Research

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

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

Trust of Machine Learning Code

Cisco

Evaluate Trustworthiness of Pre-Trained Neural Networks

Trusted Machine Learning

Wistron

Execute Machine Learning Software in Trusted Environment

Research Funding

National Science Foundation

  • 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

Wistron

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

Cisco

  • 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

Current Graduate Students

  • Caleb Tung. Topic: Efficient Computer Vision using Contextual Information

  • 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