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 (America/New_York) | 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.

Webinar: AI Agent 101 and Vibe Coding Demo
2026-03-11 12:00 - 13:00
This talk introduces the core concepts behind modern AI agents—from (Large Language Models) LLMs and memory to tool integration, reusable agent skills, and autonomous workflows. Participants will gain a clear understanding of how prompting, function calling, and multi-agent coordination work in practice. The webinar will include a live “vibe coding” demo to prototype use cases such as a transportation data dashboard using Google AI Studio, VS Code + AI extensions, and Claude Desktop to demonstrate how AI agents can orchestrate tools and skills to accelerate development, automate data workflows, and support transportation analytics and decision-making.
Presented by Dr. Yu Hu, Senior Software Engineer at Comcast.

MTA x NYU: Demystifying the Application Process
2026-03-13 15:00 - 16:00
Kawanza Williams serves as Senior Manager of Emerging Talent at the Metropolitan Transportation Authority (MTA), where she leads initiatives focused on talent development, retention, and strategic workforce partnerships. In her role as Employee Manager of Retention & External Partnerships, she oversees the full lifecycle of the MTA’s internship programs, guiding interns from onboarding through professional development and, when applicable, transition into permanent roles. Kawanza works closely with senior leadership, external partners, and vendors to ensure that talent pipelines align with organizational priorities and operational needs. Her leadership emphasizes structured career pathways, sustainable workforce development, and cost-conscious program design that supports long-term institutional growth. Through her work, she plays a central role in shaping how emerging professionals enter, navigate, and advance within one of the largest public transportation systems in North America.

SLH: Introduction to Bayesian Optimization and Its Applications in Transportation and AI
2026-03-27 12:30 - 13:30
This talk introduces Bayesian Optimization (BO), a sample-efficient framework for optimizing expensive, noisy black-box functions. We will cover the core ideas behind surrogate modeling (e.g., Gaussian processes) and acquisition functions (such as EI and UCB) that balance exploration and exploitation to find high-performing solutions with limited evaluations. The applications focus on two representative directions: (1) parameter calibration for transportation simulation—tuning behavioral and network parameters so simulated traffic patterns match real observations; and (2) hyperparameter optimization in machine learning—automatically selecting model and training settings to improve accuracy, robustness, and efficiency. We will also highlight practical considerations such as constraints, multi-objective trade-offs, and scalable implementations.

Presented by NYU’s Yu Tang

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

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.

Digital Twin Symposium
2026-04-20 | Pfizer Auditorium, 5 MetroTech, Brooklyn, NY, 11201, United States

Bridge Resource Program Rime Webinar: Extending the Service Life of New Jersey Bridge Decks with Internally Cured High Performance Concrete: Development, Field Implementation, and Life Cycle Cost Impacts
2026-05-01 11:00 - 12:00
Internally cured high performance concrete is a promising strategy for extending the service life of bridge decks in New Jersey. By replacing a portion of normal weight sand with prewetted lightweight fine aggregate, internal curing provides moisture during hydration, reducing self desiccation, early age shrinkage, and microcracking. These improvements can enhance durability and reduce long term maintenance demands.
This work combines complementary efforts on material development and economic evaluation. Rutgers, in coordination with NJDOT under the Federal Highway Administration EPIC2 initiative, led the development and assessment of internally cured concrete mixtures through laboratory testing and field implementation. The NYU C2SMART Center, in collaboration with Rutgers and NJDOT, evaluated the long term economic implications using life cycle cost analysis.
Since key inputs such as material premiums, maintenance timing, and service life remain uncertain, the cost evaluation used a stochastic framework informed by NJDOT feedback rather than a purely deterministic approach. Preliminary results indicate that, despite a modest increase in initial cost, internally cured high performance concrete can reduce agency costs by up to 40% and total costs by up to 45%. These findings support internal curing as a practical and cost effective approach for more durable bridge decks in New Jersey.
Presented by NYU’s Eren Kaval and Rutgers’ Michael Ruszala

Bridge Resource Program Rime Webinar: Additive Manufacturing Techniques in Infrastructure Applications
2026-05-26 14:00 - 15:00
Implementation of Additive Construction has increased rapidly across the world. However, its application still limited in infrastructure projects. The presentation offers a transformative approach to sustainable and structurally efficient construction practices by integrating additive construction (3D printing in construction) with topology optimization, innovative reinforcement strategies, and the use of locally sourced raw materials to develop in-house printable concrete mixtures suitable for both in-air and underwater additive construction of structures that meet printability requirements for topology optimization. The presentation will cover three on-going projects at Additive and Robotic Construction Laboratory at Rowan University.
Presented by Prof. Islam Mantawy, Assistant Professor in the Department of Civil and Environmental Engineering at Henry M. Rowan College of Engineering, Rowan University

Seminar: Physical Attribute-Driven NFD Approach for Bicycle Network Design
2026-06-11 15:00 - 16:00
Abstract: Urban street networks are increasingly expected to serve multiple modes simultaneously, yet tools for understanding how physical design choices affect aggregate traffic performance, translating those predictions into infrastructure investment decisions, and quantifying the equity trade-offs among those decisions remains limited. This work addresses these gaps through the estimation of an NFD functional form based on physical attributes of active mobility infrastructure which is then employed to develop network designs informed by varying equity objectives. The physical-infrastructure-informed NFD functional form links traffic performance, such as capacity, jam density, and flow dynamics, to spatial design characteristics including the among of area allocated to each mode, space for interaction between modes, and type of separation between modes. This form was calibrated using high-resolution simulation outputs from multimodal SUMO networks which allowed for differences in flow behavior across modes and designs to be captured in high density scenarios. A two-stage optimization framework was implemented to determine an optimal assignment of bike lane types across network links based on this functional form. The first stage maximizes the developed functional form to return optimal values of the spatial design characteristics. The second step makes use of a genetic algorithm (GA) to generate networks optimizing for flow, accessibility, and adherence to ethical frameworks. By evaluating candidate solutions using the estimated NFD, the algorithm selects infrastructure configurations that maximize network efficiency while also reducing congestion impacts on disadvantaged communities. This framework contributes an integration of macroscopic traffic modeling into network design for multimodal systems with additional equity-focused optimization.

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
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

BRP Webinar: Crashworthiness Analysis, Simulation, and Model Validation of Roadway and Bridge Hardware
2026-07-30 12:00 - 13:00
Authors/Presenters: Wassim Nasreddine, Chuck Plaxico, Hani Nassif, Chaekuk Na, Frank DarConte, Ali Atahan, and Kaan Ozbay

Abstract: This webinar will present the process for validating roadway and highway hardware prior to implementation. Following NCHRP Report 350, AASHTO MASH has become the standardized guideline for certifying hardware at different test levels. This process involves numerical simulation and structural analysis to assess energy absorption, occupant risk, and structural integrity of various hardware systems, followed by crashworthiness evaluation. The webinar will also highlight practical applications, key design considerations, and lessons learned to support the development of safer and more reliable transportation infrastructure.

AI Applications in Urban Transportation Management Symposium
2026-09-25
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…
