Inside Our ProgramProgram Events
Events
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Sep14
EVENT DETAILS
lessEnjoy a welcome from Dean Christopher A. Schuh and other McCormick leaders, and receive a Northwestern Engineering T-shirt. A free breakfast on the Tech East Plaza will follow.
TIME Monday, September 14, 2026 at 9:00 AM - 10:30 AM
LOCATION LR2, Technological Institute map it
CONTACT Andi Joppie andi.joppie@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science
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Sep22
EVENT DETAILS
lessEnjoy a welcome from Dean Christopher A. Schuh and other McCormick leaders, and receive a Northwestern Engineering T-shirt. A free lunch on the Tech East Plaza will follow.
TIME Tuesday, September 22, 2026 at 11:00 AM - 12:30 PM
LOCATION Ryan Family Auditorium, Technological Institute map it
CONTACT Andi Joppie andi.joppie@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science
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Sep29
EVENT DETAILSmore info
lessAbstract: A central phenomenon in modern machine learning is feature learning: rather than operating on a fixed representation of the data, successful models learn representations adapted to the prediction task. We study a tractable model of this phenomenon through a compositional variant of kernel ridge regression, where the kernel is applied after a learnable linear transformation. When the response depends on the input only through a low-dimensional predictive subspace, we show that optimizing the population objective automatically eliminates directions orthogonal to this subspace and, in certain regimes, exactly recovers it. Surprisingly, the same exact low-dimensional structure persists at finite sample sizes with high probability, even without explicitly penalizing the linear transformation to be low-dimensional.
Bio: Feng Ruan is currently an assistant professor in Department of Statistics and Data Science at Northwestern University. His research focuses on the foundations of feature learning and stochastic and nonsmooth optimization, and is supported by an NSF CAREER Award.
TIME Tuesday, September 29, 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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Oct6
EVENT DETAILSmore info
lessAbstract: We consider stochastic and finite-sum optimization problems with deterministic constraints. Existing methods typically focus on finding an approximate stochastic solution that ensures the expected constraint violations and optimality conditions meet a prescribed accuracy. However, such an approximate solution can potentially lead to significant constraint violations. To address this issue, we propose variance-reduced first-order methods that treat the objective and constraints differently. Under suitable assumptions, our proposed methods achieve stronger approximate stochastic solutions with complexity guarantees that more reliably satisfy the constraints compared to existing methods. This is joint work with Sanyou Mei (HKUST) and Yifeng Xiao (UMN).
Bio: Zhaosong Lu is a Full Professor in the Department of Industrial and Systems Engineering at the University of Minnesota. He received his Ph.D. in Operations Research from Georgia Institute of Technology. His research focuses on the theory and algorithms of continuous optimization, with applications in data science and machine learning. Dr. Lu has published extensively in leading journals, and his work has been supported by major funding agencies, including AFOSR, NSF, and ONR. He has served on several prize committees, such as the INFORMS George Nicholson Prize Committee and the ICCOPT Best Paper Award Committee. He has also served as an Associate Editor for leading journals, including Mathematics of Operations Research, SIAM Journal on Optimization, Computational Optimization and Applications, and Journal of Global Optimization.
TIME Tuesday, October 6, 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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Oct20
EVENT DETAILSmore info
lessBio
Gesualdo (Aldo) Scutari is the Barbara and Pedro Granadillo Professor in the Edwardson School of Industrial Engineering and the Elmore Family School of Electrical and Computer Engineering at Purdue University, an IEEE Fellow, and a University Faculty Scholar. Before Purdue he was an assistant professor at the State University of New York at Buffalo, and he completed his doctorate at Sapienza University of Rome under Sergio Barbarossa. His research interests include continuous optimization, equilibrium programming, and their applications to signal processing and statistical learning, and his recognitions include 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 currently serves as an Associate Editor of the SIAM Journal on Optimization.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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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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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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Dec1
EVENT DETAILSmore info
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 TBD, Technological Institute map it
CONTACT Nathan Keiller nathan.keiller@northwestern.edu EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
