Dr. David Heckerman
Distinguished Scientist, Microsoft Research
One obvious way to get more power out of association studies is to analyze more data. Large studies, however, are often confounded by relatedness among individuals, leading to the need for sophisticated statistical methods such as the linear mixed model. Unfortunately, these models have traditionally required an inordinate amount of computation.
Dr. Heckerman will show how these methods can be made very fast, facilitating their use on large data and will also describe some additional tricks to get more power out of association studies.
OPEN TO THE PUBLIC | REGISTRATION IS NOT REQUIRED
Dr. David Heckerman is manager of the Genomics Group at Microsoft Research. He is known for his work in showing the importance of probability theory in Artificial Intelligence, for developing methods to learn graphical models from data, for designing a vaccine for HIV, and for developing machine learning and statistical approaches for genomics including GWAS. At Microsoft, he has developed numerous applications including machine-learning tools in SQL Server and Commerce Server, the junk-mail filters in Outlook, Exchange, and Hotmail, the troubleshooters in Windows, and the Answer Wizard in Office.
Dr. Heckerman received his Ph.D. (1990) and M.D. (1992) from Stanford University, and is an ACM and AAAI Fellow.