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

Current Topics




Low-Power Computer Vision

Google, Facebook, Xilinx

Create efficient computer vision running on embedded sysems

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

Open-Source TensorFlow Garden


Create open-source implementation of computer vision

Image Processing for Solar Sail


Estiamate the optical force from the shape of solar Sail

Research Funding

National Science Foundation

  • 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

  • Haobo Wang. Topic: Network Management for Surveillance Video

Past Graduate Students