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DTSTART;TZID=America/New_York:20230522T153000
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SUMMARY:USDOT Free Public Webinar:  Intersection Safety Challenge Prize Competition
DESCRIPTION:The U.S. Department of Transportation (DOT) will host a webinar on May 22 to discuss the Intersection Safety Challenge Prize Competition\, which launched on April 25\, 2023. \nEach year\, roughly one-quarter of traffic fatalities and about one-half of all traffic injuries in the United States are attributed to intersections. According to the latest data from the National Highway Traffic Safety Administration (NHTSA)\, an estimated 42\,939 people died in motor vehicle traffic crashes in 2021\, a 10.1% increase compared to 39\,007 fatalities reported in 2020. From 2020 to 2021\, pedestrian and pedalcyclist fatalities and injuries increased at an alarming rate. For example\, pedestrian fatalities increased 13% and pedestrian injuries increased 11% from 2020 to 2021. In response to growing concerns regarding the safety of vulnerable road users at intersections and as part of the recent National Roadway Safety Strategy (NRSS) Call to Action\, the DOT aims to transform intersection safety through the innovative application of emerging technologies to identify and mitigate unsafe conditions involving vehicles and vulnerable road users. \nTo help address this growing problem and support U.S. DOT’s vision\, U.S. DOT is launching the Intersection Safety Challenge. This Challenge includes a multi-stage Prize Competition to encourage teams of innovators and end-users to develop and test their intersection safety systems (ISS) to compete for up to $6 million total in prizes. \nThe Challenge is considering the potential of emerging technologies to transform intersection safety and ensure equity among all road users (including vehicles and vulnerable road users). Leveraging emerging technologies to anticipate\, prevent\, and mitigate unsafe roadway conditions could augment traditional safety engineering in roadway design and intersection control. These emerging technologies could include machine vision\, machine perception\, sensor fusion\, real-time decision-making\, artificial intelligence\, and vehicle-to-everything (V2X) communications (among other approaches). These technologies in most cases rely on real-time decision-making informed by data ingested and analyzed from multiple sensor systems. \nThe webinar will discuss the U.S. DOT Intersection Safety Challenge Prize Competition\, including a program overview\, the Prize Competition structure\, and Stage 1A expectations. Please register for this webinar by visiting the following registration link: https://iscwebinar.eventbrite.com. \nFor more information about the program\, please visit the program website: https://its.dot.gov/isc. For more information about the ITS JPO\, please visit: https://www.its.dot.gov/.
URL:https://c2smart.engineering.nyu.edu/event/usdot-free-public-webinar-intersection-safety-challenge-prize-competition/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Safety in Transportation Systems,Virtual Events
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250218T140000
DTEND;TZID=America/New_York:20250218T150000
DTSTAMP:20260506T141624
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
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251112T130000
DTEND;TZID=America/New_York:20251112T140000
DTSTAMP:20260506T141624
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
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DTSTART;TZID=America/New_York:20251229T150000
DTEND;TZID=America/New_York:20251229T160000
DTSTAMP:20260506T141624
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
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DTSTART;TZID=America/New_York:20260105T150000
DTEND;TZID=America/New_York:20260105T160000
DTSTAMP:20260506T141624
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:20260506T141624
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:20260506T141624
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
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