In my early work, I demonstrated the importance of probability theory in Artificial Intelligence, and developed methods to learn graphical models from data, including methods for causal discovery. More recently, I am developing machine learning and statistical approaches for biological and medical applications, including HIV vaccine design and genomics (see https://github.com/microsoftgenomics). At Microsoft, I have developed numerous applications including data-mining tools in SQL Server and Commerce Server, the junk-mail filters in Outlook, Exchange, and Hotmail, handwriting recognition in the Tablet PC, text mining software in Sharepoint Portal Server, troubleshooters in Windows, and the Answer Wizard in Office.
· D. Heckerman. A Tutorial on Learning with Bayesian Networks. In Learning in Graphical Models, M. Jordan, ed.. MIT Press, Cambridge, MA, 1999. Also appears as Technical Report MSR-TR-95-06, Microsoft Research, March, 1995. An earlier version appears as Bayesian Networks for Data Mining, Data Mining and Knowledge Discovery, 1: 79-119, 1997.
· C. Lippert, J. Listgarten, Y. Liu, C.M. Kadie, R.I. Davidson, and D. Heckerman. FaST linear mixed models for genome-wide association studies. Nature Methods, 8: 833-835, Oct 2011 (doi:10.1038/nmeth.1681).
· C. Widmer, C. Lippert, O. Weissbrod, N. Fusi, C.M. Kadie, R.I. Davidson, J. Listgarten, and D. Heckerman. Further Improvements to Linear Mixed Models for Genome-Wide Association Studies. Scientific Reports 4, 6874, Nov 2014 (doi:10.1038/srep06874).
· O. Weissbrod, C. Lippert, D. Geiger, and D. Heckerman. Accurate liability estimation improves power in ascertained case-control studies. Nature Methods, Feb 2015 (doi:10.1038/nmeth.3285).
· H. Poon, C. Quirk, C. DeZiel, and D. Heckerman. Literome: PubMed-scale genomic knowledge base in the cloud. Bioinformatics 30, 2840-2842, June 2014.
· F. Pereyra, D. Heckerman, J. Carlson, C. Kadie, D. Soghoian, D. Karel, A. Goldenthal, O. Davis, C. DeZiel, T. Lin, J. Peng, A. Piechocka, M. Carrington, and B. Walker. HIV Control Is Mediated in Part by CD8+ T-Cell Targeting of Specific Epitopes. J. Virol 88 12937-12948, Aug 2014.
· R. Rubsamen, C. Herst, P. Lloyd, D. Heckerman. Eliciting cytotoxic T-lymphocyte responses from synthetic vectors containing one or two epitopes in a C57BL/6 mouse model using peptide-containing biodegradable microspheres and adjuvants. Vaccine 32, 4111-4116, June 2014.
· G. Alter, D. Heckerman, A. Schneidewind, L. Fadda, C. Kadie, J. Carlson, C. Oniangue-Ndza, M. Martin, B. Li, S. Khakoo, M. Carrington, T. Allen, M. Altfeld M. HIV-1 adaptation to NK-cell-mediated immune pressure. Nature, 476 (7358): 96-100, August 2011.
· J. Carlson, Z. Brumme, C. Rousseau, C. Brumme, P. Matthews, C. Kadie, J. Mullins, B. Walker, P. Harrigan, P. Goulder, D. Heckerman. Phylogenetic dependency networks: Inferring patterns of CTL escape and codon covariation in HIV-1 Gag. PLoS Computational Biology, 4(11): e1000225, November 2008.
· J. Breese, D. Heckerman, C. Kadie Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, Morgan Kaufmann, July 1998. May, 1998.
· J. Goodman, D. Heckerman, and R. Rounthwaite. Stopping Spam. Scientific American, April, 2005.
· D. Heckerman. Probabilistic interpretations for MYCIN's certainty factors. In Proceedings of the Workshop on Uncertainty and Probability in Artificial Intelligence, Los Angeles, CA, pages 9-20. Association for Uncertainty in Artificial Intelligence, Mountain View, CA, August 1985. Also in L. Kanal. and J. Lemmer, editors, Uncertainty in Artificial Intelligence, pages 167-196. North-Holland, New York, 1986.
· D. Heckerman. Probabilistic Similarity Networks. MIT Press, Cambridge, MA, 1991.