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: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
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250307T130000
DTEND;TZID=America/New_York:20250307T140000
DTSTAMP:20260416T100653
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:20250312T180000
DTEND;TZID=America/New_York:20250312T190000
DTSTAMP:20260416T100653
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:20260416T100653
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
END:VCALENDAR