BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//C2SMART Home - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:C2SMART Home
X-ORIGINAL-URL:https://c2smart.engineering.nyu.edu
X-WR-CALDESC:Events for C2SMART Home
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20230312T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20231105T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20270314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20271107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241030T100000
DTEND;TZID=America/New_York:20241030T110000
DTSTAMP:20260424T010708
CREATED:20241014T193918Z
LAST-MODIFIED:20241028T193657Z
UID:86211-1730282400-1730286000@c2smart.engineering.nyu.edu
SUMMARY:Webinar: Spatio-temporal Adaptive AI for Urban Mobility Modeling
DESCRIPTION: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. \nBio: 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.
URL:https://c2smart.engineering.nyu.edu/event/zhaonan-wang-webinar/
CATEGORIES:Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241106T170000
DTEND;TZID=America/New_York:20241106T180000
DTSTAMP:20260424T010708
CREATED:20241016T194032Z
LAST-MODIFIED:20241028T194901Z
UID:86225-1730912400-1730916000@c2smart.engineering.nyu.edu
SUMMARY:Webinar: Tension Stiffening Related to Cracking and Deflection of Reinforced Concrete
DESCRIPTION: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. \nThis 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. \nPresented by Peter H. Bischoff\, Department of Civil Engineering\, University of New Brunswick\, Fredericton\, NB\, Canada \nPeter 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.
URL:https://c2smart.engineering.nyu.edu/event/webinar-tension-stiffening-related-to-cracking-and-deflection-of-reinforced-concrete/
CATEGORIES:Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241108T103000
DTEND;TZID=America/New_York:20241108T113000
DTSTAMP:20260424T010708
CREATED:20241024T202005Z
LAST-MODIFIED:20241028T170122Z
UID:86306-1731061800-1731065400@c2smart.engineering.nyu.edu
SUMMARY:Webinar: Bridging the Gap: Enhancing Infrastructure Safety with Robust Vibration-based Monitoring
DESCRIPTION: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. \nBio: 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.
URL:https://c2smart.engineering.nyu.edu/event/webinar-bridging-the-gap-enhancing-infrastructure-safety-with-robust-vibration-based-monitoring/
CATEGORIES:Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241108T130000
DTEND;TZID=America/New_York:20241108T140000
DTSTAMP:20260424T010708
CREATED:20241007T153043Z
LAST-MODIFIED:20241108T174728Z
UID:86128-1731070800-1731074400@c2smart.engineering.nyu.edu
SUMMARY:Student Learning Hub: Online Optimization Meets Urban Transportation
DESCRIPTION:Instructor: Tao Li\, New York University \nBeginner level: No prior experience required. Basic optimization knowledge would be helpful but not required. \nDescription: 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.
URL:https://c2smart.engineering.nyu.edu/event/student-learning-hub-online-optimization-meets-urban-transportation/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250218T140000
DTEND;TZID=America/New_York:20250218T150000
DTSTAMP:20260424T010708
CREATED:20250116T203640Z
LAST-MODIFIED:20250218T163925Z
UID:87727-1739887200-1739890800@c2smart.engineering.nyu.edu
SUMMARY:Seminar: Gittins Indices for Cost-aware and Freeze-thaw Bayesian Optimization
DESCRIPTION:Presented by Qian Xie\, Cornell \nHyperparameter 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. \nIn 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.
URL:https://c2smart.engineering.nyu.edu/event/seminar-gittins-indices-for-cost-aware-and-freeze-thaw-bayesian-optimization/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Virtual Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250307T130000
DTEND;TZID=America/New_York:20250307T140000
DTSTAMP:20260424T010708
CREATED:20250304T190540Z
LAST-MODIFIED:20250304T190540Z
UID:87955-1741352400-1741356000@c2smart.engineering.nyu.edu
SUMMARY:SLH: Understanding Travel Demand through Passively-generated Mobile Data: a Python-based Mobility Analysis Workshop
DESCRIPTION: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.
URL:https://c2smart.engineering.nyu.edu/event/slh-understanding-travel-demand-through-passively-generated-mobile-data-a-python-based-mobility-analysis-workshop/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250314T120000
DTEND;TZID=America/New_York:20250314T130000
DTSTAMP:20260424T010708
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:20250411T120000
DTEND;TZID=America/New_York:20250411T130000
DTSTAMP:20260424T010708
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:20250507T130000
DTEND;TZID=America/New_York:20250507T140000
DTSTAMP:20260424T010708
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:20251016T180000
DTEND;TZID=America/New_York:20251016T190000
DTSTAMP:20260424T010708
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:20251107T120000
DTEND;TZID=America/New_York:20251107T130000
DTSTAMP:20260424T010708
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;TZID=America/New_York:20251112T130000
DTEND;TZID=America/New_York:20251112T140000
DTSTAMP:20260424T010708
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:20251121T120000
DTEND;TZID=America/New_York:20251121T130000
DTSTAMP:20260424T010708
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:20260311T120000
DTEND;TZID=America/New_York:20260311T130000
DTSTAMP:20260424T010708
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/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260313T150000
DTEND;TZID=America/New_York:20260313T160000
DTSTAMP:20260424T010708
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/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260327T123000
DTEND;TZID=America/New_York:20260327T133000
DTSTAMP:20260424T010708
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/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260501T110000
DTEND;TZID=America/New_York:20260501T120000
DTSTAMP:20260424T010709
CREATED:20260407T153044Z
LAST-MODIFIED:20260415T171316Z
UID:90727-1777633200-1777636800@c2smart.engineering.nyu.edu
SUMMARY: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
DESCRIPTION: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. \nThis 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. \nSince 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. \nPresented by NYU’s Eren Kaval and Rutgers’ Michael Ruszala
URL:https://c2smart.engineering.nyu.edu/event/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-imp/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260525T140000
DTEND;TZID=America/New_York:20260525T150000
DTSTAMP:20260424T010709
CREATED:20260407T152720Z
LAST-MODIFIED:20260416T150511Z
UID:90723-1779717600-1779721200@c2smart.engineering.nyu.edu
SUMMARY:Bridge Resource Program Rime Webinar: Additive Manufacturing Techniques in Infrastructure Applications
DESCRIPTION: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. \nPresented by Prof. Islam Mantawy\, Assistant Professor in the Department of Civil and Environmental Engineering at Henry M. Rowan College of Engineering\, Rowan University
URL:https://c2smart.engineering.nyu.edu/event/bride-resource-program-rime-webinar-additive-manufacturing-techniques-in-infrastructure-applications/
CATEGORIES:Student Events,Webinars
END:VEVENT
END:VCALENDAR