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DTSTART;TZID=America/New_York:20250312T180000
DTEND;TZID=America/New_York:20250312T190000
DTSTAMP:20260404T062849
CREATED:20250304T190125Z
LAST-MODIFIED:20250304T190352Z
UID:87951-1741802400-1741806000@c2smart.engineering.nyu.edu
SUMMARY:2025 Women in Transportation Panel & Networking
DESCRIPTION:This year’s panelists are Dr. Semiha Ergan\, Professor at NYU; Geline Canayon\, Senior Project Manager at Aimsun & Vice President of ITE MET Section; and Alia Kasem\, Senior Data Scientist at NYC DOT & Adjunct Lecturer at Hunter College. The discussion will be moderated by NYU PhD Candidate Zerun Liu.\n\n\nLight refreshments will be provided! Register for the Zoom here.
URL:https://c2smart.engineering.nyu.edu/event/2025-women-in-transportation-panel-networking/
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:20250314T120000
DTEND;TZID=America/New_York:20250314T130000
DTSTAMP:20260404T062849
CREATED:20250304T190838Z
LAST-MODIFIED:20250304T190838Z
UID:87959-1741953600-1741957200@c2smart.engineering.nyu.edu
SUMMARY:SLH: An Introduction to Multi-agent Driving Simulation with GPUDrive
DESCRIPTION: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. \nThe repository can be found at https://github.com/Emerge-Lab/gpudrive/tree/main
URL:https://c2smart.engineering.nyu.edu/event/slh-an-introduction-to-multi-agent-driving-simulation-with-gpudrive/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250411T093000
DTEND;TZID=America/New_York:20250411T103000
DTSTAMP:20260404T062850
CREATED:20250407T164354Z
LAST-MODIFIED:20250407T180200Z
UID:88400-1744363800-1744367400@c2smart.engineering.nyu.edu
SUMMARY:Seminar: Spatiotemporal Drought Analysis and Prediction: From Pattern Tracking to Impact Assessment
DESCRIPTION:The increasing frequency and severity of drought events worldwide demand innovative approaches that can effectively characterize\, track\, predict\, and assess impacts of the complex spatiotemporal dynamics of water scarcity. This presentation explores how integrated spatiotemporal methodologies enhance drought monitoring\, forecasting\, and impact assessment across diverse global regions. \nOur research demonstrates a logical progression from fundamental spatiotemporal drought tracking to advanced prediction methodologies and practical impact assessment. Beginning with approaches to construct drought tracks through space and time in India\, we identify key drought characteristics including onset location\, pathway\, duration\, severity\, and rotation. These spatial patterns reveal critical information about drought migration and evolution that provide insights into underlying climate and land surface drivers. \nBuilding on this foundation of spatiotemporal drought characterization\, we apply machine learning techniques to predict drought occurrence and intensity across Kazakhstan’s diverse climate zones. By utilizing non-contiguous drought analysis for feature extraction\, machine learning models achieve remarkable prediction accuracy at lead times of up to six months in these arid and semi-arid regions. Additionally\, deep learning techniques enhanced with ensemble empirical mode decomposition substantially improve soil moisture anomaly forecasting in China’s Huai River basin by better capturing the complex non-stationary time series characteristics of drought evolution. \nBeyond monitoring and forecasting\, we extend our approach to predict drought impacts on agriculture in Northeast India using spatiotemporal drought patterns as input for crop yield forecasting. This methodology integrates polynomial regression and artificial neural network models\, where the spatial extent and temporal evolution of drought areas serve as key predictive features. The results demonstrate that changes in drought area and its temporal aggregation provide an important pre-processing alternative for implementing machine learning models for drought impact prediction. \nThese integrated approaches\, spanning regions from Central Asia to South and East Asia\, contribute to a more robust understanding of drought dynamics by revealing how drought moves and evolves in space and time\, enabling more effective early warning systems and supporting agricultural decision-making in increasingly water-stressed environments. Across all applications and geographical contexts\, the spatiotemporal properties of drought emerge as key features that significantly enhance the performance of machine learning prediction models. \nBio: Dr. Gerald A. Corzo is an Associate Professor of Hydroinformatics at IHE Delft Institute for Water Education\, where he leads cutting-edge research that uses Artificial Intelligence and Machine Learning to address critical challenges in climate adaptation and water resource management. His research on hydrometeorological extremes has helped us better understand\, predict\, and respond to floods and droughts across the globe. As Principal Investigator on significant EU-funded projects like EU-WATCH\, Climate Intelligent (EU-CLINT) and EU-NAIADES\, Dr. Corzo has developed cutting-edge AI-driven tools that enhance decision-making in water management and climate resilience. He currently serves as an Editor of the Journal of Hydrology (Elsevier) and was recognized with the prestigious IAHS Tison Award for his contributions to hydrological sciences in 2012. Dr. Corzo’s interdisciplinary approach integrates environmental science with advanced computational technologies to create practical solutions aligned with sustainable development goals\, particularly for enhancing ecosystem resilience in vulnerable communities worldwide.
URL:https://c2smart.engineering.nyu.edu/event/seminar-spatiotemporal-drought-analysis-and-prediction-from-pattern-tracking-to-impact-assessment/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250411T120000
DTEND;TZID=America/New_York:20250411T130000
DTSTAMP:20260404T062850
CREATED:20250304T191106Z
LAST-MODIFIED:20250304T191106Z
UID:87962-1744372800-1744376400@c2smart.engineering.nyu.edu
SUMMARY:SLH: Discrete Choice Modeling for Travel Behavior Analysis: From Multinomial Logit to More Advanced Forms
DESCRIPTION: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.
URL:https://c2smart.engineering.nyu.edu/event/slh-discrete-choice-modeling-for-travel-behavior-analysis-from-multinomial-logit-to-more-advanced-forms/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250416T130000
DTEND;TZID=America/New_York:20250416T140000
DTSTAMP:20260404T062850
CREATED:20250402T154334Z
LAST-MODIFIED:20250402T154334Z
UID:88242-1744808400-1744812000@c2smart.engineering.nyu.edu
SUMMARY:Seminar: Investigating Vulnerabilities in Autonomous Vehicle Perception Algorithms
DESCRIPTION:Autonomous vehicles (AVs) rely on deep neural networks (DNNs) for critical tasks such as environment perception—identifying traffic signs\, pedestrians\, and lane markings—and executing control decisions like braking\, acceleration\, and lane changing. However\, DNNs are vulnerable to adversarial attacks\, including structured perturbations to inputs and misleading training samples that can degrade performance. This presentation begins with an overview of adversarial training\, emphasizing the impact of input sizes on DNNs’ vulnerability to cyberattacks. Subsequently\, I will share our recent findings that explore the hypothesis that DNNs learn piecewise linear relationships between inputs and outputs. This conjecture is crucial for developing both adversarial attacks and defense strategies in machine learning security. The last part of the presentation will focus on recent work on using error-correcting codes to safeguard DNN-based classifiers. \nDr. Saif Jabari is an Associate Professor of Civil and Urban Engineering at New York University Abu Dhabi (NYUAD) and a Global Network Associate Professor at the Tandon School of Engineering at NYU in Brooklyn\, NY. At NYUAD\, he is co-PI of the Center for Integrated Urban Networks (CITIES) and the Center for Stability\, Instability\, and Turbulence (SITE). He is an Associate Editor for Transportation Science and Area Editor with the new Elsevier journal Artificial Intelligence for Transportation. His research focuses on developing advanced computational methods and theoretical guarantees of performance for urban traffic management problems. The techniques integrate traffic data\, typically in high resolution\, with principles of traffic physics to address the rapidly evolving needs of the field. His current research focuses on understanding and addressing vulnerabilities in deep neural networks\, specifically as they relate to environment perception in autonomous vehicles.
URL:https://c2smart.engineering.nyu.edu/event/seminar-investigating-vulnerabilities-in-autonomous-vehicle-perception-algorithms/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250422T173000
DTEND;TZID=America/New_York:20250422T190000
DTSTAMP:20260404T062850
CREATED:20250410T193226Z
LAST-MODIFIED:20250410T193226Z
UID:88432-1745343000-1745348400@c2smart.engineering.nyu.edu
SUMMARY:AI: Forging the Future of the AEC Industry
DESCRIPTION:Artificial intelligence (AI) is revolutionizing the architecture\, engineering\, and construction (AEC) industry by driving innovation across planning\, design\, construction\, and operations. At the core of this transformation is the ability to collect\, process\, and act on vast amounts of data in ways that were previously unimaginable. \nTo explore these groundbreaking developments\, New York University and Professional Women in Construction of New York have assembled a panel of visionary leaders who will assess AI alternatives and discuss how they are shaping the future of the AEC industry. \nThis event will provide an invaluable opportunity to gain insights into how AI is redefining workflows and creating new possibilities for professionals in this field.\n \nEvent Details:\n Date: Tuesday\, April 22\, 2025\n Time:  5:30 PM – 7:00 PM (EDT)\n Location: Pfizer Auditorium\, 5 Metrotech\, Brooklyn\, NY\n \nPanelists:\nSemiha Ergan\, NYU Tandon School of Engineering\, Civil and Urban Engineering Department \nKiSeok Jeon\, STV\nMelissa Forstell McAneny. Trunk Tools\nMonica Nelson\, Gilbane Building Company\n\n \nModerator:\nMohamad Awada\, NYU Tandon School of Engineering\,Civil and Urban Engineering Department 
URL:https://c2smart.engineering.nyu.edu/event/ai-forging-the-future-of-the-aec-industry/
LOCATION:5 metro\, 5 MetroTech\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250423T140000
DTEND;TZID=America/New_York:20250423T150000
DTSTAMP:20260404T062850
CREATED:20250304T192602Z
LAST-MODIFIED:20250304T192602Z
UID:87966-1745416800-1745420400@c2smart.engineering.nyu.edu
SUMMARY:Fireside Chat with David Hammer\, co-founder of Popwheels
DESCRIPTION:This student-moderated Fireside Chat will feature David Hammer\, co-founder of Popwheels\, a cutting-edge e-bike battery swap network. David will discuss his career trajectory\, the challenges of standing up urban-scale technology in NYC\, and more!
URL:https://c2smart.engineering.nyu.edu/event/fireside-chat-with-david-hammer-co-founder-of-popwheels/
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:20250507T130000
DTEND;TZID=America/New_York:20250507T140000
DTSTAMP:20260404T062850
CREATED:20250422T173123Z
LAST-MODIFIED:20250506T154823Z
UID:88555-1746622800-1746626400@c2smart.engineering.nyu.edu
SUMMARY:Prof. Vinitsky Seminar: Robust Self-driving Emerges from Self-play
DESCRIPTION: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.
URL:https://c2smart.engineering.nyu.edu/event/prof-vinitsky-seminar-robust-self-driving-emerges-from-self-play/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250701T123000
DTEND;TZID=America/New_York:20250701T133000
DTSTAMP:20260404T062850
CREATED:20250701T154822Z
LAST-MODIFIED:20250701T154850Z
UID:88960-1751373000-1751376600@c2smart.engineering.nyu.edu
SUMMARY:Summer Programs Welcome Lunch!
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/summer-programs-welcome-lunch/
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:20250708T130000
DTEND;TZID=America/New_York:20250708T140000
DTSTAMP:20260404T062850
CREATED:20250612T172822Z
LAST-MODIFIED:20250701T154452Z
UID:88890-1751979600-1751983200@c2smart.engineering.nyu.edu
SUMMARY:Seminar: Improving the Quality of Urban Public Transport: Lessons Learned from a Novel Use of GPS Data
DESCRIPTION:In Italy\, public transport is typically operated by private companies under service contracts including penalties for non-compliance. Therefore\, detecting anomalous traffic days is key to fairly assigning delay responsibility for buses operating in mixed traffic: operators should not be penalized on days of exceptional traffic conditions. To address this\, harmonic average speeds of vehicles are used to cluster hours of the day exhibiting similar traffic patterns. For any of such traffic states\, a Generalized Additive Model is fit to the available speed percentiles in order to detect anomalous days. A case study relying on GPS data is presented for the city of Trieste.\n\nPresented by Andrea Mecchina\, PhD candidate\, University of Trieste\, Italy
URL:https://c2smart.engineering.nyu.edu/event/save-the-date-seminar-andrea-mecchina-university-of-trieste-italy/
CATEGORIES:Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250910T123000
DTEND;TZID=America/New_York:20250910T133000
DTSTAMP:20260404T062850
CREATED:20250910T154455Z
LAST-MODIFIED:20250910T154455Z
UID:89274-1757507400-1757511000@c2smart.engineering.nyu.edu
SUMMARY:CUE Distinguished Speaker Series: Katus Watson\, Jacobs
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/cue-distinguished-speaker-series-katus-watson-jacobs/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250912
DTEND;VALUE=DATE:20250913
DTSTAMP:20260404T062850
CREATED:20250805T154439Z
LAST-MODIFIED:20250805T155935Z
UID:89046-1757635200-1757721599@c2smart.engineering.nyu.edu
SUMMARY:New York Reinforcement Learning Workshop (NYRL)
DESCRIPTION:Join leading researchers from academia and industry for the inaugural NYRL Workshop. Connect\, collaborate\, and explore the latest advances in reinforcement learning with the vibrant New York metropolitan RL community. \nCo-organized by Amazon\, Columbia Business School (CBS)\, and NYU Tandon School of Engineering.
URL:https://c2smart.engineering.nyu.edu/event/new-york-reinforcement-learning-workshop-nyrl/
CATEGORIES:Conferences,Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250925T080000
DTEND;TZID=America/New_York:20250925T180000
DTSTAMP:20260404T062850
CREATED:20250828T185213Z
LAST-MODIFIED:20250828T185213Z
UID:89201-1758787200-1758823200@c2smart.engineering.nyu.edu
SUMMARY:2025 Sustainable Cities Summit: Day 2
DESCRIPTION:We are proud to support the second annual Sustainable Cities Summit in partnership with Climate Group and NYC Climate Week. This event brings together various experts from multiple organizations to discuss climate-forward cities and mainstream climate action for a sustainable future. \nDiscover how design\, data\, and collaboration are reshaping New York’s future—and inspiring cities around the world. As part of #ClimateWeekNYC\, Sidara companies are joining forces with NYU to host a full-day summit focused on implementable solutions for healthier\, more sustainable urban living. The event feature leading experts from TYLin\, Introba\, Perkins & Will\, and Currie & Brown\, alongside thought leaders from NYU\, MIT\, C40 Cities\, the City of New York\, and the Los Angeles Cleantech Incubator.
URL:https://c2smart.engineering.nyu.edu/event/2025-sustainable-cities-summit-day-2/
LOCATION:Kimmel Center\, Room 914\, 60 Washington Square South\, New York\, NY\, 10012\, United States
CATEGORIES:Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251009T163000
DTEND;TZID=America/New_York:20251009T180000
DTSTAMP:20260404T062850
CREATED:20251008T170510Z
LAST-MODIFIED:20251008T171314Z
UID:89861-1760027400-1760032800@c2smart.engineering.nyu.edu
SUMMARY:Fall 2025 Kickoff!
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/fall-2025-kickoff/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251010T080000
DTEND;TZID=America/New_York:20251010T120000
DTSTAMP:20260404T062850
CREATED:20251002T194503Z
LAST-MODIFIED:20251002T194503Z
UID:89836-1760083200-1760097600@c2smart.engineering.nyu.edu
SUMMARY:NYU Tandon and Verizon present: AI and You
DESCRIPTION:Join NYU Tandon and Verizon at an event to learn about AI and how it is involved in your everyday lives.  Speakers include: Emily Black\, Joseph Chow\, Rumi Chunara\, Emilia David\, Ashley Greenspan\, Shri Iyer\, Stacy Matlen\, Shema Mbyirukira\, Maurizio Porfiri!
URL:https://c2smart.engineering.nyu.edu/event/nyu-tandon-and-verizon-present-ai-and-you/
LOCATION:Pfizer Auditorium\, 5 MetroTech\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251016T180000
DTEND;TZID=America/New_York:20251016T190000
DTSTAMP:20260404T062850
CREATED:20251014T190229Z
LAST-MODIFIED:20251016T162246Z
UID:89870-1760637600-1760641200@c2smart.engineering.nyu.edu
SUMMARY:Author Talk: A Conversation with Michael M. Greenburg
DESCRIPTION: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. \nFood & refreshments provided.
URL:https://c2smart.engineering.nyu.edu/event/author-talk-a-conversation-with-michael-m-greenburg/
LOCATION:Dibner Library\, 5 MetroTech\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Conferences,Seminars,Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251021T123000
DTEND;TZID=America/New_York:20251021T133000
DTSTAMP:20260404T062850
CREATED:20251008T162634Z
LAST-MODIFIED:20251008T162634Z
UID:89858-1761049800-1761053400@c2smart.engineering.nyu.edu
SUMMARY:Seminar: Self-driving Vehicles as Transit in US Settings: Complementing Buses and Trains with Demand-responsive SAVs
DESCRIPTION:ABSTRACT: Shared autonomous vehicles (SAVs) can deliver door-to-door (D2D)\, first-mile last-mile (FMLM) and dynamic ride-sharing (DRS or pooled-ride) services in many cities today. This 3-part presentation explores potential improvements in US travel and transit systems by simulating interactions between fixed-route and on-demand SAV services across the Chicago and Austin regions. The first study microsimulates SAVs providing fixed-route\, fixed-stop service alongside private automobiles. System costs for each traveler type are computed across different demand profiles\, SAV sizes (seat counts)\, and other dimensions; and smaller SAVs reduce corridor costs across most scenarios. \nThe second uses POLARIS to mesoscopically simulate D2D plus FMLM and autonomous-bus services across the 20-county Chicago region’s hundreds of train stations\, thousands of bus stops\, and millions of travelers. Assuming fares of $0.50 per passenger-mile and 1 SAV for every 40 residents\, this mode is predicted to attract 15% of person-trips and lower household vehicle ownership by 40%. Adding FMLM service raises the region’s traditional transit (bus + train) split from 5.4% to 6.3%\, with each SAV serving 12% more trip requests per day. Automating the bus system (and halving headways\, with half the seats per bus) does little to mode splits. The third study simulates a full replacement of buses with autonomous services across the 6-county Austin region. Using the region’s bus stops as pickup and drop-off points\, the transit fleet size falls by 10% or more\, thanks to coordination in trip services. Subsidizing SAV fleet managers to serve current bus demand emerges as the region’s most cost-effective and welfare-improving transit option. \nBIO: Kara Kockelman is a registered professional engineer and holds a PhD\, MS\, and BS in civil engineering\, a master’s in city planning\, and a minor in economics from the University of California at Berkeley. Dr. Kockelman has been a professor of transportation engineering at the University of Texas at Austin for 27 years\, and is Associate Site Director of the NSF Industry-University Cooperative Research Center for Efficient Vehicles and Sustainable Transportation Systems and an MIT Mobility Fellow. She has authored over 240 journal articles (and two books)\, and her primary research interests include planning for shared and autonomous vehicle systems\, the statistical modeling of urban systems\, energy and climate issues\, the economic impacts of transport policy\, and crash occurrence and consequences. Her and her students’ work can be found at www.caee.utexas.edu/prof/kockelman. They hope you will join them each year in our world’s carbon-free\, cost-free Bridging Transportation Researchers conference: https://bridgingtransport.org/.
URL:https://c2smart.engineering.nyu.edu/event/seminar-self-driving-vehicles-as-transit-in-us-settings-complementing-buses-and-trains-with-demand-responsive-savs/
CATEGORIES:Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251025T090000
DTEND;TZID=America/New_York:20251025T180000
DTSTAMP:20260404T062850
CREATED:20250910T160927Z
LAST-MODIFIED:20250918T210509Z
UID:89278-1761382800-1761415200@c2smart.engineering.nyu.edu
SUMMARY:transportationcamp 2025
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/transportationcamp-2025/
CATEGORIES:Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251107T120000
DTEND;TZID=America/New_York:20251107T130000
DTSTAMP:20260404T062850
CREATED:20251016T154452Z
LAST-MODIFIED:20251016T154452Z
UID:89896-1762516800-1762520400@c2smart.engineering.nyu.edu
SUMMARY:Student Learning Hub: Introduction to Online Recommendation Systems: From Predict-Sort Architecture to Real-Time Feature Service
DESCRIPTION: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!
URL:https://c2smart.engineering.nyu.edu/event/student-learning-hub-introduction-to-online-recommendation-systems-from-predict-sort-architecture-to-real-time-feature-service/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20251111
DTEND;VALUE=DATE:20251113
DTSTAMP:20260404T062850
CREATED:20251014T212508Z
LAST-MODIFIED:20251014T212508Z
UID:89892-1762819200-1762991999@c2smart.engineering.nyu.edu
SUMMARY:Urban Tech Summit 2025 - Adaptive Intelligence: Activating Urban AI
DESCRIPTION:Hear from leading experts on how urban tech can help cities do more with less as they deal with uncertainty and prepare for future shocks.\n\n\n\nCornell Tech’s Urban Tech Summit returns on November 11-12\, 2025\, focusing on the theme of “Adaptive Intelligence: Activating Urban AI.” Organized by the Urban Tech Hub at Cornell Tech’s Jacobs Institute\, this year’s Summit will explore quick wins and ways cities can begin to assess\, test\, and amplify innovations in artificial intelligence for rapid impact. \n\nNYU Prof. Sarah Kaufman will be featured on a panel: New York City has long been shaped by automation. Traffic signals organize the streets and telephone switches connect our boroughs. As autonomous vehicles begin piloting on city roads\, a new wave of systems is posed to transform urban life. This session will examine how NYC is navigating this shift through policy\, pilot programs\, and planning\, while also considering broader trends in urban robotics that will define the next generation of automation in cities.
URL:https://c2smart.engineering.nyu.edu/event/urban-tech-summit-2025-adaptive-intelligence-activating-urban-ai/
LOCATION:Verizon Executive Education Center\, 2 West Loop Road\, NYC\, NY\, 10044
CATEGORIES:Conferences,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251112T130000
DTEND;TZID=America/New_York:20251112T140000
DTSTAMP:20260404T062850
CREATED:20251020T153149Z
LAST-MODIFIED:20251020T153518Z
UID:89965-1762952400-1762956000@c2smart.engineering.nyu.edu
SUMMARY:Seminar: Inverse Learning and Intervention of Transportation Network Equilibrium
DESCRIPTION:Abstract:\nBy 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. \nTraditional 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\nsufficient 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. \nFinally\, 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. \nBio:\nDr. 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.
URL:https://c2smart.engineering.nyu.edu/event/89965/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Student Events,Virtual Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251113T173000
DTEND;TZID=America/New_York:20251113T183000
DTSTAMP:20260404T062850
CREATED:20251105T145939Z
LAST-MODIFIED:20251111T220722Z
UID:90258-1763055000-1763058600@c2smart.engineering.nyu.edu
SUMMARY:C2SMART Career Panel
DESCRIPTION:Join us for a Career Panel featuring NYU’s Dr. Jingqin Gao & Holly Chase\, Suzana Duran Bernardes of Disney\, and Michael Willson of ARUP.  This panel will explore career trajectories\, lessons learned\, and helpful advice to students about to enter the job market. The panelists have an array of experiences from several industries and schools; come learn about their transitions from student to young professional to better inform your own path! \nModerated by ITE NYU Student Chapter Co-President Stephen Martone. Light refreshments will be provided\,
URL:https://c2smart.engineering.nyu.edu/event/c2smart-career-panel/
CATEGORIES:Seminars,Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251114T113000
DTEND;TZID=America/New_York:20251114T150000
DTSTAMP:20260404T062850
CREATED:20251016T155216Z
LAST-MODIFIED:20251016T155216Z
UID:89904-1763119800-1763132400@c2smart.engineering.nyu.edu
SUMMARY:STEM Day 2025!
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/stem-day-2025/
CATEGORIES:Student Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251118T123000
DTEND;TZID=America/New_York:20251118T133000
DTSTAMP:20260404T062850
CREATED:20251105T182735Z
LAST-MODIFIED:20251117T163854Z
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:20260404T062850
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:20260404T062850
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/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251202T123000
DTEND;TZID=America/New_York:20251202T143000
DTSTAMP:20260404T062850
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:20260404T062850
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/
CATEGORIES:Student Events,Virtual Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260105T150000
DTEND;TZID=America/New_York:20260105T160000
DTSTAMP:20260404T062850
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/
CATEGORIES:Student Events,Virtual Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260106T120000
DTEND;TZID=America/New_York:20260106T130000
DTSTAMP:20260404T062850
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/
CATEGORIES:Student Events,Virtual Events
END:VEVENT
END:VCALENDAR