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)

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

Research Interest

  • Efficient Mobile Computing
  • Deep Learning Acceleration
  • Distributed Edge Computing
  • Interpretable Deep learning


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.

Send a Message