Dissertation Defenses

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

Improving Application Connectivity with Network-conscious Systems

Assessment of Privacy Risks in Mobile and Web Applications

Application-Aware Scheduling in Deep Learning Software Stacks

Dynamic Connectivity, Hopsets, and Byzantine Agreement

Advancing Digital Safety for High-Risk Communities

The Value Equivalence Principle for Model-Based Reinforcement Learning

Context-Aware Detection and Resolution of Data Anomalies for Semi-Autonomous Cyber-Physical Systems

On Addressing the Problem of Discovery for Reinforcement Learning

Augmenting Structure with Text for Improved Graph Learning