Deep Learning for Personalized & Interpretable fMRI: From Individual Functional Networks to Fair Cross-Modal Alignment
Fri, Feb 27
|Virtual Event
This presentation covers the following: 1. How to discover personalized and predictive brain functional networks using a prompt-driven transformer (FunFormer); 2. How to align fMRI connectivity with phenotypic text and mitigate bias (NeuroLIP); 3. A demo showcasing the implementation of these frames
Time & Location
Feb 27, 2026, 9:30 a.m. – 10:30 a.m. PST
Virtual Event
Guests
About the event
Topic: Deep Learning for Personalized and Interpretable fMRI Analysis: From Individual Functional Networks to Fair Cross-Modal Alignment
Time: Feb 27, 2026 9:30AM PST / Feb 27, 2026 12:30PM EST / Feb 27, 2026 18:30PM CET / Feb 28, 2026 01:30AM CST (timezones)
The detailed Zoom meeting information will be provided in the email upon completion of your registration with your email address.
Speaker: Yanting Yang (PhD Student at the University of British Columbia)
Yanting Yang is a PhD student in the Department of Electrical and Computer Engineering at the University of British Columbia, advised by Dr. Xiaoxiao Li at the Trusted and Efficient AI (TEA) Lab. His research aims to improve the explainability, fairness, and efficiency of AI models, translating advanced machine learning algorithms into real-world healthcare applications. His work primarily involves developing novel deep learning architectures to analyze complex neuroimaging data, with a specific focus on functional Magnetic Resonance…