Sabyasachi Basu

I am a postdoctoral researcher at Microsoft Research, in Bangalore, India. I work in the DiskANN team, under Ravishankar Krishnaswamy. I am currently thinking about problems on quantization, matryoshka embeddings, and OOD queries. 

In September 2025, I received my PhD from the University of California, Santa Cruz, under the supervision of C. Seshadhri. My thesis was titled "Decomposition Techniques for Web-Scale Networks"; please go through my unnecessarily long acknowledgements that took me as much time to write as the rest of my thesis (/s). Before UCSC, I was an undergraduate at the Indian Institute of Science, Bengaluru, majoring in math. My advisors there were Vishwesha Guttal and Srikanth Iyer. I can send you a CV upon request, but most of the relevant stuff is on my website.

In the summer of 2023, I was a student researcher at Google, working with Aneesh Sharma to understand retrieval in graph embeddings and designing better training/retrieval procedures to obtain better results. In 2022, I worked in the personalization team at Walmart, and in 2020, I interned at the Data Science Institute at Lawrence Livermore National Laboratory.

My Erdős number is now 2. I do not expect it to go any higher. 

If you have tips on how to make my website more accessible (for example, to folks using screen readers), please let me know!

Research Interests

Broadly, I am interested in building algorithmic techniques that have a strong theoretical foundation, but can also be implemented efficiently in practice. Most modern computer science use cases require data; usually copious amounts of it. I like to think that the best tactic for algorithm design in this day and age is understanding the empirical properties of the data, and then building theory around it. I think of this as more of a physics-like approach to CS, rather than the more prevalent model-first approach to theory. I think it is especially crucial for those who wish to build methods to bridge theory and practice.