David
Heckerman
Chief Data Scientist, Human Longevity Inc.
E-mail: dheckerman@humanlongevity.comOur HLI-X team in Mountain View is hiring
in the areas of computational biology, quantitative genomics, biostatistics,
bioinformatics, natural language processing, and machine learning. If you are interested, please contact me
at the address above.
Research activities
I
am developing machine learning and statistical approaches for genomics. I am also working on vaccines for HIV
and the common cold (see references below). 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.
While
at Microsoft, I 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.
Selected
publications
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).
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.
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.