Yadi Shahryary - Data Scientist

Yadi Shahryary

Data Scientist

Munich, Bavaria, Germany

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Summary

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Senior

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Top School

Yadi Shahryary is a data scientist with 10 years’ experience building AI systems that accelerate scientific discovery, currently applying protein language models, AlphaFold2, and generative models at KWS Group to enable structure-aware functional annotation and novel protein design. With a PhD in Bioinformatics from TUM and a background in big data systems and computer science, she combines rigorous research with production-minded engineering to turn complex biological text and sequence data into structured, scalable resources. Her work spans LLM-driven text mining, deep learning for predictive modeling, and bespoke pipelines that integrate ESM2/ProTrans, ColabFold, and ProGen2-style models. Comfortable across Python and R, she has a track record in network analysis and applied NLP from earlier roles, and she uniquely pairs computational biology expertise with hands-on model fine-tuning to move from data curation to generative design.

10 years of coding experience

2 years of employment as a software developer

Doctor of Philosophy - PhD, Bioinformatics, Doctor of Philosophy - PhD, Bioinformatics at Technical University of Munich

Master's degree with honor, Big Data Systems, Master's degree with honor, Big Data Systems at Higher School of Economics

Bachelor of Applied Science (B.A.Sc.), Computer Science, Bachelor of Applied Science (B.A.Sc.), Computer Science at Moscow Technical University of Communication and Informatics

Russian, English, Persian

Stackoverflow

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Github Skills (44)

orange10

r-package9

spectra9

genetics9

classification9

rates9

regression9

random-forest9

clustering9

shiny-apps9

computational-biology9

visual-programming8

data-mining8

visualization8

nodejs8

Programming languages (3)

C++RPython

Github contributions (5)

jlab-code/MethylStar

Oct 2018 - Sep 2021

METHYLSTAR Project

Contributions:117 commits, 3 PRs, 103 pushes in 3 years

javascriptnodejs

jlab-code/AlphaBeta

Apr 2019 - Apr 2022

Computational inference of epimutation rates and spectra from high-throughput DNA methylation data in plants

Contributions:1 release, 66 commits, 6 PRs in 3 years

ratesgeneticsdna-methylation-datadnadna-methylation

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