Connected Cities for Smart Mobility toward Accessible and Resilient Transportation
Hosted by Morteza Bagheri, Associate Professor, Iran University of Science and Technology (IUST) Recently, the United States has faced several significant freight train derailments involving hazardous materials (HAZMAT), raising serious
Instructor: Tu Lan & Vivaldi Rinaldi, New York University Beginner level: No prior experience required. Schedule: Friday, October 11, 1:00 pm – 2:00 pm ET Description: Drones are becoming an
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
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
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
Instructor: Tao Li, New York University Beginner level: No prior experience required. Basic optimization knowledge would be helpful but not required. Description: Urban transportation networks are complex and dynamic, and
Presented by Qian Xie, Cornell Hyperparameter 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
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
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
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
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