Applying ML An Ongoing Personal Journey
Bridging Biology, Data, and Machine Learning in Cancer Science – Rileen Sinha
Rileen’s path spans from bioinformatics research and cancer genomics to applying machine learning and AI in translational medicine, giving him a front-row view of how computational biology and AI intersect to advance cancer research.
In this conversation, we’ll explore what truly matters when integrating machine learning into biomedical research, the recurring themes in cancer genomics, and how hands-on learning, competitions, and practical application can shape impactful scientific work.
He’ll cover:
His path from genomics research to applying ML in biomedicine
Lessons from hands-on learning, Kaggle competitions, and practical ML projects
The role of interdisciplinary collaboration in modern cancer research
What truly matters when integrating AI and ML into scientific discovery
Advice for learners entering computational biology and data-driven medicine
About the speaker:
Rileen Sinha is a senior computational biologist and cancer data scientist with extensive experience in genomics research, translational medicine, and AI/ML applications. He has published first-author papers in Nature Communications and Cell Reports Methods, contributed to large-scale consortia including TCGA and CPTAC, and led interdisciplinary projects integrating wet-lab and computational insights.
Rileen continues to expand his expertise in AI and machine learning through hands-on courses and competitions, bridging research and applied data science.
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