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Webinar: Spatio-temporal Adaptive AI for Urban Mobility Modeling
2024-10-30 10:00 - 11:00 (America/New_York)
Abstract: Rapidly developing mobile and sensor networks are accumulating massive volumes of human mobility data in cities. Predictive modeling on these data is a fundamental problem in building decision support systems for various urban and transportation applications. In the real world, such spatio-temporal data show multifold heterogeneity over space and non-stationarity over time, which makes the prediction task especially challenging. My research focuses on developing adaptive AI algorithms (e.g., meta learning) to enhance the robustness of mobility prediction models. Making robust predictions across space and time lays a foundation for not only next generation mobility services, but also emergency response to adverse events, including traffic accidents, pandemic lockdown, and natural disasters.
Bio: Zhaonan Wang joined NYU Shanghai in Fall 2024 as a Tenure-Track Assistant Professor jointly appointed by Urban Studies and Computer Science. Zhaonan obtained his PhD degree in 2022 from the University of Tokyo, advised by Ryosuke Shibasaki; during his PhD study, he also visited the research group led by Flora Salim, Cisco Chair at UNSW CSE. After graduation, Zhaonan did his postdoctoral research on Spatial AI under the supervision of Shaowen Wang and Jiawei Han at the University of Illinois Urbana-Champaign (CyberGIS Center & NSF I-GUIDE). Zhaonan’s research interests lie in the interdisciplinary area between urban & AI, and he’s been published in top-tier AI and data science venues, including AAAI, KDD, WWW, CIKM, ICDE, AIJ, TKDE. He’s also been invited to serve as a Program Committee member and awarded travel grants for multiple times. During his PhD, Zhaonan won a MEXT (Japanese government) scholarship in Top Global University Initiative, Top-10 candidates in KDD Cup 2019 Humanity RL Track, and best resource paper runner-up at ACM CIKM 2021. He also had close collaboration with industry, including the National Institute of Advanced Industrial Science and Technology (AIST in Japan)’s AI Research Center, Yahoo Japan, and Toyota.

Webinar: Tension Stiffening Related to Cracking and Deflection of Reinforced Concrete
2024-11-06 17:00 - 18:00
Hardy Cross once wrote that “strength is essential and otherwise unimportant” to emphasize it makes little difference what other attributes a structure has if it is not sufficiently strong. Looking at this from a different perspective one could say that “strength is essential and otherwise unimportant … when serviceability governs” to ensure the structure performs adequately under the day-to-day service conditions once we know the structure has adequate strength. In fact, certain types of structures such as elevated slabs and fiber reinforced polymer (FRP) reinforced concrete members are often stronger than needed once serviceability requirements are satisfied, and in these types of cases the structure is designed for serviceability first and then checked for strength. Hence, safety and serviceability related to cracking and deflection play an important role in the design of concrete structures.
This presentation is focused on deflection of reinforced concrete. The role of cracking, tension stiffening and shrinkage in understanding deflection behavior of reinforced concrete is explored. In particular, emphasis is placed on the pitfalls of ignoring the concrete shrinkage. Rationale and justification are provided for the recent adoption by most codes in North America of a new approach for the effective member stiffness used to calculate deflection. Work is presently underway with colleagues from Rutgers to extend this approach to prestressed concrete.
Presented by Peter H. Bischoff, Department of Civil Engineering, University of New Brunswick, Fredericton, NB, Canada
Peter H. Bischoff is an Honorary Research Professor in the Department of Civil Engineering at the University of New Brunswick in Fredericton, New Brunswick, Canada, where he has contributed to teaching and research in reinforced and prestressed concrete. He is a fellow of the American Concrete Institute (ACI) and Canadian Society for Civil Engineering (CSCE) and has served on numerous ACI Committees during his career. Dr. Bischoff is a recognized expert in serviceability of concrete structures related to deflection and has received several awards for work in this area. He has also been involved in development of a design code for fiber reinforced concrete (FRC) and UHPC that has been implemented into the Canadian Highway Bridge Design Code S6.

Webinar: Bridging the Gap: Enhancing Infrastructure Safety with Robust Vibration-based Monitoring
2024-11-08 10:30 - 11:30
Abstract: The idea that the majority of bridges have reached the end of their service life has become widely accepted. The need for continuous monitoring of a large number of structures has become both a duty and a burden for administrations and operators. While technological advancements enable the acquisition of numerous structural parameters, effectively harnessing the vast amount of data generated is not a straightforward task. Therefore, an automated tool that can conduct end-to-end analysis with minimal effort and cost is crucial. The presented solution is applied to a monumental reinforced concrete arch bridge, and instrumented with tailored monitoring system, from sensors to the cloud-based dashboard. Modal parameters such as vibration modes, modal shapes, and damping are determined using the Operational Modal Analysis (OMA) algorithm, specifically 2 nd order blind identification, in an automated process. The analysis is performed through robust software for automatic modal identification, providing high quality results purified by any environmental effects. Finally, by enhancing the potential of the cloud for measurement data storage, the implementation of advanced data management tools is being considered as interesting emerging future prospects.
Bio: Giacomo Imposa is a structural engineer with a PhD in Structural Health Monitoring (SHM) from Iuav University of Venice, Italy, specializing in dynamic identification of masonry structures and bridges. His academic pursuits have primarily unfolded in Italy, with a notable stint in Portugal, in Guimaraes, where he was hosted by Prof. Lourenco, the most important expert in the world regarding Heritage buildings. At Kistler, since January, Giacomo has been globally engaged in advancing new bridge-related applications concerning structural monitoring in his capacity as a business developer. His responsibilities encompass the entire measurement chain, spanning from sensors to data, encapsulated through bridge reporting.

Student Learning Hub: Online Optimization Meets Urban Transportation
2024-11-08 13:00 - 14:00
Instructor: Tao Li, New York University
Beginner level: No prior experience required. Basic optimization knowledge would be helpful but not required.
Description: Urban transportation networks are complex and dynamic, and as a result, offline planning alone may not ensure effective real-time management. Therefore, this course introduces online optimization methods with a focus on transportation applications. We’ll start with gradient descent in conventional convex optimization, move to online gradient descent, and briefly explore multi-agent online learning and associated equilibrium convergence. We’ll also discuss challenges in deploying online optimization in urban transportation.

Seminar: Gittins Indices for Cost-aware and Freeze-thaw Bayesian Optimization
2025-02-18 14:00 - 15:00 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
Presented by Qian Xie, Cornell
Hyperparameter optimization is crucial in real-world applications such as machine learning model training, robotics control, material design, and plasma physics. In transportation, hyperparameter optimization plays a significant role in applications like traffic flow prediction, dynamic pricing, route planning, and public transportation scheduling, where complex models need to be fine-tuned to achieve optimal performance. These scenarios are often modeled as black-box functions, which take hyperparameters as inputs and output performance metrics. Bayesian optimization is a powerful framework for efficiently optimizing such black-box functions, especially when evaluations are time-consuming or expensive. However, practical factors such as varying function evaluation costs and observable partial feedback during function evaluation remain under-explored in this framework. My research leverages Gittins indices, which are inherently cost-aware and feedback-aware, by drawing connections to Pandora’s Box problems and Markovian/Bayesian bandits, where Gittins indices are Bayesian optimal.
In the first half of my talk, I will present my published work, which adapts Gittins indices into a cost-aware acquisition function class for Bayesian optimization, demonstrating competitive empirical performance, particularly in medium-to-high dimensions. In the second half, I will discuss my ongoing work on developing Gittins indices for freeze-thaw Bayesian optimization involving decisions on early stopping and switching of hyperparameter tests based on partial feedback.

SLH: Understanding Travel Demand through Passively-generated Mobile Data: a Python-based Mobility Analysis Workshop
2025-03-07 13:00 - 14:00
The ubiquity of GPS-equipped mobile devices has enabled the collection of human mobility data with high spatiotemporal granularity. Indeed, there now exists an ecosystem of both data providers and consulting agencies centered around collecting, processing, and extracting insights from this type of data. Though much of the raw data cannot be made public due to privacy-preserving agreements, the academic community can still access geographically aggregated data. This workshop will cover the (Python-based) preprocessing and wrangling of aggregated mobility data in New York state provided by SafeGraph. It will highlight concepts including (but not limited to) origin-destination matrices, the four-step travel demand model, self-selection bias in passively-generated data, and demand prediction. It will also describe how to fuse such data with the American Community Survey (ACS) providing sociodemographic information at the census block group-level. Relevant packages that will be leveraged and explained include GeoPandas and scikit-Mobility. Intermediate familiarity with Python, data structures, and object-oriented programming is recommended.

SLH: An Introduction to Multi-agent Driving Simulation with GPUDrive
2025-03-14 12:00 - 13:00
In this seminar, I will introduce GPUDrive, a high-performance, data-driven driving simulator that operates at 1 million FPS. GPUDrive is built on the Madrona Game Engine and uses GPU acceleration to enable scalable multi-agent simulation. I will discuss how this simulator can be used to train reinforcement learning agents efficiently, drawing from a recent paper where we demonstrate its application to the Waymo Open Motion Dataset. Additionally, I will walk through tutorials on how to set up and use the simulator.
The repository can be found at https://github.com/Emerge-Lab/gpudrive/tree/main

SLH: Discrete Choice Modeling for Travel Behavior Analysis: From Multinomial Logit to More Advanced Forms
2025-04-11 12:00 - 13:00
Abstract: In this course, we will discuss the decision theory of random utility maximization and discrete choice models (DCMs) including multinomial logit (MNL), nested logit (NL), mixed logit (MXL), and agent-based mixed logit (AMXL). You will learn about their applications in travel behavior analysis (e.g., travel mode choice, activity scheduling choice, etc.) and how to build DCMs with long-shape and wide-shape choice datasets in R and Python. A recent study on New York State travel mode choice will be introduced as an example.

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.

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.

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!

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.

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!

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

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

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.
