Connected Cities for Smart Mobility toward Accessible and Resilient Transportation
Instructor: Matthew Maggio, Software Engineer Hands-on exercise: Yes Level: No prior experience required. Schedule: April 22, 2022 | 1:00 PM – 2:00 PM ET Mobile development can seem overwhelming at first, however Google’s
Bus Stop Chatter is a new series from C2SMART which brings transportation students into conversation with some of today's leading mobility companies to discuss innovation, disruption, and possibility in today's smart cities. In this installment, we will be joined by Laura Fox, General Manager at Lyft's CitiBike program, to discuss the role of shared mobility systems, from bikes to taxis, in driving the evolution of smart cities and urban mobility. Laura will share updates and highlights from Lyft's micromobility programming, and discuss bike safety with C2SMART student Suzana Duran Bernardes, followed by an audience Q&A.
From preparing for the final stretch of your degree to choosing your next steps after graduating, navigating the job market and charting your career path can feel overwhelming. Join C2SMART
Traffic accidents are among the leading causes of death for people aged 5–35 worldwide, causing transport externalities and thus unsustainability. At the same time, the introduction of almost fully connected and automated vehicles (CAVs) (levels 3–4 of SAE) is already a reality. Although CAVs go in the direction of smart mobility, their sustainability is still questionable because their deployment in open traffic introduces unexplored risks. The seminar will present how virtual reality and simulation are being used as a tool to replace naturalistic observations in the real-world and their pros and cons, in three different research projects on CAVs safety among which is a European Research Council Grant.
Instructor: Hector Landes, New York University Hands-on exercise: Yes Level: No prior experience required. Schedule: Wednesday October 26, 2022, 1:00pm – 2:00pm Description: This course will be a general introduction to how to use open
Instructor: Talha Azfar, Ph.D Candidate at UTEP Hands-on exercise: Yes Level: No prior experience required. Schedule: Friday, November 4, 2022, 1:00pm – 2:00pm Description: Realistic digital geographical models of real-world locations are a necessary starting
Instructor: Zhanhang Li, Rutgers University Hands-on exercise: Yes Level: No prior experience required. Schedule: Wednesday, November 11, 2022, 1:00pm – 2:00pm Description: This course will provide students with a foundational understanding of machine learning
Instructor: Fan Zuo, New York University Schedule: Friday March 10, 11:00am-12:00pm Description: In this course, you will learn about ChatGPT, a state-of-the-art language model developed by OpenAI. You will gain
Graph Neural Networks (GNNs) are a powerful tool for analyzing and processing graph-structured data. This course covers the basics of GNNs, including graph convolutional networks, graph attention networks and graph wavelet networks, including how they work and how to implement them. The session aims to provide a solid understanding of GNN modeling for machine learning practitioners, data scientists, and anyone interested in AI advancements.
The C2SMART Center and the NYU chapter of the Institute of Transportation Engineers (ITE) are excited to host our annual Women in Transportation Panel Discussion, including an opportunity for attendees to network and socialize. This year's panel boasts three amazing women working in the transportation industry who will share their perspectives and experiences.
In this hands-on course, you’ll learn how to use Unity 3D to build 3D models of road networks. You’ll start with the basics of Unity 3D, including its interface, GameObjects, components, and scripting. From there, you’ll dive into building 3D models of road networks, covering elements such as roads, buildings, and terrain.
This course covers the basics of graph neural networks (GNNs), including graph convolutional networks, graph attention networks and graph wavelet networks. The session aims to provide a solid understanding of GNN modeling for machine learning practitioners, data scientists, and anyone interested in AI advancements.