Distinguished Scientist, AmazonE-mail: email@example.com
I am developing machine learning and statistical approaches for a variety of applications including genomics and vaccine design. In my early work, I demonstrated the importance of probability theory in Artificial Intelligence, developed methods to build what are now called AI chatbots, and developed methods to learn graphical models from data including methods for causal discovery.
While at Microsoft, I developed numerous applications including machine-learning 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. 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.
J. Breese and D. Heckerman. Decision-theoretic troubleshooting: A framework for repair and experiment. In Proceedings of Twelfth Conference on Uncertainty in Artificial Intelligence, Portland, OR, pages 124-132. Morgan Kaufmann, August 1996. Microsoft copy. Local copy.
D. Heckerman and E. Horvitz. Inferring Informational Goals from Free-Text Queries. In Proceedings of Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, Morgan Kaufmann, July 1998. Microsoft copy. Local copy.
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. A similar manuscript appears as Bayesian Networks for Data Mining, Data Mining and Knowledge Discovery, 1: 79-119, 1997.
D. Heckerman, C. Meek, and G. Cooper A Bayesian Approach to Causal Discovery. In C. Glymour and G. Cooper, editors, Computation, Causation, and Discovery, pages 141-165. MIT Press, Cambridge, MA, 1999. Local copy. Also in Spirtes et al., Causation, Prediction, and Search, Second Edition, MIT Press 2000.
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.
S. T. Dumais, J. Platt, D. Heckerman and M. Sahami. Inductive Learning Algorithms and Representations for Text Categorization. Proceedings of ACM-CIKM98, November, 1998. Microsoft copy. Local copy.
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.
H. Kang, N. Zaitlen, C. Wade, A. Kirby, D. Heckerman, M. Daly, and E. Eskin, Efficient Control of Population Structure in Model Organism Association Mapping, Genetics, 178:1709-1723, March, 2008 (doi: 10.1534/genetics.107.080101).
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). Preprint.
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). Preprint
D. Heckerman, D. Gurdasani, C. Kadie, C. Pomilla, T. Carstensen, H. Martin, K. Ekoru, R.N. Nsubuga, G. Ssenyomo A. Kamali, P. Kaleebu, C. Widmer, and M.S. Sandhu. Linear mixed model for heritability estimation that explicitly addresses environmental variation. PNAS, 113: 7377–7382, July 2016 (doi: 10.1073/pnas.1510497113).
More about the Heckermans here.