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.
Topic |
Sponsor |
Summary |
|---|---|---|
Computer Vision at Edge |
National Science Foundation |
Create efficient computer vision for edge device s |
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 |
Trusted Machine Learning |
Wistron |
Execute Machine Learning Software in Trusted Environment |
Research Funding
National Science Foundation
2025, PI, 2504445 “Cyberinfrastructure for Multi-Stream Architectures Applied to Computer Vision: Efficiency via Co-Design of Network Architectures and Framework Operators”
2024, Co-PI, 2343596, “Exploring The Risks and Rewards of Large Language Models in Enabling Energy-Efficient Data Center Software Infrastructure”
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
Qualcomm
2024, Qualcomm Faculty Research Award, ``Efficient Computer Vision”
Wistron
2023, PI: Dongyan Xu, Co-PI: Yung-Hsiang Lu, “Execute Machine Learning Software in Trusted Environment”
Cisco
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
Facebook
2019 and 2020, Low-Power Computer Vision Challenge
2017, Computer Vision using Contextual Information
Google
2020, Open-Source TensorFlow Model Garden
2018, Low-Power Computer Vision Challenge
Xilinx
2019, Low-Power Computer Vision Challenge
2020, Low-Power Computer Vision Challenge
HP
2013, Adaptive Power Management for Laser Printers