🏠 Amsterdam NL 🇳🇱
Software development, machine learning, data, scientific research
Docteur ès Sciences EPFL in physics Lausanne CH 🇨🇭
Specialized on: numerical electronic structure, quantum simulations.
Thesis: Electronic Transport in 2D Materials with Strong Spin-orbit Coupling (03/2017);
supervisor: Oleg Yazyev
Master of Science Chalmers in applied physics Göteborg SE 🇸🇪
Thesis: Spintromechanical Aspects of Charge Transport in Nanostructures (06/2012);
supervisor: Robert Shekhter
B.Sc. in Physics cum laude V.N. Karazin’s State University Kharkiv UA 🇺🇦
Coursera: Machine Learning from Stanford University
Engineering scientific software @ QuTech TU Delft 🇳🇱
I led development of an open-source ML research software in python for material properties miniff: formulated tech requirements and designed the software; organized a team of 2-4 people with different backgrounds towards Agile workflow and priority goals; optimized and scaled the code; prepared, released and integrated the package for running in distributed HPC environment; attracted users and coordinated various parties (scientists, HPC platform, industry users).
Research and engineering @ Caltech US 🇺🇸
Performed R&D in numerical computer simulations of properties of novel materials. As a part of a bigger team (10-20 team members) implemented new functionality in the open-source python software project pyscf; contributed to strategic decisions and assigning priorities; refactored, prepared unit tests and documentation.
Pursued PhD @ EPFL CH 🇨🇭
Contributed to scientific research @ Seoul National University, KR 🇰🇷
Performed scientific research @ Chalmers, SE 🇸🇪
More on github/pulkin
A machine learning project in python to simulate molecular dynamics with classical force fields. Combines the power of cython, numpy and torch to deliver maximal performance in a high-quality python code. Demonstrates my passion for using cython when it comes to solving bottlenecks.
A large collaboration across universities and public companies towards high-performance quantum chemistry in python. Implemented with numpy and pure-C. Proud of having worked with these people.
My experiment in teleporting python runtimes from within the stack. Uses cPython bytecode (and relies on dill for object serialization). Integrates with AWS EC2. A sole project and idea I am very proud of.
A simple computational package for basis transformations. Implemented with python, numpy and cython. One of my cleanest code where I discovered the power of python build.
A micropython (python dialect) port to a popular cellular network module A9G written in C. Enjoyed supporting others in their hardware projects while digging into embedded firmware design.
💰 Personal Swiss NSF grant to study abroad
80k CHF, 18 months, postdoctoral level (Early Postdoc.Mobility) grant P2ELP2_175281
💰 Personal computing time at national supercomputing facilities (SURF NL) Approximate equivalent of 26k EUR, 24 months project 45873
🥇 Olympiad in Physics for University Students (national in Ukraine) – first prize
🏅 Youth Physicists Tournament (national in Ukraine, team) – multiple prizes
🥇 Open Olympiad in Applied Physics (MIPT Moscow) – first prize
💰 Kharkiv City Mayor and Kharkiv State Governor scholarships for gifted youth
🥇 Dozens of prizes in physics and informatics (olympiads, student projects; top-10 and top-1 in national competitions)
💰 Multiple scholarships
Software development in 🐍 Python (7 years): scientific stack: numpy, torch, matplotlib; notebooks; HPC and parallel/distributed/concurrent computing (MPI, OpenMP, multiprocessing, async); performance-driven development with C and cython; styling, testing, documenting, packaging; other: FastAPI, django, OpenCV, OpenCL, bytecode.
C/C++: HPC and parallel environments (MPI, OpenMP); Lapack; embedded platforms; interfacing other languages; decompiling and reverse-engineering.
Infrastructure: git, CI/CD (Travis, Gitlab-CI, Azure pipelines), docker, HPC, AWS (EC2, S3).
IDEs: Pycharm, vim.
Machine learning: supervised learning (DNN, linear fits, logistic fits, SVM); unsupervised learning (PCA/SVD, K-means, anomaly detection); dataset generation, feature extraction, adversarial models.
Soft skills: critical analysis, problem solving, communicating (organizing discussions, presenting, paper/grant/documentation writing), full-cycle project management (idea - funding - implementation - reporting), supervision.
English (prof), Ukrainian (mother), Russian, French (basic), Dutch (basic).
Sports, ✈ travels, cross-stitching, soldering, 🔒 lock picking, 🕹️ board and video games, open-source projects.