Summary
Rafael Stekolshchik is a veteran Machine Learning Algorithms Engineer with a Ph.D. in Mathematics and over 35 years of experience designing and implementing algorithms and software systems across communications, computer vision, 3D geometry and network management. He combines deep theoretical expertise—author of 40 articles and a book—with hands-on development in C++, Python, Matlab and R, recently focusing on 3D geometric ML and deep reinforcement learning using PyTorch. His career spans embedded and distributed systems (NMS/EMS, SDH, MPLS, SNMP), real-time signal and image processing, and advanced algorithmic work such as SLAM, Kalman/Particle filters, and skeletal animation for 3D prototyping. Notably, he has repeatedly bridged academic research and product engineering, turning mathematical methods (PCA/SVD, Kolmogorov–Smirnov tests, MST clustering) into production-ready code and protocols. Based in Petah Tikva, Israel, Rafael brings rare breadth from low-level IPC and protocol gateways to modern ML pipelines, with a consistent record of shipping complex, performance-sensitive systems.
9 years of coding experience
40 years of employment as a software developer
Nanodegree Program, Deep Reinforcement Learning, Nanodegree Program, Deep Reinforcement Learning at Udacity
Ph.D., Mathematics, Ph.D., Mathematics at Kiev University
M.Sc., Mathematics, M.Sc., Mathematics at Voronež University
Math School 34, Chisinau (Kishinev)
Hebrew, English, Russian