Student Learning Hub: Introduction to Online Recommendation Systems: From Predict-Sort Architecture to Real-Time Feature Service
2025-11-07 12:00 - 13:00 (America/New_York)
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
2026-05-25 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
