Improving Mobile Internet Performance with Cross-Device Network Transport and Cross-Layer Application Adaptation

In-Memory Acceleration for General Data Parallel Applications

Incorporating Expert Interpretation and Reasoning to Guide Model Selection in Machine Learning

Towards Free, Open, and Ubiquitous Hardware Design

Constructing Meaning, Piece by Piece: A Computational Cognitive Model of Human Sentence Comprehension

Predictable Performance and Low Cost for Geo-Distributed Applications

Augmenting Structure with Text for Improved Graph Learning

On Addressing the Problem of Discovery for Reinforcement Learning

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

The Value Equivalence Principle for Model-Based Reinforcement Learning

Advancing Digital Safety for High-Risk Communities

Dynamic Connectivity, Hopsets, and Byzantine Agreement

Application-Aware Scheduling in Deep Learning Software Stacks

Assessment of Privacy Risks in Mobile and Web Applications

Improving Application Connectivity with Network-conscious Systems

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

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

Toward Practical Application-Aware Big Data Systems

Integrating visualization grammars with the task language of data analysts

Learning General and Correct Procedural Knowledge in a Cognitive Architecture

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

Towards Human Action Understanding in Social Media Videos using Multimodal Models

Efficient utilization of heterogeneous compute and memory systems

A Cognitive Architecture Realization of Event Cognition Capabilities

Bringing Practical Security to Vehicles

Automating the Verification of Distributed Systems

In-SRAM Computing for Neural Network Acceleration

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

Compiler Support for Robust and High Performance Autonomous Driving Environments

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

Optimizing Emerging Graph Applications Using Hardware-Software Co-Design

Building User-Driven Edge Devices

Acceleration Techniques of Sparse Linear Algebra on Emerging Architectures

Towards Generalizable Neural Networks for Graph Applications

Theory of Mind in Collaborative Tasks between Embodied Agents

Efficient and Dependable Deep Learning Systems

Sequential Decision Making for Large Scale Machine Learning

Enabling Practical Deployments of Privacy-Preserving Technologies