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Degrees: Ph.D., Intelligent systems program under biomedical informatics track, University of Pittsburgh, 2024

Research Interests: My research focuses on developing interpretable machine learning models for precision oncology. As a postdoctoral researcher at the University of Pittsburgh, I work on integrating multi-omics data—including somatic genomic alterations, gene expression, and clinical variables—to predict drug response and survival outcomes in cancer patients. I design attention-based variational autoencoders and reinforcement learning frameworks to enhance both the performance and interpretability of predictive models, with an emphasis on actionable insights for clinical decision-making. My current work addresses the challenges of drug resistance, tumor heterogeneity, and combination therapy through biologically grounded, data-driven approaches, particularly in the context of colon rectal cancer and other solid tumors.