Fireside Chat with David Hammer, co-founder of Popwheels
2025-04-23 14:00 - 15:00 (America/New_York) | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
This student-moderated Fireside Chat will feature David Hammer, co-founder of Popwheels, a cutting-edge e-bike battery swap network. David will discuss his career trajectory, the challenges of standing up urban-scale technology in NYC, and more!

Prof. Vinitsky Seminar: Robust Self-driving Emerges from Self-play
2025-05-07 13:00 - 14:00 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
Self-play has powered breakthroughs in two-player and multi-player games. Here we show that self-play is a surprisingly effective strategy in another domain. We show that robust and naturalistic driving emerges entirely from self-play in simulation at unprecedented scale — 1.6~billion~km of driving. This is enabled by Gigaflow, a batched simulator that can synthesize and train on 42 years of subjective driving experience per hour on a single 8-GPU node. The resulting policy achieves state-of-the-art performance on three independent autonomous driving benchmarks. The policy outperforms the prior state of the art when tested on recorded real-world scenarios, amidst human drivers, without ever seeing human data during training. The policy is realistic when assessed against human references and achieves unprecedented robustness, averaging 17.5 years of continuous driving between incidents in simulation.

Summer Programs Welcome Lunch!
2025-07-01 12:30 - 13:30 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201

Seminar: Improving the Quality of Urban Public Transport: Lessons Learned from a Novel Use of GPS Data
2025-07-08 13:00 - 14:00 | NY
In Italy, public transport is typically operated by private companies under service contracts including penalties for non-compliance. Therefore, detecting anomalous traffic days is key to fairly assigning delay responsibility for buses operating in mixed traffic: operators should not be penalized on days of exceptional traffic conditions. To address this, harmonic average speeds of vehicles are used to cluster hours of the day exhibiting similar traffic patterns. For any of such traffic states, a Generalized Additive Model is fit to the available speed percentiles in order to detect anomalous days. A case study relying on GPS data is presented for the city of Trieste.

Presented by Andrea Mecchina, PhD candidate, University of Trieste, Italy

CUE Distinguished Speaker Series: Katus Watson, Jacobs
2025-09-10 12:30 - 13:30 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201

New York Reinforcement Learning Workshop (NYRL)
2025-09-12 | NY
Join leading researchers from academia and industry for the inaugural NYRL Workshop. Connect, collaborate, and explore the latest advances in reinforcement learning with the vibrant New York metropolitan RL community.
Co-organized by Amazon, Columbia Business School (CBS), and NYU Tandon School of Engineering.

2025 Sustainable Cities Summit: Day 2
2025-09-25 08:00 - 18:00 | Kimmel Center, Room 914, 60 Washington Square South, New York, NY, 10012, United States
We are proud to support the second annual Sustainable Cities Summit in partnership with Climate Group and NYC Climate Week. This event brings together various experts from multiple organizations to discuss climate-forward cities and mainstream climate action for a sustainable future.
Discover how design, data, and collaboration are reshaping New York’s future—and inspiring cities around the world. As part of #ClimateWeekNYC, Sidara companies are joining forces with NYU to host a full-day summit focused on implementable solutions for healthier, more sustainable urban living. The event feature leading experts from TYLin, Introba, Perkins & Will, and Currie & Brown, alongside thought leaders from NYU, MIT, C40 Cities, the City of New York, and the Los Angeles Cleantech Incubator.

NYU Tandon and Verizon present: AI and You
2025-10-10 08:00 - 12:00 | Pfizer Auditorium, 5 MetroTech, Brooklyn, NY, 11201, United States
Join NYU Tandon and Verizon at an event to learn about AI and how it is involved in your everyday lives. Speakers include: Emily Black, Joseph Chow, Rumi Chunara, Emilia David, Ashley Greenspan, Shri Iyer, Stacy Matlen, Shema Mbyirukira, Maurizio Porfiri!

Author Talk: A Conversation with Michael M. Greenburg
2025-10-16 18:00 - 19:00 | Dibner Library, 5 MetroTech, Brooklyn, NY, 11201, United States
This Thursday is the NYU Press Author Talk with Mr. Michael Greenburg. The book focuses on William LeMessurier, the structural engineer who discovered a fatal flaw in his building’s design and his decision to blow the whistle on himself. Please make sure you register for the event here if you plan to attend.
Food & refreshments provided.

Seminar: Self-driving Vehicles as Transit in US Settings: Complementing Buses and Trains with Demand-responsive SAVs
2025-10-21 12:30 - 13:30 | NY
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 | NY

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 | NY
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,

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.

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

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.

Women in Transportation 2026
2026-04-08 17:30 - 19:00 | NY

Morphing Structures for Civil Engineering, from Gridshells to Burrowing Robots
2026-04-14 12:30 - 13:30 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
Shape-morphing metamaterials, mechanical systems and structures are designed to predictably achieve large shape changes when actuated. While pervasive in aerospace and mechanical engineering, these systems are seldom considered in civil engineering applications. This talk will concentrate on the morphing mechanics of some of the structures designed in our lab, highlighting some of their potential civil applications.
First, we will present a strategy to turn flat arrangements of structural elements into pop-up domes, investigating their deployment mechanics, load-bearing capacity and comparing them to existing structures such as gridshells. Then, we will illustrate how morphing structures can serve as the backbone of burrowing robots for geotechnical engineering applications, where they need to engage in complex interactions with surrounding soils.

Bridge Resource Program Rime Webinar: Fusion of SHM and WIM Data for Minimizing the Cracking Potential of Concrete Bridge Decks
2026-06-23 12:00 - 13:00 | NY
This webinar focuses on the fusion of Structural Health Monitoring (SHM) data and Weigh-In-Motion (WIM) data to predict strain responses from live truck loads and support early-age crack mitigation strategies. A digital twin of a multi-span bridge was developed using a Finite Element Model (FEM) and calibrated based on the SHM data. The webinar will present how truck traffic and load data from WIM and structural responses from SHM can be used in the FEM under realistic loading scenarios during construction to support decision-making for the pouring sequence. The fusion of SHM, WIM and FEM provides advanced predictions of structural behavior allowing for optimization of the concrete placement schedules by selecting a window of time to mitigate cracking while minimizing traffic restrictions.
This webinar will cover innovative methods for obtaining and combining analysis for
Nondestructive Material testing – Testing Concrete Samples and temperature over time for early age nondestructive strength evaluation of the structural element
Developing a Truck live load profile – Onsite Weigh-In-Motion (WIM) System profiles truck traffic
Bridge superstructure behavior under known truck load – Bridge Instrumentation using strain gauges, accelerometers, and deflectometers
Long term concrete shrinkage and thermal strain of bridge deck – Embedded sensor assessment of bridge deck & girder elements
Digital twin – Finite Element Model for identifying strains that can produce cracking
Post install evaluation – Crack Mapping and Analysis
Presented by Rutgers’s Prof. Hani Nassif, Dr. Chaekuk Na, and Michael Ruszala

AI Applications in Urban Transportation Management Symposium
2026-09-25 | NY
The New York City Department of Transportation (NYCDOT) and Federal Highway Administration (FHWA), supported by organizers at New York University’s C2SMART Center, and the City College of New York’s University Transportation Research Center, is seeking to convene industry professionals, public agencies, researchers, and roadway users for a symposium focused on the future deployment of Artificial Intelligence (AI) applications in urban transportation management.
Details to come…
