Thomas Ahle
Lead Of ML And Staff Research Scientist at Normal Computing
Copenhagen, Capital Region of Denmark
Summary
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Rockstar🎓
Top SchoolThomas Ahle is a lead machine learning researcher and engineer with 19 years of experience building efficient, production-ready AI systems from academia to industry. Currently Head of ML at Normal Computing after leading ML Efficiency research at Facebook, he has a track record of inventing algorithms that shrink and speed large models—ranging from embedding-table clustering and sketching-based Bayesian inference to low-memory transformers for on-device NLP. His background blends top-tier algorithmic research (SODA/FOCS publications and optimal LSH structures) with hands-on systems work, including production chatbots, high-throughput data pipelines, and a widely used open-source chess engine. A pragmatic mathematician, he often turns sophisticated analyses into simple, fast primitives (e.g., optimized hash functions and sketching kernels) that scale in real deployments. Based in Copenhagen, he pairs deep theoretical insight with a knack for shipping robust, optimized code in both research and product settings.
19 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at IT-Universitetet i KøbenhavnInternational Baccalaureate, English, Physics, Mathematics, International Baccalaureate, English, Physics, Mathematics at Ecole Européenne de BruxellesSTX, Physics, Mathematics, STX, Physics, Mathematics at Birkerød GymnasiumBachelor of Arts (BA), Computer Science, First, Bachelor of Arts (BA), Computer Science, First at University of OxfordUddannelse, Project Development, Uddannelse, Project Development at Dansk Ungdoms FællesrådMaster of Science (MS), Computer Science, Master of Science (MS), Computer Science at Københavns Universitet
English, Danish, German, Swedish, Norwegian
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Github Skills (39)
algorithm10
algorithms10
c-language10
uml10
python10
mysql10
image10
testing10
machine-learning10
data-structure10
listview10
android10
android-view10
data-structures10
hash-functions10
Programming languages (15)
Github contributions (5)
stanfordnlp/dspy
Feb 2024 - Dec 2024
DSPy: The framework for programming—not prompting—language models
Role in this project:
Back-end Developer
Back-end DeveloperContributions:22 reviews, 44 PRs, 24 pushes in 10 months
Contributions summary:Thomas primarily focused on developing and refining features within the DSPy framework, as evidenced by the creation of a new Signature class with associated tests and types. Their work also involved updating the load/dump state functionality to utilize the newly created signature, enhancing the framework's internal workings. Furthermore, the user made significant contributions to the test suite, implementing functional tests to ensure the correctness and reliability of the framework's core functionalities.
nlpbertknowledgepredictlanguage-models
rurban/smhasher
Sep 2020 - Sep 2020
Hash function quality and speed tests
Role in this project:Back-end Developer
Back-end DeveloperContributions:1 review, 8 commits, 7 PRs in 7 days
Contributions summary:Thomas primarily contributed to the implementation of hash functions within the `smhasher` repository, as indicated by the commit messages and code changes. They introduced new hash function implementations, specifically focusing on multiply-shift and polynomial hashing techniques. The contributions also involved optimization efforts to improve the performance of the hash functions, particularly handling odd byte numbers, and fixing potential memory issues, demonstrating a focus on speed and correctness.
speedhashsha256hash-functionc-plus-plus
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