Data Analysis and Manipulation through a Constrained Optimization Lens - Alexandra Meliou (University of Massachusetts Amherst, USA)
Title: Data Analysis and Manipulation through a Constrained Optimization Lens
Speaker: Alexandra Meliou, (Professor at the College of Information and Computer Sciences at the University of Massachusetts Amherst, USA, and a visiting researcher at the Archimedes Research Unit, Athena Research Center, Greece)
Abstract: Constrained optimization problems are at the core of prescriptive analytics: deriving optimal decisions given a set of constraints and objectives. Traditional solutions to such 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. These problems are exacerbated by the unprecedented scale 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. I will discuss how deep integration between the DBMS, predictive models, and optimization software creates opportunities for rich prescriptive-query functionality with good scalability and performance. Summarizing some of our main results and ongoing work in this area, I will highlight challenges related to usability, scalability, data uncertainty, and dynamic environments. Beyond classic prescriptive analytics applications, I will invite us to revisit a broad class of traditional data manipulation problems, such as outlier detection, data generation, and training set selection, through a constrained optimization lens, arguing for a vision of expanded constrained optimization capabilities within data management systems, to provide unified abstractions and operators to model and solve these problems.
Short Bio: Alexandra Meliou is a Professor at the College of Information and Computer Sciences at the University of Massachusetts Amherst, and a visiting researcher at the Archimedes Institure, Athena RC. Her research focuses on problems related to the use and understandability of data and data-driven systems, with contributions in the areas of causality, explanations, data quality, fairness, and prescriptive analytics. Prior to joining UMass Amherst, she was a postdoctoral researcher at the University of Washington. She received her PhD and MS degrees from the University of California Berkeley. She is currently the vice-chair of SIGMOD, member of the VLDB Endowment Board of Trustees, member of the PVLDB Advisory Board, co-chair of the Joint DB Task Force on Reviewing Processes, chair of DBCares, and served as a PC co-chair for SIGMOD 2024. 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.
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