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DTSTART;TZID=America/New_York:20251118T123000
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DTSTAMP:20260404T035641
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UID:90266-1763469000-1763472600@c2smart.engineering.nyu.edu
SUMMARY:CUE Distinguished Seminar Series: Simulating the Physical and the Connected: Neural Models for Structures and Networks
DESCRIPTION: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. \nDr. 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.
URL:https://c2smart.engineering.nyu.edu/event/cue-distinguished-seminar-series-simulating-the-physical-and-the-connected-neural-models-for-structures-and-networks/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251120T140000
DTEND;TZID=America/New_York:20251120T150000
DTSTAMP:20260404T035641
CREATED:20251020T191118Z
LAST-MODIFIED:20251120T173151Z
UID:89969-1763647200-1763650800@c2smart.engineering.nyu.edu
SUMMARY:Seminar: AI-empowered Digital Twin for Traffic Safety Analysis
DESCRIPTION: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.\n\nBio: 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.
URL:https://c2smart.engineering.nyu.edu/event/seminar-ai-empowered-digital-twin-for-traffic-safety-analysis/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251121T120000
DTEND;TZID=America/New_York:20251121T130000
DTSTAMP:20260404T035641
CREATED:20251016T154633Z
LAST-MODIFIED:20251016T154633Z
UID:89901-1763726400-1763730000@c2smart.engineering.nyu.edu
SUMMARY:Student Learning Hub: Building a Scalable Multithreaded Processing System on AWS Cloud
DESCRIPTION: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!
URL:https://c2smart.engineering.nyu.edu/event/student-learning-hub-building-a-scalable-multithreaded-processing-system-on-aws-cloud/
LOCATION:NY
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251202T123000
DTEND;TZID=America/New_York:20251202T143000
DTSTAMP:20260404T035641
CREATED:20251107T191349Z
LAST-MODIFIED:20251107T191420Z
UID:90272-1764678600-1764685800@c2smart.engineering.nyu.edu
SUMMARY:AECOM Fireside Chat & Panel
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/cmaa/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251229T150000
DTEND;TZID=America/New_York:20251229T160000
DTSTAMP:20260404T035641
CREATED:20251219T185245Z
LAST-MODIFIED:20251219T185245Z
UID:90481-1767020400-1767024000@c2smart.engineering.nyu.edu
SUMMARY:NYU MS Open House Series\, Session 1
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/nyu-ms-open-house-series-session-1/
LOCATION:NY
CATEGORIES:Student Events,Virtual Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260105T150000
DTEND;TZID=America/New_York:20260105T160000
DTSTAMP:20260404T035641
CREATED:20251219T185405Z
LAST-MODIFIED:20251219T185405Z
UID:90484-1767625200-1767628800@c2smart.engineering.nyu.edu
SUMMARY:NYU MS Open House Series\, Session 2
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/nyu-ms-open-house-series-session-2/
LOCATION:NY
CATEGORIES:Student Events,Virtual Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260106T120000
DTEND;TZID=America/New_York:20260106T130000
DTSTAMP:20260404T035642
CREATED:20251219T185543Z
LAST-MODIFIED:20251219T185543Z
UID:90487-1767700800-1767704400@c2smart.engineering.nyu.edu
SUMMARY:NYU MS Open House Series\, Session 3
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/nyu-ms-open-house-series-session-3/
LOCATION:NY
CATEGORIES:Student Events,Virtual Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260107T090000
DTEND;TZID=America/New_York:20260107T100000
DTSTAMP:20260404T035642
CREATED:20251219T185637Z
LAST-MODIFIED:20251219T185637Z
UID:90489-1767776400-1767780000@c2smart.engineering.nyu.edu
SUMMARY:NYU MS Open House Series\, Session 4- Last one!
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/nyu-ms-open-house-series-session-4-last-one/
LOCATION:NY
CATEGORIES:Student Events,Virtual Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260109T140000
DTEND;TZID=America/New_York:20260109T150000
DTSTAMP:20260404T035642
CREATED:20251201T170925Z
LAST-MODIFIED:20251201T170925Z
UID:90461-1767967200-1767970800@c2smart.engineering.nyu.edu
SUMMARY:Seminar: A Large-scale Simulation Platform and AI-driven Operational Strategies for On-demand Ride Services
DESCRIPTION: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. \nBio: 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\nIntelligence 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.
URL:https://c2smart.engineering.nyu.edu/event/seminar-a-large-scale-simulation-platform-and-ai-driven-operational-strategies-for-on-demand-ride-services/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260311T120000
DTEND;TZID=America/New_York:20260311T130000
DTSTAMP:20260404T035642
CREATED:20260304T145623Z
LAST-MODIFIED:20260304T145623Z
UID:90556-1773230400-1773234000@c2smart.engineering.nyu.edu
SUMMARY:Webinar: AI Agent 101 and Vibe Coding Demo
DESCRIPTION: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. \nPresented by Dr. Yu Hu\, Senior Software Engineer at Comcast.
URL:https://c2smart.engineering.nyu.edu/event/webinar-ai-agent-101-and-vibe-coding-demo/
LOCATION:NY
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260313T150000
DTEND;TZID=America/New_York:20260313T160000
DTSTAMP:20260404T035642
CREATED:20260309T151845Z
LAST-MODIFIED:20260309T152634Z
UID:90578-1773414000-1773417600@c2smart.engineering.nyu.edu
SUMMARY:MTA x NYU: Demystifying the Application Process
DESCRIPTION: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.
URL:https://c2smart.engineering.nyu.edu/event/mta-x-nyu-demystifying-the-application-process/
LOCATION:NY
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260327T123000
DTEND;TZID=America/New_York:20260327T133000
DTSTAMP:20260404T035642
CREATED:20260304T152522Z
LAST-MODIFIED:20260304T152522Z
UID:90559-1774614600-1774618200@c2smart.engineering.nyu.edu
SUMMARY:SLH: Introduction to Bayesian Optimization and Its Applications in Transportation and AI
DESCRIPTION: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.\n\nPresented by NYU’s Yu Tang
URL:https://c2smart.engineering.nyu.edu/event/slh-introduction-to-bayesian-optimization-and-its-applications-in-transportation-and-ai/
LOCATION:NY
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260408T173000
DTEND;TZID=America/New_York:20260408T190000
DTSTAMP:20260404T035642
CREATED:20260324T161026Z
LAST-MODIFIED:20260326T180459Z
UID:90686-1775669400-1775674800@c2smart.engineering.nyu.edu
SUMMARY:Women in Transportation 2026
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/women-in-transportation-2026/
LOCATION:NY
CATEGORIES:Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260420
DTEND;VALUE=DATE:20260421
DTSTAMP:20260404T035642
CREATED:20260225T185002Z
LAST-MODIFIED:20260317T154358Z
UID:90543-1776643200-1776729599@c2smart.engineering.nyu.edu
SUMMARY:Digital Twin Symposium
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/digital-twins-for-aec-facility-managers/
LOCATION:Pfizer Auditorium\, 5 MetroTech\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Conferences,Student Events
END:VEVENT
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