معرفی
Tony Shaska is a Visiting Professor in the Department of Mathematics at the University of Michigan, specializing in Algebraic Geometry, Number Theory, and their applications to cryptography and machine learning. He holds a B.S. from the University of Michigan (1994), and M.S. and Ph.D. from the University of Florida (1998, 2001).
His research spans several interconnected fields, with primary focus on superelliptic curves, Jacobians, weighted projective spaces, and isogeny-based cryptography. He has made significant contributions to computational algebra, particularly in computing loci of genus 2 curves with (n,n)-split Jacobians. More recently, he has pioneered the application of machine learning to algebraic geometry problems, developing novel frameworks like Graded Transformers and Graded Neural Networks that integrate symbolic mathematics with geometric learning approaches.
His publication record shows remarkable productivity with numerous papers in 2024-2025 covering cutting-edge intersections of pure mathematics with AI and cryptography. His work demonstrates an evolving trajectory from classical algebraic geometry toward innovative applications in post-quantum cryptography and geometric deep learning.
Shaska has been active in organizing academic events including NATO workshops on cryptography and has filed recent patents related to graded transformer architectures and graded codes for blockchain security.
