Biography

My fascination with modeling the Navier–Stokes equations to solve complex fluid dynamics problems began during my undergraduate studies in Mechanical Engineering at Kwame Nkrumah University of Science and Technology. Translating physical principles into mathematical formulations and bringing them to life through computation inspired me to pursue advanced training in simulation and modeling. This passion has led me to a PhD in Computational Science at The University of Texas at El Paso. My research focuses on developing algorithms and numerical schemes to solve high-order partial differential equations on high-performance computing clusters. I have hands-on experience running large-scale workloads on HPC systems, optimizing performance and scalability for scientific applications. In addition, I am exploring how deep learning techniques can complement traditional numerical methods, accelerating PDE solvers and opening new avenues for addressing complex multi-scale problems. I am also AWS Solutions Architect Associate certified, which strengthens my ability to bridge high-performance computing with modern cloud platforms. As Secretary of the SIAM UTEP Student Chapter, I contribute to fostering collaboration and technical growth among students and researchers. Ultimately, I am driven by a commitment to harness computation to expand the frontiers of scientific discovery.

Academic Status

PhD Student - 2nd

Research Area/Department

Applied Mathematics

Major/Specialty

Computational Science

Degrees Earned or in Progress

BSc. Mechanical Engineering

Academic Preparation

Machine Learning, Advanced Scientific Computing, Deep Learning for Partial differential Equations, Mathematical Modelling, Numerical Analysis

Research/Academic Interests

My research interests include machine learning, high-performance computing, and cloud infrastructure management, with a focus on optimizing GPU-accelerated workloads and scalable systems for scientific discovery.

Computational and Data Science Areas

Applied Mathematics; Artificial Intelligence and Intelligent Systems; Mechanical Engineering; Other Computer and Information Sciences; Other Engineering and Technologies

Motivation

My fascination with how the Navier–Stokes equations could be modeled to solve complex fluid dynamics problems began during my undergraduate studies in Mechanical Engineering at Kwame Nkrumah University of Science and Technology. The ability to translate physical principles into mathematical formulations and then bring them to life through computation inspired me to pursue advanced training in simulation and modeling. This passion has led me to a PhD in Computational Science at The University of Texas at El Paso. My research focuses on developing algorithms and numerical schemes to solve high-order partial differential equations. In addition, I am exploring how deep learning techniques can complement traditional numerical approaches to accelerate PDE solvers and provide new ways of tackling complex multi-scale problems. Complementing my academic path, I bring professional experience as a Site Reliability Engineer and Linux System Administrator, where I automated workflows, implemented monitoring solutions, and maintained large-scale distributed systems. I am also AWS Solutions Architect Associate certified, allowing me to bridge HPC with modern cloud platforms. As Secretary of the SIAM UTEP Student Chapter, I contribute to fostering collaboration and technical growth. Ultimately, I am driven by a commitment to harness computation to push the boundaries of scientific discovery.

Lightning Talk Title

Physics-Informed Machine Learning for High Performance Computing

Keywords (Maximum 20 words)

Scientific Computing; Machine Learning; Deep Learning; Computational Mechanics; High Performance Computing; Phase Field Crystal; Microstructure Evolution; Additive Manufacturing