SFU MOCAD Seminar: Deanna Needell

In this talk, we will discuss several areas of recent work centered around the themes of fairness and foundations in machine learning as well as highlight the challenges in this area. We will discuss recent results involving linear algebraic tools for learning, such as methods in non-negative matrix factorization that include tailored approaches for fairness. Then, we will discuss new foundational results that theoretically justify phenomena like benign overfitting in neural networks. Lastly, we will mention some recent results on observational multiplicity, and how those can be utilized to improve equity. Throughout the talk, we will include example applications from collaborations with community partners, using machine learning to help organizations with fairness and justice goals. This talk includes work joint with Erin George, Kedar Karhadkar, Lara Kassab, and Guido Montufar.