Curriculum Vitae

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Zhimin Li · Pittsburgh, PA · zhl157@pitt.edu

Education

• Ph.D., Electrical and Computer Engineering, University of Pittsburgh · January 2024–present
• M.S., Information Science, Northeastern University · January 2023–January 2024 (transferred to University of Pittsburgh)
• B.S., Computer Science, University of Pittsburgh · September 2019–December 2022
• B.S. studies, Architectural Environment and Energy Application Engineering, Hefei University of Technology · September 2017–June 2019 (transferred to University of Pittsburgh)

Research interests

Efficient and Trustworthy AI; Edge and Embedded AI; Vision-Language Models; DNN Acceleration; AI Fairness and Privacy.

Research experience

• Privacy-Preserving Wearable Vision-Language Models · November 2025–present · Advisor: Jingtong Hu
• Achieve Fairness in AI-Assisted Mobile Healthcare Apps · November 2023–November 2025 · Advisor: Jingtong Hu
• Efficient Framework for Real-Time Object Detection on the Edge · March–November 2023 · Advisor: Yanzhi Wang
• Digital Avatar on Mobile Device · August–November 2023 · Advisor: Yanzhi Wang
• DNN Data Management on Hybrid Memory Systems · February–September 2021 · Advisor: Dazhao Cheng

Read the research summaries

Publications

See the full publication list, with links to each paper.

Work and teaching experience

• Research Assistant, Northeastern University, Electrical and Computer Engineering · September–December 2023. DNN pruning, DVFS for mobile DNNs, and mobile deployment of generative and detection models.
• Research Assistant, University of Pittsburgh, Electrical and Computer Engineering · May 2022–December 2023. CPU–FPGA DRAM data transfer and CNN accelerator optimization.
• Teaching Assistant, University of Pittsburgh, SCI–Computer Science · Fall 2022–present. CS 445 Data Structures; ECE 1110 Computer Organization and Architecture; ECE 1150 Computer Networks.

Honors and academic service

• Reviewer: CVPR, IJCAI, ICCAD, DAC, and IEEE Transactions on Cyber-Physical Systems.
• Dean’s List: Summer and Spring 2022; Summer 2021; Fall 2020; Spring 2020 and 2021.

Skills

• Programming and systems: Python, Linux, CUDA, Docker, Git, FastAPI.
• Machine learning: PyTorch, vision-language models, multimodal learning, diffusion models, object detection, model compression, privacy-preserving ML, AI fairness.
• Infrastructure and edge AI: Model inference, on-device and split inference, GPU computing, DVFS, FPGA acceleration, hybrid memory systems.
• Models and frameworks: Qwen, MiniCPM, GLM, Stable Diffusion, YOLOv5, nanoGPT, Whisper.