Anastasis develops and studies universal deep learning models capable of leveraging geometric structures in data science and time-series problems. He is now an Assistant Professor at McMaster University, Ontario, Canada.

 

Research interests

Geometric Deep Learning

  • Developed the first neural model which can proveably approximate any function while implementing exact constraint satisfaction with B. Zamanlooy, I. Dokmanic, T. Liu.
  • Developed a simple framework for building universal approximators between any differentiable manifold with L. Papon and E. Bilokopytov.
  • Introduced the universal the first known class of UAP (Universal Approximation Property)-Invariant feature maps with C. Hyndman.  
  • Identified the first known homotopic obstructions to non-Euclidean universal approximation with L. Papon. 

 

 

Foundations of Data Science

  • Developed the first probability-measure valued universal approximator and showed that it can approximate any regular conditional distribution.
  • Introduced the first regret-optimal descent algorithms via control and variational approach to meta-optimization with P. Casgrain.
  • Developed the first deep neural model capable of uniformly approximating any piecewise continuous function with finitely many pieces with B. Zamanlooy.

 

Mathematical Finance

  • Introduced the first penalty for arbitrage-free learning with C. Hyndman.
  • Introduce the fastest matrix completion algorithm for rapid low-rank + sparse decomposition of asset's covariance matrices with J. Teichmann, C. Herrera, F. Krach, and Google Research's P. Ruyssen.