Pranoy Ray | Computational Materials Scientist

Research Highlights & Concept Demos

Molecular Voxelization Framework

Cheminformatics DFT Forever Chemicals

Pipelines for unsupervised capture of rotationally and translationally-invariant spatial features of a molecular structure, and its subsequet chemical effects (including but not limited to defects) on a voxelized grid.

Actual input: Voxelized BCC SQS structure

[Paper 1 : IMMI] [Paper 2: Advanced Science]

AI/ML-informed Multiscale Modeling

Materials Design Bayesian Optimization DoE

Capturing multiscale phase-separation phenomena through calibration of coarse-grained force fields with MD simulations

Actual output: CGMartini3-MOBO scheme

[Paper 1: npj Comp.Mat.] [Paper 2: chemRxiv]

Forest Fires Modeling

PDE Agent-Based Models Weather Agents

Simulating wildfire propagation dynamics and their interaction with environmental factors (e.g. temp., wind, humidity). Actual data collected from 2018 in California.

Actual output: PDE Solver Timelapse

[Github Repo]

Overview

Computational Materials & Data Scientist specializing in Materials Informatics using Bayesian statistics for multiscale modeling. Expertise includes developing efficient feature engineering methods for robust materials process-structure-property relationships, process optimization, and integrating physics-based constraints with data-driven AI/ML models on HPC systems. Innovative R&D professional with a track record of driving technical deliverables (for chemical/semiconductor/federal labs) and leading diverse teams in fast-paced research environments. Presently a final year PhD candidate, expecting to graduate in May 2026.

Latest News

Nov 2025: Journal article on Lean CNNs highlighted by TMS Editors as a standout article [open]
Aug 2025: Elected to the Emerging Leaders Advisory Board (ELAB @ GT) [open]
Mar 2025: Named Woodruff School Fellow @ GT.
Jan 2024: Named Novelis Graduate Scholar 2024 [open]
Aug 2023: Elected as the Internal VP of MEGA (Mechanical Engineering Graduate Association) [open]
Dec 2022: Named EIIF 2022 Fellow : Invited Talk delivered @ 2nd EIIF (Oahu, Hawaii)
May 2022: Named CMS3 2022 Fellow : Texas A&M University (College Station, Texas)
Aug 2018: News feature: A startup which Pranoy co-founded [open] [news article]

Experience

Graduate Research Assistant
Georgia Institute of Technology (Aug 2021 - Present)
1. Architected and published robust Foundation Model workflows for scientific domains, integrating physics-based constraints with deep learning to solve complex inverse problems. Developed interpretability methods for 3D convolutional architectures (Lean CNNs), analyzing feature activation maps to understand structure-property relationships in high-dimensional scientific data.
2. Developed and published hybrid physics-AI models for multi-scale materials design, optimizing polymer physics in computational models, utilizing Uncertainty Quantification (UQ) and sensitivity analysis to correlate simulation data with experimental outputs.
Data Science Manager
Multiscale Technologies Inc (Jan 2024 - Aug 2024)
Directed AI/ML strategies to enhance process discovery, significantly reducing trial-and-error iterations for transnational industry-based (semiconductor \& chemical) clients and US national laboratories. Supervised and led a team of Data Scientists and engineers across the USA, France, India, and Pakistan to deliver exploratory materials design solutions for multiple Fortune 100 clients.Implemented and developed impactful integration workflows between product, solutions, and engineering teams adapted to MOT/Root Cause Analysis protocols.
Research Assistant
Bhabha Atomic Research Centre (May 2019 - Aug 2021)
Accomplished multiple projects on materials design for solid-state Hydrogen Storage with the application of DFT and AIMD simulations. Theoretically discovered 3 distinct novel material systems for alternative fuels (Hydrogen Storage) using DFT & MD
Project & Industrial Intern
Hindustan Aeronautics Limited (May 2018 - July 2018)
Deployed projects: (1) Conducted root cause analysis on manufacturing defects in investment casting, applying process control measures relevant to yield improvement. (2) Developed preventive measures for quality control using Lean management

Education

Doctor of Philosophy (PhD)

Georgia Institute of Technology, USA | 2021 - Present | Materials Informatics

Advisor: Dr. Surya R. Kalidindi

Master of Science (MS)

Georgia Institute of Technology, USA | 2022 - 2024

Computational Science & Engineering [degree]

Bachelor of Technology (B.Tech)

National Institute of Technology, Durgapur, India | 2016 - 2020

Metallurgical & Materials Engineering

Professional Certifications

Management of Technology

Scheller College of Business, GeorgiaTech (2025)

Computer Materials Science & Engineering

GeorgiaTech (2023)

CMS3 Summer School

Texas A&M University (2022)

Publications & Talks

Invited Talks

Lean CNNs for materials discovery: Trinity College | Dublin, Ireland - Oct 2025
Bayesian frameworks for advanced materials design: Novelis NGRTC | Kennesaw, GA - Oct 2024
Bayesian Optimization of CG Topologies: TMS Fall Meeting | Pittsburgh, PA - Oct 2024
Feature engineering of ECD fields: 2nd EIIF (TokyoTech) | Oahu, HI - Dec 2022

Conferences

Statistical quantification of helicity in linear PFAS: ACS Spring Meeting (Atlanta) - Mar 2026
Structure-aware Bayesian optimization: SES Annual Technical Meeting (Atlanta) - Oct 2025
Accelerating Molecular Design: MSE Research Showcase (GT) - Oct 2024
Ti-doped Carbon Nanostructure: DAE Computational Chemistry Symposium (Mumbai) - Nov 2019

Selected Awards

[2025] Woodruff School Fellow: Georgia Tech.
[2024] Novelis Graduate Scholar: Novelis Inc.
[2022] EIIF Fellow: TokyoTech & Strategic Energy Institute.
[2022] CMS3 Fellow: NSF + Texas A&M.

Service to Community

Reviewer & Committee Member: SciPy Conference (2023, 2024, 2025)
Peer Reviewer: NeurIPS 2025, CVPR 2025, PEARC 2025, ICLR 2026
Peer Reviewer (Journals): JOM, Chemical Papers
Session Chair & Reviewer: Undergraduate Research Symposium, GT
Board Member: Emerging Leaders Advisory Board @ GT
Internal VP: MEGA (MechE Grad Association)
Contributor: JARVIS (NIST), Mozilla Foundation
Leadership: President EDC, Treasurer Strokes (NITD)

Technical Skills

Machine Learning/AI: Neural Networks, Gaussian Process Regression, Normalizing Flows, Autoencoder, Convolutions
Programming & Data Science: Python (with key libraries like PyTorch, GPyTorch, BOTorch, Pyvista, SciPy, scikit-learn), R, Java, C
Engineering Simulation: VASP, LAMMPS, GROMACS, DFT, Molecular Dynamics, CG-Martini3, ORCA, MACE
HPC/Cloud Platforms: MPI, AWS, GCP, Azure, Shell Scripting (SLURM/PBS), Flask, Firebase, Hadoop, BigData
Advanced Graduate Courses: Parallel Computing (HPC), Density Functional Theory, Uncertainty Quantification, DoE, ML, DL
Process Informatics & Experimental Design: Bayesian Optimization, Design of Exp., Pareto Frontier Analysis, Active Learning