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

Current Topics




Computer Vision at Edge

National Science Foundation

Create efficient computer vision for edge device s

Drone Video

National Science Foundation

Create video datasets captured by drone

Observe Crowd Size in COVID-19

National Science Foundation

Estimate crowd sizes worldwide during COVID-19

Computer Vision at Edge Devices

National Science Foundation

Create Cyber Infrastructure for Edge Computing

Low-Power Computer Vision

Google, Facebook, Xilinx

Improve efficiency of computer vision on embedded sysems

Active Vision + Drone


Use real-time computer vision to control drones

Open-Source TensorFlow Garden


Create open-source implementation of computer vision

Research Funding

National Science Foundation

  • 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

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

  • Abhinav Goel. Topic: Efficient Computer Vision using Hierarchical Neural Network

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

  • Xiao Hu. Topic: Efficient Object Tracking on Embedded Systems

Past Graduate Students