STEM Day 2025!
2025-11-14 11:30 - 15:00 (America/New_York)

CUE Distinguished Seminar Series: Simulating the Physical and the Connected: Neural Models for Structures and Networks
2025-11-18 12:30 - 13:30 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
Advances in machine learning are transforming how we model and design engineered systems, from industrial components to large-scale infrastructure networks. This seminar presents recent developments in physics-based neural networks and neural operators for fast, accurate physics-based simulation. The first part focuses on individual structures and introduces models that accelerate design computations and improve generalization across geometries and parameters. The second part focuses on graph neural network (GNN) models for infrastructure networks and their use in developing digital twins of transportation systems. These GNNs enable rapid computation of network metrics—such as shortest paths, connectivity, and dynamic traffic flows—and can incorporate physics constraints like travel demand influence and flow conservation for enhanced accuracy and consistency. Case studies on urban networks demonstrate how such models can support resilient and data-driven decision making for asset management, mobility, and emergency response.
Dr. Hadi Meidani is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign (UIUC). His research focuses on uncertainty quantification, scientific and physics-informed machine learning, and fast computational methods for infrastructure and engineering design. He earned his Ph.D. in Civil Engineering, his M.S. in Electrical Engineering, and his M.S. in Structural Engineering from the University of Southern California (USC). Prior to joining UIUC, he was a postdoctoral scholar in the Department of Aerospace and Mechanical Engineering at USC and in the Scientific Computing and Imaging Institute at the University of Utah. Dr. Meidani is the Chair of the Machine Learning Committee of the ASCE Engineering Mechanics Institute. At UIUC, he is the Founding Chair of the AI in CEE Task Force in his department. Dr. Meidani is the recipient of an NSF CAREER Award for his work on fast computational models for infrastructure networks.

Seminar: AI-empowered Digital Twin for Traffic Safety Analysis
2025-11-20 14:00 - 15:00 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
Abstract: Traffic safety research faces the paradox of rarity—the most critical crashes occur so infrequently that passive methods relying on historical records cannot accurately estimate traffic risks. This talk first reviews current methodologies and highlights their limitations in handling rare, high-risk events. Building on these insights, we chart a new path that fuses active safety analysis of near-miss events and generative AI. As a preliminary foundation for the generative AI–enabled safety analysis, our recent work provides digital-twin–enabled active safety analysis through three pivotal components: (1) a physics-grounded active safety module that identifies near-miss events across diverse traffic contexts; (2) group-wise interaction modeling that captures multi-agent behaviors in traffic; and (3) a high-fidelity digital twin integrating detailed vehicle dynamics, tire–road interaction, and the above active-safety and interaction models. Together, these elements enable proactive risk prevention, expanding safety analysis beyond observed outcomes to the full spectrum of what could happen, and lay the foundation for Vision Zero.

Bio: Dr. Yang Zhou is an assistant professor in the Zachry Department of Civil and Environmental Engineering at Texas A&M University, and career initiation fellow of Texas A&M Institute of data science. He received his Ph.D. degree and Master degree from the University of Wisconsin-Madison and the University of Illinois at Urbana and Champaign, respectively. Before joining Texas A&M, Yang worked as a postdoctoral research associate supported by the Department of Civil and Environmental Engineering, University of Wisconsin-Madison. Yang has over ten years of experience in connected automated vehicle control and analysis, traffic flow analysis, AI applications on transportation, and high-fidelity simulation. Yang has PIed multiple federal and local grants such as FHWA-EAR and SS4A. Yang has published more than 70 top-tier transportation journals, including Transportation Research Part B, Transportation Research Part C, and IEEE Transactions on Intelligent Transportation Systems.

Student Learning Hub: Building a Scalable Multithreaded Processing System on AWS Cloud
2025-11-21 12:00 - 13:00
This lecture introduces the design and implementation of a serverless multithreaded processing system using AWS Step Functions, AWS Lambda, and Amazon S3. The session covers how to orchestrate parallel tasks, manage state transitions, and scale processing pipelines efficiently on the AWS cloud platform. Emphasis will be placed on cloud-native scalability, fault tolerance, and cost optimization through automation and serverless architecture. Hosted by NYU’s Zhi Jie (Jeffrey) Wang!

AECOM Fireside Chat & Panel
2025-12-02 12:30 - 14:30 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201

NYU MS Open House Series, Session 1
2025-12-29 15:00 - 16:00

NYU MS Open House Series, Session 2
2026-01-05 15:00 - 16:00

NYU MS Open House Series, Session 3
2026-01-06 12:00 - 13:00

NYU MS Open House Series, Session 4- Last one!
2026-01-07 09:00 - 10:00

Seminar: A Large-scale Simulation Platform and AI-driven Operational Strategies for On-demand Ride Services
2026-01-09 14:00 - 15:00 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
Abstract: In this work, we develop a novel multi-functional and open-sourced simulation platform for on-demand ride service operations, which can simulate the behaviors and movements of various agents (including drivers and passengers) on a real transportation network. It provides a few accessible portals for users to train and test various optimization algorithms, especially reinforcement learning algorithms, for a variety of tasks, including on-demand matching, idle vehicle repositioning, and dynamic pricing. Evaluated by experiments based on real-world datasets, the simulator is demonstrated to be an efficient and effective test bed for various tasks related to on-demand ride service operations.
Bio: Dr. Jintao Ke is an Assistant Professor in the Department of Civil Engineering at the University of Hong Kong (HKU). Dr. Ke received his B.S. degree (2016) in Civil Engineering from Zhejiang University, and his PhD degree (2020) in Civil and Environment Engineering from Hong Kong University of Science and Technology. His research interests include on demand mobility services, transportation big data analytics, multimodal transportation system optimization, transportation pricing, spatiotemporal traffic prediction, etc. He has published more than 50 SCI/SSCI indexed research papers in top-tier journals in the field of transportation research and data mining, such as Transportation Research Part A-F, IEEE Transactions on
Intelligence Transportation System, IEEE Transactions on Knowledge and Data Engineering, IEEE Internet of Things, Computer-Aided Civil and Infrastructure Engineering. He has been ranked as the World's Top 2% most-cited scientists by Stanford University since 2023. He is serving as an Editorial Board Member of Transportation Research Part C, Transportation Research Part E, and Travel Behavior and Society.

Digital Twins for AEC & Facility Managers
2026-04-20 | Pfizer Auditorium, 5 MetroTech, Brooklyn, NY, 11201, United States
