Seminar: Self-driving Vehicles as Transit in US Settings: Complementing Buses and Trains with Demand-responsive SAVs
2025-10-21 12:30 - 13:30 (America/New_York)
ABSTRACT: Shared autonomous vehicles (SAVs) can deliver door-to-door (D2D), first-mile last-mile (FMLM) and dynamic ride-sharing (DRS or pooled-ride) services in many cities today. This 3-part presentation explores potential improvements in US travel and transit systems by simulating interactions between fixed-route and on-demand SAV services across the Chicago and Austin regions. The first study microsimulates SAVs providing fixed-route, fixed-stop service alongside private automobiles. System costs for each traveler type are computed across different demand profiles, SAV sizes (seat counts), and other dimensions; and smaller SAVs reduce corridor costs across most scenarios.
The second uses POLARIS to mesoscopically simulate D2D plus FMLM and autonomous-bus services across the 20-county Chicago region’s hundreds of train stations, thousands of bus stops, and millions of travelers. Assuming fares of $0.50 per passenger-mile and 1 SAV for every 40 residents, this mode is predicted to attract 15% of person-trips and lower household vehicle ownership by 40%. Adding FMLM service raises the region’s traditional transit (bus + train) split from 5.4% to 6.3%, with each SAV serving 12% more trip requests per day. Automating the bus system (and halving headways, with half the seats per bus) does little to mode splits. The third study simulates a full replacement of buses with autonomous services across the 6-county Austin region. Using the region’s bus stops as pickup and drop-off points, the transit fleet size falls by 10% or more, thanks to coordination in trip services. Subsidizing SAV fleet managers to serve current bus demand emerges as the region’s most cost-effective and welfare-improving transit option.
BIO: Kara Kockelman is a registered professional engineer and holds a PhD, MS, and BS in civil engineering, a master’s in city planning, and a minor in economics from the University of California at Berkeley. Dr. Kockelman has been a professor of transportation engineering at the University of Texas at Austin for 27 years, and is Associate Site Director of the NSF Industry-University Cooperative Research Center for Efficient Vehicles and Sustainable Transportation Systems and an MIT Mobility Fellow. She has authored over 240 journal articles (and two books), and her primary research interests include planning for shared and autonomous vehicle systems, the statistical modeling of urban systems, energy and climate issues, the economic impacts of transport policy, and crash occurrence and consequences. Her and her students’ work can be found at www.caee.utexas.edu/prof/kockelman. They hope you will join them each year in our world’s carbon-free, cost-free Bridging Transportation Researchers conference: https://bridgingtransport.org/.

transportationcamp 2025
2025-10-25 09:00 - 18:00

Student Learning Hub: Introduction to Online Recommendation Systems: From Predict-Sort Architecture to Real-Time Feature Service
2025-11-07 12:00 - 13:00
This session introduces how modern online recommendation systems, like TikTok shop, deliver personalized content in real time. I’ll walk through the overall system architecture and then focus on the predict-sort stage, explaining how BFS (Bytedance Feature Service) and UDA (User Data Accessor) enable large-scale feature computation using a DAG-based (Directed Acyclic Graph) operator framework and a domain-specific language (DSL). Participants will gain insight into how these components work together to achieve millisecond-level predictions. Hosted by NYU’s Hongdao Meng!

Urban Tech Summit 2025 - Adaptive Intelligence: Activating Urban AI
2025-11-11 - 2025-11-12 | Verizon Executive Education Center, 2 West Loop Road, NYC, NY, 10044
Hear from leading experts on how urban tech can help cities do more with less as they deal with uncertainty and prepare for future shocks.

Cornell Tech’s Urban Tech Summit returns on November 11-12, 2025, focusing on the theme of “Adaptive Intelligence: Activating Urban AI.” Organized by the Urban Tech Hub at Cornell Tech’s Jacobs Institute, this year’s Summit will explore quick wins and ways cities can begin to assess, test, and amplify innovations in artificial intelligence for rapid impact.

NYU Prof. Sarah Kaufman will be featured on a panel: New York City has long been shaped by automation. Traffic signals organize the streets and telephone switches connect our boroughs. As autonomous vehicles begin piloting on city roads, a new wave of systems is posed to transform urban life. This session will examine how NYC is navigating this shift through policy, pilot programs, and planning, while also considering broader trends in urban robotics that will define the next generation of automation in cities.

Seminar: Inverse Learning and Intervention of Transportation Network Equilibrium
2025-11-12 13:00 - 14:00 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
Abstract:
By 2035, nearly half of all new vehicles in the United States will be connected, generating unprecedented volumes of mobility data. Leveraging emerging connected mobility data, this talk establishes an AI-enabled inverse learning framework to transform the transportation network equilibrium modeling paradigm, which has been the foundation of system planning and management for over seventy years.
Traditional transportation network equilibrium models are time-consuming and costly to calibrate. This talk presents the inverse learning of user equilibrium as a novel framework for constructing nonparametric, context-dependent network equilibrium models directly from empirical travel patterns. We compare nonparametric and parametric approaches, mathematically clarifying the trade-offs among behavioral realism, data availability, and computational cost. The proposed neural-network-based nonparametric framework can automatically discover any well-posed network equilibrium model given
sufficient data, without relying on predefined behavioral assumptions. In contrast, the semi-parametric approach is more computationally tractable, as it simplifies the inverse learning problem into a sequence of convex optimizations.
Finally, we apply the inverse learning framework to a long-term network design problem for the city of Ann Arbor, Michigan. Using real-world crowdsourced data, we learned a context-dependent equilibrium model and introduce a certified, auto-differentiation-accelerated algorithm to solve the resulting distributionally robust bi-level network design problem under contextual uncertainty.
Bio:
Dr. Zhichen Liu is an Assistant Professor in the Stony Brook University Department of Civil Engineering. Her research focuses on innovating next-generation modeling and computational tools for mobility and logistics systems, with an emphasis on connectivity, electrification, and automation. She received her Ph.D. in Civil Engineering and M.S. in Industrial and Operational Engineering from the University of Michigan, and previously served as a Visiting Scientist at General Motors. Dr. Liu is a recipient of the Rackham Predoctoral Fellowship and was honored as the sole global awardee of the prestigious Helene M. Overly Memorial Scholarship by the WTS International Foundation.

C2SMART Career Panel
2025-11-13 17:30 - 18:30
Join us for a Career Panel featuring NYU’s Dr. Jingqin Gao & Holly Chase, Suzana Duran Bernardes of Disney, and Michael Willson of ARUP. This panel will explore career trajectories, lessons learned, and helpful advice to students about to enter the job market. The panelists have an array of experiences from several industries and schools; come learn about their transitions from student to young professional to better inform your own path!
Moderated by ITE NYU Student Chapter Co-President Stephen Martone. Light refreshments will be provided,

STEM Day 2025!
2025-11-14 11:30 - 15:00

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
