About Me
Hi there!
I am currently a Member of Technical Staff at Argmax where we’re working on on-device foundation model inference. Previously, I also worked at Groundlight AI and ThirdAI where I worked on a variety of machine learning and engineering problems. Prior to that, I was a student at The University of Chicago where I double-majored in Computer Science and Computational and Applied Mathematics.
Among other things, I am also the maintainer of flatnav, a robust and memory-efficient library for performing vector search at scale.
My research interests, broadly defined, are in information retrieval, learning theory and machine learning systems. On the IR side of things, I enjoy working on efficient algorithms for high-dimensional vector search on dense embeddings. On the ML systems side, I am mostly excited about ways we can make inference faster for foundation models.
Preprints and Publications
Vihan Lakshman, Blaise Munyampirwa, Julian Shun, Benjamin Coleman. Breaking the Curse of Dimensionality: On the Stability of Modern Vector Retrieval. (https://arxiv.org/pdf/2512.12458)
Berkin Durmus, Blaise Munyampirwa , Eduardo Pacheco, Atila Orhon, Andrey Leonov. SDBench: A Comprehensive Benchmark Suite for Speaker Diarization. (https://arxiv.org/pdf/2507.16136)
Blaise Munyampirwa, Vihan Lakshman, Benjamin Coleman. Down with the Hierarchy: The H in HNSW stands for Hubs. (https://arxiv.org/pdf/2412.01940)
Deep learning detects actionable molecular and clinical features directly from head/neck squamous cell carcinoma histopathology slides. J. Dolezal, J.N. Kather, S. Kochanny, J. Schulte, A. Patel, B. Munyampirwa, S. Morin, A. Srisuwananukorn, N. Cipriani, D. Basu, A. Pearson. International Journal of Radiation Oncology, Biology, Physics, Volume 106, Issue 5, 1165 (https://www.redjournal.org/article/S0360-3016(19)34202-6/abstract)
Talks and presentations
Optimizing HNSW in the age of vector databases
Talk at Amazon Search, Palo Alto, CA
[Slides]