Extended Bio

I’m Shreyas. Like most people, I was born at a very young age.

Since I’ve grown up, I’ve been an applied math researcher and scientific software developer, mostly working on modeling and computational simulations for problems in fundamental & applied sciences. I’ve worked across areas — physics, biology, drug discovery, machine learning — trying to make complex systems a little more understandable (and computable).

I’m a computational scientist at Flexcompute. Here, we build industry-leading physics simulation tools for aerodynamics & multi-physics application areas. I work on various aspects of scientific computing & computational science. I focus on geometric algorithms, their robust implementation, and performance engineering within the context of surface & volume meshing for computational fluid dynamics (CFD) simulations.

I hold a master of science in computer science from the Manning College of Information and Computer Sciences at UMass Amherst. My degree focused on efficient algorithm design and high performance software techniques. I was awarded a competitive Bay State scholarship for this degree, and earnt it while concurrently working with the team at Flexcompute. Before this, I earnt two bachelor of science degrees in computer science and applied mathematics, also from UMass Amherst. In my time at UMass, I was a teaching assistant for mathematics, computer science, and computational physics courses.

In my day-to-day, I ponder upon numerical algorithms, mathematical optimization, computational geometry, and dynamical systems. In the time that’s left, I write efficient, robust, fast, C++ code to make my pondering fruitful. Sometimes, I translate interesting biological and physical phenomena into mathematical models.

I’m a part of the Buttenschoen Lab at UMass. Here, we study cell organization and other biological phenomena using mathematical & computational tools. My undergraduate thesis with the lab studied collective long-range migration, and the cell-cell communication mechanisms that help achieve it.

My previous research experiences include working with Pfizer’s Scientific Computing & HPC group on quantum algorithms & mathematical optimization for computational drug discovery. I’ve also worked with the Fair and Explainable Decision-Making Lab at UMass on convex optimization for fair machine learning models, and Brown University’s PALM Research Lab on generative machine learning.

Updates

2026

  • May 2026 - I completed my Master of Science in Computer Science at UMass Amherst!
  • Jan 2026 - I was promoted in my role at Flexcompute! My main contributions over the past year included robustness and feature contributions to Flexcompute’s in-house meshing workflows. Learn more about our meshing suite here.

2025

2024

  • Dec 2024 - I had two big milestones: graduated from UMass with dual-BS degrees, and completed my thesis in mathematical biology.
  • Sep 2024 - I visited Columbia University for the GROW Conference 2024.
  • Jul 2024 - I spent a week visiting the University of Utah’s Math-Bio Group for a Graduate School Preview. Here, I worked with Samantha Linn on exact mean cover times for a run-and-tumble particle. Take a look at our slide deck.
  • May 2024 - I began an internship at Pfizer! I’m a Scientific Computing & HPC intern, and I’m working on developing quantum algorithms for computational drug development.
  • Apr 2024 - I presented my work with Dr Cyrus Cousins on Convex Optimization for Fair ML at the 30th Massachusetts Undergraduate Research Conference.
  • Mar 2024 - I spent a weekend participating in Columbia University Engineering’s EngAGE program for students interested in pursuing doctoral studies in quantitative sciences!

2023

  • Aug 2023 - I concluded my summer program at Brown University, and presented posters at the Leadership Alliance National Syposium and the Brown Summer Research Symposium.
  • Jun 2023 - I joined Brown University’s REU - ‘Artificial Intelligence for Computational Creativity’ - working with the PALM Research Lab (PI: Dr Chen Sun) on Generative Modeling techniques.