🔭 I am a recent PhD graduate from the University of Massachusetts Amherst, specializing in Process Modeling, Computational Chemistry, and Catalysis, with a certificate in Statistical and Computational Data Science. I have extensive experience in DFT, MC, and MD simulations for modeling catalytic reactions, analyzing material properties, and optimizing chemical processes.

🌱 I am currently expanding my expertise in materials informatics, high-throughput screening, and machine learning applications for materials and catalysis research to drive data-driven innovation in the field. I am also enhancing my coding and algorithmic problem-solving skills by practicing on LeetCode. Additionally, I am actively taking courses to develop a strong understanding of drug discovery principles, including ADMET properties, QSAR modeling, and PK/PD analysis, along with gaining familiarity with CADD methodologies such as molecular docking and structure-based virtual screening.

👯 I am looking to collaborate on computational chemistry projects, materials design, reaction engineering, process modeling, and drug discovery, particularly those involving simulations, molecular-level insights, and data-driven approaches.

🤔 I am looking for help with exploring industry roles that align with my expertise, transitioning from academia to applied research, and leveraging my skills in computational methods to address real-world challenges in materials science, chemical engineering, machine learning, and drug discovery. I am also open to postdoctoral opportunities in these areas, especially for those involving machine learning and AI.

💬 Ask me about catalysis, atomistic simulations, adsorption modeling, high-performance computing (HPC), Python-based computational workflows, machine learning, deep learning and applying data science techniques to chemical research.

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