Happening @ Michigan https://events.umich.edu/list/rss RSS Feed for Happening @ Michigan Events at the University of Michigan. Write-Togethers (October 25, 2021 9:00am) https://events.umich.edu/event/85156 85156-21625654@events.umich.edu Event Begins: Monday, October 25, 2021 9:00am
Location: Off Campus Location
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these remote sessions, participants access a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

Where
Google doc link:
https://docs.google.com/document/d/1wWLfQZ2ZNbfEeiUCUoKRE6y1l98mAKZA7NsjCpyn604/edit

More information:
https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Livestream / Virtual Wed, 11 Aug 2021 09:47:35 -0400 2021-10-25T09:00:00-04:00 2021-10-25T12:00:00-04:00 Off Campus Location Sweetland Center for Writing Livestream / Virtual Write-Togethers
Write-Togethers (November 1, 2021 9:00am) https://events.umich.edu/event/85156 85156-21625655@events.umich.edu Event Begins: Monday, November 1, 2021 9:00am
Location: Off Campus Location
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these remote sessions, participants access a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

Where
Google doc link:
https://docs.google.com/document/d/1wWLfQZ2ZNbfEeiUCUoKRE6y1l98mAKZA7NsjCpyn604/edit

More information:
https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Livestream / Virtual Wed, 11 Aug 2021 09:47:35 -0400 2021-11-01T09:00:00-04:00 2021-11-01T12:00:00-04:00 Off Campus Location Sweetland Center for Writing Livestream / Virtual Write-Togethers
Write-Togethers (November 8, 2021 9:00am) https://events.umich.edu/event/85156 85156-21625656@events.umich.edu Event Begins: Monday, November 8, 2021 9:00am
Location: Off Campus Location
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these remote sessions, participants access a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

Where
Google doc link:
https://docs.google.com/document/d/1wWLfQZ2ZNbfEeiUCUoKRE6y1l98mAKZA7NsjCpyn604/edit

More information:
https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Livestream / Virtual Wed, 11 Aug 2021 09:47:35 -0400 2021-11-08T09:00:00-05:00 2021-11-08T12:00:00-05:00 Off Campus Location Sweetland Center for Writing Livestream / Virtual Write-Togethers
Dissertation Defense: Dry runs and PWRD aggregation: Two new methods for extracting power from careful observation of a field experiment's context (November 11, 2021 10:00am) https://events.umich.edu/event/88814 88814-21658547@events.umich.edu Event Begins: Thursday, November 11, 2021 10:00am
Location: West Hall
Organized By: Department of Statistics

Abstract:

Well-conducted field experiments, broadly construed to contain both randomized controlled trials and quasi- experiments, involve extensive planning with substantive deliberation. Such deliberation has the potential to fuel and strengthen the analysis stage of the study. Each field experiment is unique, from the subgroups on which effects are expected to concentrate to the design of the study itself. Reliance on off the-shelf methods to analyze field experiments may exclude this potentially valuable information that, if handled properly, would provide a greater opportunity to detect an effect. In this dissertation, we propose two novel methods that look to extract information unique to a specific study and translate it into additional power. We demonstrate these methods on a large-scale education intervention aimed at correcting the stalled reading trajectories of early elementary students.

The first method, PWRD aggregation, converts the theory of change behind a class of education interventions into a test statistic that maximizes the Pitman efficiency over standard methods, thus providing greater power. The scheme emphasizes cohorts and years-of-follow-up on which effects are expected to accrue with appropriate attention paid to the relative precision of estimates within cohorts. While PWRD aggregation increases power, confidence interval estimation is more difficult. To alleviate this problem, we partition our parameter space into three regions: equivalence, superiority, and inferiority. In the first, we employ PWRD aggregation to provide the greatest opportunity to detect an effect. In the latter two regions, we employ a standard method such that when we are able to detect an effect, interpretation of the point estimate and confidence interval proceeds in a typical fashion.

The second method we propose is a dry run simulation scheme that creates a pseudo-experiment replicating the initial randomized trial in a manner that preserves blinding to impact estimates. This procedure, which uses real rather than synthetic data, provides a sandbox in which various models may be tested to discover the model specification that most precisely estimates an artificially imposed treatment effect. The dry run method allows the statistician advising field experiments to estimate expected losses for each of a variety of methods, enabling them to elect a novel or unfamiliar method if it demonstrably outperforms methods more familiar to the broader team. When applied to the reading intervention that motivated dry runs, results from this method challenged received notions about covariate choice, suggesting we control for covariates beyond pre-test scores.

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Workshop / Seminar Mon, 01 Nov 2021 09:42:44 -0400 2021-11-11T10:00:00-05:00 2021-11-11T12:00:00-05:00 West Hall Department of Statistics Workshop / Seminar Timothy Lycurgus Defense Flyer
Write-Togethers (November 15, 2021 9:00am) https://events.umich.edu/event/85156 85156-21625657@events.umich.edu Event Begins: Monday, November 15, 2021 9:00am
Location: Off Campus Location
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these remote sessions, participants access a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

Where
Google doc link:
https://docs.google.com/document/d/1wWLfQZ2ZNbfEeiUCUoKRE6y1l98mAKZA7NsjCpyn604/edit

More information:
https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Livestream / Virtual Wed, 11 Aug 2021 09:47:35 -0400 2021-11-15T09:00:00-05:00 2021-11-15T12:00:00-05:00 Off Campus Location Sweetland Center for Writing Livestream / Virtual Write-Togethers
Write-Togethers (November 29, 2021 9:00am) https://events.umich.edu/event/85156 85156-21625659@events.umich.edu Event Begins: Monday, November 29, 2021 9:00am
Location: Off Campus Location
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these remote sessions, participants access a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

Where
Google doc link:
https://docs.google.com/document/d/1wWLfQZ2ZNbfEeiUCUoKRE6y1l98mAKZA7NsjCpyn604/edit

More information:
https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Livestream / Virtual Wed, 11 Aug 2021 09:47:35 -0400 2021-11-29T09:00:00-05:00 2021-11-29T12:00:00-05:00 Off Campus Location Sweetland Center for Writing Livestream / Virtual Write-Togethers
Visual Methods Towards Autonomous Underwater Manipulation (December 1, 2021 3:00pm) https://events.umich.edu/event/89533 89533-21664052@events.umich.edu Event Begins: Wednesday, December 1, 2021 3:00pm
Location: Ford Robotics Building
Organized By: Michigan Robotics

Chair: Matthew Johnson Roberson

Extra-terrestrial ocean worlds like Europa offer tantalizing targets in the search for extant life beyond the confines of Earth's atmosphere. However, reaching and exploring the underwater environments of these alien worlds is a task with immense challenges. Unlike terrestrial based missions, the exploration of ocean worlds necessitates robots which are capable of fully automated operation. These robots must rely on local sensors to interpret the scene, plan their motions, and complete their mission tasks. Manipulation tasks, such as sample collection, are particularly challenging in underwater environments, where the manipulation platform is mobile, and the environment is unstructured.

This dissertation addresses some of the challenges in visual scene understanding to support autonomous manipulation with underwater vehicle manipulator systems (UVMSs). The developed visual methods are demonstrated with a lightweight vision system, composed of a vehicle mounted stereo pair and a manipulator wrist mounted fisheye camera, that can be easily integrated on existing UVMSs. While the stereo camera primarily supports 3D reconstruction of the manipulator working area, the wrist mounted camera enables dynamic viewpoint acquisition for detecting objects, such as tools, and extending the scene reconstruction beyond the fixed stereo view. An objective of this dissertation was also to apply deep learning based visual methods to the underwater domain. While deep learning has greatly advanced the state-of-the-art in terrestrial based visual methods across diverse applications, the challenges of accessing the underwater environment and collecting underwater datasets for training these methods has hindered progress in advancing visual methods for underwater applications.

Following is an overview of the contributions made by this dissertation. The first contribution is a novel deep learning method for object detection and pose estimation from monocular images. The second contribution is a general framework for adapting monocular image-based pose estimation networks to work on full fisheye or omni-directional images with minimal modification to the network architecture. The third contribution is a visual SLAM method designed for UVMSs that fuses features from both the wrist mounted fisheye camera and the vehicle mounted stereo pair into the same map, where the map scale is constrained by the stereo features, and the wrist camera can actively extend the map beyond the limited stereo view. The fourth contribution is an open-source tool to aid the design of underwater camera and lighting systems. The fifth contribution is an autonomy framework for UVMS manipulator control and the vision system that was used throughout this dissertation work, along with experimental results from field trials in natural deep ocean environments, including an active submarine volcano in the Mediterranean basin. This chapter also describes the collection and processing of underwater datasets, captured with our vision system in these natural deep ocean environments. These datasets supported the development of the visual methods contributed by this dissertation.

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Lecture / Discussion Mon, 29 Nov 2021 08:34:50 -0500 2021-12-01T15:00:00-05:00 2021-12-01T17:00:00-05:00 Ford Robotics Building Michigan Robotics Lecture / Discussion underwater autonomous rover and model
Write-Togethers (December 6, 2021 9:00am) https://events.umich.edu/event/85156 85156-21625660@events.umich.edu Event Begins: Monday, December 6, 2021 9:00am
Location: Off Campus Location
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these remote sessions, participants access a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

Where
Google doc link:
https://docs.google.com/document/d/1wWLfQZ2ZNbfEeiUCUoKRE6y1l98mAKZA7NsjCpyn604/edit

More information:
https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Livestream / Virtual Wed, 11 Aug 2021 09:47:35 -0400 2021-12-06T09:00:00-05:00 2021-12-06T12:00:00-05:00 Off Campus Location Sweetland Center for Writing Livestream / Virtual Write-Togethers
EEB dissertation defense: Combining quantitative and population genetics to map phenotype to genotype in Ipomoea (December 6, 2021 10:00am) https://events.umich.edu/event/88980 88980-21659413@events.umich.edu Event Begins: Monday, December 6, 2021 10:00am
Location: Off Campus Location
Organized By: Ecology and Evolutionary Biology

Sonal presents her doctoral dissertation. Check your email or contact eeb.gradcoord@umich.edu at least two hours prior to the event for the passcode.

Illustration: Sonal Gupta

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Livestream / Virtual Wed, 17 Nov 2021 16:24:11 -0500 2021-12-06T10:00:00-05:00 2021-12-06T11:00:00-05:00 Off Campus Location Ecology and Evolutionary Biology Livestream / Virtual Illustration showing plants above and belowground with diagram of phenotype, environment and genotype
Semantic-Aware Robotic Mapping in Unknown, Loosely Structured Environments (December 13, 2021 10:00am) https://events.umich.edu/event/89873 89873-21666163@events.umich.edu Event Begins: Monday, December 13, 2021 10:00am
Location: Off Campus Location
Organized By: Michigan Robotics

Chair: Ryan Eustice

Abstract:
Robotic mapping is the problem of inferring a representation of a robot’s surroundings using noisy measurements as it navigates through an environment. As robotic systems move toward more challenging behaviors in more complex scenarios, such systems require richer maps so that the robot understands the significance of the scene and objects within.
This dissertation focuses on semantic-aware robotic mapping in unknown, loosely structured environments. The first contribution is a Bayesian kernel inference semantic mapping framework that formulates a unified probabilistic model for occupancy and semantics, and provides a closed-form solution for scalable dense semantic mapping. This framework significantly reduces the computational complexity of learning-based continuous semantic mapping and achieves high accuracy in the meantime. Next, a novel and flexible multi-task multi-layer Bayesian mapping framework is proposed to provide even richer environmental information. A two-layer robotic map of semantics and traversability is built as a strong example. Moreover, it is readily extendable to include more layers according to needs. Both mapping algorithms were verified using publicly available datasets or through experimental results on a Cassie-series bipedal robot. Finally, instead of modeling the terrain traversability using metrics defined by domain knowledge, an energy-based deep inverse reinforcement learning method is developed to learn the traversability from demonstrations. The proposed method considers robot proprioception and can learn reward maps that lead to more energy-efficient future trajectories. Experiments are conducted using a dataset collected by a Mini-Cheetah robot in different environments.

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Lecture / Discussion Tue, 07 Dec 2021 16:37:23 -0500 2021-12-13T10:00:00-05:00 2021-12-13T12:00:00-05:00 Off Campus Location Michigan Robotics Lecture / Discussion 3d map generated by robotic sensors
EEB dissertation defense: Major Histocompatibility Complex (MHC) evolution in platyrrhine monkeys: duplications and hybridization as sources of adaptive genetic variation (December 17, 2021 10:00am) https://events.umich.edu/event/89258 89258-21661608@events.umich.edu Event Begins: Friday, December 17, 2021 10:00am
Location: Rackham Graduate School (Horace H.)
Organized By: Ecology and Evolutionary Biology

Adrian presents his dissertation

Image credit: Milagros Gonzalez

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Presentation Fri, 03 Dec 2021 10:34:28 -0500 2021-12-17T10:00:00-05:00 2021-12-17T11:00:00-05:00 Rackham Graduate School (Horace H.) Ecology and Evolutionary Biology Presentation Mantled howler monkey (Alouatta palliata) surrounded by green leaves
Write-Together (January 10, 2022 9:00am) https://events.umich.edu/event/90106 90106-21667888@events.umich.edu Event Begins: Monday, January 10, 2022 9:00am
Location: North Quad
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Winter 2022 schedule:
January 10, 24, 31
February 7, 14, 21
March 7, 14
March 21, 28 - REMOTE ONLY
April 4, 11

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

More information about joining virtually can be found at https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Workshop / Seminar Thu, 16 Dec 2021 14:24:12 -0500 2022-01-10T09:00:00-05:00 2022-01-10T12:00:00-05:00 North Quad Sweetland Center for Writing Workshop / Seminar Write-Together flyer
Write-Together (January 24, 2022 9:00am) https://events.umich.edu/event/90106 90106-21667890@events.umich.edu Event Begins: Monday, January 24, 2022 9:00am
Location: North Quad
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Winter 2022 schedule:
January 10, 24, 31
February 7, 14, 21
March 7, 14
March 21, 28 - REMOTE ONLY
April 4, 11

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

More information about joining virtually can be found at https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Workshop / Seminar Thu, 16 Dec 2021 14:24:12 -0500 2022-01-24T09:00:00-05:00 2022-01-24T12:00:00-05:00 North Quad Sweetland Center for Writing Workshop / Seminar Write-Together flyer
Write-Together (January 31, 2022 9:00am) https://events.umich.edu/event/90106 90106-21667891@events.umich.edu Event Begins: Monday, January 31, 2022 9:00am
Location: North Quad
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Winter 2022 schedule:
January 10, 24, 31
February 7, 14, 21
March 7, 14
March 21, 28 - REMOTE ONLY
April 4, 11

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

More information about joining virtually can be found at https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Workshop / Seminar Thu, 16 Dec 2021 14:24:12 -0500 2022-01-31T09:00:00-05:00 2022-01-31T12:00:00-05:00 North Quad Sweetland Center for Writing Workshop / Seminar Write-Together flyer
Write-Together (February 7, 2022 9:00am) https://events.umich.edu/event/90106 90106-21667892@events.umich.edu Event Begins: Monday, February 7, 2022 9:00am
Location: North Quad
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Winter 2022 schedule:
January 10, 24, 31
February 7, 14, 21
March 7, 14
March 21, 28 - REMOTE ONLY
April 4, 11

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

More information about joining virtually can be found at https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Workshop / Seminar Thu, 16 Dec 2021 14:24:12 -0500 2022-02-07T09:00:00-05:00 2022-02-07T12:00:00-05:00 North Quad Sweetland Center for Writing Workshop / Seminar Write-Together flyer
Write-Together (February 14, 2022 9:00am) https://events.umich.edu/event/90106 90106-21667893@events.umich.edu Event Begins: Monday, February 14, 2022 9:00am
Location:
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Winter 2022 schedule:
January 10, 24, 31
February 7, 14, 21
March 7, 14
March 21, 28 - REMOTE ONLY
April 4, 11

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

More information about joining virtually can be found at https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Workshop / Seminar Thu, 16 Dec 2021 14:24:12 -0500 2022-02-14T09:00:00-05:00 2022-02-14T12:00:00-05:00 Sweetland Center for Writing Workshop / Seminar Write-Together flyer
Write-Together (February 21, 2022 9:00am) https://events.umich.edu/event/90106 90106-21667894@events.umich.edu Event Begins: Monday, February 21, 2022 9:00am
Location: North Quad
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Winter 2022 schedule:
January 10, 24, 31
February 7, 14, 21
March 7, 14
March 21, 28 - REMOTE ONLY
April 4, 11

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

More information about joining virtually can be found at https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Workshop / Seminar Thu, 16 Dec 2021 14:24:12 -0500 2022-02-21T09:00:00-05:00 2022-02-21T12:00:00-05:00 North Quad Sweetland Center for Writing Workshop / Seminar Write-Together flyer
Write-Together (March 7, 2022 9:00am) https://events.umich.edu/event/90106 90106-21667896@events.umich.edu Event Begins: Monday, March 7, 2022 9:00am
Location: North Quad
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Winter 2022 schedule:
January 10, 24, 31
February 7, 14, 21
March 7, 14
March 21, 28 - REMOTE ONLY
April 4, 11

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

More information about joining virtually can be found at https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Workshop / Seminar Thu, 16 Dec 2021 14:24:12 -0500 2022-03-07T09:00:00-05:00 2022-03-07T12:00:00-05:00 North Quad Sweetland Center for Writing Workshop / Seminar Write-Together flyer
Ph.D. Defense: Huiwen Jia (March 9, 2022 10:00am) https://events.umich.edu/event/92365 92365-21690340@events.umich.edu Event Begins: Wednesday, March 9, 2022 10:00am
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

Chairs: Siqian Shen and Cong Shi
Title of dissertation: Adaptive Optimization and Learning for Service Systems

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Presentation Fri, 04 Mar 2022 13:13:31 -0500 2022-03-09T10:00:00-05:00 2022-03-09T11:00:00-05:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Presentation Huiwen Jia
Dissertation Defense: Wilkinson Daniel Wong Gonzales (March 10, 2022 2:00pm) https://events.umich.edu/event/93106 93106-21700726@events.umich.edu Event Begins: Thursday, March 10, 2022 2:00pm
Location: Off Campus Location
Organized By: Department of Linguistics

Linguistics PhD candidate Wilkinson Daniel Wong Gonzales will defend his dissertation on Thursday, March 10, at 2 pm. Title: “Truly a language of our own” A corpus-based, experimental, and variationist account of Lánnang-uè in Manila.

Committee co-chairs are Marlyse Baptista and Sarah G. Thomason.

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Other Tue, 08 Mar 2022 09:41:16 -0500 2022-03-10T14:00:00-05:00 2022-03-10T16:00:00-05:00 Off Campus Location Department of Linguistics Other
EEB dissertation defense: Phylogenomic perspectives on evolutionary history: examples from the flowering plant lineage Ericales (March 10, 2022 2:00pm) https://events.umich.edu/event/91627 91627-21681046@events.umich.edu Event Begins: Thursday, March 10, 2022 2:00pm
Location: Rackham Graduate School (Horace H.)
Organized By: Ecology and Evolutionary Biology

Drew presents his doctoral dissertation.

This event is presented in a hybrid format, both in person and livestreamed via Zoom. Please see your email or contact eeb.gradcoord@umich.edu for the passcode at least two hours prior to the event.

Image: Canon ball tree, Couroupita guianensis (flowers) in Rio de Janeiro, Brazil. In front of the Imperial palace, corner of Rua da Assembleia. From Creative Commons (copyright free).

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Presentation Tue, 01 Mar 2022 12:16:11 -0500 2022-03-10T14:00:00-05:00 2022-03-10T15:00:00-05:00 Rackham Graduate School (Horace H.) Ecology and Evolutionary Biology Presentation Canon ball tree, Couroupita guianensis (flowers) in Rio de Janeiro, Brazil. In front of the Imperial palace, corner of Rua da Assembleia
Dissertation Defense: Joy Peltier (March 14, 2022 9:00am) https://events.umich.edu/event/93107 93107-21700728@events.umich.edu Event Begins: Monday, March 14, 2022 9:00am
Location: Off Campus Location
Organized By: Department of Linguistics

Linguistics PhD candidate Joy Peltier will defend her dissertation on Monday, March 14, at 9 a.m. Title: “Little Words” in Contact and in Context: Pragmatic Markers in Kwéyòl Donmnik, English, and French.

Committee chair is Marlyse Baptista.

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Other Tue, 08 Mar 2022 09:45:23 -0500 2022-03-14T09:00:00-04:00 2022-03-14T11:00:00-04:00 Off Campus Location Department of Linguistics Other
Write-Together (March 14, 2022 9:00am) https://events.umich.edu/event/90106 90106-21667897@events.umich.edu Event Begins: Monday, March 14, 2022 9:00am
Location: North Quad
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Winter 2022 schedule:
January 10, 24, 31
February 7, 14, 21
March 7, 14
March 21, 28 - REMOTE ONLY
April 4, 11

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

More information about joining virtually can be found at https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Workshop / Seminar Thu, 16 Dec 2021 14:24:12 -0500 2022-03-14T09:00:00-04:00 2022-03-14T12:00:00-04:00 North Quad Sweetland Center for Writing Workshop / Seminar Write-Together flyer
EEB dissertation defense: Evolution and conservation in a changing world: empirical and conceptual lessons from bats, salamanders and beyond (March 17, 2022 1:00pm) https://events.umich.edu/event/91544 91544-21680451@events.umich.edu Event Begins: Thursday, March 17, 2022 1:00pm
Location: Rackham Graduate School (Horace H.)
Organized By: Ecology and Evolutionary Biology

Giorgia defends her doctoral dissertation. This will be a hybrid event (in person and virtual).

See your email or contact eeb.gradcoord@umich.edu for the Zoom link and passcode.

Illustration by Giorgia Auteri

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Presentation Thu, 10 Mar 2022 16:48:21 -0500 2022-03-17T13:00:00-04:00 2022-03-17T14:00:00-04:00 Rackham Graduate School (Horace H.) Ecology and Evolutionary Biology Presentation Drawing of a bat hanging upside down by Giorgia Auteri
Ph.D. Defense: Luze Xu (March 18, 2022 11:00am) https://events.umich.edu/event/92586 92586-21692661@events.umich.edu Event Begins: Friday, March 18, 2022 11:00am
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

Chair: Jon Lee
Title of Dissertation: Treating Some Difficulties in Mixed-Integer Nonlinear Optimization

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Presentation Fri, 04 Mar 2022 13:14:45 -0500 2022-03-18T11:00:00-04:00 2022-03-18T12:00:00-04:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Presentation Luxe Xu
Write-Together (March 21, 2022 9:00am) https://events.umich.edu/event/90106 90106-21667898@events.umich.edu Event Begins: Monday, March 21, 2022 9:00am
Location: Off Campus Location
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Winter 2022 schedule:
January 10, 24, 31
February 7, 14, 21
March 7, 14
March 21, 28 - REMOTE ONLY
April 4, 11

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

More information about joining virtually can be found at https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Workshop / Seminar Thu, 16 Dec 2021 14:24:12 -0500 2022-03-21T09:00:00-04:00 2022-03-21T12:00:00-04:00 Off Campus Location Sweetland Center for Writing Workshop / Seminar Write-Together flyer
Dissertation Defense: Rawan Bonais (March 21, 2022 9:30am) https://events.umich.edu/event/93385 93385-21704098@events.umich.edu Event Begins: Monday, March 21, 2022 9:30am
Location: Off Campus Location
Organized By: Department of Linguistics

PhD candidate Rawan Bonais will defend her dissertation on Monday, March 21, at 9:30 a.m.

Title: "The Role of Transfer/Substrate Influence in the Development of Gulf Pidgin Arabic"

PhD defenses in the Linguistics Department are open to the public. Anyone is welcome to attend if interested.

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Other Tue, 15 Mar 2022 09:14:16 -0400 2022-03-21T09:30:00-04:00 2022-03-21T11:30:00-04:00 Off Campus Location Department of Linguistics Other
Mechanisms of Action and Sources of Variability in Neurostimulation for Chronic Pain (March 25, 2022 12:00pm) https://events.umich.edu/event/93458 93458-21704629@events.umich.edu Event Begins: Friday, March 25, 2022 12:00pm
Location: North Campus Research Complex Building 10
Organized By: Biomedical Engineering

Chronic pain is a debilitating neurological disorder which affects hundreds of millions of people worldwide. Neurostimulation therapies, such as spinal cord stimulation (SCS) and dorsal root ganglion stimulation (DRGS), are non-addictive alternatives for managing chronic neuropathic pain that is refractory to conventional medical management. SCS and DRGS apply sequences of brief electrical impulses to neural tissue. However, not all patients receiving these therapies obtain adequate pain relief, and patient outcomes are not improving despite decades of clinical experience and advancements in stimulation technology. This dissertation addresses two crucial knowledge gaps limiting the success of neurostimulation therapies: 1) we do not understand the physiological mechanisms of electrical stimulation-induced pain relief, and 2) we do not understand the sources of variability affecting the neural response to stimulation.

The first portion of this thesis examined the mechanisms of action of DRGS. We developed statistical models of neural element (i.e., cell bodies, axons) locations in histological samples of human dorsal root ganglia (DRG) tissue. Next, we employed a histologically informed field-cable modeling approach to study the neural response to DRGS. We coupled a finite element method model of the potential distribution generated by DRGS to multi-compartment cable models of DRG neurons to simulate which types of sensory neurons are activated by therapeutic DRGS. Our data suggest that clinical DRGS directly activates the subset of sensory neurons that code non-painful touch sensations, which may trigger pain-inhibition neural networks in the spinal cord dorsal horn.

The second portion of this thesis investigated how biological variability at different scales (e.g., single cells, patient anatomy) affected the neural response to stimulation. We implemented a Markov Chain Monte Carlo (MCMC) method to parametrize populations of neurons with heterogeneous ion channel expression profiles. We incorporated this approach in our field-cable model of DRGS and showed that variability in ion channel expression can affect the stimulation amplitude required to generate activity in target neurons. We further applied this population-modeling approach to investigate how pathology induced changes in ion channel expression can affect the behavior of neural circuits governing sensory transmission. Finally, we developed a framework for constructing patient-specific field-cable models of patients receiving SCS. This framework captured the effect of key anatomical details (e.g., the amount of cerebrospinal fluid between a patient’s SCS electrode array and the spinal cord) on neural activation during stimulation. Furthermore, this patient-specific modeling framework allows the comparison of model predictions of neural activation during SCS with clinical data, such as patient-reported outcomes (e.g., pain relief).

The results of this dissertation suggest that DRGS may share mechanisms of action with other neurostimulation therapies for pain management, such as SCS. This dissertation also developed frameworks for studying the effect of biological variability on the nervous system’s response to electrical stimulation. To develop safe and effective therapies for neurological disorders, it is crucial to understand both the physiological mechanisms of symptom relief, and how the neural response to therapy may vary across cells, circuits, and patients. This dissertation provides novel insights on both aspects as they relate to neurostimulation for chronic pain.

Date: Friday, March 25, 2022
Time: 12:00 PM EST
Zoom link: https://umich.zoom.us/j/93382361344 (password: neuron)
Chair: Dr. Scott F. Lempka

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Class / Instruction Tue, 15 Mar 2022 15:42:02 -0400 2022-03-25T12:00:00-04:00 2022-03-25T13:00:00-04:00 North Campus Research Complex Building 10 Biomedical Engineering Class / Instruction BME PhD Defense
Dissertation Defense: Yushi Sugimoto (March 28, 2022 9:00am) https://events.umich.edu/event/93744 93744-21707959@events.umich.edu Event Begins: Monday, March 28, 2022 9:00am
Location: Off Campus Location
Organized By: Department of Linguistics

PhD candidate Yushi Sugimoto will defend his dissertation on Monday, March 28, at 9 am.

Title: "Underspecification and (im)possible derivations: Toward a restrictive theory of grammar"

PhD defenses in the Linguistics Department are open to the public. Anyone is welcome to attend if interested.

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Other Mon, 21 Mar 2022 09:47:54 -0400 2022-03-28T09:00:00-04:00 2022-03-28T11:00:00-04:00 Off Campus Location Department of Linguistics Other
Write-Together (March 28, 2022 9:00am) https://events.umich.edu/event/90106 90106-21667899@events.umich.edu Event Begins: Monday, March 28, 2022 9:00am
Location: Off Campus Location
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Winter 2022 schedule:
January 10, 24, 31
February 7, 14, 21
March 7, 14
March 21, 28 - REMOTE ONLY
April 4, 11

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

More information about joining virtually can be found at https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Workshop / Seminar Thu, 16 Dec 2021 14:24:12 -0500 2022-03-28T09:00:00-04:00 2022-03-28T12:00:00-04:00 Off Campus Location Sweetland Center for Writing Workshop / Seminar Write-Together flyer
Write-Together (April 4, 2022 9:00am) https://events.umich.edu/event/90106 90106-21667900@events.umich.edu Event Begins: Monday, April 4, 2022 9:00am
Location: North Quad
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Winter 2022 schedule:
January 10, 24, 31
February 7, 14, 21
March 7, 14
March 21, 28 - REMOTE ONLY
April 4, 11

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

More information about joining virtually can be found at https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Workshop / Seminar Thu, 16 Dec 2021 14:24:12 -0500 2022-04-04T09:00:00-04:00 2022-04-04T12:00:00-04:00 North Quad Sweetland Center for Writing Workshop / Seminar Write-Together flyer
Write-Together (April 11, 2022 9:00am) https://events.umich.edu/event/90106 90106-21667901@events.umich.edu Event Begins: Monday, April 11, 2022 9:00am
Location: North Quad
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Winter 2022 schedule:
January 10, 24, 31
February 7, 14, 21
March 7, 14
March 21, 28 - REMOTE ONLY
April 4, 11

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

More information about joining virtually can be found at https://lsa.umich.edu/sweetland/graduates/write-together-sessions.html

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Workshop / Seminar Thu, 16 Dec 2021 14:24:12 -0500 2022-04-11T09:00:00-04:00 2022-04-11T12:00:00-04:00 North Quad Sweetland Center for Writing Workshop / Seminar Write-Together flyer
Thesis Defense: Robust and Computationally Efficient Methods for High-Throughput Drug Screening Studies (April 14, 2022 11:00am) https://events.umich.edu/event/94583 94583-21751043@events.umich.edu Event Begins: Thursday, April 14, 2022 11:00am
Location: Off Campus Location
Organized By: Department of Statistics Dissertation Defenses

Abstract: There is an increasing emphasis on the utility of large-scale biological experiments to advance our understanding of human biological processes. The analysis of data from these studies, however, can face challenges associated with data size and complexity. For instance, two large pharmacogenomic databases, the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Cell Line Encyclopedia (CCLE), have been widely used to explore genetic predictors of drug sensitivity and to develop and study hypotheses about new anti-cancer therapies. At the same time, several papers have reported only moderate levels of agreement in drug sensitivity estimates between GDSC and CCLE with the discordance largely attributed to experimental and analytical factors, including differences in cell viability assay, range of tested drug concentrations, and construction of dose-response curves. There has been no published in-depth exploration, however, of the raw drug screening data from GDSC and CCLE. Therefore, we examine the raw data from both studies and identify technical variation such as complex spatial biases and batch-specific outliers. We show how these errors propagate through downstream calculations of relative viability and measures of drug sensitivity. Additionally, we note that technical error can interact with aspects of plate design such as the location of control wells along plate edges and the consistent orientation of drugged wells across replicates creating challenges for analysis. These findings highlight the importance of exploring the raw drug screening data prior to pursuing an analysis. They also inform a number of strategies for improving experimental design, such as randomized plate layouts.

To eliminate the effects of such between-plate variation in high-throughput drug screening studies, intensity measurements for treated wells are often normalized to the control wells. Such normalization allows for comparability across plates and across studies. However, within-plate variability, including spatial biases, cannot be alleviated by normalization to the controls. Therefore, we provide a normalization framework that addresses multiple types of spatial effects and can handle complex plate layouts. We carefully apply this normalization framework to the drug screening data from GDSC. Our normalization produces more reliable measures of drug sensitivity than current methods.

Finally, many existing methods for high-dimensional classification, including those used for pharmacogenomic data, require a substantial amount of computing time and power. Specifically, the use of cross-validation for tuning parameter selection and error estimation can be particularly time-consuming. Therefore, we introduce an approximate leave-one-out cross-validation approach for principal component linear discriminant analysis that is computationally more efficient than existing methods. In particular, our method obviates the need to select tuning parameter values and optimizes computational efficiency through a series of matrix downdates. We apply our method to simulated data as well as to pharmacogenomic data from GDSC. For the type of genomic data for which this method is intended, it has comparable accuracy to existing approaches, while improving on computation time.

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Presentation Wed, 13 Apr 2022 08:52:02 -0400 2022-04-14T11:00:00-04:00 2022-04-14T13:00:00-04:00 Off Campus Location Department of Statistics Dissertation Defenses Presentation Defense Flyer
Thesis Defense: An Accurate and Scalable Approach to Classifying High-Dimensional Data With Dense Latent Structure (April 15, 2022 2:00pm) https://events.umich.edu/event/94584 94584-21751044@events.umich.edu Event Begins: Friday, April 15, 2022 2:00pm
Location: Weiser Hall
Organized By: Department of Statistics Dissertation Defenses

Abstract:
The primary aim of a classification analysis is to learn the relationship between a set of features and a discrete variable of primary interest so that good predictive accuracy is achieved on new out-of-sample observations. In many modern large-scale datasets, this task is complicated by the high-dimensionality of the data, as well as the presence of unobserved variables besides the variable of primary interest. Frequently, these unobserved variables induce variation across a large proportion of the features, while the variable of primary interest affects a much smaller proportion of features, resulting in variation that is both dense and latent. This variation presents both challenges and opportunities. Some of these unobserved variables may be partially correlated with the class label, and thus useful for learning the predictive relationship between the features and the class label. Others, however, may be uncorrelated with the class label and thus hold no such useful information. If the effects stemming from the variable of primary interest are sparse or weak, as they are thought to be in many applications, then the dense latent effects may obscure them.

To address the challenges posed by dense latent variation while leveraging any benefits they may confer, we propose the cross-residualization classifier (CRC). Through a decomposition and ensemble procedure, the CRC adapts to the nature of the dense latent variation in the data by first estimating and residualizing out the latent variation, training a classifier on the residuals, and then reintegrating the latent variation in a final ensemble classifier. The dense latent variation is thus accounted for without discarding any potentially predictive information. Numerical simulations comparing the CRC with other popular methods used for genomic classification demonstrate that our method of separating and reintegrating the latent variables can improve classification accuracy.

Applying high-dimensional classifiers like the CRC in practice requires scalable software that can accommodate both the size and high-dimensionality of large-scale datasets. Not all classifier implementations are equipped to handle data of this nature, either because they slow down significantly when the number of features is large or have large memory requirements that cannot be easily accommodated by the typical user (e.g., requiring the data to be stored locally in memory). Any resampling steps that are undertaken (e.g., cross-validation for selecting a tuning parameter or for estimating the out-of-sample error rate) only exacerbate these computational challenges. We focus on strategies to address such issues in the context of the CRC, which is intended for large-scale data of this nature and also contains extensive resampling steps. We address two of the most time-consuming and memory intensive parts of the CRC by reformulating two key parts of the algorithm -- the cross residualization algorithm, as well as the feature selection step embedded within one of the component classifiers, whose tuning parameter we eliminate. These contributions enable the CRC algorithm to be implemented in a scalable way and facilitate its application to large-scale datasets, particularly those that cannot be stored in memory locally. These reformulations not only improve the CRC computationally, but also reveal opportunities to improve the CRC from a statistical standpoint, which we explore. Numerical experiments on both simulated and genomic data illustrate these computational gains, as well as accompanying statistical gains. Additionally, we present an R software package, crc, which contains our scalable implementation, and provide details on various user-facing options that can be used to meet the statistical needs and computational demands of any particular application.

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Presentation Wed, 13 Apr 2022 08:59:04 -0400 2022-04-15T14:00:00-04:00 2022-04-15T16:00:00-04:00 Weiser Hall Department of Statistics Dissertation Defenses Presentation Defense Flyer
Statistical Learning for Latent Attribute Models (April 25, 2022 10:00am) https://events.umich.edu/event/94704 94704-21761597@events.umich.edu Event Begins: Monday, April 25, 2022 10:00am
Location: West Hall
Organized By: Department of Statistics Dissertation Defenses

Abstract:
Latent variable models are popularly used in unsupervised learning to uncover the unobservable latent structures underlying observed data and have seen great successes in representation learning in many applications and scientific disciplines. Latent attribute models, also known as cognitive diagnosis models or diagnostic classification models, are a special family of discrete latent variable models that have been widely applied in modern psychological and biomedical research with diagnostic purposes. Despite the wide usage in various fields, the models' discrete nature and complex restricted structures pose many new challenges for efficient learning and statistical inference. Moreover, with large-scale item and subject pools emerging in modern educational and psychological measurements, efficient algorithms for uncovering latent structures of both items and subjects are desired. This dissertation studies four important problems that arise in this context.

The first part develops novel methodologies and efficient algorithms to learn the latent and hierarchical structures in latent attribute models. Specifically, researchers in many applications are interested in hierarchical structures among the latent attributes, such as prerequisite relationships among target skills in educational settings. However, in most cognitive diagnosis applications, the number of latent attributes, the attribute-attribute hierarchical structures, the item-attribute dependence structure, as well as the item-level diagnostic model, need to be fully or partially pre-specified, which however may be subjective and misspecified as noted by many recent studies. In this part, we consider the problem of jointly learning these latent quantities and hierarchical structures from observed data with minimal model assumptions. A penalized likelihood approach is proposed for joint learning, an Expectation Maximization (EM) algorithm is developed for efficient computation, and statistical consistency theory is established under mild conditions.

The second part generalizes the methodologies in part I to simultaneously infer the subgroup structures of both subjects and items. We consider model-based co-clustering algorithms and aim to automatically select the numbers of clusters and uncover latent block structures. Specifically, based on latent block models, we propose a penalized co-clustering approach which is capable of learning the numbers of clusters and inner block structures simultaneously. An efficient EM algorithm has been developed and comprehensive simulation studies demonstrate its superiority.

The third part concerns the important yet unaddressed problem of testing the latent hierarchical structures in latent attribute models. In specific, testing the hierarchical structures is shown to be equivalent to testing the sparsity structure of the proportion parameter vector. However, due to the irregularity of the problem, the asymptotic distribution of the popular likelihood ratio test becomes nonstandard and tends to provide unsatisfactory finite sample performance under practical conditions.To tackle these challenges, we discuss the conditions of testability issues, provide statistical understandings of the failures, and propose a practical resampling-based procedure.

The fourth part introduces a unified estimation framework to bridge the gap between parametric and nonparametric methods in cognitive diagnosis, to better understand their relationship. In particular, a number of parametric and nonparametric methods for estimating latent attribute models have been developed and applied in a wide range of contexts. However, in the literature, a wide chasm exists between these two families of methods, and their relationship to each other is not well understood. Driven by this divide, we propose a unified framework and provide both theoretical analysis and practical recommendations in various cognitive diagnosis contexts.

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Presentation Tue, 19 Apr 2022 08:11:18 -0400 2022-04-25T10:00:00-04:00 2022-04-25T12:00:00-04:00 West Hall Department of Statistics Dissertation Defenses Presentation Chenchen Defense
Thesis Defense: Byoungwook Jang (April 25, 2022 12:00pm) https://events.umich.edu/event/94588 94588-21752805@events.umich.edu Event Begins: Monday, April 25, 2022 12:00pm
Location: West Hall
Organized By: Department of Statistics Dissertation Defenses

Abstract:



Decomposition models for understanding mean and covariance structures from high-dimensional data have attracted a lot of attention in recent years. This thesis visits selected machine learning problems with applications in topic modeling, neuroimaging, and experimental designs and tackles challenges in these applications by incorporating decomposable structures.

The first part of the thesis looks into the statistical learning problems for the applications with decomposable mean structures, namely topic modeling and multi-spectral imaging. The goal of topic modeling and multi-spectral unmixing is to decompose the spectrum for each document (or pixel) in the corpus (or the image of a scene) to find latent topics (or spectrums of materials present in multi-spectral images). In topic modeling applications, the number of latent variables is a lot less than the ambient dimension. This allows us to estimate the topic simplex with the geometric approach by minimizing the volume of the topic polytope. In our second application, we aim to trace neurons present in multi-spectral images, called Brainbow images, which capture individual neurons in the brain and allow researchers to distinguish different neurons based on unique com binations of fluorescent colors. Brainbow images, however, have an over-defined problem as the number of unique neuron color combinations is greater than the number of spectral channels. Thus, we reformulate the neuron tracing problem as a hidden Markov model with underlying neuronal processes as latent variables to decompose the observed Brainbow images into individual neurons.

The second part of the thesis studies the decomposition of covariance models for tensor-variate data to introduce a scalable and interpretable structure. In the tensor-variate analysis, the observed data often exhibit spatio-temporal structure, and it is desirable to simultaneously learn partial cor relation for each mode of the tensor data. However, estimating the unstructured covariance model for tensor-variate data scales quadratically in terms of the product of all dimensions of the tensor. Instead, we introduce a Kronecker sum model for the square root factor of the precision matrix. This model assumption results in a decomposable covariance matrix motivated by a well-known Sylvester equation.

For the last part of the thesis, we visit the linear contextual bandit problem with missing values to understand the effect of missing probabilities on the cumulative regret, showing that the regret degrades due to missingness by at most the square of minimum sampling probability. By separating the missing values from the context vectors in the covariance model, we can estimate the linear parameter over time without explicitly imputing the missing values. Our method is ap plied to the experimental design for collecting gene expression data by sequentially selecting class discriminating DNA probes.

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Presentation Thu, 14 Apr 2022 09:45:48 -0400 2022-04-25T12:00:00-04:00 2022-04-25T14:00:00-04:00 West Hall Department of Statistics Dissertation Defenses Presentation West Hall
Autonomous System for Legged Robots: From Calibration and Pose Estimation to CLF Reactive Motion Planning (April 26, 2022 11:30am) https://events.umich.edu/event/94368 94368-21735841@events.umich.edu Event Begins: Tuesday, April 26, 2022 11:30am
Location: Off Campus Location
Organized By: Michigan Robotics

Chair: Jessy Grizzle

ABSTRACT:
Legged robots are accessible to unstructured environments, which enables legged robots to aid in package delivery, terrain exploration, search and rescue, and disaster relief, and becoming assistants in our homes. Two-legged robots (i.e., bipedal robots) with tall and slim shapes can easily adapt to structures built for humans (narrow staircases or passages). Besides assisting the elderly or people with physical disabilities, research on bipedal robots and exoskeletons habilitates humans to improve productivity.

The capabilities of bipedal robots have yet to be unleashed to serve the society due to a number of challenges. One key challenge involves problems in sensor fusion, pose estimation, and smooth motion planning, as these aspects are critical for a bipedal robot to autonomously walk toward a distant destination and to smoothly avoid obstacles detected from different calibrated sensors while maintaining its stability.

In this defense, I am going to introduce a full autonomy system that allows bipedal robots to 1) acquire multi-modal data from a calibrated perception suite; 2) estimate their poses in textureless environments; 3) detect and avoid dynamic obstacles; 4) traverse unexplored, unstructured environments and undulating terrains; 5) perform point-to-point topometric navigation. All the research presented in this dissertation focuses on advancing the state-of-the-art algorithms that will achieve autonomy of bipedal robots — Cassie Blue and Digit.

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Presentation Tue, 12 Apr 2022 12:57:46 -0400 2022-04-26T11:30:00-04:00 2022-04-26T13:30:00-04:00 Off Campus Location Michigan Robotics Presentation How a robot sees the Wave Field as it walks
Thesis Defense: Advances in Sequential Decision Making Problems with Causal and Low-rank Structures (April 27, 2022 10:00am) https://events.umich.edu/event/94585 94585-21751045@events.umich.edu Event Begins: Wednesday, April 27, 2022 10:00am
Location: West Hall
Organized By: Department of Statistics Dissertation Defenses

Abstract: Bandits and Markov Decision Processes are powerful sequential decision making paradigms that have been widely applied to solve real world problems. However, existing algorithms often suffer from high sample complexity due to the large action space. In this thesis, we present several contributions to reduce the sample complexity by exploiting the problem structure.
In the first part, we study how to utilize the given causal information represented as a causal graph along with associated conditional distributions for bandit problems. We propose two algorithms, causal upper confidence bound (C-UCB) and causal Thompson Sampling (C-TS), that enjoy improved cumulative regret bounds compared with algorithms that do not use causal information. Further, we extend C-UCB and C-TS to the linear bandit setting. We also show that under certain causal structures, our algorithms scale better than the standard bandit algorithms as the number of interventions increases.

In the second part, we further explore how to utilize the given causal information for Markov Decision Processes. We introduce causal Markov Decision Processes, a new formalism for sequential decision making which combines the standard Markov Decision Process formulation with causal structures over state transition and reward functions. We propose the causal upper confidence bound value iteration (C-UCBVI) algorithm that exploits the causal structure and improves the performance of standard reinforcement learning algorithms that do not take causal knowledge into account. To tackle the large state space problem in Markov Decision Process, we further formulate causal factored Markov Decision Process and design new algorithms with reduced regret. Lastly, we explore the connection between linear Markov Decision Process and causal Markov Decision Process.

In the third part, we tackle the challenging setting where the causal information is unknown. We propose mild identifiability conditions and design new causal bandit algorithms for causal trees, causal forests and a general class of causal graphs. We prove that the regret guarantees of our algorithms greatly improve upon those of standard multi-armed bandit algorithms. Lastly, we prove our mild conditions are necessary: without them one cannot do better than standard bandit algorithms.
In the fourth part, we investigate a challenging problem associated with the causal structure: unobserved confounders. We study to what extent the unobserved confounders affect the estimation in the offline policy evaluation problem in reinforcement learning. We give the first minimax lower bound for error due to unobserved confounder. We also analyze two algorithms and show they are minimax optimal. Lastly, we propose a new model-based method and show it is never worse than the model-free method proposed in prior work.

In the last part, we explore another problem structure, the low-rank property of the ground truth parameter. We study linear bandits and generalized linear bandits, and we present algorithms via a novel of combination of online-to-confidence-set conversion and the exponentially weighted average forecaster constructed by a covering of low-rank matrices. To get around the computational intractability of covering based approaches, we propose an efficient algorithm using the subspace exploration technique. Our theoretical and empirical results demonstrate the effectiveness of utilizing the low-rank structures in reducing the regret.

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Presentation Wed, 13 Apr 2022 09:06:40 -0400 2022-04-27T10:00:00-04:00 2022-04-27T12:00:00-04:00 West Hall Department of Statistics Dissertation Defenses Presentation Defense Flyer
Topics in Sequential Decision Making and Algorithmic Fairness (April 28, 2022 10:00am) https://events.umich.edu/event/94746 94746-21764391@events.umich.edu Event Begins: Thursday, April 28, 2022 10:00am
Location: Off Campus Location
Organized By: Department of Statistics Dissertation Defenses

Abstract:
The ability to collect and process data has greatly expanded the areas of application for data driven inference, predictions, and decisions. How to collect and modify data is dependent upon the ultimate goal. Two areas of research with focus on these questions are sequential decision making and algorithmic fairness. Sequential decision making is the process of a learner choosing an action, observing the outcome, and using this and previous information to determine the next action to take. Algorithmic fairness is the overarching term used to describe when an algorithmic decision is seen as unfair to certain groups or individuals. Biases present in training data may rise from historical inequities or improper representation. This dissertation addresses four problems in these two areas: policies for contaminated stochastic multi-armed bandits, fair representation through convex-hull feasibility sampling, data debiasing, and implications of a sequential pipeline of fair/biased decisions.

We start in chapter 2 by considering the stochastic multi-armed bandit problem, with the added assumption that rewards can be contaminated some fixed proportion of the time. This reflects the scenario of a sequential decision when the reward is from a human response. Here there is no guarantee the observed reward is from the true reward distribution of the action. To account for the contamination, we propose an Upper Confidence Bound (UCB) policy that relies on robust mean estimators. We derive inequality bounds on these estimators in the contaminated setting and give upper bounds on the regret, showing they are comparable to UCB policies in the standard stochastic setting. Through simulations, we show the effectiveness or our policies under different types of contamination.


Bias in training data is often split into two categories, representation bias and historical bias. Representation bias refers to data with no or limited samples from groups within the target population. Representation bias can result in unfair outcomes for the underrepresented groups. Historical bias refers to unwanted correlations between protected attributes and other features caused by societal inequities. It is an inherent property of the data and cannot be attenuated by more data.

Addressing representational bias, chapter 3 introduces the convex-hull feasibility sampling problem. Here we develop a framework for sequentially testing whether a known point lies within the convex hull of a set of points with unknown distributions. This represents the problem of whether or not it is possible to sample an equally representative data set among labeled groups when the distribution of the sampling sources is unknown. We provide theoretical results in the 2D setting and simulations of our policy in 2 and 3 dimensions.

In contrast, chapter 4 addresses historical bias by proposing a data debiasing method based on a factor model. The goal is to remove variation caused by protected attributes that are undesirable during training. We compute the correlation between the debiased data and the original protected attributes and show that in ideal cases there is no correlation. We show empirical results with a case study.
Chapter 5 explores how bias across multiple decisions—what we call a pipeline—impacts the final outcome. We show how fair decisions at each decision point can perpetuate a fair outcome, and also how a biased decision can prevent fair outcomes further down the pipeline. This highlights the importance of representative data at each training and decision period.

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Presentation Wed, 20 Apr 2022 09:21:42 -0400 2022-04-28T10:00:00-04:00 2022-04-28T12:00:00-04:00 Off Campus Location Department of Statistics Dissertation Defenses Presentation Defense Flyer
Contributions to nonparametric quantile analysis and quantile-based mediation analysis, with applications to lifecourse analysis in human biology (May 9, 2022 9:00am) https://events.umich.edu/event/94978 94978-21788179@events.umich.edu Event Begins: Monday, May 9, 2022 9:00am
Location: West Hall
Organized By: Department of Statistics Dissertation Defenses

This thesis develops and assesses new ways to study the conditional quantiles of a population using a sample of data that represents the population. All methods presented here build on a recently-proposed non-parametric approach to quantile regression that is analogous to local linear regression in the least-squares setting. A major challenge is that the raw local quantile estimates are cumbersome to interpret and gain insight from directly. Aiming to overcome this challenge, there are four main contributions herein. First, we demonstrate how a low-rank additive regression analysis can produce insight into a collection of local nonparametric quantile estimates. The low rank structure regularizes the noisy quantile estimates and facilitates interpretation of the findings. Second, we show how a multivariate dimension reduction approach provides a different type of insight into a collection of estimated conditional quantile functions. The third contribution of the thesis leverages the combination of nonparametric quantile estimation and low-rank regression in the context of mediation analysis. We show that this produces a novel quantile-based approach to mediation analysis that expresses direct and indirect effects in a concise and interpretable way. The final methodological contribution of the thesis is a framework for moment-based estimation of conditional covariance functions for stochastic processes. Throughout the thesis, we motivate our work through analyses looking at the proximal and distal factors predicting human blood pressure.

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Presentation Wed, 04 May 2022 14:32:56 -0400 2022-05-09T09:00:00-04:00 2022-05-09T11:00:00-04:00 West Hall Department of Statistics Dissertation Defenses Presentation Flyer
Dissertation Defense: Andrew McInnerney (May 9, 2022 11:30am) https://events.umich.edu/event/94906 94906-21784746@events.umich.edu Event Begins: Monday, May 9, 2022 11:30am
Location: Off Campus Location
Organized By: Department of Linguistics

Linguistics PhD candidate Andrew McInnerney will defend his dissertation on Monday, May 9, at 11:30 am. The title of his dissertation is “The Argument/Adjunct Distinction and the Structure of Prepositional Phrases.” Doctoral committee members include Professor Acrisio Pires (Chair), Professor Ezra Keshet, Dr. Lisa Levinson, Professor Richard L. Lewis, and Professor T. Daniel Seely, Eastern Michigan University. All are invited to attend.

ABSTRACT
This dissertation examines the traditional evidence for the Argument/Adjunct Distinction (A/AD). I begin by drawing a distinction between the semantic sense of the A/AD and the syntactic sense of the A/AD. The semantic A/AD concerns lexical encoding of thematic information; arguments are taken to be semantically encoded in the lexical representation of predicates, while adjuncts are not. I argue instead that lexical encoding of thematic information is a property in its own right; the standard evidence does motivate an understanding of the A/AD in these terms. The syntactic A/AD has to do with the external syntax of constituents. I consider nine canonical syntactic diagnostics for argumenthood (e.g. omissibility, VP-anaphora, islandhood, etc.), using prepositional phrases in the verbal domain in English as a test case, and I find that these diagnostics do not provide good evidence for the syntactic A/AD. Instead, the properties identified by the canonical argumenthood diagnostics are independent of one another; they should not be taken to as properties of a single larger distinction.
After carefully examining the evidence for the A/AD, I consider the consequences of eliminating the distinction. I focus specifically on consequences for the syntax prepositional phrases, including (i) the configuration of PPs in the verbal domain, (ii) licensing of pronouns within PPs, and (iii) pseudopassives (p-passives). The A/AD has been argued to play an important role in each of these domains, and so if the distinction is to be eliminated, it is important to explore how analyses in these domains are affected. On the structure of VP-internal PPs, I explore the possibility that PPs could be attached as sisters to functional heads in the verbal domain, potentially forming multiple n-ary-branching layers. On pronoun-licensing in PP, I defend the hypothesis that PP is split into two layers, and I argue that the lower of the two layers is a phase; assuming that Condition B is sensitive to phase domains, this enables an account of a range of relevant data. Finally, on p-passives, I consider the conditions under which p-passivization is blocked, arguing that argumenthood is not a relevant factor.

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Other Mon, 09 May 2022 11:33:11 -0400 2022-05-09T11:30:00-04:00 2022-05-09T13:30:00-04:00 Off Campus Location Department of Linguistics Other
Statistical Estimation and Inference for Large-Scale Categorical Data (May 11, 2022 10:00am) https://events.umich.edu/event/94919 94919-21785266@events.umich.edu Event Begins: Wednesday, May 11, 2022 10:00am
Location: Off Campus Location
Organized By: Department of Statistics Dissertation Defenses

Abstract:
Categorical data become increasingly ubiquitous in the modern big data era. In this dissertation, we propose novel statistical learning and inference methods on large-scale categorical data, with a special focus on latent variable models and their applications to psychometrics. In psychometric assessments, the subjects' underlying aptitude often cannot be fully captured by raw scores due to differing item difficulties. Latent variable models are popularly used to capture this unobserved proficiency. This dissertation studies two types of latent variable models with categorical responses. The first type assumes multiple discrete latent traits, commonly known as the cognitive diagnosis models (CDMs), is a special family of discrete latent variable models. The second type assumes a continuous latent score, commonly known as the item response theory (IRT) models. Although both have been widely applied in large-scale assessments with diagnostic purposes, many challenges still exist for efficient learning and statistical inference. This dissertation studies four important problems that arise in these contexts.

The first part develops novel algorithms to estimate large latent Q-matrix in CDMs. Q-matrix plays an important role in CDMs; it specifies the inter-dependence between items and subjects' latent attributes. Accurate knowledge of Q-matrix is critical for cognitive diagnosis, item categorization and assessment design. However, in practice, many assessments do not provide Q-matrix or do not have accurate Q-matrix specifications. Existing methods are not scalable with the size of Q-matrix, despite the prevalence of large Q-matrix. We propose a penalized likelihood approach, with computational complexity growing linearly with Q sizes, to learn large Q-matrix from observational data. The estimation consistency and the robustness of the proposed method across various CDMs are also established.

The second part develops learning and inference methods for a unidimensional IRT model, the Rasch model, under the missing data setting. Data missingness is prevalent in large-scale assessments; examples include SAT and GRE where responses are combined from multiple tests administered year round from a large item pool. Direct inference to compare subjects’ latent scores under the missing data setting remains open and challenging in the literature. In this part, we obtain point estimators for the latent scores and derive their asymptotic distribution under a flexible missing-entry design in double asymptotic settings. We show our estimator is statistically efficient and optimal, which is amongst the first results in the binary matrix completion literature.

The third part concerns measurement biases in IRT models. Novel estimation and inference procedures are developed for biases brought by measurement non-invariant items under the differential item functioning (DIF) framework. Existing methods either require to know anchor items, i.e. DIF-free items or to adopt regularization to ensure model identifiability where easy inference is not permitted. We propose a novel minimal L1 condition for simultaneous DIF detection and model identification. It does not require any knowledge on anchor items and permits easy inference for both binary and multiple groups settings.

The fourth part considers privacy issues for releasing tabular (categorical) data to the public. We recommend an optimal mechanism, in which data utility is maximized given a privacy constraint, under the data differential privacy (DP) framework. Common users' practices, including merging related cells or integrating multiple data sources, are considered. Valid inference procedures are developed for the associated DP privacy-protected data.

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Presentation Mon, 09 May 2022 11:43:53 -0400 2022-05-11T10:00:00-04:00 2022-05-11T14:00:00-04:00 Off Campus Location Department of Statistics Dissertation Defenses Presentation Flyer
Dissertation Defense: Kelly Wright (May 13, 2022 11:30am) https://events.umich.edu/event/94908 94908-21784748@events.umich.edu Event Begins: Friday, May 13, 2022 11:30am
Location: Off Campus Location
Organized By: Department of Linguistics

Linguistics PhD candidate Kelly Wright will defend her dissertation on Friday, May 13, at 11:30 am. Title: “Black Professionalism: Perception and Metalinguistic Assessment of Black American Speakers' Sociolinguistic Labor.” Committee chair is Patrice Beddor. All are invited to attend.

ABSTRACT

Metalinguistic awareness encompasses what a language user knows about the relation of social factors (such as age, gender, or race) to linguistic usage, distribution, meaning, or context of occurrence variance. Metalinguistic awareness, and a language user’s embodied positionality in relation to it, can be thought of as akin to how one relates to an indexical field, in that such a field is described as representing all the potential meanings a given linguistic variable can have across contexts and communities. I argue that some people are, by the nature of their embodied positionality, always already more aware of the contents of these fields. To elicit metacommentary stemming from such positionality-based awareness, a new method of sociolinguistic interview is introduced which elevates metalinguistic knowledge to a level comparable to that of speech feature. This dissertation applied this method in interviews with 17 Black professionals from Detroit, Michigan. The design included, for example, a task geared towards eliciting metacommentary on targeted African American Language terms (e.g., shawty, stressed BIN, and the N-words) that aligns with some aspects of their positionality (e.g., regionally) and diverges in others (e.g., age- and gender-based knowledges). One major theme to emerge from the metacommentary on these terms and on other components of the interview method—examined in especially close detail through three case studies—is that the current understanding of the theoretical concept of sociolinguistic labor does not fully capture these Black professionals’ reported motivations for style shifting. Rather, the notion of sociolinguistic labor needs to be enriched to include linguistic actions which are taken not only to satisfy others, but also to satisfy the self and in service of others.

Metacommentary elicited from these Black professionals on specific elements of their racialized styles that they shift away from in the workplace informed the design of the speech perception experiment also undertaken in this study, which assessed listeners’ judgments of the relative professionalism of Black professional speech styles. Targeting three non-Standard variables—fortition via TH-stopping (they versus dey); metathesis (ask versus aks);, and consonant cluster reduction (trend versus tren_)—the perception experiment asked: if Black people sound more like themselves at work, are their identities as professionals more likely to be rejected by audiences? Across three configurations of paired sentences differing in the number of non-Standard variables, the overwhelming majority of listeners, across demographic categories, prefer sentences with fewer non-Standard variables to those with more such variables from a Black professional speaker. However, the relative influences of these variables on professionalism judgments differed, with the metathesis variable aks, for example, presenting evidence of perceptual blocking, indicating that stereotypes about aks and its normative incompatibility with professionalism are operative in this study. These findings indicate that when a Black speaker shifts towards the Standard—towards Whiteness—their style appears to align with listener expectations of professionalism; this indicates that Black professionals are less successful in conveying professionalism when features of non-Standard racialized varieties are present. In consideration of the interviewees’ reports of sociolinguistic labor done to acquiesce to assimilationist Standards, and in light of the experimental evidence indicating preference of speech styles which reflect said labors, I conclude this dissertation by calling for linguists across the discipline to become better advocates for linguistic equity at local and federal levels.

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Other Mon, 09 May 2022 08:36:10 -0400 2022-05-13T11:30:00-04:00 2022-05-13T13:30:00-04:00 Off Campus Location Department of Linguistics Other
Modeling Data that Lie on a Simplex (May 19, 2022 3:00pm) https://events.umich.edu/event/95065 95065-21788407@events.umich.edu Event Begins: Thursday, May 19, 2022 3:00pm
Location: West Hall
Organized By: Department of Statistics Dissertation Defenses

Abstract:
Examples of data that lie in a simplex abound in a variety of fields, such as biology, geology, and sociology. However, it can be challenging to model this type of data, particularly because each observation must sum up to one. If not handled correctly, this might induce what Karl Pearson called "spurious correlation". As a trivial example of this, take an observation that has two values and sums up to one. Then, the values must be negatively correlated with each other. While traditional approaches for this type of data involve analyzing the log ratio transforms, this might be problematic if any of the observations have a zero as one of their values or for interpretation.

Additional challenges arise if this data changes over time. The first two chapters lay the framework for how such data may be modeled. The first chapter proposes doing so using a general affine transformation for the overall change and a sufficiently rich error model for the difference between the overall transformation and the observation at the next time point. Of the three models explored, the Rotational Geodesic Error model is most promising. However, it might not be appropriate to assume that the direction observations moved in is uniformly distributed. Using ideas from directional statistics, we discuss in the second chapter how to model directions that appear to be similar for observations with similar values. In both chapters, we run simulation studies and analyze the income proportions from Los Angeles County. In each case, our analysis is able to discover trends consistent with larger macroeconomic ones and provide further details about these trends.

The last chapter discusses tree based mixtures of probability simplices. In other words, the simplices share vertices in a way that can be represented by a tree with the root node corresponding to a vertex shared by all simplices and the leaf nodes corresponding to vertices present in one simplex. We show when such models have posterior consistency and demonstrate how to efficiently fit them using geometric methods. Indeed, we apply them to analyze a subset of articles from the New York Times, uncovering meaningful topics and interesting semantic relationships between these topics. While we leave it to future work, these methods might also be combined with the ones from previous chapters to model how sub-regions of data that lie in a simplex change over time.

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Presentation Tue, 10 May 2022 07:57:02 -0400 2022-05-19T15:00:00-04:00 2022-05-19T17:00:00-04:00 West Hall Department of Statistics Dissertation Defenses Presentation Flyer
EEB dissertation defense: Molecular determinants, fitness effects, and evolutionary consequences of gene expression (May 20, 2022 1:00pm) https://events.umich.edu/event/94418 94418-21738330@events.umich.edu Event Begins: Friday, May 20, 2022 1:00pm
Location: Rackham Graduate School (Horace H.)
Organized By: Ecology and Evolutionary Biology

Haiqing Xu defends his dissertation

Although the expression stochasticity of an individual gene is unavoidable, clustering of genes promotes the synchronization of their expression fluctuation. This phenomenon provides a fitness benefit when the expression ratio of the clustered genes needs to stay constant, for example, because of the accumulation of toxic compounds when this ratio is altered.

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Presentation Mon, 09 May 2022 11:47:50 -0400 2022-05-20T13:00:00-04:00 2022-05-20T15:00:00-04:00 Rackham Graduate School (Horace H.) Ecology and Evolutionary Biology Presentation figure illustrating gene expressions
Dissertation Defense: Inquiring Further: Essays on Epistemic Normativity (May 31, 2022 9:30am) https://events.umich.edu/event/94782 94782-21767868@events.umich.edu Event Begins: Tuesday, May 31, 2022 9:30am
Location: Angell Hall
Organized By: Department of Philosophy

COMMITTEE:
Weatherson, Brian (co-chair)
Moss, Sarah (co-chair)
Joyce, Jim
Lasonen-Aarnio, Maria
Buss, Sarah
Hershovitz, Scott (cognate, Law)


ABSTRACT:
My dissertation defends the importance of epistemic norms on what I call ‘inquiring further.’ Inquiring further is a familiar practice we all engage in when we redeliberate, gather more evidence, or double-check our beliefs. Nonetheless, many philosophers have argued that norms governing when we should gather evidence or reinquire are at most practical or moral norms. Against this, I argue that norms on inquiring further are central to our conception of responsible epistemic agency. I do this by appealing to both the roles of epistemic evaluations and our practices of holding agents epistemically accountable. My dissertation thereby expands and enriches our understanding of epistemic evaluations and normativity.

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Other Fri, 20 May 2022 08:17:42 -0400 2022-05-31T09:30:00-04:00 2022-05-31T11:30:00-04:00 Angell Hall Department of Philosophy Other
Statistical approaches for spatially-dependent functional data and their application in oceanography (June 2, 2022 8:00am) https://events.umich.edu/event/95176 95176-21788731@events.umich.edu Event Begins: Thursday, June 2, 2022 8:00am
Location: Off Campus Location
Organized By: Department of Statistics Dissertation Defenses

Abstract:
In many scientific fields, there is interest and need in analyzing data with complex dependence structures. This work is motivated by the Argo data, a dataset of measurements of the upper 2,000 meters of the world's oceans, which has revolutionized the scientific community's monitoring of oceanic temperature and salinity in the past 20 years. This data has a number of complex and challenging features for statistical analysis. It is inherently a 4-dimensional space-time dataset: one observes data in space (longitude and latitude), time, and pressure (or equivalently, depth) in the ocean. Data collection occurs at more than 100,000 locations per year and consists of hundreds of millions of measurements. Measured ocean variables display nonstationarity as their statistical properties significantly change over time and over space. The contribution of this thesis is the development of statistical methodology for this data with a focus on spatio-temporal functional data. That is, we treat the Argo data as functions of pressure that describe ocean characteristics in the upper 2,000 meters of the ocean, and we extend perspectives used in spatial statistics to functional data with the goal of addressing the complex features of the data.

In the first chapter, we introduce the Argo data and relevant literature in more detail. In the next chapter, we describe a local estimation approach for spatio-temporal functional data, using the Argo dataset as an instrumental framework for this approach. In the third chapter, we consider the theoretical statistical performance of such estimation methodology in a spatial functional-data setting. In the fourth chapter, we develop statistical methodology for clustering multivariate spatio-temporal functional data in a functional regression framework to predict dissolved oxygen concentration in the ocean. In the fifth chapter, we introduce a new Matérn model for multivariate time series and random fields that offers improved model flexibility over previously proposed models. Finally, we discuss open questions that have arisen from our work.

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Presentation Wed, 18 May 2022 08:47:03 -0400 2022-06-02T08:00:00-04:00 2022-06-02T10:00:00-04:00 Off Campus Location Department of Statistics Dissertation Defenses Presentation Defense Flyer
Dissertation Defense: Symmetry and Reformulation: On Intellectual Progress in Science and Math (June 3, 2022 1:00pm) https://events.umich.edu/event/94783 94783-21767869@events.umich.edu Event Begins: Friday, June 3, 2022 1:00pm
Location: Angell Hall
Organized By: Department of Philosophy

COMMITTEE:
Ruetsche, Laura (co-chair)
Belot, Gordon (co-chair)
Baker, David
Elvang, Henriette (cognate, Physics)

ABSTRACT:
Scientists and mathematicians routinely make progress by reformulating their problem-solving plans. Whereas many philosophers focus on competing theories, methodologies, or foundations, I focus on what I call "compatible formulations." In these cases, different problem-solving plans peacefully coexist, mutually illuminating their subject matter. My dissertation defends an account of the nature and value of compatible formulations. I argue that reformulations often provide a kind of objective, non-practical, epistemic value, which I call "intellectual significance." Meeting the constitutive aims of science and mathematics requires reformulating.

Of course, not all reformulations are intellectually significant: some are trivial notational variants. To distinguish trivial from significant reformulations, I consider four different accounts of the value of reformulating, based on instrumental, intellectual, explanatory, and metaphysical value. According to what I call "conceptualism," reformulations are significant when they provide an epistemically distinct plan for solving problems. The intellectual value of reformulating does not require corresponding explanatory or metaphysical differences, and it goes beyond practical or instrumental value. To avoid more weighty commitments, I provide expressivist accounts of (i) what it means for one formulation to provide better understanding than another and (ii) what it means for one formulation to be more fundamental. Finally, I analyze what it means for a formulation to make a property manifest or perspicuous, e.g. by "wearing it on the sleeves."

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Other Mon, 23 May 2022 16:49:52 -0400 2022-06-03T13:00:00-04:00 2022-06-03T15:00:00-04:00 Angell Hall Department of Philosophy Other
Network Inference with Applications in Neuroimaging (June 20, 2022 10:00am) https://events.umich.edu/event/95695 95695-21790561@events.umich.edu Event Begins: Monday, June 20, 2022 10:00am
Location: Off Campus Location
Organized By: Department of Statistics

Abstract:
With network data becoming ubiquitous in many applications, many models and algorithms for network analysis have been proposed. Besides the commonly studied single binary network, in which each node in the network represents a single object and the edge between two nodes represents the relationship between the two, multiple weighted networks are also frequently observed in neuroimaging. In such applications, a network can be observed for each individual, with all networks sharing the same set of nodes. In this thesis, we develop inference methods for both types of network with application to neuroimaging dataset.

For a single binary network, there exists many inference models. Yet methods for providing uncertainty estimates in addition to point estimates of network parameters are much less common. Bootstrap and other resampling procedures have been an effective general tool for estimating uncertainty from i.i.d. samples, resampling network data is substantially more complicated. In Chapter 1, we compare three different network resampling procedures from the point of view of uncertainty estimation, and propose a general procedure to construct confidence intervals for network parameters through network resampling. We find that no one procedure is universally best for all tasks, and demonstrate the pros and cons of different resampling strategies through simulation studies.

In Chapter 2, based on the motivating example of Adolescent Brain Cognitive Development (ABCD) study, we proposed an algorithm for fitting multiple response regression problems where predictors are weighted network and edge weights are used as features. While most multiple response regression methods take advantage of correlated responses by incorporating covariance structure in the error vector, the method we proposed also considers the additional information provided in network-valued predictors by adding the constraint that a common community structure is shared across different prediction coefficients. We apply the method to the ABCD dataset, and provide inference on the relationship between rest-state brain fMRI networks and multiple cognitive task performance of 9-10 year old adolescence.

While learning the community structure shared across regression coefficients corresponding to different cognitive tasks allows better interpretation of the partition of brain areas, a discrete community assignment might impose a constraint that is too strong to remain the predictive power of brain connectomes. In Chapter 3, we relax the constraint to shared low rank structure across regression coefficients, and compared the performance of common structures learnt through three different methods, namely the low rank structure and the mixed membership structure in the coefficient of common cognitive ability, and the shared low rank embeddings in all task-specific regression coefficients.

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Presentation Mon, 20 Jun 2022 10:09:38 -0400 2022-06-20T10:00:00-04:00 2022-06-20T11:30:00-04:00 Off Campus Location Department of Statistics Presentation
Formation of the First Galaxies (June 20, 2022 11:00am) https://events.umich.edu/event/95390 95390-21789882@events.umich.edu Event Begins: Monday, June 20, 2022 11:00am
Location: West Hall
Organized By: Department of Astronomy

Dissertation Defense

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Presentation Thu, 02 Jun 2022 14:15:02 -0400 2022-06-20T11:00:00-04:00 2022-06-20T13:00:00-04:00 West Hall Department of Astronomy Presentation West Hall
Linguistics Dissertation Defense (June 24, 2022 11:00am) https://events.umich.edu/event/95579 95579-21790351@events.umich.edu Event Begins: Friday, June 24, 2022 11:00am
Location: Off Campus Location
Organized By: Department of Linguistics

Linguistics PhD candidate Jian Zhu will defend his dissertation on Friday, June 24, at 11 am.
Title: A computational account of selected patterns of linguistic variation and change
Zoom link: https://umich.zoom.us/j/93720993749, password: dimsum
Co-Chairs: Pam Beddor and David Jurgens

PhD defenses in the Linguistics Department are open to the public. Anyone is welcome to attend if interested.

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Other Thu, 23 Jun 2022 10:00:18 -0400 2022-06-24T11:00:00-04:00 2022-06-24T13:00:00-04:00 Off Campus Location Department of Linguistics Other
Linguistics Dissertation Defense (June 29, 2022 9:00am) https://events.umich.edu/event/95694 95694-21790560@events.umich.edu Event Begins: Wednesday, June 29, 2022 9:00am
Location: Off Campus Location
Organized By: Department of Linguistics

Linguistics PhD candidate Moira Saltzman will defend her dissertation on Wednesday, June 29, at 9 am. Title: "A History of Jejueo." Committee co-chairs are Marlyse Baptista and Sally Thomason.

PhD defenses in the Linguistics Department are open to the public. Anyone is welcome to attend if interested.

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Other Thu, 23 Jun 2022 10:01:02 -0400 2022-06-29T09:00:00-04:00 2022-06-29T11:00:00-04:00 Off Campus Location Department of Linguistics Other
EEB Dissertation Defense: The Soil Microbiome and its Response to Permafrost Thaw in Arctic Tundra (June 29, 2022 2:00pm) https://events.umich.edu/event/95513 95513-21790025@events.umich.edu Event Begins: Wednesday, June 29, 2022 2:00pm
Location: Off Campus Location
Organized By: Ecology and Evolutionary Biology

zoom details to follow

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Lecture / Discussion Thu, 09 Jun 2022 07:48:55 -0400 2022-06-29T14:00:00-04:00 2022-06-29T16:00:00-04:00 Off Campus Location Ecology and Evolutionary Biology Lecture / Discussion hikers on a boardwalk over a grassy field with distant mountains
Dissertation Defense: (Title TBD) (July 19, 2022 1:00pm) https://events.umich.edu/event/95090 95090-21788458@events.umich.edu Event Begins: Tuesday, July 19, 2022 1:00pm
Location: Angell Hall
Organized By: Department of Philosophy

COMMITTEE:
Railton, Peter (co-chair, Philosophy)
Kross, Ethan (co-chair, Psychology)
Sripada, Chandra
Jorgensen, Renée
Gelman, Susan (cognate, Psychology)


ABSTRACT:
TBD

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Other Wed, 11 May 2022 11:41:35 -0400 2022-07-19T13:00:00-04:00 2022-07-19T15:00:00-04:00 Angell Hall Department of Philosophy Other
EEB Dissertation Defense: Diversification in the Unionidae: Investigating the role of parasitism (July 21, 2022 11:00am) https://events.umich.edu/event/95512 95512-21790024@events.umich.edu Event Begins: Thursday, July 21, 2022 11:00am
Location: Rackham Graduate School (Horace H.)
Organized By: Ecology and Evolutionary Biology

In addition to in-person, there will be a Zoom link. Please reach out to eeb.gradcoord@umich.edu at least two hours in advance for the link.

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Lecture / Discussion Thu, 07 Jul 2022 08:42:17 -0400 2022-07-21T11:00:00-04:00 2022-07-21T13:00:00-04:00 Rackham Graduate School (Horace H.) Ecology and Evolutionary Biology Lecture / Discussion EEB superimposed over leaves, a rodent, an amphibian and a bird
Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes (July 22, 2022 11:00am) https://events.umich.edu/event/95890 95890-21791380@events.umich.edu Event Begins: Friday, July 22, 2022 11:00am
Location: West Hall
Organized By: Department of Statistics Dissertation Defenses

Abstract: This thesis focuses on data that has complex spatio-temporal structure and on probabilistic graphical models that learn the structure in an interpretable and scalable manner. We target two research areas of interest: Gaussian graphical models for tensor-variate data and summarization of complex time-varying texts using topic models. This work advances the state-of-the-art in several directions. First, it introduces a new class of tensor-variate Gaussian graphical models via the Sylvester tensor equation. Second, it develops an optimization technique based on a fast-converging proximal alternating linearized minimization method, which scales tensor-variate Gaussian graphical model estimations to modern big-data settings. Third, it connects Kronecker-structured (inverse) covariance models with spatio-temporal partial differential equations (PDEs) and introduces a new framework for ensemble Kalman filtering that is capable of tracking chaotic physical systems. Fourth, it proposes a modular and interpretable framework for unsupervised and weakly-supervised probabilistic topic modeling of time-varying data that combines generative statistical models with computational geometric methods. Throughout, practical applications of the methodology are considered using real datasets. This includes brain-connectivity analysis using EEG data, space weather forecasting using solar imaging data, longitudinal analysis of public opinions using Twitter data, and mining of mental health related issues using TalkLife data. We show in each case that the graphical modeling framework introduced here leads to improved interpretability, accuracy, and scalability.

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Presentation Fri, 01 Jul 2022 11:54:34 -0400 2022-07-22T11:00:00-04:00 2022-07-22T13:00:00-04:00 West Hall Department of Statistics Dissertation Defenses Presentation West Hall
Contributions to Quantile and Superquantile Regression (July 25, 2022 9:00am) https://events.umich.edu/event/96201 96201-21792033@events.umich.edu Event Begins: Monday, July 25, 2022 9:00am
Location: Off Campus Location
Organized By: Department of Statistics Dissertation Defenses

Abstract: Understanding the heterogeneous covariate-response relationship is central to modern data analysis. Beyond the usual descriptors such as the mean and variance, quantile and superquantile (also known as the expected shortfall or conditional value-at-risk) regression can capture the differential covariate effects on the upper or lower tails of the response distribution. This dissertation studies some fundamental aspects of the statistical inference of quantile and super quantile regression.

In the first part of the dissertation, we propose a novel approach to superquantile regression with a critical modification of an optimization formulation in the recent literature. Most existing approaches for superquantile regression rely explicitly on the modeling of the conditional quantile function. In this dissertation, we offer new insights into an optimization formulation for the superquantile, based on which we provide and validate a direct approach to superquantile regression estimation without relying on additional quantile regression modeling. Operationally, the approach can be well approximated by fitting a linear quantile regression to an array of pre-estimated conditional superquantile processes. This approach achieves implicit weighting of the data, which is found to be automatically adaptive to data heterogeneity in a variety of scenarios. With certain initial estimators based on binning of the covariate space, we show the proposed superquantile regression estimator is consistent and asymptotically normal. Via theoretical and numerical comparisons, we show that the proposed approach has competitive, and often superior, performance relative to other common approaches in the literature.

In the second part of the dissertation, we study pseudo-Bayesian inference for possibly sparse quantile regression models. We find that by coupling the asymmetric Laplace working likelihood with appropriate shrinkage priors, we can deliver pseudo-Bayesian inference that adapts automatically to the possible sparsity in quantile regression analysis. After a suitable adjustment on the posterior variance, the proposed method provides asymptotically valid inference under heterogeneity. Furthermore, the proposed approach leads to oracle asymptotic efficiency for the active (nonzero) quantile regression coefficients and super-efficiency for the non-active ones. We also discuss the theoretical extension when the covariate dimension increases with the sample size at a controlled rate. By avoiding the need to pursue dichotomous variable selection as well as nuisance parameter estimation, the Bayesian computational framework demonstrates desirable inferential stability.

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Presentation Thu, 21 Jul 2022 10:06:45 -0400 2022-07-25T09:00:00-04:00 2022-07-25T11:00:00-04:00 Off Campus Location Department of Statistics Dissertation Defenses Presentation
Linguistics Dissertation Defense (August 2, 2022 10:00am) https://events.umich.edu/event/96241 96241-21792162@events.umich.edu Event Begins: Tuesday, August 2, 2022 10:00am
Location: Off Campus Location
Organized By: Department of Linguistics

Linguistics graduate candidate Yourdanis Sedarous will defend her dissertation on Tuesday, August 2, at 10 am. Title: "An experimental study on the syntax of English and Egyptian Arabic: A unified account of bilingual grammatical knowledge." Co-chairs are Marlyse Baptista and Acrisio Pires.

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Other Mon, 25 Jul 2022 09:43:27 -0400 2022-08-02T10:00:00-04:00 2022-08-02T12:00:00-04:00 Off Campus Location Department of Linguistics Other
Dissertation Defense: (Title TBD) (August 2, 2022 2:00pm) https://events.umich.edu/event/95252 95252-21789068@events.umich.edu Event Begins: Tuesday, August 2, 2022 2:00pm
Location: Angell Hall
Organized By: Department of Philosophy

COMMITTEE:
Mendlow, Gabe (co-chair)
Weatherson, Brian (co-chair)
Buss, Sarah
Hershovitz, Scott (cognate, Law)
Yaffe, Gideon (special member, Yale)


ABSTRACT:
TBD

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Other Mon, 23 May 2022 14:13:07 -0400 2022-08-02T14:00:00-04:00 2022-08-02T16:00:00-04:00 Angell Hall Department of Philosophy Other
On Some Approximate Inference Approaches in Population Genetics (August 8, 2022 8:00am) https://events.umich.edu/event/96641 96641-21792968@events.umich.edu Event Begins: Monday, August 8, 2022 8:00am
Location: Off Campus Location
Organized By: Department of Statistics Dissertation Defenses

The study of evolution has been a central focus of biology for several centuries. One of the fields concerned with the evolutionary process is population genetics. Population genetics studies the genetic composition of populations. To extract useful information from the genetic data, one of the central problems the coalescent has become the primary tool for modeling genealogies. The coalescent was proposed by Kingman in a series of path-breaking papers. Many other authors subsequently built on Kingman’s ideas, leading to a rich understanding of many mathematical and theoretical aspects of evolution.

The coalescent can be interpreted as a probabilistic model for generating random gene trees. Subsequently, it was extended to model recombinations, and also to incorporate mutation processes along the tree. Although these models can in principle be used to compute the likelihood of a given genetic data set, it is not feasible to do so in practice in many cases of interest. This is especially true when the loci have a different evolutionary history due to recombination—then, in order to evaluate the likelihood function, one must integrate out an astronomical number of possible ancestry scenarios that could have generated the data. In order to lift the computational burden that arose in practice, various approximate models have been proposed.

Two of the most important approximations are the Li Stephens haplotype copying model, and the sequentially Markov coalescent. This thesis seeks to understand the fundamental aspects of these approximate inference approaches. Chapter 2 introduces concepts and previous work to provide the context necessary for the rest of the thesis. Chapter 3 consists of joint work focused on the Bayesian posterior consistency of the sequentially Markov coalescent and the ergodicity1 of the sequentially Markov coalescent process. By slightly modifying pairwise sequentially Markov coalescent in a way that does not adversely affect inference, we prove frequentist guarantees about its posterior distribution. We also analyze the ergodicity property of the underlying sequentially Markov coalescent process using the theory of piecewise deterministic Markov process. Chapter 4 first presents a new interpretation of the Li Stephens model in terms of changepoint detection. We derive a new, efficient algorithm for determining the complete solution surface of both the haploid and diploid variants of the Li Stephens algorithm. Chapter 5 is devoted to estimators which combined information from the sample frequency spectrum and pairwise sequentially Markov coalescent. We summarize the results of this dissertation and discuss the drawbacks and some potential directions of our work in Chapter 6.

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Presentation Mon, 08 Aug 2022 10:46:58 -0400 2022-08-08T08:00:00-04:00 2022-08-08T10:00:00-04:00 Off Campus Location Department of Statistics Dissertation Defenses Presentation
Write-Together (September 9, 2022 10:00am) https://events.umich.edu/event/97717 97717-21795000@events.umich.edu Event Begins: Friday, September 9, 2022 10:00am
Location: North Quad
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

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Meeting Fri, 21 Oct 2022 09:12:33 -0400 2022-09-09T10:00:00-04:00 2022-09-09T13:00:00-04:00 North Quad Sweetland Center for Writing Meeting Write-Together Flyer
Write-Together (September 16, 2022 10:00am) https://events.umich.edu/event/97717 97717-21795001@events.umich.edu Event Begins: Friday, September 16, 2022 10:00am
Location: North Quad
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

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Meeting Fri, 21 Oct 2022 09:12:33 -0400 2022-09-16T10:00:00-04:00 2022-09-16T13:00:00-04:00 North Quad Sweetland Center for Writing Meeting Write-Together Flyer
Write-Together (September 23, 2022 10:00am) https://events.umich.edu/event/97717 97717-21795002@events.umich.edu Event Begins: Friday, September 23, 2022 10:00am
Location: North Quad
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

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Meeting Fri, 21 Oct 2022 09:12:33 -0400 2022-09-23T10:00:00-04:00 2022-09-23T13:00:00-04:00 North Quad Sweetland Center for Writing Meeting Write-Together Flyer
Write-Together (September 30, 2022 10:00am) https://events.umich.edu/event/97717 97717-21795003@events.umich.edu Event Begins: Friday, September 30, 2022 10:00am
Location: North Quad
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

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Meeting Fri, 21 Oct 2022 09:12:33 -0400 2022-09-30T10:00:00-04:00 2022-09-30T13:00:00-04:00 North Quad Sweetland Center for Writing Meeting Write-Together Flyer
Write-Together (October 7, 2022 10:00am) https://events.umich.edu/event/97717 97717-21795004@events.umich.edu Event Begins: Friday, October 7, 2022 10:00am
Location: North Quad
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

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Meeting Fri, 21 Oct 2022 09:12:33 -0400 2022-10-07T10:00:00-04:00 2022-10-07T13:00:00-04:00 North Quad Sweetland Center for Writing Meeting Write-Together Flyer
EEB dissertation defense: Host-parasite and parasite-parasite interactions drive disease dynamics at multiple levels of biological organization (October 11, 2022 10:00am) https://events.umich.edu/event/96243 96243-21792164@events.umich.edu Event Begins: Tuesday, October 11, 2022 10:00am
Location: Rackham Graduate School (Horace H.)
Organized By: Ecology and Evolutionary Biology

This event is in-person and will be livestreamed on Zoom, see link on this page.

Please reach out to eeb.gradcoord@umich.edu at least two hours in advance for the link.

Image credit: Marcin Dziuba

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Lecture / Discussion Fri, 30 Sep 2022 10:35:36 -0400 2022-10-11T10:00:00-04:00 2022-10-11T11:00:00-04:00 Rackham Graduate School (Horace H.) Ecology and Evolutionary Biology Lecture / Discussion A magnified image of a single Daphnia, which is tiny shrimp-like bug with a single large eye and a transparent body. Parasites are visible inside its body. Image credit: Marcin Dziuba
Write-Together (October 14, 2022 10:00am) https://events.umich.edu/event/97717 97717-21795005@events.umich.edu Event Begins: Friday, October 14, 2022 10:00am
Location: North Quad
Organized By: Sweetland Center for Writing

Write-Together sessions provide structure, accountability, and support for graduate writers working on writing at any stage, from papers to theses to journal articles to dissertations and more. For each of these sessions, participants can meet in-person or access a Zoom link and a shared Google document that will serve as a communal virtual space. Students will be invited to post pre-writing goals and post-writing reflections in the document. Writers can also schedule a 10-minute Zoom meeting with Sweetland faculty during each session to discuss writing questions. We will also provide weekly writing strategies to habituate students to best writing practices.

Supported by the Rackham Graduate School and the Sweetland Center for Writing.

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Meeting Fri, 21 Oct 2022 09:12:33 -0400 2022-10-14T10:00:00-04:00 2022-10-14T13:00:00-04:00 North Quad Sweetland Center for Writing Meeting Write-Together Flyer