Generally, my research interests are in computational systems biology and machine learning. Specifically, my work focuses on the development and application of new statistical learning algorithms to complex problems in systems biology.
Although these problems are quite diverse, a number of common themes appear repeatedly throughout Dr. Hartemink's work: probabilistic representations, Bayesian statistics, fusion of information from multiple sources, optimization of joint objective functions, and learning in high-dimensional spaces without over-fitting. Many of these themes are variations on two simple ideas: careful attention to biology in the development of statistical models and the use of informative Bayesian priors to both regularize and guide automated learning.
In addition to the Center for Genomic and Computational Biology, Dr. Hartemink is affiliated with the Center for Systems Biology and was the former director for the Computational Biology and Bioinformatics program. Dr. Hartemink has been at Duke since September 2001, after receiving his Ph.D. from the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology under the supervision of David Gifford, Tommi Jaakkola, and Rick Young.