Anastasios (Tasos) Sidiropoulos

Anastasios Sidiropoulos is a Co-Founder and the Chief Technology Officer of Grady, where he leads the design and engineering of the company's AI grading and feedback technology. He guides the technical direction of a platform built to support and extend the work of instructors and their teaching teams rather than replace them.

Sidiropoulos is an Associate Professor of Computer Science at the University of Illinois at Chicago, where his research spans theoretical computer science, algorithms, and the mathematical foundations of data science and machine learning. His work includes the design and analysis of algorithms for high-dimensional and geometric data, metric embeddings, graph algorithms, and learning-theoretic questions in machine learning.

This grounding is directly relevant to building AI responsibly. The algorithms, high-dimensional geometry and learning theory at the center of his research are the same mathematical foundations on which modern machine learning is built. That depth shapes how Grady's technology is engineered: with a clear understanding of what these systems can and cannot do reliably, and of the care required to apply them to something as consequential as a student's grade. He has served as a Principal Investigator in several NSF-funded research programs, and his research has appeared at leading venues in both theory and machine learning, including the Symposium on the Theory of Computing, Foundations of Computer Science, Symposium on Computational Geometry, NeurIPS, and ICLR.

Before joining the University of Illinois Chicago, Sidiropoulos was an Assistant Professor at The Ohio State University and a Research Assistant Professor at the Toyota Technological Institute at Chicago, following a postdoctoral fellowship at the University of Toronto. He earned his Ph.D. and Master of Science in computer science from MIT, where his doctoral work was advised by Piotr Indyk, the Thomas D. and Virginia W. Cabot Professor at MIT and a member of the National Academy of Sciences, whose foundational work on high-dimensional similarity search underpins techniques now widely used across machine learning and AI.