Interests:

Genomics, vaccine design, computational biology, and machine learning.

Background:

David is developing machine learning and statistical approaches for biological and medical applications including HIV vaccine design and genomics.  In his early work, he demonstrated the importance of probability theory in Artificial Intelligence, and developed methods to learn graphical models from data, including methods for causal discovery.  In his 25 years at Microsoft, he 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. He joined Human Longevity, Inc., in 2017, where he leads an advanced analytics team to tackle the analysis of large-scale medical and genomic data as their Chief Data Scientist.

Press release: http://www.prnewswire.com/news-releases/machine-learning-visionary-david-heckerman-joins-human-longevity-inc-as-chief-data-scientist-300427014.html

Representative publications:

• 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.
• 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.
• 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 Similarity Networks.  MIT Press, Cambridge, MA, 1991.