Connected Cities with Smart Transportation
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