Profile

Published

Bio: Sebastian Bruch completed his Ph.D. in Computer Science at the University of Maryland, College Park in 2013. He is interested in randomized algorithms, sketching, and nearest neighbor search. He particularly enjoys conducting theoretical investigations of empirically-successful algorithms, and developing practical algorithms that are backed by theoretical guarantees.

Sebastian is the author of “Foundations of Vector Retrieval” and the co-author of “Efficient and Effective Tree-based and Neural Learning to Rank.” His published works have appeared in leading journals including ACM TOIS, IEEE TKDE, and FnT in Information Retrieval (IR). He has also contributed to the proceedings of and served on the program committees or as reviewer for conferences including NeurIPS, ICLR, SIGIR, WSDM, SIGKDD, and the Web Conference.

After nearly a decade in industry, Sebastian returned to academia in 2024 and is currently a Senior Research Scientist at the Northeastern University in Boston, MA.

Philosophy: “Man muss noch Chaos in sich haben, um einen tanzenden Stern gebären zu können.” (Translation: You must have chaos within you to give birth to a dancing star.) –Friedrich Nietzsche

Awards and Distinctions

[2025] Best Short Student Paper Award at the 47th European Conference on Information Retrieval (ECIR)
[2024] Best Paper Runner-Up Award at the 47th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
[2024] Outstanding Program Committee Member at the 46th European Conference on Information Retrieval (ECIR)
[2022] Program Committee Member Honorable Mention at the 31st ACM Web Conference (WWW)
[2021] Outstanding Program Committee Member at the 14th ACM International Conference on Web Search and Data Mining (WSDM)
[2020] Outstanding Program Committee Member at the 13th ACM International Conference on Web Search and Data Mining (WSDM)
[2019] Best Short Paper Award at the 5th Annual ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR)

Service

[2025-03 - Present] Member of the Editorial Board of Reviewers for Journal of Machine Learning Research (JMLR)
[2023-10 - 2026-10] I am serving as an Associate Editor on the ACM Transactions on Information Systems (TOIS) Editorial Board.
[2022-10 - 2023-12] I co-edited the Special Section on Efficiency in Neural Information Retrieval for the ACM Transactions on Information Systems (TOIS) with Claudio Lucchese, Maria Maistro, and Franco Maria Nardini.
I served on the Senior Program Committee or as Meta Reviewer for the following conferences:
- Conference on Web Search and Data Mining (WSDM - Full Paper) 2022–2025
- Conference on Knowledge Discovery and Data Mining (KDD) 2022
- The Web Conference (WWW - Search Track) 2023
I served on the Program Committee or as Reviewer for the following conferences:
- International Conference on Learning Representations (ICLR) 2025
- Neural Information Processing Systems (NeurIPS) 2024–2025
- European Conference on Information Retrieval (ECIR - Full Paper Track) 2024
- European Conference on Information Retrieval (ECIR - Short Paper Track) 2023
- Conference on Web Search and Data Mining (WSDM - Full Paper) 2020–2021
- Conference on Web Search and Data Mining (WSDM - Tutorials) 2021
- Conference on Research and Development in Information Retrieval (SIGIR - Full Paper Track) 2019–2023
- Conference on Research and Development in Information Retrieval (SIGIR - Short Paper Track) 2019–2023
- The Web Conference (WWW - Search Track) 2020–2022, 2025
- Conference on Knowledge Discovery and Data Mining (KDD - Research Track) 2020–2021

Workshops

[2025-07] I co-organized the fourth international workshop on Reaching Efficiency in Neural Information Retrieval (ReNeuIR) at SIGIR 2025 in Padua, Italy, with Maik Fröbe, Tim Hagen, Franco Maria Nardini, and Martin Potthast. (Workshop Website)
[2023-07] I co-organized the second international workshop on Reaching Efficiency in Neural Information Retrieval (ReNeuIR) at SIGIR 2023 in Taipei, Taiwan, with Maria Maistro, Joel Mackenzie, and Franco Maria Nardini. (Workshop Website)
[2022-07] I co-organized the first international workshop on Reaching Efficiency in Neural Information Retrieval (ReNeuIR) at SIGIR 2022 in Madrid, Spain, with Claudio Lucchese and Franco Maria Nardini. (Workshop Website)

Seminars, Teaching, and Tutorials

[2025-04] Talk at Purdue University. “Approximate Retrieval from an Artificial Memory.” Lafayette, IN, US.
[2025-04] Talk at Northeastern University. “Approximate Retrieval from an Artificial Memory.” Boston, MA, US.
[2025-04] Talk at Technische Universität Nürnberg. “Approximate Retrieval from an Artificial Memory.” Nürnberg, Germany.
[2024-06] Talk at the Information Retrieval Lab at the University of Amsterdam. “Optimistic Query Routing in Clustering-based Approximate Maximum Inner Product Search.” Amsterdam, the Netherlands. (Virtual)
[2024-06] Talk at the Institute for Experiential AI at Northeastern University. “Order though randomness for fair ranking and sustainable retrieval in generative AI.” Boston, MA, United States.
[2023-09] I gave a keynote talk at SPIRE 2023 in Pisa, Italy.
[2023-03] Talk at Naver Labs Europe. “Sketching and Topological Organization of Sparse Vectors for Probably Approximately Correct MIPS.” Grenoble, France.
[2023-03] Talk at Università Ca’ Foscari. “Sketching and Topological Organization of Sparse Vectors for Probably Approximately Correct MIPS.” Venice, Italy.
[2023-02] I co-taught CM0638 on Learning with Massive Data at Università Ca’ Foscari in Venice, Italy in Spring 2023 with Prof. Claudio Lucchese.
[2022-03] Talk at Pinecone. “Stochastic Rankers.” New York, NY, United States. (Virtual)
[2021-03] Talk at the National Institute on Drug Abuse (NIH/NIDA). “A Model of the Orbitofrontal Cortex in Odor Sequence Problems.” Bethesda, MD, United States.
[2019-10] Neural Learning to Rank using TensorFlow Ranking: A Hands-on Tutorial. The Annual ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR 2019), Santa Clara, CA, United States.
[2019-07] Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning. The 42nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019), Paris, France.
[2019-05] Talk at the University of Virginia. “A Stochastic Treatment of Learning to Rank Scoring Functions.” Charlottesville, VA, United States.