BioE Ph.D. Proposal Presentation

September 22, 2026
Time: 10:00 am – 12:00 pm ET
Location: Marcus Nanotechnology Building, Room 1116
Virtual: https://gatech.zoom.us/j/92534368030

Advisor:
Dr. Shu Jia, Georgia Institute of Technology; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University

Committee Members:
Dr. Haonan Lin, Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University
Dr. Callie Hao, School of Electrical and Computer Engineering, Georgia Institute of Technology
Dr. Marcus Cicerone, Georgia Institute of Technology; School of Chemistry and Biochemistry, Georgia Institute of Technology
Dr. Scott Danielsen, Georgia Institute of Technology; School of Materials Science and Engineering, Georgia Institute of Technology

Toward Event-Driven Neuromorphic Super-Resolution Microscopy

Super-resolution fluorescence microscopy enables visualization beyond the diffraction limit, but most existing methods still rely on conventional frame-based detectors that integrate fluorescence signals and compress their temporal evolution. This proposal will develop an event-driven neuromorphic super-resolution microscopy framework that exploits the precise timing and threshold-crossing dynamics of event cameras as an additional source of spatial information for resolution enhancement. The work will first establish how controlled fluorescence changes are encoded into temporally evolving event responses and develop a physics-guided reconstruction algorithm based on this event-domain PSF evolution. The resulting reconstruction will then be implemented and optimized on an edge-computing platform to reduce processing latency and support real-time or near-real-time imaging. Finally, the system will be validated in progressively more demanding biological applications, including fixed subcellular structures, high-speed live-cell dynamics, and functional calcium imaging driven by intrinsic fluorescence changes under continuous illumination. Successful completion of this research will establish detector-level temporal encoding as a new dimension for super-resolution microscopy and provide a unified platform that combines improved spatial resolution, high temporal sensitivity, and efficient computation for biological imaging.