Eran Avidan
Applied AI Researcher & Architect at Intel Ignite
Tel-Aviv District, Israel
Summary
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Rockstar🎓
Top SchoolEran Avidan is an Applied AI Researcher and Architect with a decade of experience building scalable AI systems and production-grade tooling within Intel, where he has led architecture for a 200-person AI group and driven solutions that deliver over $1B annual value. He blends hands-on research in language models and agentic AI with pragmatic engineering—optimizing deployment, designing domain-specific flows, and coaching cross-functional teams from prototype to scale. His open-source contributions include performance and I/O parallelization work on Modin to scale pandas workflows (notably parallel Parquet, HDF5, Feather and to_sql support via Ray), reflecting a focus on making complex data pipelines simple and fast. Based in Tel Aviv, he mentors deep-startup founders through Intel Ignite and holds an MS in Computer Science, pairing strong academic foundations with enterprise impact.
10 years of coding experience
13 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at The Hebrew University of JerusalemB.Sc Engineering, B.Sc Engineering at Ben-Gurion University of the Negev
Github Skills (11)
dataframes10
pandas10
ray10
python10
data-science10
dataframe10
distributed-computing10
sql9
parquet9
hdf9
feather8
Programming languages (4)
Github contributions (5)
modin-project/modin
Nov 2018 - Dec 2019
Modin: Scale your Pandas workflows by changing a single line of code
Role in this project:
Data Scientist
Data ScientistContributions:13 commits, 17 PRs, 2 pushes in 1 year 1 month
Contributions summary:Eran significantly contributed to improving the performance and functionality of Modin, a library designed to scale Pandas workflows. Their work involved parallelizing data loading operations, specifically for Parquet and HDF5 files, by leveraging Ray for distributed computing. Furthermore, the user refactored Modin's I/O module and extended the capabilities of the library by including a parallel implementation for read_feather and to_sql methods, thereby broadening its file format support and database integration capabilities.
analyticspythonline-of-codedata-sciencedataframe
eavidan/modin
Nov 2018 - Mar 2020
Modin: Speed up your Pandas workflows by changing a single line of code
Contributions:1 PR, 61 pushes, 17 branches in 1 year 4 months
pythonspeedline-of-codepython3workflows
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