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Events
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Oct20
EVENT DETAILSmore info
lessAbstract: The rapid proliferation of hierarchical network architectures, such as edge-cloud systems and networks of geographically distributed data centers, has sparked growing interest in efficiently solving large-scale statistical learning tasks, especially when data is inherently distributed and lacks centralized oversight. In these architectures, communication is fast within local clusters but slow and costly across them. Many of these tasks are also high dimensional in the statistical sense: the number of parameters exceeds the sample size, and accurate estimation is possible only by exploiting low-dimensional structure, such as sparsity or low rank. Decentralization makes inference in this regime particularly challenging: no single node may hold enough samples to estimate the model on its own, even when the pooled samples are sufficient, while computation, time, and inter-node communication are tightly constrained. While statistical-computational trade-offs have been thoroughly characterized for high-dimensional inference in centralized settings, our understanding of these trade-offs within decentralized network environments remains comparatively limited. Indeed, methodologies that demonstrate robustness and accuracy in traditional low-dimensional contexts frequently underperform in high-dimensional regimes, and theoretical convergence results often fail to align with observed empirical behaviors. In this talk, we will introduce new algorithmic frameworks and analytical tools specifically tailored for decentralized high-dimensional inferential tasks. Through several vignettes, we will show how integrating statistical insights into the design and analysis of decentralized optimization algorithms sheds new light on existing gaps and misconceptions prevalent in the literature, thereby redefining our understanding of distributed inference methodologies. Time permitting, we will also outline a new decomposition approach, based on message passing, in which the network, rather than serving only to exchange information, becomes part of the algorithm design; exploiting its structure in this way can markedly speed up convergence.
Bio: Gesualdo Scutari is the Pedro and Granadillo Professor in the Edwardson School of Industrial Engineering and the Elmore Family School of Electrical and Computer Engineering at Purdue University, West Lafayette, IN, USA. Research interests focus on continuous optimization (particularly distributed and stochastic methods), equilibrium programming, and their applications in signal processing and statistical learning. Among other honors, he received the 2013 NSF CAREER Award, the 2015 IEEE Signal Processing Society Young Author Best Paper Award, and the 2020 IEEE Signal Processing Society Best Paper Award. He served as an IEEE Signal Processing Society Distinguished Lecturer (2023–2024) and has been on the editorial boards of several IEEE journals. He is currently an Associate Editor for the SIAM Journal on Optimization. He is a Fellow of IEEE.
TIME Tuesday, October 20, 2026 at 11:00 AM - 12:00 PM
LOCATION B211, Technological Institute map it
CONTACT Nathan Keiller nathan.keiller@northwestern.edu EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Oct23
EVENT DETAILS
lessApplication for degree due to The Graduate School to receive a degree in Fall
TIME Friday, October 23, 2026
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Nov10
EVENT DETAILSmore info
lessBio
Andi Wang is an assistant professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison, which he joined in August 2024 after serving as an assistant professor in the School of Manufacturing Systems and Networks at Arizona State University. He holds a PhD from the Georgia Institute of Technology (2021), a PhD from the Hong Kong University of Science and Technology (2016), and a BS from Peking University (2012). His research develops principled data science and machine learning methodologies for engineering processes and systems, including predictive analytics, monitoring, diagnostics, design optimization, and model-based control, with applications in areas such as additive and semiconductor manufacturing.TIME Tuesday, November 10, 2026 at 11:00 AM - 12:00 PM
LOCATION A230, Technological Institute map it
CONTACT Nathan Keiller nathan.keiller@northwestern.edu EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Nov17
EVENT DETAILSmore info
lessBio
Salar Fattahi is a faculty member in the Department of Industrial and Operations Engineering at the University of Michigan (promoted to Associate Professor with tenure in May 2026). He received his PhD in Industrial Engineering and Operations Research from UC Berkeley in 2020, an MS in Electrical Engineering from Columbia University, and a BS from Sharif University of Technology. He is a recipient of an NSF CAREER Award, has won multiple best paper awards from INFORMS, and serves as an Associate Editor for the INFORMS Journal on Data Science and as an area chair for NeurIPS, ICML, and ICLR. His research develops efficient and scalable computational methods that exploit structure in optimization and machine learning problems, such as sparsity and low-rankness, to achieve strong guarantees at scale.TIME Tuesday, November 17, 2026 at 11:00 AM - 12:00 PM
LOCATION A230, Technological Institute map it
CONTACT Nathan Keiller nathan.keiller@northwestern.edu EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Nov20
EVENT DETAILS
lessDissertation, PhD Final Exam, and change of grade forms due to TGS for Fall PhD candidates
TIME Friday, November 20, 2026
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Nov24
EVENT DETAILSmore info
lessBio: Zhenyu "James" Kong is the Ralph H. Bogle Jr. Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. He received his BS and MS in Mechanical Engineering from the Harbin Institute of Technology and his PhD in Industrial and Systems Engineering from the University of Wisconsin-Madison in 2004. His research focuses on sensing and analytics for smart manufacturing, modeling and diagnosis of large and complex manufacturing systems, and machine learning for manufacturing applications, and he is a Fellow of the Institute of Industrial and Systems Engineers.This talk consists of two parts. In the first part, we study the inference reliability of large language models (LLMs), with an emphasis on characterizing how prompt design affects the accuracy and stability of model outputs. We discuss recent theoretical and empirical advances in in-context learning and hallucination, and examine conditions under which LLMs produce reliable predictions. In the second part, we consider constrained bilevel optimization and develop efficient algorithms with provable guarantees. In particular, we show how error bound conditions can be leveraged to obtain fast convergence rates under mild assumptions.
TIME Tuesday, November 24, 2026 at 11:00 AM - 12:00 PM
LOCATION A230, Technological Institute map it
CONTACT Nathan Keiller nathan.keiller@northwestern.edu EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Nov25
EVENT DETAILS
lessThanksgiving Vacation begins at 6:00 PM (No classes, University offices closed)
TIME Wednesday, November 25, 2026
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Nov30
EVENT DETAILS
lessFall Classes Resume
TIME Monday, November 30, 2026
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Dec1
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lessBio: René Caldentey is a Professor of Operations Management. His primary research interests include stochastic modeling with applications to revenue and retail management, queueing theory, and finance. He has been published in numerous journals including Advances in Applied Probability, Econometrica, Management Science, Mathematics of Operations Research, M&SOM, Operations Research and Queueing Systems. He has served on the editorial board of Management Science, M&SOM, Operations Research, Production and Operations Management and the Journal of Systems and Engineering (in Spanish).
Prior to joining Booth, Caldentey was a professor in the department of Information, Operations and Management Science at New York University Stern School of Business. Before joining NYU Stern in 2001, he worked for the Chilean Central Bank and taught at the University of Chile and The Sloan School of Management at Massachusetts Institute of Technology (MIT).
Professor Caldentey received his Master of Arts in civil industrial engineering from the University of Chile and his Doctor of Philosophy in operations management from MIT.TIME Tuesday, December 1, 2026 at 11:00 AM - 12:00 PM
LOCATION A230, Technological Institute map it
CONTACT Nathan Keiller nathan.keiller@northwestern.edu EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Dec5
EVENT DETAILS
lessFall classes end
TIME Saturday, December 5, 2026
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Dec7
EVENT DETAILS
lessFall Exams begin
TIME Monday, December 7, 2026
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Dec12
EVENT DETAILS
lessFall exams end
TIME Saturday, December 12, 2026
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Dec12
EVENT DETAILSmore info
lessThe ceremony will take place on Saturday, December 12 in Pick-Staiger Concert Hall, 50 Arts Circle Drive.
TIME Saturday, December 12, 2026 at 4:00 PM - 6:00 PM
LOCATION Pick-Staiger Concert Hall map it
CONTACT Andi Joppie andi.joppie@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science