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DTSTART;TZID=America/New_York:20260313T150000
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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
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DTSTART;TZID=America/New_York:20260327T123000
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CREATED:20260304T152522Z
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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
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DTSTART;TZID=America/New_York:20260408T173000
DTEND;TZID=America/New_York:20260408T190000
DTSTAMP:20260406T042021
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/
CATEGORIES:Seminars,Student Events
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DTSTART;VALUE=DATE:20260420
DTEND;VALUE=DATE:20260421
DTSTAMP:20260406T042021
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
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