Tobias Rubel

I am a PhD candidate in computer science at the University of Maryland, College Park, advised by Laxman Dhulipala. I am supported by an NSF Graduate Research Fellowship.

I work on parallel algorithms and data structures, mostly for similarity search. My current focus is making graph-based indexes for approximate nearest neighbor search fast to build as well as fast to query, at billion-point scale. I am also interested in uniquely represented (history-independent) data structures and in randomized parallel algorithms more broadly.

Before Maryland I was a post-baccalaureate research assistant at Reed College, working on algorithms for biological networks with Anna Ritz and on models of technological innovation with Mark Bedau. I received a BA in philosophy from Reed College in 2019, with a thesis on metaphysical fundamentality advised by Paul Hovda.

• trubel@umd.edu
• Google Scholar
• UMD page
• GitHub
• CV

Research interests

• Approximate nearest neighbor search and vector databases
• Parallel and cache-efficient algorithms
• Uniquely represented and history-independent data structures
• Randomized algorithms and graph algorithms
• Algorithms for computational biology

Publications

* denotes equal contribution.

1. Fast k-NN Graph Building with Quantized PiPNN and Filtering
   Tobias Rubel, Richard Wen, Guy Blelloch, Laxman Dhulipala, Lars Gottesbüren, Jakub Łącki, Vahab Mirrokni.
   International Conference on Similarity Search and Applications (SISAP), 2026. To appear. Code.
   First place, Task 1 of the SISAP 2026 Indexing Challenge
2. Turbocharging PiPNN for Proximity and k-NN Graph Building
   Tobias Rubel, Richard Wen, Guy Blelloch, Laxman Dhulipala, Lars Gottesbüren, Jakub Łącki, Vahab Mirrokni.
   2nd Workshop on Vector Databases (VecDB) at VLDB, 2026. Full version under submission.
   Best Paper Award
3. PiPNN: A Framework for Ultra-Scalable Graph-Based Nearest Neighbor Indexing
   Tobias Rubel, Richard Wen, Laxman Dhulipala, Lars Gottesbüren, Rajesh Jayaram, Jakub Łącki.
   ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2026. arXiv · Code.
   Best Paper Award, Research Track
4. Fast, parallel, and cache-friendly suffix array construction
   Jamshed Khan, Tobias Rubel, Erin K. Molloy, Laxman Dhulipala, Rob Patro.
   Algorithms for Molecular Biology, 2024. Preliminary version in WABI 2023.
5. Dollo-CDP: a polynomial-time algorithm for the clade-constrained large Dollo parsimony problem
   Junyan Dai, Tobias Rubel, Yunheng Han, Erin K. Molloy.
   Algorithms for Molecular Biology, 2024. Preliminary version in WABI 2023.
6. Reconciling signaling pathway databases with network topologies
   Tobias Rubel*, Pramesh Singh*, Anna Ritz.
   Pacific Symposium on Biocomputing (PSB), 2022.
7. Dropping diversity of products of large US firms: models and measures
   Ananthan Nambiar*, Tobias Rubel*, James McCaull, Jon deVries, Mark A. Bedau.
   PLOS ONE, 2022.
8. Graphery: interactive tutorials for biological network algorithms
   Heyuan Zeng, Jinbiao Zhang, Gabriel A. Preising, Tobias Rubel, Pramesh Singh, Anna Ritz.
   Nucleic Acids Research, 2021.
9. Augmenting signaling pathway reconstructions
   Tobias Rubel, Anna Ritz.
   ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB), 2020.
10. Open-ended technological innovation
    Mark A. Bedau, Nicholas Gigliotti, Tobias Janssen, Alec Kosik, Ananthan Nambiar, Norman Packard.
    Artificial Life, 2019.

Software

• PiPNN: parallel C++ implementation of the PiPNN framework for building graph-based nearest neighbor indexes.
• CaPS-SA: parallel, cache-friendly suffix array construction.