Michael Gibbons-Brown

As a computational physicist, I combine physics and computer science to write and run programs that
simulate atomic interactions at the molecular level. As a science educator, I communicate complicated scientific ideas to diverse audiences in ways that are both informative and engaging.

I have over 10 years of experience using HPC clusters to research semiconductors and semiconductor
devices. My doctoral dissertation focused on the effects of defects on the thermal properties of silicon, and much of my subsequent research has centered on defects in semiconductors. I also have a post-graduate certificate in Artificial Intelligence and Machine Learning. I am currently focusing on the use of Foundation Model AI to calculate machine-learned atomic potentials that are as accurate and flexible as first-principles calculations, but which are also many orders of magnitude faster in large- scale simulations. 

I am an expert in large-scale modeling simulations, parallel processors, graphical processor units (GPU), and I can program in various languages including C/C++, Python, MATLAB, and Fortran. I have experience in all phases of the scientific process including lab management, project and program management, team leadership, budget oversight, research, grant writing, and peer-review, as well as science education and outreach.

Midland College - Professor of Physics - July 2017-Present
● Conduct lectures and give presentations to both specialists and non-specialists of varying levels
● Teach fundamental mathematical concepts underlying physics, computer science, engineering, and modern
applications such as generative AI and Foundation Models in AI/ML
● Mentor student research projects in computer science and computational physics focused on using AI/ML,
and Large Language Models
● Manage the physics department, including budgetary oversight, and grant writing
● Direct research group through a multi-year, international coral reef study
● Research, develop, and construct high-pressure measurement pods for underwater data collection
● Complete data-driven outcomes assessments to chart departmental performance
● Serve as a member of the Midland College Equity Committee and support the College’s role as a federally
designated Hispanic Serving Institution (HSI) by leading cultural initiatives in both Europe and Central America
 

Texas Tech University - Post-Doctoral Researcher in Physics- May 2012-July 2017
● Conduct research focusing on silicon, semiconductor devices, and high performance computing
● Author/coauthor numerous peer-reviewed articles published in journals such as Journal of Applied Physics,
Physical Review B, Modeling and Simulation in Materials Science and Engineering, and Nature/Scientific
Reports.
● Utilize modern computer science and applied mathematics to study complex physical systems
● Program scalable parallel processing code for HPCC and GPU clusters in OpenMP and CUDA
● Develop Machine-Learned Atomic Potentials for silicon nanowires for use in Molecular Dynamics Simulations
● Utilize Density Functional Theory to design and administer atomic-level simulations of the thermal flow and
electrical properties of silicon
● Write C/C++ and Python code used in the simulation calculations
● Analyze data and synthesize findings into easily accessible data visualizations for non-expert stakeholders
● Apply audience-specific public speaking strategies to present research at various conferences
● Develop, recommend, and implement solutions for improving and enhancing team calculations
● Maintain a current working knowledge of the field through extensive literature review

Post Graduate Certificate in Artificial Intelligence and Machine Learning
The University of Texas at Austin, 2024
 

Doctor of Philosophy (Ph.D.) in Physics
Texas Tech University, 2012
 

Bachelor of Science (B.S.) in Engineering Physics
University of North Texas, 2007