PhD Defence • Artificial Intelligence | Machine Learning — Likelihood-based Density Estimation using Deep Architectures
Priyank Jaini, PhD candidate
David R. Cheriton School of Computer Science
Priyank Jaini, PhD candidate
David R. Cheriton School of Computer Science
Zhiwei Shang, Master’s candidate
David R. Cheriton School of Computer Science
Abel Nieto Rodriguez, Master’s candidate
David R. Cheriton School of Computer Science
Henry Chen, Master’s candidate
David R. Cheriton School of Computer Science
Mohammed Alfatafta, Master’s candidate
David R. Cheriton School of Computer Science
Nalin Chhibber, Master’s candidate
David R. Cheriton School of Computer Science
Alex Williams, PhD candidate
David R. Cheriton School of Computer Science
Peoples’ work lives have become ever-populated with transitions across tasks, devices, and environments. Despite their ubiquitous nature, managing transitions across these three domains has remained a significant challenge. Current systems and interfaces for managing transitions have explored approaches that allow users to track work-related information or automatically capture or infer context, but do little to support user autonomy at its fullest.
Aravind Balakrishnan, Master’s candidate
David R. Cheriton School of Computer Science
Ashish Gaurav, Master’s candidate
David R. Cheriton School of Computer Science
Sachin Vernekar, Master’s candidate
David R. Cheriton School of Computer Science