Assistant Professor, Computer Science @ San Francisco State University (SFSU). Researching Edge AI, wireless systems, and spectrum sharing & security with a focus on energy‑aware and privacy‑preserving learning across heterogeneous devices.
Dr. Qun Wang is an Assistant Professor in the Computer Science Department at San Francisco State University (SFSU). His research bridges next‑generation wireless networks, machine learning, and edge–cloud collaboration. He investigates energy‑aware optimization, privacy‑preserving LLM inference, and spectrum sharing security to support intelligent, resilient, and sustainable infrastructures for smart cities and industrial IoT.
Previously, Dr. Wang worked at Lawrence Berkeley National Laboratory (LBNL) as a Postdoctoral Researcher and Research Affiliate, and received a Ph.D. in Electrical and Computer Engineering from Utah State University.
Received NSF Collaborative Research CISE Core Small NeTS Grant ($60,000) as Lead PI for "Intelligent Reflecting Surface Assisted Physical Layer Security Enhancement" with collaborators from UGA and UCSC.
Received NSF CRII Grant ($175,000) as Single-PI for "Spectrum Sharing and Sensing Networks" research project.
Our project "Small Size LLM Enhancement for Privacy-Preserving Network Threat Detection" received $1,500 API credits through the OpenAI Cybersecurity Grant Program.
Our research work: Binrong Zhu, Ruxue Jin, Yang Liu, Guiran Liu, Qun Wang, and Phuong Mai Nguyen, "Edge AI Agent Design for Policy-Aware Urban Waste Management," has been accepted by AIAS 2025.
Our research work: "Attention-Guided Task Complexity Prediction for Edge-Cloud LLM Collaboration" has been accepted as a poster by Bay Area Machine Learning Symposium.
Our CIDER Lab project WildfireRFM won the 1st prize in KumoRFM Hackathon, congratulations to Binrong Zhu, Guiran Liu, and Yang Liu.
Our research project has been selected for the Gilead Innovation Initiative 2025 Summer Research Award, with $5,000 to Binrong Zhu and $5,000 to Yang Liu.
Congratulations to Xu Gu for being selected as the 2025 SFSU Graduate Distinguished Achievement Award Recipient.
Congratulations to Satvik Verma, Xu Gu, and Ruxue Jin on their graduation.
Satvik Verma presented our research at AAAI 2025 Spring Symposium.
S. Verma, Q. Wang, E. W. Bethel, "Intelligent IoT Attack Detection via ODLLM," accepted by AAAI 2025 Spring Symposium.
W. Guo, Q. Wang, H. Yue, H. Sun, R. Q. Hu (2025). Efficient Phishing URL Detection Using Graph-based Machine Learning and Loopy Belief Propagation. Accepted by IEEE ICC 2025.
S. Liu, Q. Wang, Z. Qin, W. Zhang, J. Wang, X. Ma, "IRS Assisted Decentralized Learning for Wideband Spectrum Sensing." Accepted by ISICN 2025.
D. Chen, Q. Wang, H. Sun, Y. Hao, "GNN Based PLS Enhancement for Next Generation Spectrum Sharing Industrial Networks." Accepted by ISICN 2025.
Invited to attend NSF NeTS Early Career Workshop 2025 with travel support.
Received NSF Discovery Research PreK-12 Grant ($1,396,551) as Co-PI for "Design Effective and Equitable Professional Learning for Middle School Computer Science Teachers".
Congratulations to Satvik Verma and Sicheng Liu for their student abstract accepted by IEEE DSAA'24 with travel support.
Guest editor for the Special Issue "Cognitive Semantic Communications for 6G Wireless Networks", VSI: CSC-6GWN in Physical Communication (IF: 2.0).
J. Xu, Q. Wang, Y. Cao, B. Zeng, S. Liu, "A general-purpose device for interaction with llms." Accepted by Future Technologies Conference (FTC) 2024.
X. Ma, Q. Wang, H. Sun, R. Q. Hu, Y. Qian, "CSMAAFL: Client Scheduling and Model Aggregation in Asynchronous Federated Learning." IEEE ICC 2024.
M. Xiang, Y. Zhou, H. Zhang, Q. Wang, H. Sun, H. Wang, R. Q. Hu, "Exploring Communication Technologies, Standards, and Challenges in Electrified Vehicle Charging." Accepted by IEEE Open Journal of the Communications Society (IF: 6.3).
Joined San Francisco State University as Assistant Professor in Computer Science Department.
Completed postdoctoral research fellowship at Lawrence Berkeley National Lab (LBNL) focusing on 5G NR–based scientific edge computing simulation.
Received SFSU RSCA Fund Award as Co-PI for "Intelligent Wireless Spectrum Monitoring with Decentralized On-device Learning" (Internal Funding, Spring 2023).
Started postdoctoral research fellowship at Lawrence Berkeley National Lab (LBNL).
Completed Ph.D. in Electrical and Computer Engineering from Utah State University.
Earned Ph.D. in Electrical and Computer Engineering from Utah State University with focus on machine learning and network optimization.
Contributed to several key projects in mobile computing and intelligent application development during graduate studies.
Published extensively in collaborative edge computing and energy-efficient communication systems.
Cybersecurity & Intelligent Distributed Edge Research
Our lab focuses on edge AI, on-device LLMs, wireless systems, and cybersecurity. We develop efficient data processing frameworks for real-time analysis, collaborative learning, and model deployment across heterogeneous edge devices.
Research Areas:
Facilities: USRPs, OpenWRT Wi-Fi, IRS panels, Jetson edge nodes, SFSU HPC (A100), lab servers (RTX 4090).
S. Liu, Q. Wang, Z. Qin, W. Zhang, J. Wang, X. Ma, "IRS Assisted Decentralized Learning for Wideband Spectrum Sensing." ISICN 2025
D. Chen, Q. Wang, H. Sun, Y. Hao, "GNN Based PLS Enhancement for Next Generation Spectrum Sharing Industrial Networks." ISICN 2025
S. Verma, Q. Wang, E. W. Bethel, "Intelligent IoT Attack Detection via ODLLM." AAAI 2025 Spring Symposium
W. Guo, Q. Wang, H. Yue, H. Sun, R. Q. Hu, "Efficient Phishing URL Detection Using Graph-based Machine Learning and Loopy Belief Propagation." IEEE ICC 2025
Binrong Zhu, Ruxue Jin, Yang Liu, Guiran Liu, Qun Wang, and Phuong Mai Nguyen, "Edge AI Agent Design for Policy-Aware Urban Waste Management." AIAS 2025
J. Xu, Q. Wang, Y. Cao, B. Zeng, S. Liu, "A general-purpose device for interaction with llms." Future Technologies Conference (FTC) 2024
X. Ma, Q. Wang, H. Sun, R. Q. Hu, Y. Qian, "CSMAAFL: Client Scheduling and Model Aggregation in Asynchronous Federated Learning." IEEE ICC 2024
M. Xiang, Y. Zhou, H. Zhang, Q. Wang, H. Sun, H. Wang, R. Q. Hu, "Exploring Communication Technologies, Standards, and Challenges in Electrified Vehicle Charging." IEEE Open Journal of the Communications Society (IF: 6.3)
Q. Wang, F. Zhou, R. Q. Hu and Y. Qian, "Energy-Efficient Beamforming and Cooperative Jamming in IRS-Assisted MISO Networks." IEEE ICC 2020
Q. Wang, L. T. Tan, R. Q. Hu and Y. Qian, "Hierarchical Energy Efficient Mobile Edge Computing in IoT Networks." IEEE Internet of Things Journal
Q. Wang and F. Zhou, "Fair Resource Allocation in an MEC-Enabled Ultra-Dense IoT Network with NOMA." IEEE ICC Workshops 2019
Q. Wang, L. T. Tan, R. Q. Hu, and G. Wu, "Hierarchical collaborative cloud and fog computing in IoT networks." WCSP 2018
H. Sun, Q. Wang, S. Ahmed, and R. Q. Hu, "Non-orthogonal multiple access in a mmWave based IoT wireless system with SWIPT." IEEE VTC Spring 2017
H. Sun, Q. Wang, R. Q. Hu and Y. Qian, "Outage Probability Study in a NOMA Relay System." IEEE WCNC 2017
I welcome motivated students (undergraduate and graduate) to join our research team. We offer opportunities in:
Email: qunwang@sfsu.edu
Office: San Francisco State University
Computer Science Department
Links:
Google Scholar ·
LinkedIn ·
CIDER Lab
I'm open to collaborations and welcome student researchers (MS/BS). If you're interested in edge AI, on-device LLMs, or spectrum sharing, feel free to email with your CV and research interests.
Research Areas: Edge AI, Wireless Systems, Spectrum Sharing, Security & Privacy, Distributed Learning