October 3, 2024
12:15 pm
Speaker:
Qiang (Shawn) Cheng, Division of Biomedical Informatics, University of Kentucky
Where:
327 McVey Hall
(Zoom link: https://uky.zoom.us/j/82467171189)
Title:
Novel Deep Learning models and Techniques for Effective and Efficient
Handling of Multiple Data Modalities
Abstract:
Diverse areas of scientific research and everyday life, including healthcare, biomedicine, and engineering, are inundated with various data modalities, each presenting unique challenges. This talk presents several cutting-edge learning approaches designed to handle different types of data with both accuracy and computational efficiency. They include A SOTA state-space model for tabular data classification, time series long-term forecasting with linear complexity, time series classification, multi-modal data clustering with sparse tensor learning, and if time permits inferring circadian phases from multi-omics data. This talk integrates deep learning models and classical learning methods, each optimized for a particular data type, providing solutions that often achieve linear complexity and excellent scalability. It will demonstrate how these advanced techniques can significantly enhance our ability to discover patterns and extract knowledge from various complex datasets, addressing the unique challenges posed by different data types. Additionally, the talk will highlight the models’ potential impact in their respective domains.
