Peng Shi
Applied Scientist at Amazon Web Services (AWS)
Ontario, United States
Summary
🤩
Rockstar🎓
Top SchoolPeng Shi is an Applied Scientist at Amazon Web Services with a decade of experience bridging research-grade systems and production search infrastructure. A University of Waterloo PhD in Computer Science, he has deep expertise in information retrieval and backend engineering, evidenced by substantive contributions to the well-regarded Anserini Lucene toolkit—where he refactored Freebase lookup logic, added Chinese/CJK support, and improved accent-folding analysis to make indexing and search more integrable and multilingual. At AWS he applies that research-to-production mindset to build scalable, robust retrieval and ML-driven systems. Colleagues rely on him to translate complex IR algorithms into maintainable code that interoperates with other ecosystems (e.g., Pyjnius), reflecting both rigorous academic training and practical engineering focus.
10 years of coding experience
Doctor's Degree, Computer Science, Doctor's Degree, Computer Science at University of Waterloo
Github Skills (5)
lucene10
javas10
information-retrieval10
java10
python8
Programming languages (4)
Github contributions (5)
castorini/anserini
Oct 2018 - Dec 2019
Anserini is a Lucene toolkit for reproducible information retrieval research
Role in this project:
Back-end Developer
Back-end DeveloperContributions:5 commits, 10 PRs, 4 pushes in 1 year 1 month
Contributions summary:Peng primarily focused on enhancing the functionality of the `LookupFreebase` class within the `anserini` project, specifically exposing and modifying its `lookupMid` and `search` functions. They refactored the code to return results instead of printing them, which enables integration with other tools like Pyjnius. Additionally, the user added a `FreebaseAnalyzer` for accent folding and improved code consistency across indexing and lookup operations, ultimately improving the system's data retrieval capabilities. The user also incorporated support for Chinese indexing and search, including the addition of CJKAnalyzer and necessary modifications to the indexing and search functionalities.
reproducibleretrievalluceneinformation-retrievaljava
Impavidity/Omniscient
Sep 2018 - Jun 2019
Knowledge Extraction, Graph Construction and exciting Applications
Contributions:41 commits, 34 pushes in 8 months
extractionknowledgeexcitingknowledge-graphgraph
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