Background

Hello! I’m Jason Dai, a bioinformatics scientist at Thermo Fisher Scientific working to reduce organ transplant rejection by designing better diagnostics. I work with small, non-technical teams to build out their bioinformatics software and analyses toolkits.

Education

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• Gained expertise in the theory and application of cutting-edge machine learning and AI paradigms, including deep learning architectures (neural networks, transformers), Hidden Markov Models (HMMs), and reinforcement learning.
• Cultivated a robust understanding of applied mathematics essential for AI, with a focus on statistical modeling and optimization.
• Designed and implemented advanced computational techniques to extract insights and solve critical challenges across various biological and medical domains.
• Analyzed mutational signatures in relation to cancer evolution and risk at the Schwartz Lab.

 

• Developed a robust interdisciplinary foundation spanning core sciences (chemistry, organic chemistry, physics), advanced mathematics (linear algebra, statistics, advanced calculus), and the life sciences (genetics, molecular biology, biotechnology).
• Honed critical computational and analytical skills through specialized coursework in bioinformatics algorithms, programming, algorithm design, and databases.
• Gained practical engineering perspectives via studies in biotech.
• Developed several bioinformatics software pipelines at the Alexandrov Lab.