About
I am a doctoral candidate at Rensselaer Polytechnic Institute researching theoretical condensed matter physics. My work focuses on quantum computing, spintronics, and the development of novel computational methods for understanding material properties at the atomic scale. I leverage machine learning techniques and high-performance computing to advance our understanding of quantum systems.
Education
Rensselaer Polytechnic Institute, School of Science
University of Illinois at Urbana-Champaign, College of Liberal Arts and Sciences
Employment
Rensselaer Polytechnic Institute, Troy, New York
- Develop new methods for modelling spin dynamics and transport in semiconductors and metals
- Contribute new features to open source density functional theory software, QimPy (see qimpy.org)
- Developer for the BEAST electrocatalysis database at NREL (see beastdb.nrel.gov)
- Research duties span multiple grants/projects and subfields including spin dynamics, electrocatalysis, and machine learned density functional theory approaches
- Extensive experience with government and academic supercomputing resources for science, including allocation proposals, reporting, and orchestrating workloads in HPC environments
Rensselaer Polytechnic Institute, Troy, New York
- Teaching and grading for undergraduate courses, including Physics 1, Honors Physics 2, Quantum Physics 1, and Experimental Physics
- Received the Walter Eppenstein Award in May 2021, awarded annually to a graduate teaching assistant recognized for excellent teaching
Posed By a Pro LLC, Salt Lake City, Utah
- Develop an Android application which assists the user in posing photographs with a smartphone
- Work extensively with Java, the Android Software Development Kit, and Flutter for developing cross-platform software (iOS/Android)
- Coordinate with a photography expert to design a proprietary method for composing photographs
- Submitted U.S. Patent 15857490 as co-inventor of relevant methodology
University of Utah School of Medicine, Salt Lake City, Utah
- Maintain the proprietary web application which manages the admissions and records department for the School of Medicine
- Develop full-stack web applications and use containerized (Docker) workflows for deployment
- Collaborate with university admissions to identify user experience improvements
- Research and collaborate with a team of developers using a Git-based source-control workflow
- Created a cross-platform (iOS/Android) app for conveniently accessing School of Medicine resources on smartphones
University of Utah School of Medicine, Salt Lake City, Utah
Publications
Physical Review B
Quantum computing and spintronics applications require the discovery of new materials with improved spin dynamics properties. This paper develops new methods for modeling spin dynamics in materials from first principles, incorporating spin-phonon interactions and dephasing due to momentum-dependent g-factors. These methods can help discover better materials for spin-based engineering and research, such as spin transistors and CMOS qubits.
The Journal of Physical Chemistry C
We develop a database of surface calculations for important catalysis pathways intended for machine learning workflows.
Journal of Chemical Physics
We develop a new universal machine learning method for learning nonlocal functionals and apply it to a variety of problems, including classical and electronic density functional theory, the inhomogeneous Ising model, and liquid water. This single model shows excellent agreement for all.
ACS Applied Materials & Interfaces
IEEE Journal of Biomedical and Health Informatics
Projects
I am the frontend developer for the BEAST electrocatalysis database: https://beastdb.nrel.gov This is a project hosted by the National Renewable Energy Lab which uses high-throughput density functional theory calculations to produce a large amount of data useful for electrocatalysis applications. This data is available to the public and intended to be used by machine-learning pipelines for materials screening and design.
Frontend Development Density Functional Theory Machine LearningQimPy is a Python package for Quantum-Integrated Multi-PhYsics, which can perform density functional theory calculations for electronic structure, ab-initio molecular dynamics, and spin transport and dynamics calculations. QimPy uses PyTorch for performance-intensive mathematical tasks in order to leverage increasing support for Artificial Intelligence geared architecture in high performance computing facilities. As such, it supports massively parallel GPU and CPU calculations over MPI.
Python PyTorch Quantum Mechanics High Performance ComputingPatents
Patent No. 15857490
Technique for Displaying Subjects in a Composition
Awards
Rensselaer Polytechnic Institute
The Walter Eppenstein Award recognizes excellence in graduate teaching based on student evaluations and recommendations from professors. I received this award as a teaching assistant for RPI physics courses during 2020 and 2021.