Ronak Pradeep
Research Scientist at Databricks
Waterloo, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolRonak Pradeep is a PhD candidate in Computer Science at the University of Waterloo with a decade of experience applying machine learning to information retrieval and large language models. He has research and industry experience from Google, Apple, Mila, and internships at AI-focused companies, and currently works on AI at Yupp. Ronak is an active open-source contributor to Pyserini, where he added dense retrieval capabilities (DKRR-DPR retriever), query encoding and topic-creation tooling, and MS MARCO dataset generation scripts that support reproducible IR research. His background blends rigorous academic research with hands-on engineering across dense and sparse retrieval, and he often bridges model development with practical dataset and tooling improvements. Based in Waterloo, he combines deep IR expertise with a track record of shipping research-ready code used by the broader retrieval community.
10 years of coding experience
3 years of employment as a software developer
High School, High School at Chennai Public SchoolDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Waterloo
English, Malayalam, Tamil
Github Skills (10)
transformers10
machine-learning10
nlp10
information-retrieval10
python10
natural-language-processing10
bert9
pytorch8
data-engineering8
pandas7
Programming languages (6)
Github contributions (5)
castorini/pyserini
Jan 2021 - Dec 2021
Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.
Role in this project:
ML Engineer
ML EngineerContributions:108 reviews, 8 commits, 31 PRs in 11 months
Contributions summary:Ronak contributed significantly to the Pyserini repository, focusing on integrating and expanding dense retrieval capabilities. This involved adding a new retriever based on the DKRR-DPR model, including implementation of query encoding and topic creation scripts. The user also updated the codebase to use specific transformer versions and incorporated top-k sampling for MS MARCO V2 training set generation. Additionally, the user contributed scripts to generate MS MARCO v1 Document Jsonl Collection.
information-retrievalpython
castorini/nuggetizer
Jan 2025 - Apr 2026
Contributions:9 reviews, 5 PRs, 67 pushes in 1 year 2 months
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