Also see my profiles on DBLP and Google Scholar.

Pre-prints

1. VIBE: Vector Index Benchmark for Embeddings, Elias Jääsaari, Ville Hyvönen, Matteo Ceccarello, Teemu Roos, Martin Aumüller, benchmark, interactive website, datasets

Peer-reviewed research papers

1. Results of the Big ANN: NeurIPS’23 competition,
   Harsha Vardhan Simhadri, Martin Aumüller, Amir Ingber, Matthijs Douze, George Williams, Magdalen Dobson Manohar, Dmitry Baranchuk, Edo Liberty, Frank Liu, Ben Landrum, Mazin Karjikar, Laxman Dhulipala, Meng Chen, Yue Chen, Rui Ma, Kai Zhang, Yuzheng Cai, Jiayang Shi, Yizhuo Chen, Weiguo Zheng, Zihao Wan, Jie Yin, Ben Huang, accepted at NeurIPS 2025, code
2. Approximate Single-Linkage Clustering Using Graph-based Indexes: MST-based Approaches and Incremental Searchers,
   Camilla Birch Okkels, Erik Thordsen, Martin Aumüller, Arthur Zimek, and Erich Schubert, accepted at SISAP 2025.
3. Overview of the SISAP 2025 Indexing Challenge,
   Eric S. Tellez, Edgar Chavez, Martin Aumüller and Vladimir Mic, accepted at SISAP 2025.
4. Differentially Private High-Dimensional Approximate Range Counting, Revisited,
   Martin Aumüller, Fabrizio Boninsegna, Francesco Silvestri, FORC’25, prototype implementation.
5. High-dimensional Density-based Clustering Using Locality-Sensitive Hashing,
   Camilla Birch Okkels, Martin Aumüller, Viktor Bello Thomsen, Arthur Zimek, EDBT’25, implementation code, benchmarking code.
6. PLAN: Variance-Aware Private Mean Estimation,
   Martin Aumüller, Christian Janos Lebeda, Boel Nelson, Rasmus Pagh, PETS’24, code.
7. Overview of the SISAP 2024 Indexing Challenge, Eric Sadit Tellez, Martin Aumüller, Vladimir Mic, SISAP 2024.
8. On the Design of Scalable Outlier Detection Methods Using Approximate Nearest Neighbor Graphs,
   Camilla Birch Okkels, Martin Aumüller, Arthur Zimek, SISAP 2024, code
9. An Empirical Evaluation of Search Strategies for Locality-Sensitive Hashing: Lookup, Voting, and Natural Classifier Search,
   Malte Helin Johnsen, Martin Aumüller, SISAP 2024.
10. Recent Approaches and Trends in Approximate Nearest Neighbor Search, M. Aumüller, M. Ceccarello, IEEE Data Engineering Bulletin, 2023.
11. Solving k-Closest Pairs in High-Dimensional Data,
    Martin Aumüller, Matteo Ceccarello, SISAP 2023. code
12. Overview of the SISAP 2023 Indexing Challenge,
    Eric S. Tellez, Martin Aumüller, Edgar Chavez, SISAP 2023. website
13. Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedback,
    Omar Shahbaz Khan, Martin Aumüller, Björn Þór Jónsson, SISAP 2023.
14. Representing Sparse Vectors with Differential Privacy, Low Error, Optimal Space, and Fast Access,
    Martin Aumüller, Christian Janos Lebeda, Rasmus Pagh, Journal of Privacy and Confidentiality 2022, full version of CCS 2021 paper below.
15. DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search,
    Matti Karppa, Martin Aumüller, Rasmus Pagh. AISTATS 2022. code, experiments
16. Implementing Distributed Similarity Joins using Locality Sensitive Hashing,
    Martin Aumüller, Matteo Ceccarello. EDBT 2022.
17. Sampling near neighbors in search for fairness,
    Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri. Communications of the ACM 2022. See TODS version below for a full technical version.
18. Sampling a Near Neighbor in High Dimensions — Who is the Fairest of Them All?,
    Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri. TODS 2022. code
19. Fair near neighbor search via sampling,
    Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri, ACM SIGMOD Record 50, 2021. Technical perspective by Qin Zhang
20. Results of the NeurIPS’21 Challenge on Billion-Scale Approximate Nearest Neighbor Search,
    Harsha Vardhan Simhadri, George Williams, Martin Aumüller, Matthijs Douze, Artem Babenko, Dmitry Baranchuk, Qi Chen, Lucas Hosseini, Ravishankar Krishnaswamy, Gopal Srinivasa, Suhas Jayaram Subramanya, Jingdong Wang. NeurIPS 2021. code
21. Differentially private sparse vectors with low error, optimal space, and fast access,
    Martin Aumüller, Christian Lebeda, Rasmus Pagh. CCS 2021, TPDP 2021. See journal version above.
22. The Role of Local Dimensionality Measures in Benchmarking Nearest Neighbor Search,
    Martin Aumüller, Matteo Ceccarello, Information Systems 2021. Available online.
23. Running experiments with confidence and sanity,
    Martin Aumüller, Matteo Ceccarello. SISAP 2020. code
24. Locally Differential Private Sketches for Jaccard Similarity Estimation,
    Martin Aumüller, Anders Bourgeat, Jana Schmurr, SISAP 2020. code
25. Fair Near Neighbor Search: Independent Range Sampling in High Dimensions,
    Martin Aumüller, Rasmus Pagh, Francesco Silvestri, PODS 2020. See TODS version above for full technical version. code.
26. ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms,
    Martin Aumüller, Erik Bernhardsson, Alexander Faithfull. Full version of the SISAP’17 paper below. Information Systems 2020. Appeared online.
27. The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search,
    Martin Aumüller, Matteo Ceccarello, SISAP 2019 (Best Paper Award). website
28. PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors,
    Martin Aumüller, Tobias Christiani, Rasmus Pagh, Michael Vesterli, ESA 2019. slides, code, additional material
29. Simple and Fast BlockQuicksort using Lomuto’s Partitioning Scheme,
    Martin Aumüller, Nikolaj Hass. ALENEX’19. code
30. Distance-sensitive Hashing,
    Martin Aumüller, Tobias Christiani, Rasmus Pagh, Francesco Silvestri. PODS’18, slides
31. Dual-Pivot Quicksort: Optimality, Analysis and Zeros of Associated Lattice Paths,
    Martin Aumüller, Martin Dietzfelbinger, Clemens Heuberger, Daniel Krenn, Helmut Prodinger. Full version of the AofA’16 paper below. Combinatorics, Probability, and Computing, 2018. Online version.
32. ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms,
    M. Aumüller, E. Bernhardsson, A. Faithfull. SISAP 2017. See full version in Information Systems 2020 above. website
33. Parameter-free Locality Sensitive Hashing for Spherical Range Reporting,
    Thomas D. Ahle, Martin Aumüller, Rasmus Pagh. SODA 2017, slides
34. Counting Zeros in Random Walks on the Integers and Analysis of Optimal Dual-Pivot Quicksort,
    Martin Aumüller, Martin Dietzfelbinger, Clemens Heuberger, Daniel Krenn, Helmut Prodinger. AofA 2016, see full journal version in CPC above.
35. How Good is Multi-Pivot Quicksort?,
    Martin Aumüller, Martin Dietzfelbinger, Pascal Klaue. ACM Transactions on Algorithms 13(1), 2016. Additional Material
36. Optimal Partitioning for Dual-Pivot Quicksort, Martin Aumüller, Martin Dietzfelbinger, ACM Transactions on Algorithms 12(2), 2015.
37. Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash,
    Martin Aumüller, Martin Dietzfelbinger, Philipp Woelfel, invited paper, Algorithmica 70(3), 2014. Additional Material
38. Optimal Partitioning for Dual Pivot Quicksort,
    Martin Aumüller, Martin Dietzfelbinger, ICALP 2013, slides.
39. Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash,
    Martin Aumüller, Martin Dietzfelbinger, Philipp Woelfel, ESA 2012, slides.
40. Experimental variations of a theoretically good retrieval data structure,
    Martin Aumüller, Martin Dietzfelbinger, Michael Rink, ESA 2009.

Other papers

1. Reproducibility protocol for ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor search algorithms, M. Aumüller, E. Bernhardsson, A. Faithfull, manuscript. Code, Artifacts
2. Reproducibility Companion Paper: Visual Sentiment Analysis for Review Images with Item-Oriented and User-Oriented CNN, Quoc-Tuan Truong, Hady W. Lauw, Martin Aumüller, Naoko Nitta, ACM MM 2020.
3. Algorithm Engineering for High-Dimensional Similarity Search Problems (Invited Talk), Martin Aumüller. SEA 2020.
4. Benchmarking Nearest Neighbor Search: Influence of Local Intrinsic Dimensionality and Result Diversity in Real-World Datasets, Martin Aumüller, Matteo Ceccarello. Workshop EDML19. Website
5. A Simple Hash Class with Strong Randomness Properties in Graphs and Hypergraphs, Martin Aumüller, Martin Dietzfelbinger, Philipp Woelfel.

PhD Thesis

1. On the Analysis of Two Fundamental Randomized Algorithms: Multi-Pivot Quicksort and Efficient Hash Functions, Martin Aumüller, TU Ilmenau, slides of defense.

Diploma Thesis

1. An Alternative Analysis of Cuckoo Hashing with a Stash and Realistic Hash Functions, Martin Aumüller, TU Ilmenau.