Machine Learning - A Path Towards Reproducible, Scalable, and Trustworthy Science

With speaker Trilce Estrada, PhD
Professor of Computer Science at the University of New Mexico

Abstract:
Over the past years, the use of Machine Learning has become ubiquitous in most disciplines, and data-driven high-throughput applications are not the exception. Machine Learning increasingly plays a central role across the whole workflow pipeline, from workload forecasting, self-managed resource allocation, data representation, and on-the-fly analysis. As we consider a pathway towards reproducible, scalable, and trustworthy science, we must pay special attention to the impact of a "black box" culture, and how current practices in ML can advance or hinder this effort. In this talk, Estrada presents several case studies on data-driven science and highlight issues in scalability, reproducibility, and trust.

Bio:
Trilce Estrada is an associate professor in the Department of Computer Science at the University of New Mexico and the director of the Data Science Laboratory. Her research interests span the intersection of Machine Learning, High-Performance Computing, Big Data, and their applications to interdisciplinary problems in science and medicine. Estrada received an NSF CAREER award for her work on in-situ analysis and distributed machine learning. In 2019 she was named the ACM SIGHPC Emerging Woman Leader in Technical Computing. She is the Big Data aspect lead for the NSF-TCPP national curriculum development initiative that seeks to include and promote the adoption of parallel processing in the undergraduate curriculum. She is PI faculty advisor of the New México’s Critical Technology Studies Program, a multi-institutional consortium for developing human expertise for the intelligence community. Estrada obtained a PhD in Computer Science from the University of Delaware, an M.S in Computer Science from INAOE, Mexico, and a B.S in Informatics from Universidad de Guadalajara, Mexico.

Trilce Estrada wins International Emerging Woman Leader in Technical Computing Award