Nandan Thakur
Waterloo, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolNandan Thakur is a Ph.D. candidate at the University of Waterloo specializing in information retrieval and NLP, with eight years of industry and research experience spanning academia and tech labs. He focuses on heterogeneous benchmarking and robust, efficient neural retrieval methods for low-resource and specialized domains, and makes his work accessible by releasing easy-to-use code. Nandan contributed substantially to BEIR, a widely adopted open-source IR benchmark (NeurIPS 2021) with strong GitHub traction, and has implemented retrieval pipelines using Elasticsearch, DPR, DPR-style binary retrievers, and cross-encoder rerankers. His internships at Google Research, Databricks/MosaicML, and collaborations with Vectara and Huawei reflect a blend of production-minded engineering and rigorous evaluation. Notably, he brings cross-disciplinary experience from computational biology to large-scale enterprise systems, enabling practical solutions that bridge research and deployable tooling.
8 years of coding experience
4 years of employment as a software developer
BITS Pilani, Birla Institute of Technology and ScienceHigh School, High School at Modern School, Barakhamba RoadDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Waterloo
English, Hindi, Bengali, German
Github Skills (13)
elasticsearch10
elasticsearchquery10
information-retrieval10
aws-elasticsearch10
sentence-transformers10
python10
elasticsearch-api10
amazon-elasticsearch10
nlp8
machine-learning8
pytorch8
natural-language-processing8
data-engineering7
Programming languages (7)
Github contributions (5)
beir-cellar/beir
Jan 2021 - Jan 2023
A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:8 releases, 6 reviews, 344 commits in 2 years
Contributions summary:Nandan contributed primarily to the development of a retrieval system, including implementing search functionality using Elasticsearch and incorporating various models like DPR and a custom model for generating questions and improving retrieval performance. They also implemented a Binary Passage Retriever and applied a cross-encoder model for document reranking. Their work involves building the retrieval components and improving performance.
information-retrievalnlpbertbenchmarksentence-transformers
thakur-nandan/income
May 2022 - Jan 2023
INCOME: An Easy Repository for Training and Evaluation of Index Compression Methods in Dense Retrieval. Includes BPR and JPQ.
Contributions:43 commits, 6 PRs, 23 pushes in 8 months
compression
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.