Tomasz Kalinowski
New York, New York, United States
Summary
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Rockstar🎓
Top SchoolTomasz Kalinowski is an interdisciplinary data scientist and back-end developer based in New York with a decade of experience bridging biological research and production-grade ML tooling. He brings a rare combination of PhD-level training in Biological Design and hands-on software engineering, contributing substantial fixes and feature work to high-profile RStudio projects (reticulate, tensorflow, keras3, and RStudio IDE) that improve Python interoperability, tensor indexing, and deep learning APIs for R users. Tomasz excels at making complex cross-language integrations robust—examples include conda/pip environment handling, safer Python initialization, and enabling Python-style tensor slicing from R. His work demonstrates deep attention to edge cases and developer ergonomics, quietly raising reliability for many downstream data scientists.
10 years of coding experience
Doctor of Philosophy (PhD), Biological Design, Doctor of Philosophy (PhD), Biological Design at Arizona State UniversityBachelor of Science (BS), Cellular & Molecular Biology, Bachelor of Science (BS), Cellular & Molecular Biology at State University of New York at BinghamtonHigh School, High School at Stuyvesant High School
Stackoverflow
1,450reputation
28kreached
33answers
9questions
Github Skills (24)
environmental10
python10
r10
package-development10
conda10
indexing10
keras10
manage10
deep-learning10
tensorflow10
management10
slicing10
pip10
develop9
apidoc9
Programming languages (14)
Github contributions (5)
rstudio/keras3
May 2018 - Dec 2022
R Interface to Keras
Role in this project:
Back-end Developer
Back-end DeveloperContributions:15 releases, 10 reviews, 510 commits in 4 years 8 months
Contributions summary:Tomasz's commits primarily focus on modifying and extending the R interface to Keras, a Python-based deep learning library. The contributions involve implementing new activation functions, such as Swish and GELU, and ensuring proper handling of output shapes in the `layer_lambda` function. The user is also adding core functionality to the R API and updating testing frameworks.
kerasmachine-learningtensorflow
rstudio/tensorflow
Apr 2018 - Dec 2022
TensorFlow for R
Role in this project:Back-end Developer
Back-end DeveloperContributions:10 releases, 2 reviews, 202 commits in 4 years 8 months
Contributions summary:Tomasz's primary contribution involves refactoring the `[.tensorflow.tensor` function, which is used for subsetting tensors. This refactoring enables R users to access Python-style strided steps, ellipses, subsetting with tensors, and other advanced features via the `[` operator. The changes include modifications to the parsing of slice specifications and handling of one-based versus zero-based indexing, improving the usability and feature parity of the `tensorflow` package. These modifications provide a more consistent and feature-rich tensor indexing experience for users familiar with both R and Python.
machine-learningtensorflow
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