Yiping Deng is a Tech Lead II specializing in ML data infrastructure with 10 years of experience building scalable pipelines, feature stores, and hosted tooling for production ML at HubSpot. He has driven high-throughput data ingestion (terabytes/day), introduced vector DBs for RAG use cases, and manages JupyterHub and other ML engineer-facing platforms. His background spans big-data systems, database security, embedded AR device ML, and contributions to open-source AI server projects where he added profiling tools and API improvements. Academically trained in computer science and formal methods, he completed research on Hilbert’s Tenth Problem using Isabelle, reflecting a rare blend of practical engineering and mathematical rigor. Based in Dublin and AWS certified, he keeps sharp through long-distance running and an unexpected love of baking and LaTeX.
10 years of coding experience
6 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at Jacobs University Bremen
Distributed Open Source twitter and social media message search server that anonymously collects, shares, dumps and indexes data http://api.loklak.org
Role in this project:
Back-end & Documentation Engineer
Contributions:17 commits, 27 PRs, 121 comments in 1 year 7 months
Contributions summary:Yiping primarily contributed to the documentation of the API, specifically the `/api/threaddump.txt` and `/api/asset` endpoints. They fixed errors and added details to the API documentation, focusing on clarity and accuracy. Additionally, the user added a Scala-based profiling script and integrated a Weibo helper class into the project. They also refactored code and implemented build support.
SUSI.AI server backend - the Artificial Intelligence server for personal assistants https://api.susi.ai
Role in this project:
Back-end Developer
Contributions:1 issue in 1 day
Contributions summary:Yiping primarily contributed to the server-side API documentation and functionality. Their work included fixing errors and adding documentation for the thread dump API and asset API in `api.html`. They also added a Scala-based profiling script using scalaj-http, demonstrating an understanding of performance analysis and potentially server optimization. Furthermore, they integrated a Weibo helper class.
apipythonartificialmachine-learningassistants
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