Wei Dong
Managing Director at IAILABS Industrial Artificial Intelligence Inc.
Ann Arbor, Michigan, United States
Summary
👤
Senior🎓
Top SchoolWei Dong is a Managing Director and CTO with a PhD in computer science from Princeton and over a decade of experience building practical, performance-focused systems from Ann Arbor. He founded and runs Ann Arbor Algorithms, offering IT consulting and algorithmic engineering services while also serving as CTO of IAILABS, bridging academic rigor with product-oriented AI applications. His work includes contributions to the well-regarded ann-benchmarks project, where he optimized approximate nearest neighbor implementations and improved data handling and installation workflows. Earlier research and internships produced systems-level results in compression, deduplication, and proxy-based mobile optimization, including FAST-published work on multi-node dedupe. He combines deep theoretical training in CS and mathematics with hands-on optimization experience, making him adept at turning research-grade algorithms into efficient, deployable solutions.
12 years of coding experience
Bachelor of Science (BSc), Computer Science, Mathematics, Bachelor of Science (BSc), Computer Science, Mathematics at Peking UniversityPhD, Computer Science, PhD, Computer Science at Princeton University
Stackoverflow
21reputation
15kreached
1answer
0questions
Github Skills (14)
nearest-neighbor-search10
algorithm10
benchmark10
benchmarking10
machine-learning-algorithms10
python10
nearest-neighbors10
numpy10
scikit-learn9
scikit9
data-analysis8
sparse-matrix6
dockers3
docker3
Programming languages (10)
Github contributions (5)
erikbern/ann-benchmarks
Jun 2015 - Jun 2016
Benchmarks of approximate nearest neighbor libraries in Python
Role in this project:
Data Scientist
Data ScientistContributions:5 commits, 4 PRs, 24 comments in 1 year
Contributions summary:Wei primarily contributed to the implementation and refinement of approximate nearest neighbor algorithms, specifically within the context of the ann-benchmarks project. They modified existing algorithms (e.g., KGraph) and added new parameters to tune their performance. They also made changes to improve the efficiency, data handling, and installation processes. The user worked on optimizing these algorithms.
nearest-neighbor-searchpythonnearest-neighborsbenchmarkdocker
aaalgo/donkey
May 2015 - Jan 2019
A content-based search engine generator.
Contributions:135 commits, 5 PRs, 103 pushes in 3 years 9 months
search-engine
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