Xihong Lin is Professor and former Chair of Biostatistics, and Coordinating Director of the Program in Quantitative Genomics at Harvard School of Public Health, and Professor of Statistics at Harvard University. Dr. Lin works on the development and application of statistical and machine learning methods for the analysis of big and complex genomic and health data, such as large scale whole genome sequencing (WGS) studies and multi-ethnic biobanks, Electronic Health Records, and whole genome variant functional annotations. The methods and tools her lab has developed have been widely used in analyzing large scale WGS and biobank data, including the NHLBI Trans-Omics Precision Medicine Program (TOPMed), the UK biobank, and the NIH All of Us Program. Dr. Lin was elected to the US National Academy of Medicine in 2018 and the US National Academy of Sciences in 2023. She received the 2002 Mortimer Spiegelman Award from the American Public Health Association, the 2006 Presidents’ Award from the Committee of Presidents of Statistical Societies (COPSS). She also received the 2017 COPSS FN David Award, the 2022 National Institute of Statistical Sciences Sacks Award for Outstanding Cross-Disciplinary Research, and the 2022 Zelen Leadership in Statistical Science Award, and the 2024 Founder Award of the Statistical Genomics and Genetics Section of the American Statistical Association. She is an elected fellow of American Statistical Association, Institute of Mathematical Statistics, and International Statistical Institute. Dr. Lin’s statistical research has been supported by the MERIT Award (2007-2015) and the Outstanding Investigator Award (2015-2029) from the National Institute of Health. Dr. Lin is the former Chair of COPSS and the former Editor of several biostatistical journals. She has served on several US National Academies committees

2. 报告题目

Build an end-to-end scalable and interpretable data science ecosystem for big data by integrating statistics, ML, and domain sciences

3. 报告摘要

The data science ecosystem encompasses data fairness, statistical, ML methods and tools, interpretable data analysis, and trustworthy decision-making. Rapid advancements in ML have revolutionized data utilization and enabled machines to learn from data more effectively. Statistics, as the science of learning from data while accounting for uncertainty, plays a pivotal role in addressing complex real-world problems and facilitating trustworthy decision-making. In this talk, I will discuss the challenges and opportunities involved in building an end-to-end scalable and interpretable data science ecosystem that integrates statistics, ML, and domain science. I will illustrate key points using the analysis of whole genome sequencing data and electronic health records by discussing a few scalable and interpretable statistical and ML methods, tools and data science resources. This talk aims to ignite proactive and thought-provoking discussions, foster collaboration, and cultivate open-minded approaches to advance scientific discovery.

4. 报名方式

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