BEGIN:VCALENDAR METHOD:PUBLISH PRODID:-//Apple Computer\, Inc//iCal 1.0//EN X-WR-CALNAME;VALUE=TEXT:USC VERSION:2.0 BEGIN:VEVENT DESCRIPTION:Speaker: Ruishan Liu, Stanford University Talk Title: Machine learning for precision medicine Series: CS Colloquium Abstract: Toward a new era of medicine, our mission is to benefit every patient with individualized medical care. This talk explores how machine learning can make precision medicine more effective and diverse. I will first discuss Trial Pathfinder, a computational framework to optimize clinical trial designs (Liu et al. Nature 2021). Trial Pathfinder simulates synthetic patient cohorts from medical records, and enables inclusive criteria and data valuation. In the second part, I will discuss how to leverage large real-world data to identify genetic biomarkers for precision oncology (Liu et al. Nature Medicine 2022), and how to use language models and causal inference to form individualized treatment plans.\n \n This lecture satisfies requirements for CSCI 591: Research Colloquium Biography: Ruishan Liu is a postdoctoral researcher in Biomedical Data Science at Stanford University, working with Prof. James Zou. She received her PhD in Electrical Engineering at Stanford University in 2022. Her research lies in the intersection of machine learning and applications in human diseases, health and genomics. She was the recipient of Stanford Graduate Fellowship, and was selected as the Rising Star in Data Science by University of Chicago, the Next Generation in Biomedicine by Broad Institute, and the Rising Star in Engineering in Health by Johns Hopkins University and Columbia University. She led the project Trial Pathfinder, which was selected as Top Ten Clinical Research Achievement in 2022 and Finalist for Global Pharma Award in 2021. Host: Yan Liu SEQUENCE:5 DTSTART:20230411T110000 LOCATION:OHE 132 DTSTAMP:20230411T110000 SUMMARY:CS Colloquium: Ruishan Liu (Stanford University) - Machine learning for precision medicine UID:EC9439B1-FF65-11D6-9973-003065F99D04 DTEND:20230411T120000 END:VEVENT END:VCALENDAR