Research Assistants
Graduate Research Assistant
Zhenyu Lin, ECE (Fall23)
Ankita Mukherjee, EECS (Fall22)
Philip Liang, EECS (SP22)
Undergraduate Research Assistant
Brandon Huang, EE (Fall23)
Jin Chul Rhim (co-advised with Dr. Xiaorong Zhang), CompE (SP22)
Almuni
Jimmy Lu, Lowell High School, (Summer 2022)
Project: On-device EMG Pattern Recognition for Real-Time Bionic Arm Control by Deep Neural Network, Sony Spresense Developer Challenge 2022 (Grand Prize)
Employment after graduation: BS in Computer Science, UCSC
Benediction Bora, CompE (SP22)
Employment after graduation: Associate Systems Engineer, Northrop Grumman, San Diego, CA
Lisha (Kory) Zhou, Computer Science/ComE (SP21)
Employment after graduation: Software Engineer, JPMorgan Chase & Co, Palo Alto, CA
John Carlo Manuel, Skyline College, (Summer 2022)
Project: Real-time Object Detection and Distance Estimation for Exo-Glove Control
Thanh Nguyen, EECS (SP21)
Thesis Title: Real-Time Object Detection and Grasping for a Robotic Arm
Employment after graduation: Software Engineer, DIMAAG-AI, Fremont, CA
Shivam Rajesh Singh, EECS (SP21)
Project: Classification and Quality Detection of Common Fruits Using Neural Networks
Employment after graduation: Data Analytics Engineer, Abbott Labs, IL
Lab Director
Zhuwei Qin
Assistant Professor of Computer Engineering
School of Engineering
College of Science & Engineering
San Francisco State University
Address: 1600 Holloway Ave, SCI211C, San Francisco, CA 94132
Email: zwqin.sfsu.edu
Research Interest
- Efficient Mobile Computing
- Deep Learning Acceleration
- Distributed Edge Computing
- Interpretable Deep learning
Bibliography
Dr. Zhuwei Qin joined San Francisco State University (SFSU) in 2020 as a faculty member in computer engineering. He is the Director of the Mobile and Intelligent Computing Laboratory (MIC Lab). His research interests are in the broad area of efficient mobile computing, deep learning acceleration, distributed edge computing, and interpretable deep learning.
In the past years of research, he has proposed a set of computational optimization approaches for deep neural network execution on mobile devices through better neural network interpretability. Related papers are published in the top international conferences and journals such as the British Machine Vision Conference (BMVC), the International Conference on Computer-Aided Design (ICCAD), and the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). His current research focuses on deep learning acceleration for real-time mobile applications, efficient and secure edge computing systems, and emerging IoT technologies.