The fellowship mainly investigates an analysis of the state-of-the-art approaches, design and implementation of cutting-edge deep neural network models to be used on a mobile platform. It explored ways to optimize the deployment of these machine-learning models for prediction tasks on the mobile devices which requires energy efficiency and accuracy.

Optimizing a program for Graphics Processing Units (GPUs) is critical for performance, yet remains a challenge due to the non-intuitive interactions among the optimizations and the GPU architecture. Automatic optimization tuning for a GPU is demanding particularly given the exploding number of mobile GPU variants in the market.

Driver distraction has long been a critical issue drawing substantial amount of research effort. In order to reduce driver distraction for improved driving performance and safety, automotive suppliers have been endeavoring to provide optimum user interaction solutions. Until recent years, there have been growing interests in the use of gestural interfaces for in-vehicle information systems; however, little is known about how such gesture-based interactions differ from existing touch- and voice-based interactions in the context of driver distraction.

Conventional camera sensors record three color channels: red, green and blue. In this project we will investigate computational photography algorithms for cameras that record a near-infrared channel (NIR) in addition to RGB. This channel is particularly useful for biometric imaging and holds great potential in consumer imaging applications as well. The key challenge in simultaneously capturing RGB and NIR is that lens behavior depends on wavelenth and thus the NIR channel may be defocused compared to the other three.