Dissertation Defenses

Steerable AI-powered Art-making Tools

Secure and Safe Autonomous Driving: an Adversary’s Perspective

Enabling Practical Deployments of Privacy-Preserving Technologies

Sequential Decision Making for Large Scale Machine Learning

Efficient and Dependable Deep Learning Systems

Theory of Mind in Collaborative Tasks between Embodied Agents

Towards Generalizable Neural Networks for Graph Applications

Acceleration Techniques of Sparse Linear Algebra on Emerging Architectures

Building User-Driven Edge Devices

Optimizing Emerging Graph Applications Using Hardware-Software Co-Design

Efficient, Reconfigurable, and QoS-Aware Systems for Deep Neural Networks

Compiler Support for Robust and High Performance Autonomous Driving Environments

Securing Connected and Automated Vehicle through Proactive Vulnerability Analysis and Security Enhancement

In-SRAM Computing for Neural Network Acceleration

Automating the Verification of Distributed Systems

Bringing Practical Security to Vehicles

A Cognitive Architecture Realization of Event Cognition Capabilities

Efficient utilization of heterogeneous compute and memory systems

Towards Human Action Understanding in Social Media Videos using Multimodal Models

Sample-Efficient Algorithms for Hard-Exploration Problems in Reinforcement Learning

Learning General and Correct Procedural Knowledge in a Cognitive Architecture

Integrating visualization grammars with the task language of data analysts

Toward Practical Application-Aware Big Data Systems

Modeling Trading Strategies in Financial Markets with Data, Simulation, and Deep Reinforcement Learning

Advances in Deep Reinforcement Learning: Intrinsic Rewards, Temporal Credit Assignment, State Representations, and Value-equivalent Models