Research focus
About the research
Can machine learning detect cancer risk from routine clinical imaging before symptoms appear to enable earlier intervention?
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The group develops deep-learning models for personalized mammography-based risk prediction and early cancer detection. Researchers build ML methods for de-novo molecular design and retrosynthesis to change traditional drug-discovery pipelines toward data-driven generation. The team develops generative models such as BoltzGen to create protein binders for biological targets and tools like VaxSeer to predict virus evolution for vaccine strain selection. They also focus on interpretability and robustness for clinical AI to support safer, fairer deployment in healthcare settings.
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