Sabyasachi Basu (সব্যসাচী বসু) 

I am a fifth-year PhD in computer science at UC Santa Cruz (UCSC). My advisor is Prof. C. Seshadhri. Before my time at UCSC, I was an undergraduate at the Indian Institute of Science, Bengaluru, majoring in math. My advisors there were Prof. Vishwesha Guttal and Prof. Srikanth Iyer. My BS thesis was on understanding catastrophic transitions in financial markets using ecological migration as a framework. I can send you a CV upon request, but most of the relevant stuff is on my website.

Because this comes up often, I prefer being called Sabya. For a long time, most of my friends and colleagues have called me by various versions of my last name, but I prefer Sabya (or, if you're familiar with Bangla, Shobbo).  This is a picture of me from 2022, but my hair may be significantly longer now.

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.

Current Research

A lot of my work involves using theoretical insight to design efficient graph algorithms that work in practice. My ongoing research broadly tackles the following questions (a recurring theme across them involves different ways of decomposing graphs; see Research for papers and working manuscripts):

1. Dense subgraphs:

   1. Decomposing graphs into dense clusters

   2. Finding theory(and algorithms) for dense cluster discovery in directed graphs

2. Complex Networks:

   1. Understanding how complex networks organize and exploiting their structure to design better algorithms

   2. Community detection in complex networks 

3. Sublinear Algorithms:

   1. Property testing in sparse graphs

   2. Sublinear algorithms in practice

4. Constructing graph embeddings to enable efficient retrieval, with a particular emphasis on recommendation