Alexander Nozik
Director at Scientific Programming Centre
Moscow, Moscow, Russia
Summary
🤩
Rockstar🎓
Top SchoolAlexander Nozik is a director and scientific software engineer who transitioned from a career as a neutrino physicist—where he helped set a leading limit on the electron neutrino mass—to building and leading software efforts for scientific computing. He founded and maintains several open-source Kotlin libraries (DataForge, KMath, Plotly-kt, VisionForge, Control-kt), contributed key testing and integration work to the widely used Kotlin Jupyter kernel, and served as a Kotlin GDE and leader of the Russian Kotlin User Group. As Director of the Scientific Programming Centre and a department head and associate professor at MIPT, he blends research, teaching, and product leadership across physics, data analysis, and API design. His background in experiment design and slow-control systems informs pragmatic, robust tooling for researchers, while his library design reflects an emphasis on composable APIs and test-driven reliability.
11 years of coding experience
3 years of employment as a software developer
Lyceum Second SchoolMaster’s Degree, Physics, Master’s Degree, Physics at Moscow Institute of Physics and Technology (State University) (MIPT)
English, Russian
Stackoverflow
448reputation
175kreached
12answers
6questions
Github Skills (15)
kotlin10
junit10
lib10
integrate10
integrations10
testing10
dependency-management9
gradlew8
gradle8
jupyter8
windows6
databinding6
javafx6
python6
groovy6
Programming languages (16)
Github contributions (5)
Kotlin/kotlin-jupyter
Sep 2020 - Apr 2021
Kotlin kernel for Jupyter/IPython
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:15 reviews, 15 commits, 6 PRs in 7 months
Contributions summary:Alexander contributed significantly to the Kotlin Jupyter kernel project, primarily focusing on testing and library integration. Their work included writing and maintaining comprehensive tests for the kernel's core functionalities, such as code execution, completion suggestions, error handling, and library loading. They also implemented features related to library resolution, including resource builders, and integration with dependency management systems. The user's contributions indicate a strong focus on ensuring the reliability and extensibility of the kernel.
ipythonjupyterkernelkotlin
SciProgCentre/dataforge-core
Sep 2018 - Jan 2023
A clean multi-platform re-implementation of dataforge concepts
Contributions:11 releases, 5 reviews, 498 commits in 4 years 5 months
data-forgedataforgekotlinjetbrains-research
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