I enjoy turning algorithmic ideas into practical tools and understanding what makes them work well—or fail—in practice. Much of my work revolves around similarity search and differential privacy.

Research

I enjoy designing and evaluating algorithms that bridge theory and practice, with a focus on:

• Similarity Search & Benchmarking: Algorithms for high-dimensional nearest neighbor search (puffinn, danny), community benchmarks (ann-benchmarks, big-ann-benchmarks, vector-index-benchmark) that define research practices, and understanding what makes search methods work in practice.
• Differential Privacy: Private algorithms for sparse vectors, mean estimation, and approximate range counting with strong formal guarantees.

Selected Publications

• ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms, Inf. Syst., 2020
• PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors, ESA 2019
• Sampling near neighbors in search for fairness, Commun. ACM, 2022
• Differentially Private Sparse Vectors with Low Error, Optimal Space, and Fast Access, CCS 2021
• Distance-Sensitive Hashing, PODS 2018

See the full list of publications for all details.

Research projects

• Benefit and Bias of Approximate Nearest Neighbor search [2022-2025, PI, finished]
• MMX-VR: Multimedia Metadata Exploration in Virtual Reality [2023-2026, co-PI]

Team

If you're interested in working with me, feel free to reach out. I'm always happy to discuss new ideas and collaboration opportunities.

• Current PhD students
  • Fabrizio Boninsegna (at UniPD, co-supervised with Francesco Silvestri)
• Finished PhD students
  • Christian Janos Lebeda (co-supervised with Rasmus Pagh, now postdoc at INRIA, France)
  • Camilla Birch Okkels (co-supervised with Arthur Zimek)
• Research assistants
  • Alexander Theodor Bilde Pedersen (2025)
  • Viktor Bello Thomsen (2024-2025)
• Scientific Visitors
  • David Procházka (visiting PhD student, 2025)

Code & Challenges

Most code is available on GitHub. Selected projects:

• ann-benchmarks: The de-facto standard for benchmarking approximate nearest neighbor search algorithms. [Paper]
• big-ann-benchmarks: Benchmarking approximate nearest neighbor search on billion-scale and with modern workloads. [Paper NeurIPS'21 Challenge], [Paper NeurIPS'23 Challenge]
• vector-index-benchmark: ann-benchmarks++: Benchmarking ANN on modern datasets, with better visualization, and better support for HPC environments. [Paper]
• puffinn: Nearest neighbor search with guarantees. [Paper]
• danny: Distributed similarity join using LSH. [Paper]

Challenges

• 2025: SISAP Indexing Challenge: Report, Results
• 2024: SISAP Indexing Challenge: Report
• 2023: NeurIPS Practical Vector Search challenge: Report, Code
• 2023: SISAP Indexing Challenge: Report
• 2021: NeurIPS Billion-Scale ANN Challenge: Report, Code

Invited Talks, Tutorials, Workshops

• Algorithm Engineering for High-Dimensional Similarity Search at SEA'20 (Slides, Recording)
• Neural Search in Action at CVPR'23 (Slides, Recording)
• High-dimensional Approximate Nearest Neighbor Search: Applications, Algorithms, Current Challenges at DAMVISEH at SDU Odense, 2025 (Slides)
• I co-organized the first workshop on vector databases (VecDB) at ICML'25

Teaching

Current courses at IT University of Copenhagen

• Algorithmic Problem Solving (Bachelor + Master) (Spring 2021, 2022, 2023, 2024, 2025, 2026)
• Applied Algorithms (Master's level) (Autumn 2018, 2019, 2020, 2023, 2024, 2025)

Previous courses

• Introduction to Programming (Bachelor + Master) (Autumn 2018, 2019, 2020, 2021, 2022, 2023)
• Introduction to Data Science and Programming (Autumn 2024)
• Algorithms and Data Structures (Bachelor + Master) (Summer 2020, 2021)
• Algorithmic Fairness, Accountability, and Ethics (Master) (Spring 2022, 2023, 2024)
• Spring 2018, First-Year Project: Map of Denmark. Visualization, Navigation, Searching, and Route Planning (Bachelor's level)
• Autumn 2017, Programming Workshop (Master's level)
• Spring 2017, Algorithm Design Project (Master's level)
• Fall 2016, Introduction to Programming Workshop (Master's level)
• Spring 2016, Advanced Algorithms Seminar (Master's level)

Classes at Technische Universität Ilmenau (in German)

• Winter 2015 Tutorials: Efficient Algorithms, Foundations and Methods of Cryptography
• Summer 2015 Tutorials: Algorithms and Data Structures (Student's Evaluation)
• Winter 2014 Tutorials: Efficient Algorithms (Student's Evaluation)
• Summer 2014 Tutorials: Algorithms and Data Structures (Student's Evaluation)
• Winter 2013 Tutorials: Efficient Algorithms (Student's Evaluation), Complexity Theory
• Summer 2013 Tutorials: Efficient Algorithms (Undergraduate) (Student's Evaluation)
• Winter 2012 Tutorials: Efficient Algorithms (Student's Evaluation), Complexity Theory/Approximation Algorithms
• Summer 2012: Lecture and Tutorial: Efficient Algorithms (Undergraduate) (Lecture Evaluation, Seminar Evaluation)

Algorithm Visualizations

Oldies but goldies?

• Auction Matching
• Cuckoo Hashing

Academic Service

Programme committees

SEA 2018, ESA 2018 (Track B), MMM 2019, EDML 2019, MM 2019 Reproducibility, SISAP 2019, SISAP 2020 (co-chair), ICMR 2021/2022/2023 Reproducibility (co-chair), LATIN 2022, SISAP 2024, CIKM 2024, ESA 2025 (Track S), ICML'25 VecDB Workshop (co-chair), SEA 2026 (PC co-chair)