Archimedes Talk by Alexandra Meliou on "Data Analysis and Manipulation through a Constrained Optimization Lens"

On Tuesday 4 November, 2025, from 1:00 pm to 2:00 pm, at the Archimedes Amphitheatre (1 Artemidos Street, 15125, Marousi, Archimedes, Athena Research Center, Greece), Professor 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, will deliver an Archimedes talk on "Data Analysis and Manipulation through a Constrained Optimization Lens."

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.


Biography:

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 Institute, 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.