Location: Hodson 311
Title: Towards Transparency, Fairness, and Efficiency in Machine Learning
Abstract: In this talk, we will address several areas of recent work centered around the themes of transparency and fairness in machine learning as well as practical efficiency for methods with high dimensional data. We will discuss recent results involving linear algebraic tools for learning, such as methods in non-negative matrix factorization and CUR decompositions. We will showcase our derived theoretical guarantees as well as practical applications of those approaches. These methods allow for natural transparency and human interpretability while still offering strong performance. Then, we will discuss new directions in debiasing of word embeddings for natural language processing as well as an example in large-scale optimization that allows for population subgroups to have better predictors than when treated within the population as a whole. We will conclude with work on compression and reconstruction of large-scale tensorial data from practical measurement schemes. Throughout the talk, we will include example applications from collaborations with community partners. This talk will also include discussion of recent leadership experience, initiatives, and related work.
Biography: Deanna Needell earned her BA in Computer Science and Mathematics from the University of Nevada, Reno and PhD from UC Davis in Mathematics before working as a postdoctoral fellow at Stanford University. She is currently a full professor of mathematics at UCLA, the Dunn Family Endowed Chair in Data Theory, and the Executive Director for UCLA’s Institute for Digital Research and Education. Her work is in applied mathematics and data science, with a focus on large-scale optimization, linear algebraic machine learning, and fairness in machine learning. Much of her work involves community partnerships with data-driven needs such as the California Innocence Project, Homeboy Industries, and lymedisease.org. She has earned many awards including the Alfred P. Sloan fellowship, an NSF CAREER award, the IMA prize in Applied Mathematics, and is a 2022 American Mathematical Society (AMS) Fellow. She has been a research professor fellow at several top research institutes including the Mathematical Sciences Research Institute, SLMath, and Simons Institute in Berkeley. She also has served and serves as associate editor for IEEE Signal Processing Letters, Linear Algebra and its Applications, the SIAM Journal on Imaging Sciences, and Transactions in Mathematics and its Applications as well as on the organizing committee for SIAM sessions and the Association for Women in Mathematics.