News & EventsDepartment Events & Announcements
Events
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Mar25
EVENT DETAILS
lessLogistics
Date: Wednesday, March 25th, 2026
Location: Northwestern University, Ford Motor Company Engineering Design Center (Room: Hive 2350) 2133 Sheridan Rd, Evanston, IL 60201
Parking: For those driving to the workshop, attendees can park in the North Campus garage 2311 N Campus Dr #2300, Evanston, IL 60208. https://maps.northwestern.edu/txt/facility/646 You’ll exit the garage on the opposite side from the car entrance. It will be a five minute walk to the workshop from the parking garage.
Description:
This workshop focuses on how strategic agents, potentially including LLM based ones, interact in various machine learning environments and influence the system-wide outcome.
We are welcoming poster submissions around the following topics (but not restricted to):
Incentive Issues in LLMs
Mechanisms for Emerging Technologies such as Generative AIs
LLM-Based Agents & Coordination
Multi-agent Reasoning/Debating
Learning & Adaptation
Convergence in Strategic Online learning and Bandits
Preference Learning & Human Feedback
Information/Text Elicitation from GenAIs
Fairness / Ethics in LLMs
Game-Theoretic Foundations
Robustness & Adversarial Behavior in Strategic Settings
We are co-locating this workshop with EAI GameNets 2026 – 14th EAI International Conference on Game Theory for Networks GameNets is happening on Mar 26-27 in the same location
TIME Wednesday, March 25, 2026 at 1:45 PM - 6:30 PM
LOCATION Hive 2350, Ford Motor Company Engineering Design Center map it
CONTACT Indira Munoz indira.munoz@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Apr2
EVENT DETAILS
lessThursday / Seminar
April 2 / 12:00 PM
Hybrid / Mudd 3514Speaker
Tianhao Wang, University of VirginiaTalk Title
Unlocking the Value of Private Data: Differentially Private Synthetic Data GenerationAbstract
Despite massive data generation, access to sensitive datasets for research and development remains severely restricted, leading to "data poverty" and hindering innovation. Differentially private synthetic data generation offers a robust framework to create realistic, privacy-preserving datasets, unlocking their value without compromising individual confidentiality. In this talk, I will highlight our group's recent advancements in this domain, including novel algorithms and comprehensive benchmarking efforts to evaluate the utility and privacy trade-offs of synthetic data and talk about ongoing and future research directions addressing technical challenges and examining approaches across various data modalities.Biography
Tianhao Wang is an Assistant Professor at the Department of Computer Science at University of Virginia (UVA) since Jan 2022. He held a postdoc position at Carnegie Mellon University, earned his Ph.D. in Computer Science from Purdue University in 2021, and B.E. from Fudan University in 2015. His research focus is differential privacy and AI security and privacy. He has extensive publications in top security and database conferences. His work about differentially private synthetic data generation won multiple awards in NIST’s competition.TIME Thursday, April 2, 2026 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Apr6
EVENT DETAILS
lessFrom Data to Design: Rethinking
Engineering Design With Next-Gen AIBIO
Faez Ahmed is an Associate Professor of Mechanical Engineering at MIT, where he leads the DeCoDE Lab. His research focuses on AI for engineering design, including deep generative models, multimodal representations, and human–AI collaboration. His work has been recognized with the NSF CAREER Award, ASME DAC and DTM Young Investigator Awards, the Google Research Scholar Award, and the Amazon Research Award. He serves as an Associate Editor for Computer-Aided Design and Design Science.
ABSTRACT
Generative AI is transforming how we create, customize, and accelerate digital content. Yet applying these tools to engineering design introduces unique challenges, from maintaining precision under evolving requirements to working effectively in data-scarce environments and interpreting designer intent. In this talk, I will discuss these challenges and show how emerging engineering-focused foundation models are beginning to address them, reshaping workflows in areas such as vehicle design, CAD automation, and design optimization. I will highlight new opportunities enabled by generative AI that integrates multimodal data with engineering analysis and optimization, and present examples of AI-driven design co-pilots for engineering tasks. The talk will conclude with a perspective on how AI enables us to broaden design democratization, accelerate innovation cycles, and fundamentally reshape the role of engineers.
TIME Monday, April 6, 2026 at 3:00 PM - 4:00 PM
LOCATION 1-350, Ford Motor Company Engineering Design Center map it
CONTACT Jeremy Wells jeremywells@northwestern.edu EMAIL
CALENDAR McCormick - Mechanical Engineering (ME)
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Apr13
EVENT DETAILS
lessMonday / CS Seminar
April 13 / 12:00 PM
Hybrid / Mudd 3514Speaker
Arindam Banerjee, University of Illinois Urbana-ChampaignTalk Title
Reinforcement Learning and Control with Generative World ModelsAbstract
"Recent years have witnessed remarkable advances in generative modeling — from diffusion models and flow matching to autoregressive transformers and action-conditioned video models — that are rapidly closing the gap between learned simulators and the complexity of real-world dynamics. These developments open a principled path toward a new generation of reinforcement learning (RL) algorithms that harness the representational power of generative world models, naturally bridging model-based planning and model-free policy optimization within a unified framework.
In this talk, we introduce an inference-time policy optimization framework inspired by model predictive control (MPC), built around a pretrained policy and a learned world model (WM) of state transitions and rewards. While existing approaches use learned dynamics to generate imagined trajectories — either during training or at inference — they stop short of using those trajectory rollouts to optimize policy parameters on the fly. Our approach addresses this gap through a Differentiable World Model (DWM) pipeline that enables end-to-end gradient computation through WM trajectory rollouts, yielding inference-time policy optimization (ITPO) grounded in MPC. Across continuous-control benchmarks, ITPO with DWM consistently outperforms strong offline RL baselines. Beyond the core RL framework, we also discuss principled approaches to fine-tuning generative models under distribution shift, which enable the online deployment of such world-model-based policies."
Biography
Arindam Banerjee is a Founder Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign. He currently serves as the President of the Society for Artificial Intelligence and Statistics which runs the annual international AISTATS conference. He is an ACM Fellow. His research interests are in machine learning and artificial intelligence. His current research focuses on computational and statistical aspects of deep learning, spatial and temporal data analysis, generative models, and sequential decision making. His work also focuses on applications of machine learning in complex real-world and scientific domains including problems in weather and climate, ecology, and agriculture. He has won several awards over the years, including the NSF CAREER award, the IBM Faculty Award, and seven best paper awards at top-tier venues.Research Area/Interest:
Machine Learning, Artificial Intelligence---
Zoom: TBA
Panopto: TBATIME Monday, April 13, 2026 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Apr22
EVENT DETAILS
lessWednesday / CS Seminar
April 22 / 12:00 PM
Hybrid / Mudd 3514Speaker
Lev Reyzin, University of Illinois ChicagoTalk Title
On the Hardness of Learning Regular ExpressionsAbstract
"Despite the theoretical significance and wide practical use of regular expressions, the computational complexity of learning them has been largely unexplored. We study the computational hardness of improperly learning regular expressions in the PAC model and with membership queries. We show that PAC learning is hard even under the uniform distribution on the hypercube, and also prove hardness of distribution-free learning with membership queries. Furthermore, if regular expressions are extended with complement or intersection, we establish hardness of learning with membership queries even under the uniform distribution. We emphasize that these results do not follow from existing hardness results for learning DFAs or NFAs, since the descriptive complexity of regular languages can differ exponentially between DFAs, NFAs, and regular expressions.
This work is joint with Idan Attias, Nati Srebro, and Gal Vardi"
Biography
Lev Reyzin is a Professor of Mathematics, Statistics, and Computer Science at the University of Illinois Chicago and Co-Director of the IDEAL Institute. He works on the theory of machine learning, data science, and artificial intelligence. Prior to UIC, Reyzin was a Simons Postdoctoral Fellow at Georgia Tech and an NSF Computing Innovation Fellow at Yahoo! Research. Reyzin received his Ph.D. on an NSF doctoral fellowship from Yale under Dana Angluin and his bachelor’s degree from Princeton. He is currently the Chair of the Steering Committee for the ALT conference and the Editor-in-Chief of Mathematics of Data, Learning, and Intelligence. He has also served as a General Chair for FOCS 2024, the Program Chair for ISAIM 2020, and a Program Chair for ALT 2017. His work has earned awards at ICML, COLT, and AISTATS and has received extensive funding.Research Areas/Interests: theory of machine learning, data science, and artificial intelligence
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Zoom Link
Panopto LinkTIME Wednesday, April 22, 2026 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Apr27
EVENT DETAILS
lessMonday / CS Seminar
April 27 / 12:00 PM
Hybrid / Mudd 3514Speaker
Tushar ChandraTalk Title
TBAAbstract
TBABiography
TBA---
Zoom: TBA
Panopto: TBATIME Monday, April 27, 2026 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Apr29
EVENT DETAILS
lessWednesday / CS Seminar
April 29 / 12:00 PM
Hybrid / Mudd 3514Speaker
Bill FeffermanTalk Title
TBAAbstract
TBABiography
TBA---
Zoom: TBA
Panopto: TBATIME Wednesday, April 29, 2026 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May4
EVENT DETAILS
lessMonday / CS Seminar
May 4 / 12:00 PM
Hybrid / Mudd 3514Speaker
Moon DuchinTalk Title
TBAAbstract
TBABiography
TBA---
Zoom: TBA
Panopto: TBATIME Monday, May 4, 2026 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)