Hi there! I’m a computational scientist and researcher with a passion and expertise for solving complex problems at the intersection of machine learning, computational chemistry, and materials science.

As a Staff Scientist at Los Alamos National Laboratory I focus on:

• ML interatomic potentials
• Active learning for chemical data generation
• GPU-enabled PyTorch modules for excited-state simulations
• Deploying/optimizing HPC applications and consulting users

I’m also fascinated by new tech, productivity/ergonomics tools and hacks and scientific visualization. Whether it’s assembling hardware, writing the code or testing new LLMs, I like finding smarter and smoother ways to work.

See a dedicated tab for CV 📄

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Skills

High-Performance
Computing

Computational
Chemistry

Machine
Learning
Workflows

Scientific
Visualization

Project &
Conference Management

Python
PyTorch

ORCA
Gaussian
VASP

Linux
Windows
Shells

Git
Docs
Sphinx

Notion
TickTick
Obsidian

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Work Experience

Staff Scientist | Los Alamos National Laboratory (LANL) | 2024–Present

• Developing PyTorch modules for excited-state simulations
• Maintenance of HPC applications and user consulting
• Active Learning Framework (ALF) integration with HPC systems
• Lead organizer of MLCM conference series

Director’s Postdoctoral Fellow | LANL | 2022–2024

• GPU implementation of Davidson diagonalization for matrix-free methods
• Benchmarking transition path sampling using ML models
• Dataset generation and active learning workflows

Research Assistant | LANL & Utah State University | 2020–2022

• Machine learning parameterization of semi-empirical models
• Long-range electrostatics in ML interatomic potentials
• Generating datasets for training predictive models

Research Assistant | Utah State University | 2018–2020

• Investigated chemical bonding in clusters and materials
• Simulated spectra to align with experimental results
• Expanded HPC infrastructure to support computational projects

Research Internships

Center for Nonlinear Studies | LANL | Summer 2020

• Data generation for ML-driven semi-empirical quantum chemistry

Institute of Physical Organic Chemistry | Russia | summer 2018, 2019

• Designed polyfunctional materials; studied bonding/dynamics in 2D systems

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Education

PhD in Physical/Computational Chemistry | Utah State University | 2018–2022

Advisor: Prof. Alexander I. Boldyrev
magna cum laude | GPA = 4.0

B.Sc in Fundamental & Applied Chemistry | Southern Federal University | 2012–2017

magna cum laude | GPA = 4.0