Also see my profiles on DBLP and Google Scholar.

Peer-reviewed research papers

  1. Mathematical Foundations of Poisoning Attacks on Linear Regression over Cumulative Distribution Functions, Atsuki Sato, Martin Aumüller, and Yusuke Matsui, Accepted at SIGMOD 2026, 2026.
  2. High-dimensional density-based clustering using locality-sensitive hashing, Camilla Birch Okkels, Martin Aumüller, Viktor Bello Thomsen, and Arthur Zimek, Proceedings 28th International Conference on Extending Database Technology, EDBT 2025, Barcelona, Spain, March 25-28, 2025, 2025, implementation code, benchmarking code.
  3. Differentially Private High-Dimensional Approximate Range Counting, Revisited, Martin Aumüller, Fabrizio Boninsegna, and Francesco Silvestri, 6th Symposium on Foundations of Responsible Computing, FORC 2025, Stanford University, CA, USA, June 4-6, 2025, 2025, prototype implementation.
  4. 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, Similarity Search and Applications - 18th International Conference, SISAP 2025, Reykjavik, Iceland, October 1-3, 2025, Proceedings, 2025.
  5. Overview of the SISAP 2025 Indexing Challenge, Eric Sadit Tellez, Edgar Chávez, Martin Aumüller, and Vladimir Mic, Similarity Search and Applications - 18th International Conference, SISAP 2025, Reykjavik, Iceland, October 1-3, 2025, Proceedings, 2025.
  6. 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, Benjamin 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, and Ben Huang, NeurIPS 2025, 2025, code.
  7. An Empirical Evaluation of Search Strategies for Locality-Sensitive Hashing: Lookup, Voting, and Natural Classifier Search, Malte Helin Johnsen, and Martin Aumüller, Similarity Search and Applications - 17th International Conference, SISAP 2024, Providence, RI, USA, November 4-6, 2024, Proceedings, 2024.
  8. On the Design of Scalable Outlier Detection Methods Using Approximate Nearest Neighbor Graphs, Camilla Birch Okkels, Martin Aumüller, and Arthur Zimek, Similarity Search and Applications - 17th International Conference, SISAP 2024, Providence, RI, USA, November 4-6, 2024, Proceedings, 2024, code.
  9. Overview of the SISAP 2024 Indexing Challenge, Eric Sadit Tellez, Martin Aumüller, and Vladimir Mic, Similarity Search and Applications - 17th International Conference, SISAP 2024, Providence, RI, USA, November 4-6, 2024, Proceedings, 2024.
  10. PLAN: Variance-Aware Private Mean Estimation, Martin Aumüller, Christian Janos Lebeda, Boel Nelson, and Rasmus Pagh, Proc. Priv. Enhancing Technol., 2024.
  11. Solving k-Closest Pairs in High-Dimensional Data, Martin Aumüller, and Matteo Ceccarello, Similarity Search and Applications - 16th International Conference, SISAP 2023, A Coruña, Spain, October 9-11, 2023, Proceedings, 2023, code.
  12. Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedback, Omar Shahbaz Khan, Martin Aumüller, and Björn Þór Jónsson, Similarity Search and Applications - 16th International Conference, SISAP 2023, A Coruña, Spain, October 9-11, 2023, Proceedings, 2023.
  13. Overview of the SISAP 2023 Indexing Challenge, Eric Sadit Tellez, Martin Aumüller, and Edgar Chávez, Similarity Search and Applications - 16th International Conference, SISAP 2023, A Coruña, Spain, October 9-11, 2023, Proceedings, 2023, website.
  14. Recent Approaches and Trends in Approximate Nearest Neighbor Search, with Remarks on Benchmarking, Martin Aumüller, and Matteo Ceccarello, IEEE Data Eng. Bull., 2023.
  15. DEANN: Speeding up Kernel-Density Estimation using Approximate Nearest Neighbor Search, Matti Karppa, Martin Aumüller, and Rasmus Pagh, International Conference on Artificial Intelligence and Statistics, AISTATS 2022, 28-30 March 2022, Virtual Event, 2022, code, experiments.
  16. Implementing Distributed Similarity Joins using Locality Sensitive Hashing, Martin Aumüller, and Matteo Ceccarello, Proceedings of the 25th International Conference on Extending Database Technology, EDBT 2022, Edinburgh, UK, March 29 - April 1, 2022, 2022.
  17. Sampling near neighbors in search for fairness, Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, and Francesco Silvestri, Commun. ACM, 2022.
  18. Representing Sparse Vectors with Differential Privacy, Low Error, Optimal Space, and Fast Access, Christian Janos Lebeda, Martin Aumüller, and Rasmus Pagh, J. Priv. Confidentiality, 2022.
  19. Sampling a Near Neighbor in High Dimensions - Who is the Fairest of Them All?, Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, and Francesco Silvestri, ACM Trans. Database Syst., 2022.
  20. Differentially Private Sparse Vectors with Low Error, Optimal Space, and Fast Access, Martin Aumüller, Christian Janos Lebeda, and Rasmus Pagh, CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, Republic of Korea, November 15 - 19, 2021, 2021.
  21. 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, and Jingdong Wang, NeurIPS 2021 Competitions and Demonstrations Track, 6-14 December 2021, Online, 2021, code.
  22. The role of local dimensionality measures in benchmarking nearest neighbor search, Martin Aumüller, and Matteo Ceccarello, Inf. Syst., 2021.
  23. Fair near neighbor search via sampling, Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, and Francesco Silvestri, SIGMOD Rec., 2021.
  24. Reproducibility Companion Paper: Visual Sentiment Analysis for Review Images with Item-Oriented and User-Oriented CNN, Quoc-Tuan Truong, Hady W. Lauw, Martin Aumüller, and Naoko Nitta, MM '20: The 28th ACM International Conference on Multimedia, Virtual Event / Seattle, WA, USA, October 12-16, 2020, 2020.
  25. Fair Near Neighbor Search: Independent Range Sampling in High Dimensions, Martin Aumüller, Rasmus Pagh, and Francesco Silvestri, Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, PODS 2020, Portland, OR, USA, June 14-19, 2020, 2020.
  26. Differentially Private Sketches for Jaccard Similarity Estimation, Martin Aumüller, Anders Bourgeat, and Jana Schmurr, Similarity Search and Applications - 13th International Conference, SISAP 2020, Copenhagen, Denmark, September 30 - October 2, 2020, Proceedings, 2020.
  27. Running Experiments with Confidence and Sanity, Martin Aumüller, and Matteo Ceccarello, Similarity Search and Applications - 13th International Conference, SISAP 2020, Copenhagen, Denmark, September 30 - October 2, 2020, Proceedings, 2020, code.
  28. Algorithm Engineering for High-Dimensional Similarity Search Problems (Invited Talk), Martin Aumüller, 18th International Symposium on Experimental Algorithms, SEA 2020, Catania, Italy, June 16-18, 2020, 2020, slides.
  29. ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms, Martin Aumüller, Erik Bernhardsson, and Alexander John Faithfull, Inf. Syst., 2020.
  30. Simple and Fast BlockQuicksort using Lomuto's Partitioning Scheme, Martin Aumüller, and Nikolaj Hass, Proceedings of the Twenty-First Workshop on Algorithm Engineering and Experiments, ALENEX 2019, San Diego, CA, USA, January 7-8, 2019, 2019, code.
  31. PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors, Martin Aumüller, Tobias Christiani, Rasmus Pagh, and Michael Vesterli, 27th Annual European Symposium on Algorithms, ESA 2019, Munich/Garching, Germany, September 9-11, 2019, 2019, code, slides, additional material.
  32. Benchmarking Nearest Neighbor Search: Influence of Local Intrinsic Dimensionality and Result Diversity in Real-World Datasets, Martin Aumüller, and Matteo Ceccarello, Proceedings of the 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning co-located with SIAM International Conference on Data Mining (SDM 2019), Calgary, Alberta, Canada, May 4th, 2019, 2019, website.
  33. The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search, Martin Aumüller, and Matteo Ceccarello, Similarity Search and Applications - 12th International Conference, SISAP 2019, Newark, NJ, USA, October 2-4, 2019, Proceedings, 2019, website.
  34. Dual-Pivot Quicksort: Optimality, Analysis and Zeros of Associated Lattice Paths, Martin Aumüller, Martin Dietzfelbinger, Clemens Heuberger, Daniel Krenn, and Helmut Prodinger, Comb. Probab. Comput., 2019.
  35. Distance-Sensitive Hashing, Martin Aumüller, Tobias Christiani, Rasmus Pagh, and Francesco Silvestri, Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, Houston, TX, USA, June 10-15, 2018, 2018, slides.
  36. ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms, Martin Aumüller, Erik Bernhardsson, and Alexander John Faithfull, Similarity Search and Applications - 10th International Conference, SISAP 2017, Munich, Germany, October 4-6, 2017, Proceedings, 2017, code, website.
  37. Parameter-free Locality Sensitive Hashing for Spherical Range Reporting, Thomas D. Ahle, Martin Aumüller, and Rasmus Pagh, Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2017, Barcelona, Spain, Hotel Porta Fira, January 16-19, 2017, slides.
  38. Theory and Applications of Hashing (Dagstuhl Seminar 17181), Martin Dietzfelbinger, Michael Mitzenmacher, Rasmus Pagh, David P. Woodruff, and Martin Aumüller, Dagstuhl Reports, 2017.
  39. Optimal Partitioning for Dual-Pivot Quicksort, Martin Aumüller, and Martin Dietzfelbinger, ACM Trans. Algorithms, 2016.
  40. How Good Is Multi-Pivot Quicksort?, Martin Aumüller, Martin Dietzfelbinger, and Pascal Klaue, ACM Trans. Algorithms, 2016, additional material.
  41. Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash, Martin Aumüller, Martin Dietzfelbinger, and Philipp Woelfel, Algorithmica, 2014, additional material.
  42. Optimal Partitioning for Dual Pivot Quicksort - (Extended Abstract), Martin Aumüller, and Martin Dietzfelbinger, Automata, Languages, and Programming - 40th International Colloquium, ICALP 2013, Riga, Latvia, July 8-12, 2013, Proceedings, Part I, 2013, slides.
  43. Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash, Martin Aumüller, Martin Dietzfelbinger, and Philipp Woelfel, Algorithms - ESA 2012 - 20th Annual European Symposium, Ljubljana, Slovenia, September 10-12, 2012. Proceedings, 2012, slides.
  44. Experimental Variations of a Theoretically Good Retrieval Data Structure, Martin Aumüller, Martin Dietzfelbinger, and Michael Rink, Algorithms - ESA 2009, 17th Annual European Symposium, Copenhagen, Denmark, September 7-9, 2009. Proceedings, 2009.

PhD Thesis

  1. On the Analysis of Two Fundamental Randomized Algorithms - Multi-Pivot Quicksort and Efficient Hash Functions, Martin Aumüller, Technische Universität Ilmenau, Germany, 2015, slides.

Pre-prints

  1. VIBE: Vector Index Benchmark for Embeddings, Elias Jääsaari, Ville Hyvönen, Matteo Ceccarello, Teemu Roos, and Martin Aumüller, CoRR, 2025, benchmark, datasets, website.
  2. High-dimensional Spherical Range Reporting by Output-Sensitive Multi-Probing LSH, Thomas D. Ahle, Martin Aumüller, and Rasmus Pagh, CoRR, 2016.
  3. Counting Zeros in Random Walks on the Integers and Analysis of Optimal Dual-Pivot Quicksort, Martin Aumüller, Martin Dietzfelbinger, Clemens Heuberger, Daniel Krenn, and Helmut Prodinger, CoRR, 2016.
  4. A Simple Hash Class with Strong Randomness Properties in Graphs and Hypergraphs, Martin Aumüller, Martin Dietzfelbinger, and Philipp Woelfel, CoRR, 2016.

Co-authors

Thomas D. Ahle (2), Artem Babenko (1), Dmitry Baranchuk (2), Erik Bernhardsson (2), Fabrizio Boninsegna (1), Anders Bourgeat (1), Yuzheng Cai (1), Matteo Ceccarello (8), Meng Chen (1), Yue Chen (1), Yizhuo Chen (1), Qi Chen (1), Tobias Christiani (2), Edgar Chávez (2), Laxman Dhulipala (1), Martin Dietzfelbinger (10), Matthijs Douze (2), Alexander John Faithfull (2), Sariel Har-Peled (3), Nikolaj Hass (1), Clemens Heuberger (2), Lucas Hosseini (1), Ben Huang (1), Ville Hyvönen (1), Amir Ingber (1), Malte Helin Johnsen (1), Elias Jääsaari (1), Björn Þór Jónsson (1), Mazin Karjikar (1), Matti Karppa (1), Omar Shahbaz Khan (1), Pascal Klaue (1), Daniel Krenn (2), Ravishankar Krishnaswamy (1), Benjamin Landrum (1), Hady W. Lauw (1), Christian Janos Lebeda (3), Edo Liberty (1), Frank Liu (1), Rui Ma (1), Sepideh Mahabadi (3), Magdalen Dobson Manohar (1), Yusuke Matsui (1), Vladimir Mic (2), Michael Mitzenmacher (1), Boel Nelson (1), Naoko Nitta (1), Camilla Birch Okkels (3), Rasmus Pagh (13), Helmut Prodinger (2), Michael Rink (1), Teemu Roos (1), Atsuki Sato (1), Jana Schmurr (1), Erich Schubert (1), Jiayang Shi (1), Francesco Silvestri (6), Harsha Vardhan Simhadri (2), Gopal Srinivasa (1), Suhas Jayaram Subramanya (1), Eric Sadit Tellez (3), Viktor Bello Thomsen (1), Erik Thordsen (1), Quoc-Tuan Truong (1), Michael Vesterli (1), Zihao Wan (1), Jingdong Wang (1), George Williams (2), Philipp Woelfel (3), David P. Woodruff (1), Jie Yin (1), Kai Zhang (1), Weiguo Zheng (1), Arthur Zimek (3)