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Seminar: A Large-scale Simulation Platform and AI-driven Operational Strategies for On-demand Ride Services
2026-01-09 14:00 - 15:00 (America/New_York) | 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 | NY
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 | NY
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 | NY
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 | 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.

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 | NY
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 | NY
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
