Speaker Profile

Ph.D., Sr. Director of Analytics / The Lawrence J. Ellison Institute for Transformative Medicine of USC

Dr. Ruderman’s research at The Ellison Institute focuses on complex systems analysis of cancer. His computational team uses AI techniques to develop efficient image-based predictive biomarkers from pathology. His lab applies live cancer cell imaging to monitor the impact of drugs on intracellular growth signals to identify compounds that impact cancer in new ways. Dr. Ruderman earned his doctorate in theoretical physics from UC Berkeley. He performed postdoctoral research in computational neuroscience at Cambridge University, USC, and The Salk Institute. He pursued cancer research in industry, first in tumor target discovery using integrative genomics at Berlex Biosciences, then in proteomic biomarker discovery at Applied Minds. Dr. Ruderman was Founding Scientist at Applied Proteomics, a biomarker company spun out from Applied Minds in 2007. He joined USC in 2011 as Assistant Professor of Research Medicine. Dr. Ruderman has consulted in the enterprise software, animation, and photonics industries.


A Framework for Bringing Explainable AI to Medicine
Recent advances in AI and machine learning are well-timed to match the greatly increasing volumes of data that medicine is generating. I will discuss a framework for the development of medical AI that is founded on a tight feedback loop among medical domain experts, AI practitioners and AI itself.

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