2022 Women and Mathematics

Jessica Gronsbell, University of Toronto
11:45 am - 12:05 pm 
Title: Statistical learning with high volume, high noise health data 
Abstract: In this talk, I will give a brief overview of my background and my path to statistics and machine learning...

Alane Lima, Federal University of Parana
4:30 pm -4:50 pm
Title: Statistical Learning Theory and Sampling Algorithms for Complex Networks
Abstract: When dealing with problems in large scale graphs, using an exact algorithm may be inefficient in...

Abstract: Stochastic Gradient Descent (SGD) is the de facto optimization algorithm for training neural networks in modern machine learning, thanks to its unique scalability to problem sizes where the data points, the number of data points, and the...

Mirjeta Pasha, Arizona State University
7:00 pm - 7:20 pm
Title: On Deterministic and Statistical Methods for Large-scale Dynamic Inverse Problems
Abstract: Inverse problems are ubiquitous in many fields of science such as engineering, biology...

Abstract:  With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions. Explanations for black box models are not reliable, and can be misleading. If we use...