Happening @ Michigan https://events.umich.edu/list/rss RSS Feed for Happening @ Michigan Events at the University of Michigan. SEMINAR: "Unbiased Subdata Selection for Fair Classification: A Unified Framework and Scalable Algorithms" – Weijun Xie (September 16, 2021 3:00pm) https://events.umich.edu/event/86631 86631-21635241@events.umich.edu Event Begins: Thursday, September 16, 2021 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.

Title:
Unbiased Subdata Selection for Fair Classification: A Unified Framework and Scalable Algorithms

Abstract:
Fair classification concerns the issues of unintentional biases against the sensitive features (e.g., gender, race) in the conventional classification approaches. Due to the high nonconvexity of fairness measures, existing methods are often unable to model exact fairness, which can cause inferior fair classification outcomes. This paper fills the gap by developing a novel unified framework to jointly optimize accuracy and fairness. The proposed framework is versatile and can incorporate different fairness measures precisely as well as can be applicable to many classifiers, including deep classification models. Many classification models within this framework can be recast as mixed-integer convex programs, which can be solved effectively by off-the-shelf solvers when the instance sizes are moderate. We prove that in the proposed framework, when the classification outcomes are known, the resulting problem, termed “unbiased subdata selection,” is strongly polynomial-solvable and can be used to enhance the classification fairness by selecting more representative data points. This motivates us to develop an iterative refining strategy (IRS) to solve the large-scale instances, where we improve the classification accuracy and conduct the unbiased subdata selection in an alternating fashion. We numerically demonstrate that the proposed framework can consistently yield better fair classification outcomes than existing methods. This is a joint work of my Ph.D. student Qing Ye.

Bio:
Dr. Weijun Xie is an Assistant Professor of Industrial and Systems Engineering, Virginia Tech. He obtained his Ph.D. from Georgia Tech. His research interests are in theory and applications of stochastic, discrete, and convex optimization. Dr. Xie has won multiple awards including NSF Career Award, INFORMS Optimization Prize for Young Researchers, INFORMS Junior Faculty Interest Group Paper Competition, INFORMS George Nicholson Student Paper Competition. He currently serves as the Vice Chair of Optimization under Uncertainty at INFORMS Optimization Society.

]]>
Workshop / Seminar Thu, 09 Sep 2021 22:34:50 -0400 2021-09-16T15:00:00-04:00 2021-09-16T16:00:00-04:00 Off Campus Location U-M Industrial & Operations Engineering Workshop / Seminar Weijun Xie, Virginia Tech
SEMINAR: "Spectral Models for Air Transportation Networks" – Max Li (September 23, 2021 3:00pm) https://events.umich.edu/event/86630 86630-21635240@events.umich.edu Event Begins: Thursday, September 23, 2021 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.

Title: Spectral Models for Air Transportation Networks


Abstract:
Air transportation networks are canonical examples of highly interconnected and complex systems. Furthermore, these societal-scale infrastructures generate large amounts of data upon which decisions regarding operational safety, efficiency, and reliability must be made. Therefore, understanding the characteristics of performance measures such as air traffic delays is critical for developing ways to mitigate their significant economic and environmental impacts. I will focus on network behavior and performance during disruptions (e.g., thunderstorms, nor’easters, hurricanes), and discuss how airport delays can be viewed as graph-supported signals, amenable to a variety of spectral and graph signal processing-based methods. Through this analysis, I characterize the spatial distribution of delays across a network of airports, highlighting key differences in delay dynamics between different types of disruptions and among different airline networks. I will then touch on some recent work regarding low-dimensional representations of airport network delays and how to use these representations to develop control and traffic flow management strategies. Finally, I will discuss some interdisciplinary research directions of interest to me, such as the integrated modeling of air-surface multi-modal transportation networks and emerging aerospace mobility systems (e.g., UAS/AAM).

Bio:
Max is a Visiting Assistant Professor of Aerospace Engineering at the University of Michigan—Ann Arbor and will be starting as an Assistant Professor in Fall 2022. He is also currently at MITRE’s Center for Advanced Aviation System Development (CAASD) as a National Airspace System Senior Data Scientist. Max received his PhD in Aerospace Engineering from the Massachusetts Institute of Technology in 2021. He earned his MSE in Systems Engineering and BSE in Electrical Engineering and Mathematics, both from the University of Pennsylvania, in 2018. Broadly, he is interested in the analysis, control, and optimization of networks and networked processes, signal processing over irregular domains and manifolds, and geometric/topological data analysis, with an eye towards applications in air transportation systems and other societal-scale networks. He is the recipient of the Federal Aviation Administration RAISE Award (2018), a National Science Foundation Graduate Research Fellowship (2018), and the Wellington and Irene Loh Fellowship from MIT (2019), as well as several best paper awards from ICRAT and the ATM R&D Seminar, two joint FAA- Eurocontrol conferences.

]]>
Workshop / Seminar Thu, 09 Sep 2021 22:36:44 -0400 2021-09-23T15:00:00-04:00 2021-09-23T16:00:00-04:00 Off Campus Location U-M Industrial & Operations Engineering Workshop / Seminar Max Li, University of Michigan
SEMINAR: "Detecting equivalence between iterative algorithms for optimization" – Madeleine Richards Udell (September 30, 2021 3:00pm) https://events.umich.edu/event/86946 86946-21637614@events.umich.edu Event Begins: Thursday, September 30, 2021 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.

Title: Detecting equivalence between iterative algorithms for optimization

Abstract:
When are two algorithms the same? How can we be sure a recently proposed algorithm is novel, and not a minor twist on an existing method? In this talk, we present a framework for reasoning about equivalence between a broad class of iterative algorithms, with a focus on algorithms designed for convex optimization. We propose several notions of what it means for two algorithms to be equivalent, and provide computationally tractable means to detect equivalence. Our main definition, oracle equivalence, states that two algorithms are equivalent if they result in the same sequence of calls to the function oracles (for suitable initialization). Borrowing from control theory, we use state-space realizations to represent algorithms and characterize algorithm equivalence via transfer functions. Our framework can also identify and characterize some algorithm transformations including permutations of the update equations, repetition of the iteration, and conjugation of some of the function oracles in the algorithm. A software package named Linnaeus implements the framework and makes it easy to find other iterative algorithms that are equivalent to an input algorithm. More broadly, this framework and software advances the goal of making mathematics searchable.
Based on joint work with Shipu Zhao and Laurent Lessard


Bio:
Madeleine Udell is Assistant Professor of Operations Research and Information Engineering and Richard and Sybil Smith Sesquicentennial Fellow at Cornell University. She studies optimization and machine learning for large scale data analysis and control, with applications in marketing, demographic modeling, medical informatics, engineering system design, and automated machine learning. She has received several awards, including an Alfred P. Sloan Research Fellowship (2021), a National Science Foundation CAREER award (2020), an Office of Naval Research (ONR) Young Investigator Award (2020), a Cornell Engineering Research Excellence Award (2020), an INFORMS Optimization Society Best Student Paper Award (as advisor) (2019), and INFORMS Doing Good with Good OR (2018). Her work is supported by grants from the NSF, ONR, DARPA, the Canadian Institutes of Health, and Capital One.
Her research in optimization centers on detecting and exploiting novel structures in optimization problems, with a particular focus on convex and low rank problems. These structures lead the way to automatic proofs of optimality, better complexity guarantees, and faster, more memory-efficient algorithms. She has developed a number of open source libraries for modeling and solving optimization problems, including Convex.jl, one of the top tools in the Julia language for technical computing.
Her research in machine learning centers on methods for imputing missing data in large tabular data sets. Her work on generalized low rank models (GLRMs) extends principal components analysis (PCA) to embed tabular data sets with heterogeneous (numerical, Boolean, categorical, and ordinal) types into a low dimensional space, providing a coherent framework for compressing, denoising, and imputing missing entries. This research enables novel applications in medical informatics, quantitative finance, marketing, causal inference, and automated machine learning, among others.
At Cornell, Madeleine has advised more than 50 students and postdocs. She has developed several new courses in optimization and machine learning, earning the Douglas Whitney ’61 Engineering Teaching Excellence Award in 2018.
Madeleine completed her PhD at Stanford University in Computational & Mathematical Engineering in 2015 under the supervision of Stephen Boyd, and a one year postdoctoral fellowship at Caltech in the Center for the Mathematics of Information hosted by Professor Joel Tropp. At Stanford, she was awarded a NSF Graduate Fellowship, a Gabilan Graduate Fellowship, and a Gerald J. Lieberman Fellowship, and was selected as the doctoral student member of Stanford's School of Engineering Future Committee to develop a road-map for the future of engineering at Stanford over the next 10–20 years. She received a B.S. degree in Mathematics and Physics, summa cum laude, with honors in mathematics and in physics, from Yale University.

]]>
Workshop / Seminar Tue, 14 Sep 2021 18:34:09 -0400 2021-09-30T15:00:00-04:00 2021-09-30T16:00:00-04:00 Off Campus Location U-M Industrial & Operations Engineering Workshop / Seminar Madeleine Richards Udell, Cornell University
Panel and Q&A: Academic/Research job search (October 5, 2021 3:00pm) https://events.umich.edu/event/87779 87779-21645942@events.umich.edu Event Begins: Tuesday, October 5, 2021 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

As part of our Tuesday afternoon series, we are holding a faculty panel to answer some questions from PhD students to help them learn more about applying and interviewing, and be prepared for all aspects of the job search process in academia or research labs and similar settings. The PhD job market is often described as a marathon, and many aspects of it can be mysterious to first-time job seekers. If you are a PhD student currently on the job market, or considering going on the academic job market in the future, we particularly encourage you to attend. (The event is open to all IOE students and faculty.)

]]>
Workshop / Seminar Fri, 01 Oct 2021 13:41:14 -0400 2021-10-05T15:00:00-04:00 2021-10-05T16:00:00-04:00 Off Campus Location U-M Industrial & Operations Engineering Workshop / Seminar IOE Seminar generic
SEMINAR: "Avoiding Weather Hazards in General Aviation: Display Interpretation as a Contributing factor" - Elizabeth L. Blickensderfer (October 14, 2021 3:00pm) https://events.umich.edu/event/87593 87593-21644208@events.umich.edu Event Begins: Thursday, October 14, 2021 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.

Title: Avoiding Weather Hazards in General Aviation: Display Interpretation as a Contributing factor

Abstract: Pending

Bio:
Dr. Beth L. Blickensderfer has over 20 years of experience in human-machine systems research and development using both qualitative and quantitative research methods. She has designed and validated numerous training programs for purposes such as teaching general aviation pilots to interpret and understand weather displays and fostering crew resource management skills in helicopter and fixed-wing pilots. In addition, she has developed metrics to assess human performance in a range of domains and tasks such as aviation operations, nurses responding to cardiac arrest, and tennis teams. Her other recent work includes investigating patient safety at a community hospital, measuring general aviation pilots' knowledge and skill at interpreting weather displays, and performing cognitive task analyses to identify safety issues inherent to Live-Virtual-Constructive flight training environments for the U.S. Navy. Dr. Blickensderfer has held leadership positions in both the Human Factors and Ergonomic Society as well as Division 21 Applied Experimental and Engineering Psychology of the American Psychological Association. She earned an M.S in Industrial/Organizational Psychology and a Ph.D. in Human Factors Psychology from the University of Central Florida.

]]>
Workshop / Seminar Thu, 30 Sep 2021 13:42:23 -0400 2021-10-14T15:00:00-04:00 2021-10-14T16:00:00-04:00 Off Campus Location U-M Industrial & Operations Engineering Workshop / Seminar Elizabeth L. Blickensderfer, Embry-Riddle Aeronautical University
SEMINAR: "Industrial & Systems Engineering at the University of Florida and Recent Automation and Situation Awareness Modeling Research" - David Kaber (November 4, 2021 3:00pm) https://events.umich.edu/event/86873 86873-21637055@events.umich.edu Event Begins: Thursday, November 4, 2021 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.

Title: Industrial & Systems Engineering at the University of Florida and Recent Automation and Situation Awareness Modeling Research

Abstract:
In this talk, I will provide information on the current state of the Department of Industrial and Systems Engineering (ISE) at the University of Florida (UF), including faculty, research thrusts and laboratories, as well as funding and some current projects. The second part of the talk will focus on methods of modeling degrees of automation in human-in-the-loop systems as well as the impact on operator situation awareness (SA) responses. We contend that methods of some prior research do not represent engineering models, per se. Furthermore, discrete and ordinal characteristics of other models limit reliable prediction of operator performance with automation. This research defines an “automation rate” (AR) function involving classification of all system functions according to stages of information processing, calculation of the AR for each stage, setting weighting factors for these rates, and finally obtaining an overall AR for the system. The practicality and feasibility of this model are verified through a case study analysis. In addition, we formulate a new model of operator SA responses to AR, based on existing empirical research findings. Through the case analysis and mathematical proof, the rationality of the form of these new models is demonstrated. This work lays the foundation for subsequent optimization of automated system design for operator SA. This study provides an example of current human-systems engineering research through the UF ISE Department.

Bio:
David Kaber is currently the Dean’s Leadership Professor and Chair of the Department of Industrial and Systems Engineering at the University of Florida (UF). Prior to joining UF, Kaber was a distinguished professor of industrial engineering at North Carolina State University where he also served as the Director of Research for the Ergonomics Center of North Carolina. Kaber’s primary area of research interest is human-systems engineering with a focus on human-automaton interaction, including design and analysis for situation awareness in complex human in-the-loop systems. Domains of study for his research have included physical work systems, industrial safety systems, robotic systems, transportation systems and healthcare. Kaber is a senior member of IEEE and junior-past Editor-in-Chief of the IEEE Transactions on Human-Machine Systems. He is a fellow of Institute of Industrial Engineers and the Human Factors & Ergonomics Society. Kaber is also a Certified Human Factors Professional (BCPE) and a Certified Safety Professional (BCSP).

]]>
Workshop / Seminar Mon, 27 Sep 2021 16:17:44 -0400 2021-11-04T15:00:00-04:00 2021-11-04T16:00:00-04:00 Off Campus Location U-M Industrial & Operations Engineering Workshop / Seminar David Kaber, University of Florida
SEMINAR: "A DDDAMS-based Surveillance and Crowd Control via UAVs and UGVs" – Young-Jun Son (November 11, 2021 3:00pm) https://events.umich.edu/event/86680 86680-21635404@events.umich.edu Event Begins: Thursday, November 11, 2021 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.

Title: A DDDAMS-based Surveillance and Crowd Control via UAVs and UGVs

Abstract:
In this talk, we first introduce a dynamic data driven adaptive multi-scale simulation (DDDAMS) based planning and control framework that we have developed for effective and efficient surveillance and crowd control via unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The framework is composed of integrated planner, integrated controller, and decision module for DDDAMS. The integrated planner, which is designed in an agent-based simulation (ABS) and Unity-based game engine, devises best control strategies for each function of 1) crowd detection, 2) crowd tracking, and 3) UAV/UGV motion planning. The integrated controller then controls real UAVs/UGVs for surveillance tasks via 1) sensory data collection and processing, 2) control command generation based on strategies provided by the decision planner, and 3) control command transmission via radio to the real system. The decision module for DDDAMS enhances computational efficiency of the framework via dynamic switching of fidelity of simulation and information gathering. Finally, we will share the results of our field demo, which successfully integrated a fast running simulator, a real-time simulator, and the real system (viz. UAVs, UGVs, and crowd).

Bio:
Dr. Young-Jun Son is a Professor and the Head of Systems and Industrial Engineering Department at The University of Arizona. He is a Department Editor of the Institute of Industrial and Systems Engineers (IISE) Transactions, and serve on the editorial board for six other international journals. He is an IISE Fellow, and has received several research awards such as the Society of Manufacturing Engineers (SME) 2004 Outstanding Young Manufacturing Engineer Award, the IIE 2005 Outstanding Young Industrial Engineer Award, the Industrial and Systems Engineering Research Conference (ISERC) Best Track Paper Award (in 2005, 2008, 2009, 2016, 2018, 2019), and the Best Paper of the Year Award (2007) in International Journal of Industrial Engineering. His research works have been sponsored by NSF, AFOSR, USDOT, USDA, USDOE, NIST, among others. He can be reached at son@sie.arizona.edu.

]]>
Workshop / Seminar Thu, 09 Sep 2021 22:19:27 -0400 2021-11-11T15:00:00-05:00 2021-11-11T16:00:00-05:00 Off Campus Location U-M Industrial & Operations Engineering Workshop / Seminar Young-Jun Son, University of Arizona
SEMINAR: "Optimization Models to Increase Supplier Autonomy and Resource Utilization" – Jennifer Ann Pazour (November 18, 2021 3:00pm) https://events.umich.edu/event/86681 86681-21635405@events.umich.edu Event Begins: Thursday, November 18, 2021 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.

Title: Optimization Models to Increase Supplier Autonomy and Resource Utilization

Abstract:
Underutilized resources exist all around us. When at a stoplight, notice the empty seats and cargo spaces in the vehicles around you. Think about the monolithic distribution centers that are a mismatch for most businesses’ seasonal and fluctuating space and throughput requirements. To harness these and other underutilized resources, organizations need to think differently about how resources are acquired, managed, and allocated to fulfill requests. This talk will focus on one such solution: on-demand systems that match requests to independent, decentralized suppliers who are not employed nor controlled by the platform. In these situations, the platform cannot be certain a supplier will accept an offered request. To mitigate this selection uncertainty, a platform can offer each supplier a menu of requests to choose from. However, such menus need to be created carefully because of the trade-off between increasing selection probability and reduced systematic control. In addition to a complex decision space, supplier selection decisions are vast and have systematic implications, impacting the platform’s revenue, other suppliers’ experiences (in the form of duplicate selections) and the request waiting times. Thus, we present a multiple scenario approach, repeatedly sampling potential supplier selections, solving the corresponding two-stage decision problems, and combining the multiple different solutions through a consensus algorithm. Our specialized dynamic methods to create and push personalized recommendations to a set of freelance suppliers have broad applications to crowdsourced delivery, ride sharing, and volunteer management where providing choices can help interleaving of tasks with other activities and has the potential to increase resource utilization of decentralized assets. Extensive computational results using the Chicago Region as a case study illustrate that our method outperforms a set of benchmark policies, and is tractable for real-time deployment. We quantify the value of anticipating supplier selection, offering menus to suppliers, offering requests to multiple suppliers at once, and holistically generating menus with the entire system in mind. Our method leads to more balanced assignments by sacrificing some easy wins towards better system performance over time and for all stakeholders involved, including increased revenue for the platform, and decreased match waiting times for suppliers and requests.

This research is partially funded by the National Science Foundation award 1751801 and through a Johnson and Johnson WiSTEM2D fellowship. This is joint work with kind and talented people, including Rosemonde Ausseil, Hannah Horner, John Mitchell, Shahab Mofidi, and Marlin Ulmer.

Bio:
Jen Pazour is an Associate Professor and the Undergraduate Coordinator of Industrial and Systems Engineering at Rensselaer Polytechnic Institute (RPI) in Troy, NY. Her research and teaching focus on the development and use of mathematical models to guide decision making for logistics and supply chain challenges. Jen is a recipient of a National Science Foundation Faculty Early Career Development (CAREER) Award (2018), a Johnson & Johnson Women in STEM2D Scholars Award (2018), a National Academies of Science Gulf Research Program Early-Career Fellowship (2016), and a Young Investigator Award from the Office of Naval Research (2013). She was awarded the 2019 Rensselaer Alumni Teaching Award, the 2018 IISE Logistics and Supply Chain Division Teaching Award, and the 2017 IISE Dr. Hamed K. Eldin Outstanding Early Career IE in Academia Award. She is an Associate Editor of IISE Transactions and Military Operations Research. She has served as the chair of the INFORMS professional recognition committee, chair of the INFORMS undergraduate operations research prize, the communications chair of the IISE Logistics and Supply Chain division and is on the IISE Transaction Social Media Team. She proudly holds three degrees in Industrial Engineering (a B.S. from South Dakota School of Mines and Technology, and a M.S. and Ph.D. from the University of Arkansas). More information can be found at her research and teaching blog: http://jenpazour.wordpress.com/

]]>
Workshop / Seminar Thu, 09 Sep 2021 22:25:52 -0400 2021-11-18T15:00:00-05:00 2021-11-18T16:00:00-05:00 Off Campus Location U-M Industrial & Operations Engineering Workshop / Seminar Jennifer Ann Pazour, Rensselaer Polytechnic Institute
SEMINAR: "Finding low-dimensional structure in messy data" – Laura Balzano (December 2, 2021 3:00pm) https://events.umich.edu/event/86875 86875-21637058@events.umich.edu Event Begins: Thursday, December 2, 2021 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.

Title: Finding low-dimensional structure in messy data

Abstract:
In order to draw inferences from large, high-dimensional datasets, we often seek simple structure that models the phenomena represented in those data. Low-rank linear structure is one of the most flexible and efficient such models, allowing efficient prediction, inference, and anomaly detection. However, classical techniques for learning low-rank models assume your data have only minor corruptions that are uniform over samples. Modern research in optimization has begun to develop new techniques to handle realistic messy data — where data are missing, have wide variations in quality, and/or are observed through nonlinear measurement systems.

In this talk I will give a high-level overview of recent research in this area. Then I will focus on the problem of learning linear subspace structure from multiple data sources of varying quality. This is common in problems like sensor networks or medical imaging, where different measurements of the same phenomenon are taken with different quality sensing (eg high or low radiation). In this context, learning the low-rank structure via PCA suffers from treating all data samples as if they are equally informative. I will discuss our theoretical results on weighted PCA. I will then present new algorithms for the non-convex probabilistic PCA formulation of this problem and a novel SDP relaxation.

Bio:
Laura Balzano is an associate professor of Electrical Engineering and Computer Science, and of Statistics by courtesy, at the University of Michigan. She is recipient of the NSF Career Award, ARO Young Investigator Award, AFOSR Young Investigator Award, and faculty fellowships from Intel and 3M. She received the Vulcans Education Excellence Award at the University of Michigan. Her main research focus is on modeling with big, messy data — highly incomplete or corrupted data, uncalibrated data, and heterogeneous data — and its applications in a wide range of scientific problems. Her expertise is in statistical signal processing, matrix factorization, and optimization. Laura received a BS from Rice University, MS from the UCLA, and PhD from the University of Wisconsin in Electrical and Computer Engineering.

]]>
Workshop / Seminar Mon, 13 Sep 2021 14:33:25 -0400 2021-12-02T15:00:00-05:00 2021-12-02T16:00:00-05:00 Off Campus Location U-M Industrial & Operations Engineering Workshop / Seminar Laura Balzano, University of Michigan
The ARPA-E GO competition: The good, the bad and the ugly (March 24, 2022 3:00pm) https://events.umich.edu/event/92760 92760-21695327@events.umich.edu Event Begins: Thursday, March 24, 2022 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

Seminar Abstract:
The Advanced Research Projects Agency-Energy (ARPA-E) recently organized the "Grid Optimization" Competition, offering a total of $2.4M in prizes.

ARPA-E's goal is to "accelerate the development of transformational and disruptive methods for solving the most pressing power system problems" in a market where revenues reach close to $400B yearly.

In this talk, we will go over the computational challenges underlying these optimization problems and present the solution approach adopted by the team that ranked first across all divisions.

Presenter Bio:
Hassan Lionel Hijazi received a Ph.D. in Computer Science from Aix-Marseille University while working at Orange Labs - France Telecom R&D.
During his early career, Hassan was part of the Computer Science Laboratory of the Ecole Polytechnique in France and a senior lecturer at the Australian National University. Hassan is currently a staff scientist at Los Alamos National Laboratory. His main field of expertise is mixed-integer nonlinear optimization with applications in energy systems. Hassan was the laureate of the 2015 Rising Star Award by the Australian Society for Operations Research. In 2021, Hassan was the winner of the ARPA-E Grid Optimization Competition Challenge 2, ranking 1st across all divisions. Hassan continues to work on gravity, a modeling language for mathematical optimization.

This presentation is part of our IOE Departmental Seminar (899) Series.
The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend.

]]>
Presentation Fri, 04 Mar 2022 13:15:49 -0500 2022-03-24T15:00:00-04:00 2022-03-24T16:00:00-04:00 Off Campus Location U-M Industrial & Operations Engineering Presentation Hassan Hijazi
899 Seminar Series: Karmel S. Shehadeh, Lehigh University (March 31, 2022 3:00pm) https://events.umich.edu/event/93814 93814-21708495@events.umich.edu Event Begins: Thursday, March 31, 2022 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

In this talk, we present two distributionally robust optimization (DRO) models for a mobile facility (MF) fleet sizing, routing, and scheduling problem (MFRSP) with time-dependent and random demand, as well as methodologies for solving these models. Specifically, given a set of MFs, a planning horizon, and a service region, our models aim to find the number of MFs to use (i.e., fleet size) within the planning horizon and a route and time schedule for each MF in the fleet. The objective is to minimize the fixed cost of establishing the MF fleet plus a risk measure (expectation or mean conditional value-at-risk) of the random operational costs over all demand distributions defined by an ambiguity set. In the first model, we use an ambiguity set based on the demand’s mean, support, and mean absolute deviation. In the second model, we use an ambiguity set that incorporates all distributions within a 1-Wasserstein distance from a reference (empirical) distribution. To solve these DRO models, we propose a decomposition-based algorithm. In addition, we derive valid lower bound inequalities that efficiently strengthen the master problem in the decomposition algorithm, thus improving convergence. We also derive two families of symmetry-breaking constraints that improve the solvability of the proposed models. Finally, we present extensive computational experiments comparing the operational and computational performance of the proposed models and a stochastic programming model, demonstrating where significant performance improvements could be gained and derive insights into the MFRSP.

Presenter Bio:
Dr. Karmel S. Shehadeh is an Assistant Professor of Industrial Systems and Engineering (ISE) at Lehigh University. She currently serves as one of the directors of the Operations Research Division at the Institute of Industrial and Systems Engineers. Before joining Lehigh, she was a Presidential and Dean Postdoctoral Fellow at Heinz College of Information Systems and Public Policy at Carnegie Mellon University. She holds a doctoral degree in Industrial and Operations Engineering from the University of Michigan, a master's degree in Systems Science and Industrial Engineering from Binghamton University, and a bachelor's in Biomedical Engineering from Jordan University of Science and Technology.

Shehadeh’s broad methodological research expertise and interests include integer programming, stochastic optimization, and scheduling theory and algorithms development. Her primary application areas and expertise are in healthcare operations and analytics. Her research group is currently working on solving emerging and challenging real-world optimization problems within and outside healthcare operations. These include healthcare scheduling and capacity planning, home care, hospital readmission, facility location, and disaster response operations.

]]>
Workshop / Seminar Tue, 22 Mar 2022 09:06:54 -0400 2022-03-31T15:00:00-04:00 2022-03-31T16:00:00-04:00 Off Campus Location U-M Industrial & Operations Engineering Workshop / Seminar Karmel: 899 Seminar Series
Low Back Disorder Causality? (April 7, 2022 3:00pm) https://events.umich.edu/event/92931 92931-21698084@events.umich.edu Event Begins: Thursday, April 7, 2022 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

Seminar Abstract:
Low back disorders (LBDs) are a common world-wide problem that leads to disability, pain and excessive medical costs. While one can find volumes of information relating to the casual pathways associated with LBDs, our rates of LBDs have been increasing over the decades and the treatment costs have increased by over 300 percent in recent years. This situation begs the question: What do we really know about LBD causality? This presentation will review the efforts that have been underway at the Spine Research Institute over the past several decades to unravel the casual pathway puzzle. These efforts involve observational field studies, biomechanical laboratory studies, modeling and more recent attempts to phenotype patients using patient-centered studies. These efforts will be presented as a pattern of evidence to better appreciate the casual pathways associated with LBD that can be used to inform prevention as well as treatment efforts.

Presenter Bio:
William S. Marras, Ph.D.

William S. Marras holds the Honda Chair in Integrated Systems Engineering at the Ohio State University and serves as the Director of the Spine Research Institute at the Ohio State University where he leads NIH, NSF, DoD and privately funded research efforts. Dr. Marras holds joint academic appointments in the Department of Orthopaedic Surgery, the Department of Neurosurgery, and the Department of Physical Medicine & Rehabilitation. His research is focused on understanding multidimensional causal pathways for spine disorders through quantitative epidemiologic evaluations, laboratory biomechanical studies, personalized mathematical modeling, and clinical studies of the lumbar and cervical spines. His findings have been published in over 300 peer-reviewed journal articles, hundreds of refereed proceedings, and numerous books and book chapters including a book entitled The Working Back: A Systems View. Professor Marras has been active in the National Research Council (NRC) having served on over a dozen boards and committees and has served as Chair of the Board on Human Systems Integration for multiple terms. He has also served as Editor-in-Chief of Human Factors and is currently Deputy Editor of Spine. Dr. Marras holds Fellow status in six professional societies and is an elected member of the National Academy of Engineering (the National Academy of Science, Engineering and Medicine), recorded a TEDx talk entitled “Back Pain and your Brain” and has been featured on NPR’s All Things Considered.

]]>
Presentation Fri, 04 Mar 2022 13:32:49 -0500 2022-04-07T15:00:00-04:00 2022-04-07T16:00:00-04:00 Off Campus Location U-M Industrial & Operations Engineering Presentation William Marras