Inside Our ProgramProgram Events
Events
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Apr7
EVENT DETAILSmore info
lessJim Dai
ORIE, Cornell University
Stochastic processing networks (SPNs) model the operations of many complex systems, such as data centers and communication networks. These networks have been an active subject of research for more than 40 years. In this talk, I will introduce M-COF (Multi-scale Closed-form Optimization Framework), a new framework for the optimal control of a class of SPNs known as multi-class queueing networks. M-COF is scalable, can incorporate practical performance metrics such as tail-latency or fairness, and is rooted in recently developed multi-scale heavy-traffic theory. I will also discuss the relationship between M-COF (simulation-free) and recent methods (with simulation) based on deep PDEs and deep reinforcement learning. This talk is based on joint work with Jin Guang (Chicago Booth) and Lucy Huo (HKUST).
Bio: Jim Dai is the Leon C. Welch Professor of Engineering in the School of Operations Research and Information Engineering at Cornell University. Prior to joining Cornell, he held the Chandler Family Chair Professorship in the School of Industrial and Systems Engineering at the Georgia Institute of Technology, where he was a faculty member from 1990 to 2012.
Jim Dai received his BA and MA in mathematics from Nanjing University and his PhD in mathematics from Stanford University. He is an elected Fellow of the Institute of Mathematical Statistics and an elected Fellow of the Institute for Operations Research and the Management Sciences (INFORMS). His awards include the Erlang Prize (1998), the ACM SIGMETRICS Achievement Award (2018), and the INFORMS von Neumann Theory Prize (2024). He served as Editor-in-Chief of Mathematics of Operations Research from 2012 to 2019.
Jim Dai’s research interests include fluid and diffusion models of queueing networks, Stein’s method, stochastic processing networks and their applications to ride-hailing platforms, data centers, and hospital inpatient flow management.
TIME Tuesday, April 7, 2026 at 11:00 AM - 12:00 PM
LOCATION Suite 1400, Krebs Classroom, North Campus Parking Garage map it
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Apr7
EVENT DETAILS
lessThis lecture introduces Differential Privacy (DP) as a mathematically rigorous framework for privacy-preserving data analysis, motivated by the well-documented failures of traditional anonymization approaches such as linkage attacks and the mosaic effect. The lecture covers the formal definition of DP, key properties including the privacy budget (ε), and the mechanisms used to achieve it — Laplace, Exponential, and Gaussian — alongside practical considerations around the privacy-utility tradeoff. Real-world deployments at organizations are examined, with a forward-looking discussion of the emerging challenges of applying DP to large language model training.
TIME Tuesday, April 7, 2026 at 5:30 PM - 6:30 PM
LOCATION M152, Technological Institute map it
CONTACT Master of Science in Machine Learning and Data Science Program mlds@northwestern.edu EMAIL
CALENDAR Master of Science in Machine Learning and Data Science (MLDS)
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Apr8
EVENT DETAILS
lessThis lecture introduces data anonymization as a formal approach to protecting individual privacy when releasing datasets, motivating the need for rigorous guarantees through the well-known failures of naive de-identification such as linkage attacks and quasi-identifier re-identification. The lecture covers core Statistical Disclosure Control (SDC) concepts and a range of anonymization techniques including generalization, suppression, pseudonymization, and data perturbation. Practical methodology for applying anonymization in context-specific settings is presented with attention to the inherent privacy-utility tradeoff that practitioners must navigate.
TIME Wednesday, April 8, 2026 at 12:00 PM - 1:00 PM
LOCATION McCormick Education Center, Room 1400 (Krebs), North Campus Parking Garage map it
CONTACT Master of Science in Machine Learning and Data Science Program mlds@northwestern.edu EMAIL
CALENDAR Master of Science in Machine Learning and Data Science (MLDS)
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Apr9
EVENT DETAILS
lessThis lecture introduces Homomorphic Encryption (HE), a powerful cryptographic technique that enables computations to be performed directly on encrypted data without requiring access to a secret key, such that the decrypted result is identical to what would be obtained from computing on the original plaintext. The session covers the core concepts and mathematical foundations of HE, including additive and multiplicative homomorphism, the role of noise in fully homomorphic schemes, and practical tools such as Microsoft SEAL and HELib. Applications spanning healthcare, finance, machine learning, and cloud computing are examined alongside the key deployment challenge of computational overhead.
TIME Thursday, April 9, 2026 at 5:30 PM - 6:30 PM
LOCATION M152, Technological Institute map it
CONTACT Master of Science in Machine Learning and Data Science Program mlds@northwestern.edu EMAIL
CALENDAR Master of Science in Machine Learning and Data Science (MLDS)
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Apr21
EVENT DETAILS
lessIEMS Weekly Seminar Series
TIME Tuesday, April 21, 2026 at 11:00 AM - 12:00 PM
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Apr28
EVENT DETAILS
lessAbstract: Join Jim Roth, Northwestern alumnus and President of Customer Success at Salesforce, for an insightful talk on building a successful career in a rapidly evolving technological landscape. Learn how Jim’s personal career journey and his industrial engineering degree helped him get to where he is today. Drawing on his experience leading global operations at Dell Technologies and Salesforce—including driving service transformations and deploying autonomous AI agents—Jim will explore how artificial intelligence is fundamentally reshaping the workforce and what it means for engineers starting their careers.
Following the session, you’ll walk away with actionable advice on:
The Value of Your Degree: How engineering equips you for the AI-driven future of work.
In-Demand Skills: The specific capabilities businesses are actively looking for in undergraduate and graduate hires to stay "future-ready," including familiarity with AI tools.
Maximizing Your College Experience: Tips, tricks, and recommended opportunities to pursue while still in school to set your career up for success.
Whether you're curious about AI or just want solid career tips, this session will help you level up your future.TIME Tuesday, April 28, 2026 at 11:00 AM - 12:00 PM
LOCATION Suite 1400, Krebs Classroom, North Campus Parking Garage map it
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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May5
TIME Tuesday, May 5, 2026 at 11:00 AM - 12:00 PM
LOCATION L440, Technological Institute map it
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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May19
TIME Tuesday, May 19, 2026 at 11:00 AM - 12:00 PM
LOCATION L440, Technological Institute map it
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
