Martin Aumüller - Associate Professor

Martin Aumüller

Associate Professor

Copenhagen, Capital Region of Denmark

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Summary

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Martin Aumüller is an associate professor at ITU Copenhagen with 12 years of experience bridging theoretical computer science and practical systems, specializing in randomized algorithms and approximate nearest neighbor methods. He has a strong academic trajectory from TU Ilmenau to a PhD-equivalent Dr. rer. nat., combined with hands-on software development dating back to work on Opera Mini/Mobile. His open-source contributions include meaningful backend improvements to widely used ANN projects such as Spotify's Annoy and the ann-benchmarks suite, where he added Hamming-distance support and integrated additional libraries to broaden benchmarking capabilities. Comfortable moving between low-level C++ optimizations and Python benchmarking frameworks, he focuses on improving core algorithmic performance and developer-facing tooling. Based in Copenhagen, he pairs rigorous research with practical engineering, often surfacing non-obvious efficiencies in distance calculations and batch query processing.

12 years of coding experience

9 years of employment as a software developer

Dr. rer. nat., Informatik, Dr. rer. nat., Informatik at Technische Universität Ilmenau

German, English, Russian, Swedish, Japanese

Github Skills (17)

algorithm10

algorithms10

python10

nearest-neighbors10

approximate-nearest-neighbor-search10

data-structure10

cplus10

nearest-neighbor-search10

data-structures10

cpp10

faiss10

build-system9

cmake9

cprogramming-language8

c-language8

Programming languages (9)

JuliaC++CSSCRustHTMLJupyter NotebookRuby

Github contributions (5)

erikbern/ann-benchmarks

Aug 2016 - Sep 2022

Benchmarks of approximate nearest neighbor libraries in Python

Role in this project:

Backend Developer

Contributions:20 reviews, 302 commits, 77 PRs in 6 years 1 month

Contributions summary:Martin primarily contributed to the development of the `ann-benchmarks` repository by integrating and adapting external libraries for approximate nearest neighbor search. Their work included adding and configuring dependencies like `ann-filters`, implementing wrappers for algorithms such as those from `FAISS` and `PUFFINN`, and establishing batch query capabilities. These modifications extended the framework's functionality, allowing it to evaluate additional algorithms and improve the benchmarking process.

pythonnearest-neighbornearestdockerbenchmark

spotify/annoy

Oct 2017 - Mar 2018

Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

Role in this project:

Back-end Developer

Contributions:6 commits, 2 PRs, 8 comments in 5 months

Contributions summary:Martin contributed significantly to the `annoy` repository, focusing on the implementation of Hamming space and Hamming distance support. They introduced new structures and functions related to Hamming distance calculations and optimized existing code within the `annoylib.h` file. Additionally, they addressed formatting issues and compiler-related problems. Their work involved modifying core distance calculation methods and node structures, indicating a focus on improving the core functionality of the approximate nearest neighbors search algorithm.

memorymemory-usagepythonapproximate-nearest-neighbor-searchkd-tree

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