Intelligent traffic signal strategies can drastically improve traffic and reduce air pollutant emissions. Using realistic large-scale simulations and Deep Reinforcement Learning, we train intelligent agents for traffic lights control that outperform traditional approaches.

This project deals with database management. Specifically, the project team is investigating a system to support data scientists, called Data Civilizer.

Current shared computing platforms, from small clusters to large datacenters, suffer from low utilization, wasting billions of dollars in energy and infrastructure every year. Low utilization stems from a disconnect between layers of the hardware and software stack. The goal of this proposal is to investigate and develop integrated intra- and inter-node resource management techniques that provide both near-peak utilization and guaranteed high performance in shared environments.

A revenue management approach for air-cargo that combines machine learning prediction with decision-making using deep reinforcement learning.

We are exploiting big data for image and video manipulation. Our work solves fundamental and challenging computer graphics problems with applications to various impactful domains including: computational photography, multimedia and video content post-production.

Inferring road graphs from satellite imagery is a challenging computer vision task. The key idea in QSM is a novel encoding scheme, graph-tensor encoding (GTE), which encodes the road graph into a tensor representation.