Oleg Kachan

Graduated from: Higher School of Economics, MS Applied Mathematics and Informatics
Research interests: Topological Data Analysis, Geometric Methods in Machine Learning, fMRI Data Analysis
Thesis title: Topological and Geometrical Approach to fMRI Data Analysis

Academic mobility:

• Visiting Graduate Researcher, Institute for Pure and Applied Mathematics, UCLA, USA (March-June 2019).
  Geometry and Learning from Data in 3D and Beyond [link]

Additional education:

2019

• Geometric Data Analysis Workshop (University of Chicago, USA)
• Topological Methods in Machine Learning and Artificial Intelligence Summer School (Prof. Gunnar Carlsson, CofC, USA)

2018

• Topology and Neuroscience Workshop (EPFL, CH)
• Topological Data Analysis and Persistent Homology Summer School (CIRM, IT)
• Optimal Transport Summer School (Lake Como School of Advanced Studies, IT)

Teaching:

2018, 2019, 2020

• TA, Geometric Methods in Machine Learning (Skoltech, RU)

Publications:

• Kachan O. “Persistent Homology-driven Projection Pursuit”, DiffCVML 2020 (accepted)
• Bernstein A., Burnaev E., Sharaev M., Kondrateva E., Kachan O. “Topological Data Analysis in Computer Vision”, ICMV 2019
• Bernstein, A. V., Burnaev, E., Kachan, O. “Reinforcement learning for computer vision and robot navigation,” [International Conference on Machine Learning and Data Mining in Pattern Recognition], 258–272, Springer (2018).
• Kachan, O., Yanovich, Y., Abramov, E. Vector Fields Alignment on Manifolds via Contraction Mappings, Uchenye Zapiski Kazanskogo Universiteta: Seriya Fiziko-Matematicheskie Nauki, 160(2), 300-308. (2018)