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• Feb

  17

  IEMS Nemhauser and Nelson Best Student Paper Seminar

  Department of Industrial Engineering and Management Sciences (IEMS)

  11:00 AM A230, Technological Institute

  EVENT DETAILS

  Nemhauser Best Paper Award: Chutong Gao, Redundancy or Flexibility? Interplay between their Values in Designing Fork-Join Systems

  Coauthors: Seyed Iravani, Ohad Perry

  Citation: We consider fork-join systems with redundancy and flexibility. Fork-join systems empower numerous modern online service platforms, such as AI agent workflows, among other real-world applications. In a 𝑘-requirement fork-join (FJ𝑘, 𝑘 ≥ 2) system, each job “forks” into multiple tasks processed in parallel, among which 𝑘 tasks are required to “join” for the completion of the job. To minimize the mean job sojourn times in FJ𝑘 systems, two strategies—redundancy and flexibility—are often employed. Redundancy deploys 𝑛 (> 𝑘) stations and sends one task from a job to each, but only 𝑘 out of 𝑛 tasks are needed to complete the job (rendering the remaining 𝑛 − 𝑘 tasks redundant). Flexibility, on the other hand, enables dynamic service capacity allocation. To understand the impact on the mean sojourn time of each strategy and their interplay, we propose a unified modeling framework that simultaneously considers: (i) how many redundant stations should be deployed, and (ii) how the service capacities should be allocated among all stations. We primarily focus on exponentially distributed task sizes. When flexibility is allowed, we prove a class of longest-queue policies are global-optimal, regardless of having redundancy or lack thereof—so redundancy has no marginal value in the existence of flexibility. In the absence of flexibility, we prove in FJ2 systems that “a little redundancy goes a long way”, in a twofold sense: To achieve heavy traffic optimality relative to the global-optimal system, it suffices to (i) employ only one extra station, and (ii) allocate an arbitrarily small capacity 𝜖 > 0 to that extra station. This suggests that (even a little) redundancy is as attractive as (full) flexibility in reducing sojourn times. Numerical studies further support the above insights in FJ𝑘 systems with 𝑘 > 2. We further consider a family of Weibull task-size distributions, where exponential becomes a special case. We show that when the variability of task sizes is low, redundancy can reduce the stability region (and thus harm both throughput and the sojourn time), so it should not be implemented. For highly variable task sizes, redundancy alone can expand the stability region, and flexibility on top of that further expands the stability region. But the flexible control policies need to be selected with extra caution—otherwise, implementing flexibility can harm the system’s stability.

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  Nelson Best Paper Award: Tong Xu, Integer programming for learning directed acyclic graphs from nonidentifiable Gaussian models

  Coauthors: Armeen Taeb, Simge Küçükyavuz, Ali Shojaie

  Citation: We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model. State-of-the-art structure learning methods for this setting have at least one of the following shortcomings: (i) they cannot provide optimality guarantees and can suffer from learning suboptimal models; (ii) they rely on the stringent assumption that the noise is homoscedastic, and hence the underlying model is fully identifiable. We overcome these shortcomings and develop a computationally efficient mixed-integer programming framework for learning medium-sized problems that accounts for arbitrary heteroscedastic noise. We present an early stopping criterion under which we can terminate the branch-and-bound procedure to achieve an asymptotically optimal solution and establish the consistency of this approximate solution. In addition, we show via numerical experiments that our method outperforms state-of-the-art algorithms and is robust to noise heteroscedasticity, whereas the performance of some competing methods deteriorates under strong violations of the identifiability assumption. The software implementation of our method is available as the Python package micodag.

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  TIME Tuesday, February 17, 2026 at 11:00 AM - 12:00 PM

  LOCATION A230, Technological Institute    map it

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  CONTACT Kendall Minta    kendall.minta@gmail.com EMAIL

  CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)

• Feb

  3

  IEMS Seminar 02/03 Kibaek Kim Winter 2026

  Department of Industrial Engineering and Management Sciences (IEMS)

  11:00 AM A230, Technological Institute

  EVENT DETAILSmore info

  Title: Privacy-Preserving Federated Learning at Scale: From Algorithms to Supercomputers

  Bio: I am a Computational Mathematician in Laboratory for Applied Mathematics, Numerical Software, and Statistics, Mathematics and Computer Science Division at Argonne National Laboratory, and a Senior Scientist at-Large at the University of Chicago Consortium for Advanced Science and Engineering. My research focuses on federated learning algorithms and software development, as well as modeling and numerical algorithms for large-scale optimization on high-performance computing systems and GPUs. My work is applied to areas including electric grid systems, healthcare, and key scientific domains of interest to the Department of Energy. Before joining Argonne, I obtained a Ph.D. degree in Industrial Engineering and Management Sciences from Northwestern University. I am a recipient of DOE Early Career Research Program award. I serve as associate editors in Mathematical Programming Computation and Naval Research Logistics and board members for COIN-OR Foundation and IISE Energy Systems.

  Abstract: Federated learning (FL) is increasingly attractive for AI for Science because it enables collaborative model training across institutions and facilities without centralizing sensitive data. In this talk, I will give a brief overview of our DOE-funded AI for Science effort and the multidisciplinary team building scalable learning workflows for scientific and energy applications. I will then introduce privacy-preserving federated learning, focusing on practical mechanisms (e.g., secure aggregation and differential privacy) and the core tradeoffs they impose on accuracy, communication, and systems performance.

        The second half of the talk will focus on our recent queue-aware FL protocol designed for cross-facility training on leadership-class HPC systems. Unlike conventional synchronous or fully asynchronous FL, our algorithm treats batch-scheduler delays as a first-class systems signal: it adapts local work and aggregation to time-varying queue and execution delays to reduce straggler effects while controlling staleness. I will present the key ideas and empirical results from deploying FL across Aurora, Frontier, Polaris, and Perlmutter, highlighting end-to-end performance, stability under heterogeneous runtimes, and what this implies for building reliable, privacy-preserving, multi-site AI training pipelines for science.

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  TIME Tuesday, February 3, 2026 at 11:00 AM - 12:00 PM

  LOCATION A230, Technological Institute    map it

  ADD TO CALENDAR

  CONTACT Kendall Minta    kendall.minta@gmail.com EMAIL

  CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)

• Feb

  10

  IEMS Seminar 2/10 Feng Ruan Winter 2026

  Department of Industrial Engineering and Management Sciences (IEMS)

  11:00 AM A230, Technological Institute

  EVENT DETAILSmore info

  IEMS Seminar 02/10 Feng Ruan

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  TIME Tuesday, February 10, 2026 at 11:00 AM - 12:00 PM

  LOCATION A230, Technological Institute    map it

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  CONTACT Kendall Minta    kendall.minta@gmail.com EMAIL

  CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)

• Mar

  3

  IEMS Seminar 03/03 Shane Henderson Winter 2026

  Department of Industrial Engineering and Management Sciences (IEMS)

  11:00 AM A230, Technological Institute

  EVENT DETAILSmore info

  IEMS Seminar 03.03 Shane Henderson

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  TIME Tuesday, March 3, 2026 at 11:00 AM - 12:00 PM

  LOCATION A230, Technological Institute    map it

  ADD TO CALENDAR

  CONTACT Kendall Minta    kendall.minta@gmail.com EMAIL

  CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)

• Mar

  10

  IEMS Seminar 3/10 Nabarun Deb Winter 2026

  Department of Industrial Engineering and Management Sciences (IEMS)

  11:00 AM A230, Technological Institute

  EVENT DETAILSmore info

  IEMS Seminar 03/10 Nabarun Deb

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  TIME Tuesday, March 10, 2026 at 11:00 AM - 12:00 PM

  LOCATION A230, Technological Institute    map it

  ADD TO CALENDAR

  CONTACT Kendall Minta    kendall.minta@gmail.com EMAIL

  CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)