Research Projects

My research interests lie in machine learning, wireless communications and networking, and information theory. Current research topics include:

• Transformers and large language models (LLMs)

• Multi-armed bandits and reinforcement learning

• Federated learning and distributed/decentralized learning

• Privacy-preserving machine learning

• Novel applications of machine learning and artificial intelligence

Active Projects

• PI, National Science Foundation (NSF), Communications, Circuits, and Sensing-Systems (CCSS) core program, ‘‘Collaborative Research: Optimized Testing Strategies for Fighting Pandemics: Fundamental Limits and Efficient Algorithms’’.

• PI, National Science Foundation (NSF), Computer and Network Systems (CNS) core program, ‘‘Collaborative Research: CNS Core: Timely Computing and Learning over Communication Networks’’.

• PI, National Science Foundation (NSF), Computer and Network Systems (CNS) core program, ‘‘CNS Core: Medium: When Next Generation Wireless Networks Meet Machine Learning’’.

• Co-PI, National Science Foundation (NSF), Communications, Circuits, and Sensing-Systems (CCSS) core program, ‘‘Energy-Efficient Broadband Spectrum Sensing in Real Time Based on a Frequency-Domain Analog Signal Processor’’.

Selected Completed Projects

• PI, National Science Foundation (NSF), Spectrum and Wireless Innovation enabled by Future Technologies (SWIFT) program, ‘‘Learning-Efficient Spectrum Access for No-Sensing Devices in Shared Spectrum’’.

• PI, National Science Foundation (NSF) and Intel, Machine Learning for Wireless Networking Systems (MLWiNS) program, ‘‘Dino-RL: A Domain Knowledge Enriched Reinforcement Learning Framework for Wireless Network Optimization’’.

• PI, National Science Foundation (NSF), CAREER program,‘‘CAREER: When Energy Harvesting Meets ‘Big Data’: Designing Smart Energy Harvesting Wireless Sensor Networks’’.

• PI, National Science Foundation (NSF), Communications, Circuits, and Sensing-Systems (CCSS) core program, ‘‘Energy-aware Sparse Sensing’’.

• Co-PI, Department of Energy (DOE), ‘‘Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties’’, Phase I and Phase II.