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|Date||Monday, October 28th, 2019|
|Room: Refreshments||Weniger Hall, Room 245 (Statistics Conference Room)|
|Time: Tea and Refreshments with Faculty and Speaker||3:00 pm to 3:45 pm|
|Room: Seminar||Weniger Hall, Room 149|
|Time: Seminar||4:00 pm to 4:50 pm|
|Cost||Free and open to the public|
Scalable Analysis of Massive Health Data: Challenges and Opportunities
Massive data from genome, exposome, and phenome are becoming available at a rapidly increasing rate with no apparent end in sight. Examples include Whole Genome Sequencing data, smartphone data, wearable devices, Electronic Health Records and biobanks. The emerging field of Health Data Science presents statisticians, computer scientists and informaticians, and quantitative scientists, with many exciting research and training opportunities and challenges. Success in health data science requires scalable statistical inference integrated with computational science, information science and domain science. In this talk, I discuss some of such challenges and opportunities, and emphasize the importance of incorporating domain knowledge in health data science method development and application. I illustrate the key points using several use cases, including analysis of data from large scale Whole Genome Sequencing (WGS) association studies, integrative analysis of different types and sources of data using causal mediation analysis, reproducible and replicable research, and cloud computing. I will discuss the data and analytic sources and tools being developed in the ongoing large scale whole genome sequencing studies of the NHGRI Genome Sequencing Program and the NHLBI Trans-Omics Precision Medicine Program of over 500,000 genomes.
For more information about Xihong Lin, click here.