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Student Learning Hub: Building a Scalable Multithreaded Processing System on AWS Cloud
2025-11-21 12:00 - 13:00 (America/New_York)
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

Digital Twins for AEC & Facility Managers
2026-04-20 | Pfizer Auditorium, 5 MetroTech, Brooklyn, NY, 11201, United States
