Computational Chemist · Bioinformatician · TechBio Enthusiast
Research Scientist at UR Advanced Therapeutics
I hold a Ph.D. in Biotechnology, followed by two postdoctoral fellowships. At Soongsil University (South Korea), I completely transitioned into a computational data analyst by mastering High-Performance Computing (HPC) and essential technical skill sets. During this time, I developed automated scripts to execute high-throughput virtual screening of massive compound libraries and engineered robust cheminformatics pipelines to accelerate drug discovery workflows. Later, at the University of Vienna (Austria), I focused on building ML-driven preclinical frameworks in collaboration with Roche Pharmaceutical, correlating historical in vivo data points to reduce control groups. Currently, I am a Research Scientist at UR Advanced Therapeutics, supporting the team with the molecular modeling of peptides using advanced molecular dynamics (MD) simulations, as well as developing multiomics data analysis pipelines.
Throughout my career, I have been driven by a deep interest in uncovering insights at the intersection of computation and biology. I specialize in AI-assisted drug discovery, cheminformatics, and computational chemistry, with an ongoing focus on extending my expertise into multiomics data analysis. I maintain a strong enthusiasm for programming languages, advanced TechBio concepts, and their direct application to solving complex biological challenges.
Researching, developing, and integrating next-generation AI workflows—including Agentic AI, Retrieval-Augmented Generation (RAG), and local LLMs via Ollama—to build data-to-insight pipelines for bioinformatics and computational chemistry.
Passionate about open-source tools, reproducible workflows, and collaborative research. I strive to build analyses that are transparent, version-controlled, and easy for others to validate and extend.
Finding balance on the court playing Badminton, hiking outdoors, enjoying Cricket, and time with friends and colleagues.
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