About Me

Academic Journey

I'm a graduate student in the Applied Mathematics, Statistics & Scientific Computation (AMSC) program at the University of Maryland, College Park. My background sits at the intersection of computer science, meteorology, and applied mathematics, but I am also interested in roles where I can use that mix to solve data-intensive and engineering-focused problems.

My journey into interdisciplinary work began at Pennsylvania State University, where I earned double B.S. degrees in Meteorology and Atmospheric Science, and Computer Science in May 2023. That training gave me a strong foundation in programming, quantitative modeling, data analysis, and problem solving across both physical and computational systems.

That interdisciplinary work shapes how I approach problems. I like moving between theory and implementation, whether that means designing experiments, building data pipelines, debugging model behavior, or turning complex technical ideas into reliable, usable tools.

Current Research

My current research is centered on data assimilation and predictive modeling for geophysical systems, especially ocean and atmospheric applications. In practice, that means combining observational data with large numerical models, evaluating forecast quality, and improving how information moves through an end-to-end computational workflow.

A central part of what I do is implementing and testing various data assimilation systems, running repeated forecast-assimilation cycles, and working with both real and synthetic observations. I also design observing system simulation experiments to evaluate how different data sources and system choices affect downstream performance.

A large part of this work involves building and maintaining the surrounding workflow. I run large model forecasts on HPC systems, manage repeated processing windows, validate outputs against reference datasets, and generate diagnostics for monitoring and analysis. I have also developed tools for statistics, visualization, quality checks, and error analysis to support model evaluation and decision-making.

More broadly, I enjoy work that bridges quantitative reasoning with production-style execution. I am especially interested in machine learning and data-driven approaches when they are paired with strong modeling, diagnostics, and reliable computational workflows.

Technical Interests

My technical interests mainly involves Python-based scientific computing, machine learning, numerical modeling, data assimilation, and parallel computing.

Beyond Academia

In addition to my research, I have a deep passion for coffee brewing. I find joy in the process of making a great cup of coffee, as there are so many variables involved, and any change can significantly impact the final cup. This is where my nerdy side comes into play; I love analyzing and experimenting with variables such as brewing techniques, grind sizes, extraction times, and water chemistry... etc. to achieve the perfect cup. In January 2025, I became a licensed Q Grader, further deepening my appreciation for coffee quality and sensory analysis. For me, crafting coffee is not just a routine, but a way to balance my focus and creativity. You'll often find me brewing a fresh cup, which keeps me energized and inspired throughout my research sessions.