Research Topics and Sponsors

Current Topics

Dr. Lu’s research focuses on improving efficiency of computer systems and analyze large amounts of data from distributed sources.




Computer Vision at Edge

National Science Foundation

Create efficient computer vision for edge device s

Analyze Drone Video

National Science Foundation

Create video datasets captured by drone

Computer Vision at Edge Devices

National Science Foundation

Create Cyber Infrastructure for Edge Computing

AI Institute: Cybersecurity

National Science Foundation

Agent-Based Cyber Threat Intelligence and Operation

AI for Future Musicians

National Science Foundation

AI-Based Software to help music performers

Trust of Machine Learning Code


Evaluate Trustworthiness of Pre-Trained Neural Networks

Efficient Computer Vision


Detect and Eliminate Redundant Data on Edge Devices

Trusted Machine Learning


Execute Machine Learning Software in Trusted Environment

Research Funding

National Science Foundation

  • 2023, Co-PI, 2326198, “Artificial Intelligence Technology for Future Music Performers

  • 2023, Senior Personnel, “AI Institute for Agent-Based Cyber Threat Intelligence and Operation (ACTION)

  • 2021, PI, 2120430, “CNS Collaborative Research: CAR:NEW: Research Infrastructure for Real-Time Computer Vision and Decision Making via Mobile Robots

  • 2021, PI, 2107230, “OAC Core: Advancing Low-Power Computer Vision at the Edge

  • 2021, PI, 2104709, “CDSE: Collaborative: Cyber Infrastructure to Enable Computer Vision Applications at the Edge Using Automated Contextual Analysis

  • 2020, PI, Collaborative:RAPID:Leveraging New Data Sources to Analyze the Risk of COVID-19 in Crowded Locations

  • 2019, PI, CCRI: Planning: Collaborative Research: Planning to Develop a Low-Power Computer Vision Platform to Enhance Research in Computing Systems

  • 2017, PI, Summit of Software Infrastructure for Managing and Processing Big Multimedia Data at the Internet Scale

  • 2015, PI, SI2-SSE: Analyze Visual Data from Worldwide Network Cameras

  • 2015, PI, I-Corps: Business Analytics for Large Scale Intelligence

  • 2014, PI, US-Singapore Workshop: Collaborative Research: Understand the World by Analyzing Many Video Streams

  • 2013, Co-PI, Planning Grant: I/UCRC for Net-Centric Software and Systems Center Research Center

  • 2010, Co-PI, CI-ADDO-NEW: Collaborative Research: Development of DARwIn Humanoid Robots for Research, Education and Outreach

  • 2009, Co-PI, CRI: II-NEW: Adaptive Robotic Testbed for Wireless Sensor Networks and Autonomous Systems

  • 2008, Co-PI, CRI: Planning - A Testbed for Compiler-supported Scalable Error Monitoring and Diagnosis for Reliable and Secure Sensor Networks

  • 2007, Co-PI, NeTS-NOSS: AIDA: Autonomous Information Dissemination in RAndomly Deployed Sensor Networks

  • 2007, Co-PI, CPATH EAE: Extending a Bottom-Up Education Model to Support Concurrency from the First Year

  • 2007, Co-PI, CT-ISG: Compiler-Enabled Adaptive Security Monitoring on Networked Embedded Systems

  • 2006, PI, CPA: Cross-Layer Energy Management by Architectures, Operating Systems, and Application Programs

  • 2005, Co-PI, CSR-EHS: Resource-Efficient Monitoring, Diagnosis, and Programming Support for Reliable Networked Embedded Systems

  • 2004, PI, CAREER: A Unified Approach for Energy Management by Operating Systems

  • 2003, Co-PI, IIS: Distributed Energy-Efficient Mobile Robots


  • 2023, PI: Dongyan Xu, Co-PI: Yung-Hsiang Lu, “Execute Machine Learning Software in Trusted Environment”


  • 2023, PI: Yung-Hsiang Lu, Co-PI: James Davis, “Efficient Computer Vision for Edge Devices”.

  • 2022, PI: James Davis, Co-PI: Yung-Hsiang Lu, “Trustworthy Re-use of Pre-Trained Neural Networks”.

Sandia National Laboratory

  • 2020, Large Scale Network Simulation for Video Surveillance


  • 2019 and 2020, Low-Power Computer Vision Challenge

  • 2017, Computer Vision using Contextual Information


  • 2020, Open-Source TensorFlow Model Garden

  • 2018, Low-Power Computer Vision Challenge


  • 2019, Low-Power Computer Vision Challenge

  • 2020, Low-Power Computer Vision Challenge


  • 2013, Adaptive Power Management for Laser Printers

Current Graduate Students

  • Jiwoo Kim. Topic: Autonomous UAV Navigation

  • Nick Eliopoulos. Topic: Efficient Computer Vision using Transformers

  • Cheng-Yun Yang. Topic: Active and Real-Time Computer Vision

  • Purvish Jatin Jajal. Topic: Evaluate Pre-Trained Machine Models

  • Gowri Ramshankar. Topic: Trust of Machine Learning Software

Past Graduate Students