Ramil Nugmanov
Senior Principal Scientist at Johnson & Johnson Innovative Medicine
Beerse, Antwerp, Belgium
Summary
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Senior🎓
Top SchoolRamil Nugmanov is a Senior Principal Scientist based in Beerse, Antwerp with 11 years of experience at the intersection of cheminformatics, Python engineering, and predictive modeling of chemical reactivity and bioactivity. He progressed from academic research and teaching in organic chemistry and chemoinformatics to lead applied modeling efforts at Johnson & Johnson Innovative Medicine, translating molecular insight into practical drug discovery tools. An open-software enthusiast, Ramil contributes to core ML infrastructure—improving PyTorch data loading and sampling utilities to support more efficient batched workflows. He holds a PhD in Organic Chemistry and a master’s in Chemoinformatics and molecular modeling, blending deep domain knowledge with production-grade software development. Notably, he pairs hands-on Python data-mining skills with a scientist’s rigor, enabling reproducible, scalable modeling pipelines for cheminformatics problems.
11 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy - PhD, Organic Chemistry, Doctor of Philosophy - PhD, Organic Chemistry at A.E. Arbuzov Institute of Organic and Physical Chemistry of Russian Academy of SciencesMaster's degree, Chemoinformatics and molecular modeling, Master's degree, Chemoinformatics and molecular modeling at Kazan State University
Русский, English
Github Skills (12)
data-loading10
machine-learning10
pytorch10
data-api10
python10
datasets10
data-set10
autograd9
deep-learning9
tensor9
neural-network8
numpy7
Programming languages (6)
Github contributions (5)
pytorch/pytorch
Oct 2022 - Oct 2023
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
ML Engineer
ML EngineerContributions:18 reviews, 7 PRs, 24 comments in 1 year
Contributions summary:Ramil's contributions primarily revolve around enhancements and modifications to the PyTorch data loading and sampling utilities. They have added features and fixed bugs related to custom samplers, batch samplers, and the `StackDataset`, improving how datasets are handled and optimized within the PyTorch framework. Their work includes additions of APIs, adjustments of type hints, and implementations supporting batched sampling, improving data loading and usability for machine learning tasks.
pythongpu-accelerationdeep-learninggpunumpy
stsouko/CGRtools
Jul 2016 - Sep 2023
CGRs, molecules and reactions manipulation
Contributions:1 release, 14 reviews, 177 PRs in 7 years 2 months
manipulationmoleculesreactionscgrchemoinformatics
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