AIISC Seminar (Invited Talk)

Zoom: https://us06web.zoom.us/j/8440139296?pwd=b09lRCtJR0FCTWcyeGtCVVlUMDNKQT…

The unprecedented processing demand, posed by the explosion of big data, challenges researchers to design efficient and adaptive machine learning algorithms that do not require persistent retraining and data annotation and avoid learning redundant information. This capability is essential in adopting AI in healthcare and biomedical applications. Inspired by learning techniques of intelligent biological agents, identifying transferable knowledge across learning problems has been a significant research focus to improve machine learning algorithms. Towards this mission, this talk covers how the challenges of knowledge transfer can be addressed through embedding spaces that capture and store hierarchical knowledge.We first focus on the problem of cross-domain knowledge transfer and show how this idea can address the challenges of learning with unannotated data, including, in medical image segmentation.We then investigate the problem of cross-task knowledge transfer in sequential learning settings. Here, the goal is to identify relations and similarities of multiple machine learning tasks to improve performance across tasks that are encountered temporally one at a time. We show how the core idea can help to address catastrophic forgetting and learning from distributed private data.Finally, we focus on potential and new research directions to expand past results.

About the Speaker: Mohammad Rostami is a faculty member at the USC Department of Computer Science with a joint appointment at the Department of Electrical and Computer Engineering. He is alsoa research leadat the USC Information Sciences Institute. Before USC, he received his PhD from the University of Pennsylvania, where he was awarded the Joseph D'16 and Rosaline Wolf Best PhD Dissertation Award. His research focus is on machine learning in data scarce regimes, focusing on practical applications in healthcare.His research has been recognized by several awards, including, IJCAI Distinguished Student Paper Award, AAAI New Faculty Highlights, and Keston Research Award. More at: https://viterbi.usc.edu/directory/faculty/Rostami/Mohammad