Ran Liu - Machine Learning PhD Student - School of Electrical and Computer Engineering
Date: April 22nd
Time: 3:30 PM – 5:00 PM ET
Location: Coda C1103 Lindberg
Meeting Link: https://gatech.zoom.us/j/97333964943?pwd=UEJBQ2MzU2pXZk1RQmRzeGtkYXh2Zz09
Committee
Dr. Eva Dyer (Advisor), Biomedical Engineering, Georgia Institute of Technology
Dr. Anqi Wu, Computational Science and Engineering, Georgia Institute of Technology
Dr. Zsolt Kira, Interactive Computing, Georgia Institute of Technology
Dr. Vidya Muthukumar, Electrical and Computer Engineering, Industrial and Systems Engineering, Georgia Institute of Technology
Dr. Vince Calhoun, Electrical and Computer Engineering, Georgia Institute of Technology
Abstract
Deep learning (DL) methods have significantly advanced the fields of neuroscience and physiology. However, conventional DL methods that are tailored to specific populations and tasks are no longer adequate in comprehending large-scale, multimodal, and multitask physiological datasets. In this thesis, we propose methods that aim to improve DL methods from the perspective of: (i) Generalizability, enabling applications across diverse modalities, tasks, and subjects, and (ii) Explainability, enabling researchers to understand and potentially customize the learning process to suit specific distributions. These improvements are not only crucial for physiological datasets, which typically require domain knowledge to comprehend, but also improve deep learning methodologies and benefit the broader ML community.