Happening @ Michigan https://events.umich.edu/list/rss RSS Feed for Happening @ Michigan Events at the University of Michigan. Research Talk: Lauren Czerniak (October 3, 2023 3:00pm) https://events.umich.edu/event/110536 110536-21830047@events.umich.edu Event Begins: Tuesday, October 3, 2023 3:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

Seminar Abstract:
Hospital pharmacy managers make decisions for thousands of different drugs. This motivates inventory policies that are (a) quick to solve and (b) easy to implement. Further, these inventory policies must capture the two key characteristics in a hospital pharmacy inventory system: perishability and supply chain disruptions. This talk will focus on closed-form solutions for a perishable lost-sales (R,S) periodic review inventory system with supply chain disruptions. Using a drug case study, the talk will also provide insights on the (i) impact of ignoring perishability and supply chain disruptions, (ii) importance of accounting for the length and time between supply chain disruptions, and (iii) influence of stochastic demand.

Presenter Bio:
Lauren Czerniak is an Industrial and Operations Engineering PhD candidate at the University of Michigan where she is being co-advised by Mark S. Daskin and Mariel S. Lavieri. Her research focuses on developing and applying stochastic models to address current challenges in healthcare with applications in pharmaceutical drugs, glaucoma, and concussion management. She is a Rackham Merit Fellow and a recipient of the National Science Foundation Graduate Research Fellowship.

]]>
Lecture / Discussion Tue, 26 Sep 2023 12:09:21 -0400 2023-10-03T15:00:00-04:00 2023-10-03T16:00:00-04:00 Off Campus Location U-M Industrial & Operations Engineering Lecture / Discussion Lauren Czerniak
PhD Research Talk: Naichen Shi (October 10, 2023 3:00pm) https://events.umich.edu/event/113411 113411-21830970@events.umich.edu Event Begins: Tuesday, October 10, 2023 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

Seminar Abstract:
In myriad statistical applications, data is collected from related but heterogeneous sources. These sources share some commonalities while containing idiosyncratic characteristics. Is it possible to recover the shared and source-specific factors? We show that under appropriate conditions on the alignment of source-specific factors, the problem is well-defined, and both shared and source-specific factors are identifiable under a constrained matrix factorization objective. To solve this objective, we propose a new class of matrix factorization algorithms called heterogeneous matrix factorization. HMF is easy to implement, enjoys local linear convergence under suitable assumptions, and is intrinsically distributed. Through a variety of empirical studies, we showcase the advantageous properties of HMF and its potential application in feature extraction and anomaly detection. We also show HMF's capabilities in handling large noise and missing entries.


Presenter Bio:
Naichen Shi is a Ph.D. candidate from the department of industrial and operations engineering at the University of Michigan, where he is advised by Dr. Raed Al Kontar. His research focuses on data analytics and optimization and their applications in manufacturing systems. In particular, he is interested in applying efficient optimization algorithms to understand the intrinsic patterns of complex systems. Naichen is an active member of his communities and has served as a reviewer for multiple journals and conferences, including Technometrics, NeurIPS, and AISTATS. See his personal website (https://sites.google.com/umich.edu/ncs/home) for more detailed information about him.

]]>
Lecture / Discussion Tue, 03 Oct 2023 12:14:57 -0400 2023-10-10T15:00:00-04:00 2023-10-10T16:00:00-04:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Lecture / Discussion Industrial and Operations Engineering Building
PhD Defense: Lauren Czerniak (November 2, 2023 11:00am) https://events.umich.edu/event/113412 113412-21830971@events.umich.edu Event Begins: Thursday, November 2, 2023 11:00am
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

Lauren Czerniak is an Industrial and Operations Engineering Ph.D. Candidate at the University of Michigan where she is being co-advised by Mark Daskin and Mariel Lavieri. Her research focuses on developing and applying stochastic models to address current challenges in healthcare with applications in pharmaceutical drugs, glaucoma, and concussion management. She is a Rackham Merit Fellow and a recipient of the National Science Foundation Graduate Research Fellowship. Feel free to check out her website to learn more about her interests, research, teaching, and service.

]]>
Lecture / Discussion Mon, 02 Oct 2023 19:45:00 -0400 2023-11-02T11:00:00-04:00 2023-11-02T12:00:00-04:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Lecture / Discussion Lauren Czerniak
PhD Research Talk: Baoyu Zhou (November 7, 2023 3:00pm) https://events.umich.edu/event/114348 114348-21832769@events.umich.edu Event Begins: Tuesday, November 7, 2023 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

Seminar Abstract:
In this talk, I will present some recents works on the design, analysis, and implementation of practical algorithms for solving stochastic optimization problems with constraints, while such problems arise from important applications including artificial intelligence, inventory control, power systems, etc. The first part of this talk focuses on some new understandings of an inexact regularized L-shaped algorithm for two-stage stochastic programming problems. Under common assumptions including fixed recourse and bounded (sub)gradients, we provide the number of iterations, operations, and samples that the algorithm needs to find a near-optimal solution, where the radius of the convergence neighborhood depends on the level of the inexactness of objective function estimates. In the second part, I will introduce a sequential quadratic programming method for minimizing a stochastic objective function subject to deterministic constraints. In addition to presenting the theoretical convergence behavior, we compare the empirical performance of our proposed method with other alternatives to demonstrate the advantages of our algorithm. In the end, I will discuss some of my future research directions.


Presenter Bio:
Baoyu Zhou is a postdoctoral researcher at the University of Michigan (Department of Industrial and Operations Engineering) and the University of Chicago (Booth School of Business), working with Professors Albert S. Berahas, Haihao Lu, and John R. Birge. He received his doctoral and master's degree in Industrial and Systems Engineering (ISE) from Lehigh University, advised by Professor Frank E. Curtis. Before joining Lehigh, he received his bachelor's degree in Mechanical Engineering from Shanghai Jiao Tong University. He was a Givens Associate in the Mathematics and Computer Science Division at Argonne National Laboratory and a Research Intern at Facebook AI Research. He won the Van Hoesen Family Best Publication Award at Lehigh ISE Department in 2021 and received the Elizabeth V. Stout Dissertation Award at the P.C. Rossin College of Engineering and Applied Science in 2022. His research focuses on developing, analyzing, and implementing practical algorithms for solving large-scale continuous optimization problems.

]]>
Lecture / Discussion Mon, 23 Oct 2023 15:39:21 -0400 2023-11-07T15:00:00-05:00 2023-11-07T16:00:00-05:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Lecture / Discussion Baoyu Zhou
PhD Research Talk: Jingwen Tang (November 14, 2023 3:00pm) https://events.umich.edu/event/114643 114643-21833243@events.umich.edu Event Begins: Tuesday, November 14, 2023 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

Seminar Abstract:
We study a feature-based pricing problem with demand censoring in an offline data-driven setting. In this problem, a firm is endowed with a finite amount of inventory, and faces a random demand that is dependent on the offered price and the covariates (from products, customers, or both). Any unsatisfied demand that exceeds the inventory level is lost and unobservable. The firm does not know the demand function but has access to an offline dataset consisting of quadruplets of historical covariates, inventory, price, and potentially censored sales quantity. Our objective is to use the offline dataset to find the optimal feature-based pricing rule so as to maximize the expected profit. Through the lens of causal inference, we propose a novel data-driven algorithm that is motivated by survival analysis and doubly robust estimation. We derive a finite sample regret bound to justify the proposed offline learning algorithm and prove its robustness. Extensive numerical experiments demonstrate the robust performance of our proposed algorithm in accurately estimating optimal prices on both training and testing data. Furthermore, these experiments highlight the value of considering demand censoring in the context of feature-based pricing.

Presenter Bio:
Jingwen Tang a fifth-year Ph.D. candidate at the Department of Industrial and Operations Engineering (IOE) at the University of Michigan, Ann Arbor advised by Professor Cong Shi. Her research interests lie broadly in learning algorithms and data-driven optimization. Specific topics include sequential decision-making under uncertainty and online and offline learning algorithms, especially their applications in supply chain management and revenue management.

]]>
Lecture / Discussion Thu, 09 Nov 2023 12:08:29 -0500 2023-11-14T15:00:00-05:00 2023-11-14T16:00:00-05:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Lecture / Discussion Jingwen Tang
PhD Defense: Xinyu Fei (November 30, 2023 10:00am) https://events.umich.edu/event/114311 114311-21832661@events.umich.edu Event Begins: Thursday, November 30, 2023 10:00am
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

Xinyu Fei is a Ph.D. student in Industrial and Operations Engineering. Her research focuses on developing models and efficient algorithms for solving large-scale nonconvex optimization problems in complex networks. Methods include stochastic integer programming, distributed optimization, parallel computing, and statistical learning. Applications include traffic signal control, resource allocation/re-distribution, and coordination of reopen/closedown decisions during pandemic emergency response.

Advisor: Siqian Shen
Position Sought: Industry, Academia
Availability: 2024

]]>
Lecture / Discussion Sat, 21 Oct 2023 13:30:22 -0400 2023-11-30T10:00:00-05:00 2023-11-30T11:00:00-05:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Lecture / Discussion Xinyu Fei
PhD Research Talk: Yaohui Guo (December 5, 2023 3:00pm) https://events.umich.edu/event/115330 115330-21834446@events.umich.edu Event Begins: Tuesday, December 5, 2023 3:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

Seminar Abstract:
Trust has been identified as a critical factor for effective human-robot teaming. However, existing literature on trust modeling predominantly focuses on dyadic human-robot teams, and there is little, if not no, research on trust modeling in multi-human-multi-robot (MHMR) teams. To fill this research gap, we propose the Trust Inference and Propagation (TIP) model, a mathematical framework for computational trust modeling in MHMR teams. The TIP model accounts for both the direct and indirect experiences that a human agent has with a robot, successfully capturing the underlying trust dynamics and significantly outperforming a baseline model. In addition, to foster trust in MHMR teams, we develop an online learning algorithm for real-time, optimal team formation in dynamic, collaborative environments. Specifically, we model the teaming problem as a matching linear bandit and show the proposed algorithm achieves sublinear regret, which offers a promising avenue for real-time, optimal team formation that goes beyond static or pre-planned strategies.


Presenter Bio:
Yaohui Guo is a Ph.D. candidate in the Department of Industrial and Operations Engineering at the University of Michigan. His research focuses on human-robot/AI collaboration, specifically in developing algorithms that enhance robots' interaction abilities by accurately interpreting human internal states. He has received Master's degrees in Robotics and Mathematics from the University of Michigan, and a Bachelor's degree in Mechanical Engineering and Automation from Xi’an Jiaotong University. His research is supported by the Rackham Predoctoral Fellowship.

]]>
Lecture / Discussion Tue, 28 Nov 2023 10:21:22 -0500 2023-12-05T15:00:00-05:00 2023-12-05T16:00:00-05:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Lecture / Discussion Yaohui Guo
PhD Defense: Jeffrey Choy (January 11, 2024 2:00pm) https://events.umich.edu/event/116731 116731-21837857@events.umich.edu Event Begins: Thursday, January 11, 2024 2:00pm
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

Doctoral student, Jeffrey Choy, will present his dissertation titled "Studies in Financial Frontiers: Robo-Advising and Interconnected Markets".

]]>
Presentation Mon, 08 Jan 2024 18:15:46 -0500 2024-01-11T14:00:00-05:00 2024-01-11T15:00:00-05:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Presentation Industrial and Operations Engineering Building
PhD Defense: Zhongzhu Chen (February 23, 2024 10:30am) https://events.umich.edu/event/118360 118360-21840936@events.umich.edu Event Begins: Friday, February 23, 2024 10:30am
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

Zhongzhu Chen is currently a third-year joint Ph.D. candidate in Operations Research & Scientific Computing at the Department of Industrial and Operations Engineering (IOE) and Michigan Institute for Computational Discovery & Engineering (MICDE) at the University of Michigan, Ann Arbor. Before joining Umich, he received his bachelor’s degree in Mathematics, Data Science from the School of Mathematical Science and my economics minor from the National School of Development at Peking University, China, in 2019. Zhongzhu's research interests are numerical optimization, discrete optimization, large-scale optimization, statistics, information theory, machine learning.

]]>
Lecture / Discussion Sun, 04 Feb 2024 10:28:07 -0500 2024-02-23T10:30:00-05:00 2024-02-23T11:30:00-05:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Lecture / Discussion Zhongzhu Chen
PhD Defense: Jingwen Tang (March 14, 2024 9:00am) https://events.umich.edu/event/119641 119641-21843133@events.umich.edu Event Begins: Thursday, March 14, 2024 9:00am
Location: Industrial and Operations Engineering Building
Organized By: U-M Industrial & Operations Engineering

Jingwen Tang a fifth-year Ph.D. candidate at the Department of Industrial and Operations Engineering (IOE) at the University of Michigan, Ann Arbor advised by Professor Cong Shi. Her research interests lie broadly in learning algorithms and data-driven optimization. Specific topics include sequential decision-making under uncertainty and online and offline learning algorithms, especially their applications in supply chain management and revenue management.

]]>
Lecture / Discussion Mon, 04 Mar 2024 13:40:44 -0500 2024-03-14T09:00:00-04:00 2024-03-14T10:00:00-04:00 Industrial and Operations Engineering Building U-M Industrial & Operations Engineering Lecture / Discussion Jingwen Tang
PhD Defense: Patrik Schuler (March 21, 2024 10:00pm) https://events.umich.edu/event/120519 120519-21844851@events.umich.edu Event Begins: Thursday, March 21, 2024 10:00pm
Location: Off Campus Location
Organized By: U-M Industrial & Operations Engineering

Patrik Schuler's research interests include user-centered design, human-autonomy/robot interaction, and cognitive ergonomics. He was born in Uznach, Switzerland, and grew up in Charlotte, North Carolina. Before attending college, Patrik served five years as a radio repairman in the United States Marine Corps. He would eventually like to apply his human factors knowledge to conduct research in a military domain.

]]>
Lecture / Discussion Wed, 20 Mar 2024 19:20:19 -0400 2024-03-21T22:00:00-04:00 2024-03-21T23:00:00-04:00 Off Campus Location U-M Industrial & Operations Engineering Lecture / Discussion