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Clarifying the Mysteries of Choosing Statistical and Machine-Learning Methods in Genomics Research
Virtual presentation by Jingyi Jessica Li, a professor in the Department of Statistics (primary) and the Departments of Biostatistics, Computational Medicine, and Human Genetics (secondary) at UCLA. Li leads a research group called the Junction of Statistics and Biology, where she and her students focus on developing statistical and computational methods to answer important questions in biological and biomedical sciences and to extract key information from genomics and health-related data.
In this lecture, Lin will clarify common confusions in genomics data analysis by connecting cutting-edge genomics research questions with fundamental statistical and machine-learning methods. In particular, she will focus on the distinctions and choices among the methods that are apparently similar but fundamentally different, so that quantitative genomics researchers will have clear guidelines to follow in their development of bioinformatics tools. Register online.
Li has received multiple awards and grants from National Institutes of Health, the National Science Foundation (NSF), and other institutions, including the Johnson & Johnson WiSTEM2D Scholars Award (2018), a Sloan Research Fellowship (2018), an NSF Faculty Early Career Development (CAREER) Program Award (2019), and a spot on the MIT Technology Review 35 Innovators Under 35 China list (2020).
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