I am a computational physicist with a Ph.D. in Physics from the University of Central Florida. I specialize in data analysis, high-performance computing, and numerical modeling. With over 8 years of programming experience in Python and shell scripting and a self-taught foundation in C++ and SQL, I bring a robust technical toolkit to solving complex problems.My research has focused on modeling surface phenomena and adsorption processes at the atomic scale, leveraging Density Functional Theory (DFT) and high-performance computing to uncover novel material properties. Key projects include:Investigating the electronic structures and catalytic behaviors of single-atom catalysts for energy-efficient reactions.Exploring ligand-coordinated catalysts and their stabilization mechanisms led to actionable insights for material design.Modeling hybrid organic-inorganic interfaces to predict and simulate atomic-scale behaviors.Developing workflows to analyze large-scale datasets, streamlining calculations, and reducing processing times by 30%.Collaborating with international teams from the USA, Spain, and Germany, I contributed to cutting-edge research published in high-impact journals while gaining invaluable experience in interdisciplinary problem-solving and team dynamics. These efforts honed my skills in managing complex datasets, numerical optimization and designing simulations to validate theoretical predictions.Passionate about finance, I am pivoting to quantitative finance to apply my research-driven mindset and technical expertise to financial modeling and algorithmic trading. My independent study includes exploring algorithmic trading strategies, portfolio optimization, and financial time-series analysis, building a bridge between my computational expertise and quantitative finance challenges.Let’s connect if you're interested in discussing quantitative research, data science, or the intersection of technology and finance!