David
Heckerman
Distinguished Scientist, Amazon
E-mail: heckerma@hotmail.comResearch activities
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.
Selected
publications
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.
J. Goodman, D. Heckerman, and R. Rounthwaite. Stopping Spam. Scientific American, April, 2005. Microsoft 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).