Thesis: SPAD Image Sensors with Embedded Intelligence
This research focuses on integrating recurrent and spiking neural networks within or near SPAD sensors to enable efficient, real-time, intelligent edge processing, especially for biomedical imaging. The work encompasses sensor IC design, processor architecture, FPGA implementation, software development, neural network training and evaluation, mathematical modeling, fluorescence lifetime imaging, and optical system setup. Supervisor: Prof. Edoardo Charbon and Dr. Bruschini Claudio.
Dissertation: Subcellular localization of long noncoding RNA with interpretable deep learning
This study explores the application of natural language processing techniques to RNA sequences for predicting the subcellular localization of long noncoding RNAs. The developed prediction software is deployed on a server, providing biologists with accessible and reliable tools for analysis. Supervisors: Prof. Hong-Bin Shen and Prof. Xiaoyong Pan