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Fan's research interests include efficient AI hardware and algorithm, AI chip design, in-memory computing circuits and architecture, adversarial and trustworthy AI system.

Research Interest: Efficient 3D modeling through NeRF/3DGS

Research Interest: AI accelerator hardware

Research Interest: 3DGS

Research Interest: Efficient and Secure AI

Research Interest: AI Chip

Research Interest: AI Hardware-Software Co-Design

Ph.D. thesis: Efficient and Secure Deep Learning Inference System, A Software and Hardware Co-Design Perspective; Current: Associate Professor in Shanghai Jiaotong University; Research website: https://elliothe.github.io/

Ph.D. thesis: Processing-in-Memory for Data-Intensive Applications, From Device to Algorithm Current: Assistant Professor in New Jersey Institute of Technology Research Website: https://www.shaahinangizi.com/

Ph.D. thesis: Exploration of Security and Privacy Challenges through Adversarial Weight Perturbation in Deep Learning Models. Current: Assistant Professor of The State University of New York, Binghamton

Ph.D. thesis: Efficient Deep Learning at Edge, From Efficient and Dynamic Inference to On-Device Learning Current: Assistant Professor, University of North Carolina at Charlotte Website: https://lyang-666.github.io/

Ph.D. thesis: Efficient In- and Near-Memory Computing System With Hardware and Software Co-Design Current: Google

Ph.D. thesis: Compute-in-memory Circuits and Architectures for Efficient Acceleration of AI and Data Centric Workloads Current: Apple

Master thesis title: NeRF Robustness Study Against Adversarial Bit Flip Attack

Master thesis title: Deep Learning based Air Quality Prediction with Time Series Sensor Network Data

Master thesis title: Accelerating Genome Quantification in FPGA

Master thesis title: Exploring FPGA Implementation for Binarized Neural Network Inference