Haitian Scientific Society Seminar Series

Saturday February 24, 2024


Zoom-Link: https://umassboston.zoom.us/j/94777986513




Unveiling the power of metric learning: from diverse domains to enhanced optimization


Maryam Bagherian

University of Massachusetts Boston



AbstractMetric learning serves as an approach for uncovering hidden structures within high-dimensional spaces. Through the acquisition of a suitable distance metric, algorithms reliant on distance measurements can more effectively capture the inherent structure of data points, resulting in enhanced performance. In contrast to single metric learning methods, the effectiveness of multi-metric and geometric metric learning becomes evident in handling intricate data distributions and diverse data characteristics. These alternative approaches offer heightened flexibility and interpretability, making them especially valuable for representation learning in intricate non-linear multi-modal datasets. In this context, I provide a concise introduction to the concepts of distance metric learning and introduce methods for extending its applicability to high-dimensional spaces, graphs and manifolds. 



Bio: Maryam Bagherian obtained her PhD in Mathematics from the University of South Florida, Tampa, in 2019. She held postdoctoral positions at the University of Michigan-Ann Arbor, Michigan Institute for Data Science, and Yale University before joining UMass Boston as an assistant professor in Fall 2023. Her research interests revolve around optimization in high-dimensional spaces, with applications in physics and biology.