I am a Professor of Computer Science and Biology at Duke University. I am the faculty director of the Office of University Scholars and Fellows, I previously directed the Graduate Program in Computational Biology and Bioinformatics, and I am an active member of the Center for Genomic and Computational Biology and the Center for Advanced Genomic Technologies. I have been at Duke since September 2001, when I received my 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.

My research interest is the development of new algorithms in statistical machine learning and artificial intelligence, and on the application of those methods to complex problems in computational genomics. Specific application areas include regulatory genomics and systems biology, although I am also interested in other domains. Current high-level projects include:

  • discovering principles and mapping networks of transcriptional regulation,
  • understanding the role of chromatin organization in enacting this regulation, and
  • revealing the mechanisms that control dynamic cellular processes, like the eukaryotic cell cycle.

I have also done earlier work in:

  • reconstructing accurate protein-protein and domain-domain interaction networks,
  • understanding how information flows in the brain during sensory processing and learning tasks,
  • identifying imprinted genes, their regulatory mechanisms, and their implications for disease, and
  • improving the diagnosis and treatment of disease using high-throughput clinical data.

Although these problems are quite diverse, a number of common themes appear repeatedly throughout my 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.

Contact Information

Prof. Alexander J. Hartemink
Duke University
Department of Computer Science

mailing address: Computer Science, Box 90129, Durham NC 27708-0129
package address: 308 Research Drive, LSRC D239, Durham NC 27708
office location: LSRC Building, Room D239

tel: (919) 660-6514
fax: (919) 660-6519

ORCID: ORCID iD iconhttps://orcid.org/0000-0002-1292-2606