David
Heckerman
Distinguished Scientist, Amazon
E-mail: heckerma@hotmail.comResearch activities
I
am leading a team at Amazon that is contributing scientific and machine
learning expertise to a research collaboration with Fred Hutch that explores
the development of personalized treatments for certain forms of cancer. The
team at Fred Hutch is in the process of completing Phase I of the clinical
trial, and Amazon and Fred Hutch recently published initial results here and here. The personalized
treatment is investigational and hasn’t been approved by any regulatory
body for any use.
I have
been developing machine learning and statistical approaches for a variety of
applications including genomics, vaccine design, AI chatbots, and social-media
algorithms. 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 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 and Clippy in Office.
Selected
publications
J. Veatch, W. Gwin, E.T.
Hall, S. Bhatia, S.M. Lee, L.M. Tachiki, S.S. Tykodi,
N.J. Miller, K.G. Paulson, F.W. Schmitz, A. Heit, T. Zhang, A. Rizzi, M.
Iuliano, B. Seaton, G. Roy, B. Lee, L. Martin, M. Walker, A. Harley, A.I. Safo,
S.A. Danziger, H. Tang, P. Le, L. Price, G. Sadeh, B. Hoane, S. Stockwell, B.
Sarkis, N. Levato, A. Vaidya, N. Sood, C. Yeung, E.W. Newell, R.K. Strong, D.
Heckerman, A.G. Chapuis. A
phase I clinical trial of a personalized neoantigen vaccine in PD-1 inhibitor
refractory metastatic melanoma. The 40th Annual Meeting of the Society for
Immunotherapy of Cancer. Washington DC, October 2025.
G.M. Souza, M.A. Van Sluys,
C.G. Lembke, H. Lee, G.R.A. Margarido, C.T. Hotta, J.W. Gaiarsa,
A.L. Diniz, M. de Medeiros Oliveira, S. de Siqueira Ferreira, M.Y. Nishiyama
Jr, F. ten-Caten, G.T. Ragagnin, P. de Morais
Andrade, R.F. de Souza, G.G. Nicastro, R. Pandya, C. Kim, H. Guo, A.M. Durham,
M. S. Carneiro, J. Zhang, X. Zhang, Q. Zhang, R. Ming, M.C.Schatz,
R. Davidson, A.H. Paterson, and D. Heckerman. Assembly of the 373k gene
space of the polyploid sugarcane genome reveals reservoirs of functional
diversity in the world's leading biomass crop. GigaScience,
8(12), Dec 2019 (doi.org/10.1093/gigascience/giz129).
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).
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
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).
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.
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.
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).
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.
Goodman, D. Heckerman, and R. Rounthwaite.
Stopping
Spam. Scientific American, April, 2005. Microsoft
copy.
I.
Cadez, D. Heckerman, C. Meek, P. Smyth, and S. White, Visualization
of Navigation Patterns on a Web Site Using Model Based Clustering, Data
Mining and Knowledge Discovery, 7:399-424, 2003
D. Geiger and D. Heckerman. Parameter
Priors for Directed Acyclic Graphical Models and the Characterization of
Several Probability Distributions. The Annals of Statistics,
30: 1412-1440, 2002.
D.
Heckerman, D. Chickering, C. Meek, R. Rounthwaite, C. Kadie. Dependency
Networks for Density Estimation, Collaborative Filtering, and Data
Visualization. Journal of Machine Learning Research. 1:49-75,
2000.
S.
T. Dumais, J. Platt, D. Heckerman and M. Sahami. Inductive
Learning Algorithms and Representations for Text Categorization. Proceedings of ACM-CIKM98,
November, 1998. Winner of CIKM Test of Time Award 2017.
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.
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.
D.
Heckerman. A Tutorial on Learning with Bayesian
Networks. In Learning in Graphical Models, M. Jordan,
ed.. MIT Press, Cambridge, MA, 1999.
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.
D.
Geiger, D. Heckerman. A
Characterization of the Dirichlet Distribution Through Global and Local
Independence. The Annals of Statistics, 25:1344-1369,
1997.
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.
D.
Heckerman, R. Shachter. Decision-Theoretic
Foundations for Causal Reasoning. Journal of Artificial
Intelligence Research, 3:405-430, 1995.
D.
Heckerman. Probabilistic Similarity
Networks. MIT Press,
Cambridge, MA, 1991.
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.