BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//College of Engineering - University of Wisconsin-Madison - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://engineering.wisc.edu
X-WR-CALDESC:Events for College of Engineering - University of Wisconsin-Madison
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20240310T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20241103T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20250309T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20251102T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20260308T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20261101T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20270314T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20271107T070000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260410T120000
DTEND;TZID=America/Chicago:20260410T130000
DTSTAMP:20260428T135157
CREATED:20251017T150858Z
LAST-MODIFIED:20260330T173550Z
UID:10001353-1775822400-1775826000@engineering.wisc.edu
SUMMARY:Reinforcement Learning for Digital Health Interventions in the Dyadic Setting
DESCRIPTION:UW-ISyE looks forward to welcoming Susan Murphy Professor of Statistics and of Computer Science and Associate Faculty at the Kempner Institute\, Harvard University. \n\n\n\n\n\n\n\nWe present our ongoing work on the development of an online reinforcement learning (RL) algorithm for dyadic digital intervention settings in which the task for the RL algorithm is to assist the target person with a difficult illness be adherent to behavioral activities.   To achieve this goal the RL algorithm will not only deliver digital interventions to the target person but also deliver interventions to assist the carepartner to manage caregiving burden and help the two individuals improve their relationship.  That is\, different RL components target different elements of the dyad.  The RL algorithm is a multi-agent RL algorithm in which the 3 agents make decisions on the 3 elements of the dyad.  We incorporate domain knowledge in the form of approximal causal directed acyclic graphs to speed up online learning in this sparse data setting.  This work is motivated by our development of the ADAPTS-HCT multi-agent RL algorithm\, designed to improve medication adherence by young adults who have undergone a blood and bone marrow transplant.  The RL algorithm will be deployed in summer 2026. \n\n\n\n\n\nBio: Susan A. Murphy is Mallinckrodt Professor of Statistics and of Computer Science and Associate Faculty at the Kempner Institute\, Harvard University.  Her research focuses on improving sequential decision making via the development of online\, real-time reinforcement learning algorithms.  Her lab is involved in multiple deployments of these algorithms in digital health.  She is a member of the US National Academy of Sciences and of the US National Academy of Medicine.  In 2013 she was awarded a MacArthur Fellowship for her work on experimental designs to inform sequential decision making.  She is a Fellow of the College on Problems in Drug Dependence\, Past-President of Institute of Mathematical Statistics\, Past-President of the Bernoulli Society and a former editor of the Annals of Statistics.    
URL:https://engineering.wisc.edu/event/reinforcement-learning-for-digital-health-interventions-in-the-dyadic-setting/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2025/10/cohengraphic-4.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260320T120000
DTEND;TZID=America/Chicago:20260320T130000
DTSTAMP:20260428T135157
CREATED:20260311T134410Z
LAST-MODIFIED:20260311T134944Z
UID:10001490-1774008000-1774011600@engineering.wisc.edu
SUMMARY:ISyE - Modeling to Inform Intervention Planning and Deployment for Infectious Disease Control
DESCRIPTION:UW-ISyE looks forward to welcoming Pinar Keskinocak from Georgia Tech. \n\n\n\n\n\n\n\n Infectious diseases continue to impact millions of people every year around the world\, despite many advances in medicine and technology. Pharmaceutical interventions such as testing\, vaccines\, or treatment\, may not be available\, and when they are\, resources for their deployment are often very limited. Other challenges (e.g.\, logistical\, societal) may also hamper deployment. Decisions regarding what\, when\, and how to deploy need to incorporate various short- and long-term considerations\, as well as human behaviors such as following non-pharmaceutical intervention recommendations or uptake of available pharmaceutical interventions. In this presentation\, we will illustrate these challenges using a few examples from infectious diseases that are targeted for elimination or eradication\, and share results from modeling studies to help inform these complex decisions. \n\n\n\n\n\nBio: Pinar Keskinocak is the H. Milton and Carolyn J. Stewart School Chair and Professor in the School of Industrial and Systems Engineering (ISyE) at Georgia Tech. She is the co-founder and Director of the Center for Health and Humanitarian Systems (CHHS). Previously\, she served as Associate Chair for Faculty Development in ISyE\, College of Engineering ADVANCE Professor\, and Interim Associate Dean for Faculty Development and Scholarship. Her research spans supply chain management and applications in health and humanitarian systems\, with a multidisciplinary\, multi-stakeholder perspective. Recent work has focused on disease modeling\, evaluating interventions\, resource allocation\, process improvement for healthcare delivery\, and disaster preparedness\, response\, and recovery. Her research has appeared in leading journals and has been supported by government agencies\, industry\, NGOs\, and foundations. Her leadership and service within Georgia Tech\, professional communities\, and nationwide have been extensive\, including serving as the President of INFORMS in 2020. Dr. Keskinocak is a Fellow of INFORMS and IISE. She is the recipient of the INFORMS President’s Award (2024)\, George E. Kimball Medal (2024)\, and Women in Operations Research and Management Science Award.
URL:https://engineering.wisc.edu/event/isye-modeling-to-inform-intervention-planning-and-deployment-for-infectious-disease-control/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/jpeg:https://engineering.wisc.edu/wp-content/uploads/2026/03/cohengraphic-1.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260313T120000
DTEND;TZID=America/Chicago:20260313T130000
DTSTAMP:20260428T135157
CREATED:20260303T152254Z
LAST-MODIFIED:20260304T164648Z
UID:10001481-1773403200-1773406800@engineering.wisc.edu
SUMMARY:ISyE - Deterministic Benchmarks to Inform Sequential Decisions Using Lookahead
DESCRIPTION:UW-ISyE looks forward to welcoming Itai Gurvich from Northwestern University. \n\n\n\n\n\n\n\nDynamic programming is a canonical tool for solving complex sequential decision problems in operations. Yet\, because it suffers from the curse of dimensionality\, one often must rely on approximations. Among these\, deterministic—or “fluid”—approximations have long served as tractable benchmarks that reveal key structural properties of optimal or near-optimal policies in dynamic resource allocation problems across service operations and revenue management. \n\n\n\nWhile such fluid approximations have been widely—and often ad hoc—applied\, this talk presents a systematic framework for leveraging them to design high-quality control policies through what we call fluid lookahead. I will illustrate the approach in a family of finite-horizon revenue management problems\, showing how fluid lookahead captures key structural properties of the true optimal policies and\, perhaps surprisingly\, achieves near-optimal performance with only a few lookahead steps.  \n\n\n\nThis is joint work with Daniel Loredo Duran (PhD student) and Jan A. Van Mieghem. \n\n\n\n\n\nBio: Itai Gurvich is a Professor at the Kellogg School of Management\, Northwestern University. He earned his Ph.D. from Columbia University’s Graduate School of Business in 2008 and joined Kellogg the same year. From 2016 to 2020\, he was on the faculty of Cornell University’s campus in New York City (Cornell Tech) before returning to Kellogg in 2021. \n\n\n\nProfessor Gurvich’s research focuses on the performance analysis and optimization of processing networks\, as well as the theory of stochastic-process approximations. His work has been recognized with the INFORMS Applied Probability Society’s Best Publication Award. He has served as the Stochastic Models Area Editor for Operations Research and as Chair of the INFORMS Applied Probability Society.
URL:https://engineering.wisc.edu/event/isye-deterministic-benchmarks-to-inform-sequential-decisions-using-lookahead/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/03/cohengraphic-2.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260227T120000
DTEND;TZID=America/Chicago:20260227T130000
DTSTAMP:20260428T135157
CREATED:20260211T205013Z
LAST-MODIFIED:20260211T205140Z
UID:10001458-1772193600-1772197200@engineering.wisc.edu
SUMMARY:ISyE - Cognitive engineering for higher education – A view from both sides of design
DESCRIPTION:Photographer: Meredith Forrest Kulwicki\n\n\n\nUW-ISyE looks forward to welcoming Ann Bisantz\, a SUNY Distinguished Professor of Industrial and Systems Engineering at the University at Buffalo\, where she also serves as Vice Provost and Dean for Undergraduate Education. \n\n\n\nUS institutions of higher education are large\, complex systems affected by both internal and external factors\, answering to multiple stakeholders with often conflicting goals; which must be managed in the face of short-and long-term uncertainties and limited resources; and where there is substantial risk – both for individuals impacted by the system (i.e.\, students) and arguably for society writ large\, if they fail in their missions. They are examples of “intentional” systems – where constraints and priorities are drawn from human-created structures rather than physical laws. Increasingly\, these systems are managed\, at least in part\, through data reporting and analyses which bring together variables of interest across functions and at multiple scales. Creating meaningful reports and visualizations of these complex data sets in support of decision-makers is a critical function of a modern university. \n\n\n\nMethods of cognitive engineering have been used across a variety of complex\, high risk systems to support design of automation\, information displays\, and decision-support tools. Frameworks such as cognitive work analysis provide models to reveal both the demands stemming from the work domain\, as well as the knowledge\, skills and strategies that experts bring to bear on those demands. Typically\, these methods are used prospectively\, are inputs to a larger system design process. \n\n\n\nThis presentation combines expertise in higher education administration with a cognitive engineering research perspective to inform a work domain model of one significant university sub-system\, undergraduate education administration\, drawing parallels with a well-studied health care system. It proposes a process of visualization co-creation as an alternative work-centered approach to successful design of decision-support tools\, and concludes with shared lessons learned from a comparison of this just-in-time approach to design based in prospective analysis and modelling. \n\n\n\n\n\nBio: Dr. Bisantz is a SUNY Distinguished Professor of Industrial and Systems Engineering at the University at Buffalo\, which she also serves as Vice Provost and Dean for Undergraduate Education. Her contributions to the field of human factors engineering include investigating new techniques for displaying complex and uncertain information to decision makers; supporting the transition in complex work domains from legacy and manual information systems to more integrated\, supportive IT systems; modeling human judgment and decision making; extending cognitive engineering methods which can be used to model complex human-technology work domains; and understanding aspects of human trust in automated systems. This research has been conducted in a number of complex work environments\, including health care\, military systems\, emergency management\, and transportation; and has included interdisciplinary collaborations with researchers in health care and information fusion. She has been PI or CoI on over 15M in funded research projects from agencies including the National Science Foundation\, Agency for Healthcare Research and Quality\, and numerous defense agencies. Bisantz is the past recipient of an NSF CAREER award\, was recognized with a SUNY Chancellor’s Award for Research and Creative Activity\, and is a Fellow of the Human Factors and Ergonomics Society (HFES). She has served as an Associate Editor or on the Editorial Board of a number of journals and has served HFES as a member-at-large of the Executive Council. She is the recipient of the HFES Fitts’ Education award for her contributions to human factors education and was the 2020 HFES WOMAN Mentor of the year. She is past chair of the UB ISE department and since 2018 has served the UB as Vice Provost and Dean\, where she is responsible for university-wide leadership for undergraduate education\, including curriculum\, policy\, student success\, advising coordination\, general education\, undergraduate research\, and the UB Honors College. In 2024 Bisantz was appointed to the National Academy of Engineering Board on Human-System Integration. Her PhD is from the Georgia Institute of Technology.
URL:https://engineering.wisc.edu/event/isye-cognitive-engineering-for-higher-education-a-view-from-both-sides-of-design/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/02/bisantz.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260213T120000
DTEND;TZID=America/Chicago:20260213T130000
DTSTAMP:20260428T135157
CREATED:20260202T183625Z
LAST-MODIFIED:20260202T191233Z
UID:10001449-1770984000-1770987600@engineering.wisc.edu
SUMMARY:ISyE - Online Fault Detection for High-dimensional Data Streams under Resource Constraints
DESCRIPTION:With the rapid advances in sensing and communication technologies\, most complex systems are continuously monitored by sensors that provide a variety of streaming data with rich information about the system’s performance. Monitoring such high-dimensional streaming data in real-time is critical to detect anomalies and system failures. Nonetheless\, resource constraints on sensing\, computation\, and communication make traditional monitoring and anomaly detection methods impractical. This talk introduces a family of adaptive and active learning strategies for online fault detection that explicitly account for the limitations associated with resource constraints. By dynamically selecting which data to sample\, process\, or transmit\, these methods achieve efficient monitoring without sacrificing statistical reliability. I will discuss applications in networked and partially observed systems\, real-time anomaly detection with mobile sensors\, and online batch fault diagnosis. The unifying theme is the integration of statistical learning\, sequential decision-making\, and uncertainty quantification to enable scalable\, data-efficient online monitoring under resource constraints. \n\n\n\n\n\nBio: Ana Maria Estrada Gomez is an assistant professor at the Edwardson School of Industrial Engineering at Purdue University. She received a B.Sc. in industrial engineering and a B.Sc. in mathematics from la Universidad de los Andes\, Bogota\, Colombia\, in 2013 and 2015\, respectively. She also holds a M.Sc. in industrial engineering from la Universidad de los Andes (2015)\, and a M.Sc. in statistics from Georgia Tech (2018). In 2021\, she received her PhD in industrial engineering with a specialization in statistics from Georgia Tech. Her research interests lie in developing efficient methodologies and algorithms for modeling\, monitoring\, and diagnosing complex systems that collect high-dimensional data\, using statistics and machine learning tools. She is the recipient of the SPES + Q&P Best Student Paper Award from ASA\, the QSR Best Poster Award from INFORMS\, and the IISE Doctoral Colloquium Best Poster Award. She has also been appointed as a Latina Trailblazer in Engineering Fellow by Purdue’s College of Engineering.
URL:https://engineering.wisc.edu/event/isye-online-fault-detection-for-high-dimensional-data-streams-under-resource-constraints/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/02/cohengraphic-1.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260209T120000
DTEND;TZID=America/Chicago:20260209T130000
DTSTAMP:20260428T135157
CREATED:20260202T152053Z
LAST-MODIFIED:20260202T154653Z
UID:10001448-1770638400-1770642000@engineering.wisc.edu
SUMMARY:ISyE - Bridging Machine Learning and Optimization for Human-Centered AI
DESCRIPTION:From healthcare delivery to resilient power grid management\, predictive and prescriptive analytics tools have the potential to improve decision-making for some of today’s most pressing problems\, yet their impact is often limited by the technical barriers required to access these tools and to interpret and trust their results. This talk will explore how the synthesis of machine learning and optimization can lower these barriers to advance human-centered artificial intelligence (AI). The first part of the talk will demonstrate how generative AI can broaden access to optimization tools through an interactive decision-support framework\, developed in collaboration with Microsoft Outlook\, that leverages large language models to translate natural-language user requests into underlying constraint programming models. The second part of the talk will focus on trust\, showing how optimization can identify regions where machine learning models make fixed predictions that preclude individuals from changing their outcomes\, such as a loan applicant who can never be approved regardless of their actions. We will conclude by outlining broader opportunities for integrating AI and optimization\, moving toward a future in which advanced analytics tools are as accessible and trustworthy for managers at a local food bank as they are for applied scientists at Amazon. \n\n\n\n\n\nBio: Connor Lawless is a Postdoctoral Fellow at the Stanford Institute for Human- Centered Artificial Intelligence advised by Ellen Vitercik and Madeleine Udell. His research blends tools from optimization\, machine learning\, and human-computer interaction to make advanced analytics tools more accessible and trustworthy. He received his PhD in Operations Research from Cornell University where he was advised by Oktay Gunluk\, and previously spent time at Microsoft Research\, IBM Research\, and the Royal Bank of Canada.
URL:https://engineering.wisc.edu/event/isye-bridging-machine-learning-and-optimization-for-human-centered-ai/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/02/cohengraphic.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260202T120000
DTEND;TZID=America/Chicago:20260202T130000
DTSTAMP:20260428T135157
CREATED:20260126T144839Z
LAST-MODIFIED:20260126T144841Z
UID:10001444-1770033600-1770037200@engineering.wisc.edu
SUMMARY:ISyE - Interaction-Centered Design and Evaluation for Trustworthy Human-AI Work Systems
DESCRIPTION:As artificial intelligence becomes increasingly embedded in work systems\, questions of trust extend beyond whether people accept or rely on algorithms to how humans and AI jointly perform\, adapt\, and sustain trustworthy decisions over time. In this talk\, I present research that frames trustworthiness as a system-level property built through the iterative design\, evaluation\, and integration of human-AI interactions. I argue that interaction—not the human or the AI alone—is the fundamental unit for designing and evaluating trustworthy work systems. I illustrate this perspective through empirical studies of AI-enabled decision support in safety- and mission-critical domains\, including identity verification and intelligence analysis. Across these studies\, I show how human and AI strengths and weaknesses depend on interaction design; why explainable AI designs can produce mismatches between perceptual and performance measures of system trustworthiness; and how interaction-centered design can concurrently translate theoretical and operational trustworthiness models into experimentally testable systems. This work advances foundations for building human-AI systems that are effective\, efficient\, ethical\, and capable of sustaining human judgment over time. \n\n\n\n\n\nBio: Myke C. Cohen is a final-year Ph.D. student in Human Systems Engineering at Arizona State University and an Associate Scientist at Aptima\, Inc. His work sits at the intersection of human factors engineering\, complex adaptive systems\, and applied cognitive science\, with a focus on the design and evaluation of AI-enabled decision systems in safety- and mission-critical environments. He has led and contributed to projects sponsored by the U.S. Department of Homeland Security\, DARPA\, and the Department of Defense. Myke is a recipient of the HFES Student Member with Honors Award\, and was named an Ira A. Fulton Schools of Engineering Dean’s Fellow and the inaugural CHART Scholar at Arizona State University. Prior to his doctoral studies\, he served as an Instructor of Industrial Engineering at the University of the Philippines Diliman\, where he earned his B.S. in Industrial Engineering.
URL:https://engineering.wisc.edu/event/isye-interaction-centered-design-and-evaluation-for-trustworthy-human-ai-work-systems/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/jpeg:https://engineering.wisc.edu/wp-content/uploads/2026/01/cohengraphic.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260130T120000
DTEND;TZID=America/Chicago:20260130T130000
DTSTAMP:20260428T135157
CREATED:20260122T132927Z
LAST-MODIFIED:20260122T144835Z
UID:10001442-1769774400-1769778000@engineering.wisc.edu
SUMMARY:ISyE - Advancement of Large-Scale 3D Printing
DESCRIPTION:Most advances for the integration of 3D printing (3DP) into production settings have focused on small-scale 3DP with manufacturing of components such as aerospace fuel nozzles\, aircraft’s hydraulic components\, and military hardware to name a few. But when it comes to large-scale applications 3DP research has been minimally explored. Defining large-scale 3DP as additive manufacturing technologies aimed at applications such as remanufacturing of large components in aerospace\, automotive\, and agricultural industries\, to construction and infrastructure\, research in large-scale 3DP is nascent and needed. Alongside cost\, establishment of 3DP technologies for large-scale applications has been hindered by the interdisciplinarity required to identify technological design needs\, manufacturing\, validation\, and implementation of these technologies while securing safety standards. Therefore\, this presentation will discuss outcomes of studies revolving around industrial and manufacturing engineering practices that can contribute to advancing 3DP technologies for large-scale 3DP applications. Concluding remarks will suggest to opportunities that can lead to identification of interdisciplinary research collaborations to further advance the field. \n\n\n\n\n\nBio: Dr. Iris V. Rivero is the Paul and Heidi Brown Preeminent Chair and Department Chair of Industrial and Systems Engineering at the University of Florida (UF). She received her B.S.\, M.S.\, and Ph.D. in Industrial & Manufacturing Engineering from Penn State University. Her research group\, iMED (Interdisciplinary Manufacturing Engineering and Design) laboratory\, specializes in the design and validation of additive and hybrid manufacturing techniques for the processing of a wide array of material systems ranging from biopolymers\, metal alloys\, to concrete. She was a faculty fellow at NASAs Marshall Space Flight Center\, and her research has been funded by the National Science Foundation\, Department of Energy\, and NASA\, to name a few. She has over 90 peer-reviewed publications and over 100 invited talks and peer-reviewed presentations. She is a fellow in the Institute of Industrial and Systems Engineers (IISE) and in SME (formerly the Society of Manufacturing Engineers).
URL:https://engineering.wisc.edu/event/isye-finding-the-needle-in-the-haystack-how-gradient-descent-converges-to-low-dimensional-solutions-in-over-parameterized-models-2/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/01/riverographic.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260129T120000
DTEND;TZID=America/Chicago:20260129T130000
DTSTAMP:20260428T135157
CREATED:20260121T184709Z
LAST-MODIFIED:20260121T184711Z
UID:10001441-1769688000-1769691600@engineering.wisc.edu
SUMMARY:ISyE - Finding the needle in the haystack: How gradient descent converges to low-dimensional solutions in over-parameterized models.
DESCRIPTION:In contemporary machine learning\, realistic models exhibit increasing nonconvexity and overwhelming overparameterization. This nonconvex nature often leads to numerous undesirable or “spurious” local solutions\, while overparameterization exacerbates the risk of overfitting. Yet\, simple “short-sighted” algorithms\, such as gradient descent (GD) or its variants\, often find the needle in the haystack: they converge to the correct\, low-dimensional solutions even when such structures are neither explicitly encoded in the model nor required by the algorithm. This talk delves into explaining this desirable performance of GD-based algorithms by studying their fine-grained trajectory on over-parameterized models\, spanning from low-rank models to deep neural networks.   \n\n\n\n\n\nBio: Salar Fattahi is an Assistant Professor of Industrial and Operations Engineering at the University of Michigan. He received his Ph.D. from the University of California\, Berkeley in 2020. He is the recipient of a National Science Foundation CAREER Award and the Deans’ MLK Spirit Award. His research focuses on optimization and machine learning and has been recognized with multiple nominations and awards\, including the INFORMS Junior Faculty Interest Group Best Paper Award\, the INFORMS Data Mining Best Paper Award\, and the INFORMS Computing Society Best Student Paper Award. He currently serves as Vice Chair for Machine Learning in the INFORMS Optimization Society\, as an Associate Editor for the INFORMS Journal on Data Science\, and as an Area Chair for several premier conferences\, including NeurIPS\, ICML\, and ICLR.
URL:https://engineering.wisc.edu/event/isye-finding-the-needle-in-the-haystack-how-gradient-descent-converges-to-low-dimensional-solutions-in-over-parameterized-models/
LOCATION:2188 Mechanical Engineering Building\, 1513 University Avenue\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/01/fattahigraphic.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260126T120000
DTEND;TZID=America/Chicago:20260126T130000
DTSTAMP:20260428T135157
CREATED:20260114T182823Z
LAST-MODIFIED:20260114T182825Z
UID:10001399-1769428800-1769432400@engineering.wisc.edu
SUMMARY:ISyE - From Dyads to Teams: Modeling Human Trust Dynamics and Behaviors in Human-Autonomy Interactions
DESCRIPTION:Technology is evolving rapidly\, and human interaction with autonomous technologies is no longer confined to one-to-one decision-support settings. Intelligent agents are increasingly working alongside groups of people in diverse contexts such as defense\, transportation\, and manufacturing. Consequently\, there is a growing need to design trust- and behavior-aware adaptive agents that allow humans and autonomous systems to leverage their complementary strengths\, while promoting values unique to human society. To this end\, our research focuses on modeling human trust and behavioral dynamics over time across various teaming scenarios. In this seminar\, I will first introduce our work on classifying and predicting trust dynamics profiles using individuals’ personal characteristics\, uncovering who exhibits which trust trajectory and why. I will then present a study examining individual variability in trust bias (contrast versus assimilation) and its effects on coordination and decision-making when humans collaborate with multiple autonomous agents simultaneously. This work further extends to understanding distinct perceptions and teamwork strategies in hierarchical mixed human-agent teams. Time permitting\, I will also discuss our research on the design of AI-supported emergency navigation systems that promote human altruism and trust.   \n\n\n\n\n\nBio: Hyesun Chung is a final-year Ph.D. candidate in Industrial and Operations Engineering at the University of Michigan. She was recently named a Barbour Fellow and received the Student Member with Honors Award from the Human Factors and Ergonomics Society (HFES). Prior to joining the University of Michigan as a doctoral student\, she earned three bachelor’s degrees in Industrial Engineering\, Business\, and Industrial Design\, as well as an M.S. in Industrial Engineering\, all from Seoul National University in South Korea. Building on her interdisciplinary background\, she is a human factors engineer and human-computer interaction researcher who integrates computational and statistical methods with psychological theory to better understand and design human-AI interaction and teaming.
URL:https://engineering.wisc.edu/event/isye-from-dyads-to-teams-modeling-human-trust-dynamics-and-behaviors-in-human-autonomy-interactions/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/01/chunggraphic.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260121T120000
DTEND;TZID=America/Chicago:20260121T130000
DTSTAMP:20260428T135157
CREATED:20260113T150154Z
LAST-MODIFIED:20260113T150157Z
UID:10001398-1768996800-1769000400@engineering.wisc.edu
SUMMARY:ISyE - GPU-Accelerated Linear Programming and Beyond
DESCRIPTION:The rapid progress in GPU computing has revolutionized many fields\, yet its potential in mathematical programming\, such as linear programming (LP)\, has only recently begun to be realized. This talk aims to provide an overview of recent advancements in GPU-based first-order methods for LP\, with a particular focus on the design and development of cuPDLPx. The extensions to GPU-based optimization beyond LP\, including convex quadratic programming and semidefinite programming\, will also be discussed. \n\n\n\n\n\nBio: Jinwen Yang is a final-year Ph.D. student at the University of Chicago\, advised by Professor Haihao Lu. His research interests are in optimization\, with a particular focus on optimization algorithms tailored to modern hardware (like GPUs) and intended for practical applications. He obtained B.S. in Mathematics and Applied Mathematics from Fudan University.
URL:https://engineering.wisc.edu/event/isye-gpu-accelerated-linear-programming-and-beyond/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/01/yanggraphic.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260115T120000
DTEND;TZID=America/Chicago:20260115T130000
DTSTAMP:20260428T135157
CREATED:20260106T182252Z
LAST-MODIFIED:20260108T134323Z
UID:10001390-1768478400-1768482000@engineering.wisc.edu
SUMMARY:ISyE - Auto-Conditioned First-Order and Stochastic Optimization Methods
DESCRIPTION:First-order methods are widely used to tackle data science and machine learning problems with complex structures\, such as nonconvexity\, nonsmoothness\, and stochasticity. However\, in many real-world scenarios\, the problem structure and parameters can be unknown or ambiguous\, creating significant challenges for algorithm design and stepsize selection. \n\n\n\nIn this talk\, I will present a novel class of first-order methods\, termed auto-conditioned methods\, that are universal for solving various classes of optimization problems without requiring prior knowledge of problem parameters or resorting to any line search or backtracking procedures. In the first part of the talk\, we focus on convex optimization and propose a uniformly optimal method for smooth\, weakly smooth\, and nonsmooth problems. In the second part of the talk\, we consider smooth but possibly nonconvex optimization\, and propose a novel parameter-free projected gradient method with the best-known unified complexity for convex and nonconvex problems. We then generalize the method to the stochastic setting\, achieving new universal complexity bounds that are nearly optimal for both convex and nonconvex problems. The advantages of the proposed methods are demonstrated by encouraging numerical results. \n\n\n\n\n\nBio: Tianjiao Li is a postdoctoral associate at the MIT Sloan School of Management and Operations Research Center. He received his Ph.D. in Operations Research from the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech\, where he was advised by Prof. George Lan and Prof. Ashwin Pananjady. His research interests lie in the theory and methodology of nonlinear optimization\, stochastic optimization\, and reinforcement learning\, with a central focus on bridging rigorous theoretical development with practical relevance\, especially in data science and artificial intelligence. His work has been recognized as an honorable mention in the INFORMS George Nicholson Student Paper Competition and as second place in the INFORMS Optimization Society Student Paper Award.
URL:https://engineering.wisc.edu/event/isye-auto-conditioned-first-order-and-stochastic-optimization-methods/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/01/LIgraphic.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20260112T120000
DTEND;TZID=America/Chicago:20260112T130000
DTSTAMP:20260428T135157
CREATED:20260105T133949Z
LAST-MODIFIED:20260105T140449Z
UID:10001389-1768219200-1768222800@engineering.wisc.edu
SUMMARY:ISyE - Choice-based Operations At Scale: Complementarity and Dynamic Decisions
DESCRIPTION:Assortment and inventory decisions lie at the core of supply chain and retail operations. In practice\, these decisions face two fundamental challenges arising from complex customer choice behavior. First\, customers often purchase complementary products across categories\, which makes category-level decisions interdependent. Second\, inventory is limited and customers arrive over time\, so product availability changes dynamically as items stock out. Most work in choice-based operations has focused on single-category and static settings\, while research addressing these two challenges remains relatively limited. Existing approaches often either oversimplify customer preferences in choice modeling or rely on algorithms that are not tractable in large-scale settings. In this talk\, I will present two projects that address these challenges. The first proposes a Markovian framework to model cross-category complementarity\, supporting scalable estimation and joint assortment optimization. The second introduces a unified algorithmic framework for dynamic assortment and inventory optimization under MNL choice\, with provable guarantees in both personalized and non-personalized settings. Together\, these works offer scalable tools for decision-making in complex\, data-driven supply chain environments. \n\n\n\n\n\nBio: Shuo Sun is a PhD candidate in Industrial Engineering and Operations Research at the University of California\, Berkeley. Her research focuses on modeling and algorithm design for supply chain and revenue management using optimization and machine learning. Her work has received several recognitions\, including the INFORMS Daniel H. Wagner Prize and a finalist distinction in the INFORMS RMP Jeff McGill Best Student Paper Award. She has publications in leading conferences and papers published or under revision at leading operations and analytics journals\, as well as industry experience at Amazon and JD.com on retail operations problems.
URL:https://engineering.wisc.edu/event/isye-colloquium-shuo-sun/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2026/01/sungraphic.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251121T120000
DTEND;TZID=America/Chicago:20251121T130000
DTSTAMP:20260428T135157
CREATED:20251105T181612Z
LAST-MODIFIED:20251105T181614Z
UID:10001366-1763726400-1763730000@engineering.wisc.edu
SUMMARY:Closing the Loop on Machine Learning: A Perturbation Analysis Approach to Decision-Dependent Distribution Shift
DESCRIPTION:UW-ISyE looks forward to welcoming Roy Dong Assistant Professor in the Industrial & Enterprise Systems Engineering department at the University of Illinois at Urbana-Champaign. \n\n\n\nRoy Dong – research assistant professor\, electrical and computer engineering\n\n\n\nAs data-driven methods are deployed in real-world settings\, the processes that generate the observed data will often react to the decisions of the learner. For example\, a data source may have some incentive for the algorithm to provide a particular label (e.g. approve a bank loan)\, and manipulate their features accordingly. In this talk\, I will present our recent work on analyzing this decision-dependent distribution shift through the lens of perturbation analysis in control theory. This framework allows us to consider settings with multiple equilibria and characterize the regions of attraction for each equilibrium\, which is observed in practice: learning algorithms can settle into echo chambers\, and characterizes the set of initial conditions which leads to each ultimate outcome. Additionally\, I will discuss how these methods can be computationally calculated using integral quadratic constraints\, how they can be made distributionally robust\, and how it can be used for trajectory predictions for robotic crowd navigation. \n\n\n\n\n\nBio: Roy Dong is an Assistant Professor in the Industrial & Enterprise Systems Engineering department at the University of Illinois at Urbana-Champaign. He received a BS Honors in Computer Engineering and a BS Honors in Economics from Michigan State University in 2010. He received a PhD in Electrical Engineering and Computer Sciences at the University of California\, Berkeley in 2017\, where he was funded in part by the NSF Graduate Research Fellowship. Prior to his current position\, he was a postdoctoral researcher in the Berkeley Energy & Climate Institute\, a visiting lecturer in the Industrial Engineering and Operations Research department at UC Berkeley\, and a Research Assistant Professor in the Electrical and Computer Engineering department at the University of Illinois at Urbana-Champaign. His research uses tools from control theory\, economics\, statistics\, and optimization to understand the closed-loop effects of machine learning\, with applications in cyber-physical systems such as the smart grid\, modern transportation networks\, and autonomous vehicles.
URL:https://engineering.wisc.edu/event/closing-the-loop-on-machine-learning-a-perturbation-analysis-approach-to-decision-dependent-distribution-shift/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/jpeg:https://engineering.wisc.edu/wp-content/uploads/2025/11/donggraphic.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251010T120000
DTEND;TZID=America/Chicago:20251010T130000
DTSTAMP:20260428T135157
CREATED:20250929T154550Z
LAST-MODIFIED:20250929T154957Z
UID:10001337-1760097600-1760101200@engineering.wisc.edu
SUMMARY:Understanding Generalization of Diffusion Models: Structured Data and Memorization
DESCRIPTION:UW-ISyE looks forward to welcoming Minshuo Chen\, assistant professor with the Department of Industrial Engineering & Management Sciences at Northwestern University \n\n\n\n\n\n\n\nDiffusion models achieve state-of-the-art performance in various high-dimensional data modeling tasks. These empirical successes challenge conventional wisdom while raising critical concerns. On the one hand\, in high-dimensional applications\, diffusion models’ strong performance appears to circumvent the curse of dimensionality. On the other hand\, memorization emerges as an unwanted byproduct\, limiting creativity and raising safety and privacy issues. In this talk\, we theoretically decipher these observations. The first part develops statistical learning guarantees of diffusion models for low-dimensional manifold data—an assumption aligns well with many practical datasets. We prove that diffusion models can learn data distributions at rates governed by the intrinsic dimension and curvature of the data. The second part establishes separation in memorization and generalization through the statistical learning and network approximation lens. Building on these insights\, we propose a pruning-based method that reduces memorization while maintaining generation quality. \n\n\n\n\n\nBio: Minshuo Chen is an assistant professor with the Department of Industrial Engineering & Management Sciences at Northwestern University. He was an associate research scholar with the Department of Electrical and Computer Engineering at Princeton University from 2022 to 2024. He completed his Ph.D. from the School of Industrial and Systems Engineering at Georgia Tech\, majoring in Machine Learning. His research focuses on developing principled methodologies and theoretical foundations of deep learning\, with a particular interest in 1) generative models including diffusion models\, 2) foundations of machine learning\, such as optimization and sample efficiency\, and 3) reinforcement learning.
URL:https://engineering.wisc.edu/event/understanding-generalization-of-diffusion-models-structured-data-and-memorization/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2025/09/chengraphic.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20251003T120000
DTEND;TZID=America/Chicago:20251003T130000
DTSTAMP:20260428T135157
CREATED:20250918T131424Z
LAST-MODIFIED:20250918T193205Z
UID:10001332-1759492800-1759496400@engineering.wisc.edu
SUMMARY:Strong duals for mixed integer programs.
DESCRIPTION:UW-ISyE looks forward to welcoming Dr. Santanu Dey\, Professor at  H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology.  \n\n\n\n\n\n\n\nWe develop two general-purpose strong dual formulations for binary MINLPs\, motivated by sensitivity analysis and distributed computation. For mixed binary quadratic programs (MBQPs)\, we show that the copositive dual of Burer’s completely positive reformulation has no duality gap when the feasible region is bounded or the objective is convex. Since the right-hand side of the original MBQP appears only in the dual’s objective\, this formulation enables sensitivity analysis. For nearly decomposable nonlinear mixed binary programs\, we propose a hierarchy of relaxations that preserve decomposability. The first level coincides with the classical Lagrangian relaxation\, while higher levels yield progressively tighter bounds\, culminating in a strong dual. We analyze the quality of these bounds for various types of MILPs. This is joint work with Diego Cifuentes and Jingye Xu. \n\n\n\n\n\nBio: Santanu S. Dey is an Anderson-Interface professor and director of doctorial recruiting and admissions in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. Dr. Dey’s research interests are in the area of non-convex optimization\, and in particular mixed integer linear and nonlinear programming. His research is partly motivated by applications of non-convex optimization problems arising in areas such as electrical power engineering\, process engineering\, civil engineering\, logistics\, and statistics. He currently serves on the editorial board of Mathematical Programming A\, Mathematics of Operations Research and SIAM Journal on Optimization. He has previously served as an area editor for Mathematical Programming C and associate editor of INFORMS Journal on Computing. He has won the INFORMS Nicholson student paper competition\, IBM Faculty Award\, the Class of 1969 Teaching Fellow at Georgia Tech\, the NSF CAREER award\, the INFORMS Energy Natural Resources and Environment best paper award\, and the INFORMS optimization society Balas Prize.
URL:https://engineering.wisc.edu/event/strong-duals-for-mixed-integer-programs/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/png:https://engineering.wisc.edu/wp-content/uploads/2025/09/deygraphic.avif
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20250926T120000
DTEND;TZID=America/Chicago:20250926T130000
DTSTAMP:20260428T135157
CREATED:20250903T155005Z
LAST-MODIFIED:20250918T163656Z
UID:10001309-1758888000-1758891600@engineering.wisc.edu
SUMMARY:Optimization\, ML and AI in Operations Management 
DESCRIPTION:UW-ISyE looks forward to welcoming Dr. Georgia Perakis\, Professor at the MIT Sloan School of Management. \n\n\n\n\n\n\n\nData-driven decision-making has garnered a growing interest due to the increase in data availability in recent years. With that growth many opportunities as well as challenges arise. Optimization\, Machine Learning (ML) and more generally\, AI play and can play even more an important role to address important challenges in a variety of Operations Management applications. In this talk\, we will discuss some of these applications and will highlight the importance and challenges of integrating optimization with ML in data-driven decision-making. We will also discuss some concrete examples of these synergies in pricing and healthcare. As a result\, we will also discuss how we can optimize over already trained objective functions that arise from neural network models in order to recommend better decisions. \n\n\n\n\n\nBio: Georgia Perakis is the William F. Pounds Professor and a Professor of Operations Management\, Operations Research & Statistics at the MIT Sloan School of Management. She is also serving as co-director of the Operations Research Center. On July 1 she started her sabbatical at Harvard Business School where she is spending the year as a Visiting Scholar. For the past year and a half\, she served as the John C Head III Dean (Interim) at MIT Sloan and before that\, she served as an Associate Dean for Social and Ethical Responsibility in Computing (SERC) in the Schwarzman College of Computing and MIT Sloan. Her research has received many awards and focuses on analytics/AI\, in particular\, in the intersection of optimization and machine learning with applications in pricing\, revenue management\, supply chain\, sustainability and healthcare among others. She received the PECASE Award from the Office of the President on Science and Technology. In 2016\, she was elected as an INFORMS Fellow\, and in 2021 as Distinguished MSOM Fellow.  \n\n\n\nPerakis has passion for supervising PhD\, masters\, and undergraduate students\, having graduated 34 PhD and 63 master’s students. She has received numerous awards for teaching including the Graduate Student Council Teaching Award (2002)\, the Samuel M. Seegal Award (2012)\, the Jamieson Prize for excellence in Teaching (2014)\, the Teacher of the Year Award (2017) and the Outstanding Teaching Award (2024) at MIT Sloan. Perakis is currently the Editor in Chief of the M&SOM journal and has served on the editorial board at a number of other journals. She holds a BS in mathematics from the University of Athens as well as an MS in applied mathematics and a PhD in applied mathematics from Brown University.
URL:https://engineering.wisc.edu/event/optimization-ml-and-ai-in-operations-management/
LOCATION:1163 Mechanical Engineering\, 1513 Engineering Dr.\, Madison\, WI\, 53706\, United States
CATEGORIES:Colloquium,Industrial & Systems Engineering
ATTACH;FMTTYPE=image/jpeg:https://engineering.wisc.edu/wp-content/uploads/2025/09/perakisgraphic.avif
END:VEVENT
END:VCALENDAR