Quantum Chemistry Researcher · Applied Machine Learning · High-Performance Computing

I'm a computational scientist with a Ph.D. in Chemical Physics and 10+ years of experience at the intersection of quantum chemistry, machine learning, and large-scale scientific computing. My work spans autonomous AI systems for scientific discovery, massively parallel quantum chemistry algorithms, and deep learning for molecular design.

• Quantum Chemistry — Electronic structure methods, many-body theory, tensor decomposition algorithms
• Applied Machine Learning — Graph Neural Networks, deep learning for molecular property prediction, surrogate-driven atomistic modeling
• High-Performance Computing — Massively parallel algorithms, GPU/CPU cluster optimization, distributed systems
• Agentic AI — Multi-agent frameworks, autonomous scientific workflows, simulation-aware decision loops

Developer of MPQC, a high-performance quantum chemistry package written in C++. I designed and optimized massively parallel tensor-decomposition algorithms for high-level quantum chemistry simulations, scaling across CPU clusters with parallel filesystem integration.

Contributor to ChemGraph, an agentic software framework for computational chemistry workflows. Built agentic AI automation pipelines using the LangGraph framework, integrating simulation, optimization, and data analysis backends with ML models.

• Postdoctoral Fellow at Sandia National Labs — computational design of molecular emitters through quantum simulation and structure-property relationships; simulation of light-initiated excited state reactions; designed and deployed agentic AI systems for autonomous scientific discovery using LangGraph; developed GNN models for molecular design in photochemical and energy applications
• Postdoctoral Fellow at California Institute of Technology — built ML models for organic molecular property prediction
• Postdoctoral Fellow at Virginia Tech — co-developed MPQC and optimized massively parallel quantum chemistry algorithms for high-accuracy chemistry simulations

• Varun Rishi et al. "Quantifying Design Principles for Light-Emitting Materials with Inverted Singlet–Triplet Energy Gaps" — J. Phys. Chem. Lett. 16, 5213 (2025)

Full publication list on Google Scholar (13+ publications)

• Principal Investigator, DOE-Sandia LDRD Grant (2023) — "Towards efficient light emitters via computational design of molecules with inverted singlet-triplet gaps"
• Sanibel Prize for Outstanding PhD Thesis, Quantum Theory Project, University of Florida (2018)

Languages: Python · C++ · Fortran

ML/AI: PyTorch · TensorFlow · Sci-kit Learn · PyTorch Geometric · LangGraph · GNNs · Transformers · Diffusion Models

Scientific Computing: Numpy · Pandas · PySCF · Psi4 · MPQC

HPC & Cloud: AWS · MPI · OpenMP · BLAS · CUDA · Kubernetes · Docker

Certifications: AWS ML Engineer Associate (2025) · NVIDIA Agentic AI Professional (2026) · Deep Learning Specialization, DeepLearning.AI (2025)

📍 Ontario, Canada