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
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	Cisco	Evaluate Trustworthiness of Pre-Trained Neural Networks
Efficient Computer Vision	Cisco	Detect and Eliminate Redundant Data on Edge Devices
Trusted Machine Learning	Wistron	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

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

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

• Ph.D.

  • 2023 Caleb Tung, Ph.D. Thesis: “Efficient and Consistent Convolutional Neural Networks for Computer Vision”

  • 2022 Abhinav Goel, Ph.D., Thesis: “Tree-Based Unidirectional Neural Networks for Low-Power Computer Vision on Embedded Devices”

  • 2017 Anup Mohan, Ph.D., Thesis: “Cloud Resource Management for Big Visual Data Analysis from Globally Distributed Network Cameras”.

  • 2016 Ahmed Kaseb, Ph.D., Thesis: “A Cost-Effective Cloud-Based System for Analyzing Big Real-Time Visual Data From Thousands of Network Cameras”.

  • 2012 Jing (Jackie) Feng, Ph.D., Thesis: “Energy-Efficient Collaborative Data Transmission in Wireless Sensor Networks”.

  • 2011 Karthik Kumar, Ph.D. Thesis: “Application-Based Energy Efficient Mobile and Server Computing”, Bilsland Dissertation Fellowship.

  • 2010 Yamini Nimmagadda, Ph.D., Thesis: “Resource-Driven Transmission, Display, and Processing of Multimedia in Mobile Devices”.

  • 2008 Changjiu Xian, Ph.D., Thesis: “Collaborative Power Management between Operating Systems and Applications”.

  • 2008 Nathaniel (Eddie) Pettis, Ph.D., Thesis: “Automatic Configuration and Selection of Power Management Policies”. Bilsland Dissertation Fellowship and a finalist of Chorafas Top Doctoral Award.

  • 2007 Yongguo Mei, Ph.D., Thesis: “Energy-Efficient Mobile Robots”.

  • 2006 Le Cai, Ph.D., Thesis: “Joint Power Management of Memory and Hard Disks”

• MS

  • 2022 Xiao Hu. M.S., Thesis: “Efficient Multi-Object Tracking on Unmanned Aerial Vehicle”

  • 2020 Sara Aghajanzadeh, M.S., Thesis: “Camera Placement Meeting Restrictions of Computer Vision”.

  • 2019 Ryan Dailey, M.S., Thesis: “Automated Discovery of Network Cameras in Heterogeneous Web Pages”.

  • 2019 Aniesh Chawla, M.S., Thesis: “Automated System for Identifying Usable Sensors in a Large Scale Sensor Network for Computer Vision Applications”.

  • 2018 Yifan Li, M.S., Thesis: “Study of Pedestrian Jaywalking in Campus Area by Analyzing Network Camera Data”.

  • 2018 Fengjian Pan, M.S., Thesis: “Faster CNN-based Object Detection with Adaptive Network Selection on Embedded System”.

  • 2016 Youngsol Koh, M.S., Thesis: “Efficient Data Processing from Network Camera and its Application”.

  • 2015 Ganesh Prahlad Rao Gingade, M.S., Thesis: “Hybrid Power Management for Office Equipment”.

  • 2015 Wenyi Chen, M.S., Thesis: “Dynamic Resource Allocation for Large-Scale Streaming Data”.

  • 2008 Karthik Kumar, M.S., Thesis: “Energy Conservation for Content-Based Image Retrieval on Mobile Devices”.

  • 2007 Douglas Herbert, M.S., Thesis: “Wireless Sensor Network Debugging Using Invariant Insertion”.

  • 2006 Jeffrey Brateman, M.S., Thesis: “Frequency and Speed Setting for Energy Conservation in Autonomous Mobile Robots”.