Speaker: Alexandra Meliou
Location: Soda 380
Date: November 8, 2023
Time: 11AM – 12PM PST
Title: “Systems for Human Data Interaction”
Abstract: Decision makers in a broad range of domains, such as finance, transportation, manufacturing, and healthcare, often need to derive optimal
decisions given a set of constraints and objectives. Traditional solutions to such constrained optimization problems are typically application-specific, complex, and do not generalize. Further, the usual workflow requires slow, cumbersome, and error-prone data movement between a database and predictive-modeling and optimization packages. All of these problems are exacerbated by the unprecedented size of modern data-intensive optimization problems. The emerging research area of in-database prescriptive analytics aims to provide seamless domain-independent, declarative, and scalable approaches powered by the system where the data typically resides: the database. In this talk, I will give an overview of our results in designing declarative support for constrained optimization problems, and algorithms for scaling the evaluation of such queries to large data. I will also discuss some of our ongoing work and strategies for addressing key challenges.
Alexandra Meliou is an Associate Professor and Associate Chair of Faculty Development in the College of Information and Computer Sciences, at the University of Massachusetts Amherst. Prior to joining UMass, she was a Postdoctoral Research Associate at the University of Washington. Alexandra received her PhD degree from the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley. She has received recognitions for research, teaching, and service, including a CACM Research Highlight, an ACM SIGMOD Research Highlight Award, an ACM SIGSOFT Distinguished Paper Award, an NSF CAREER Award, a Google Faculty Research Award, multiple Distinguished Reviewer Awards, and a Lilly Fellowship for Teaching Excellence. Her research focuses on data provenance, causality, explanations, data quality, and algorithmic fairness.