Contact Information

Prof. Alexander J. Hartemink
Duke University
Department of Computer Science

mailing address: Computer Science, Box 90129, Durham NC 27708-0129
package address: 308 Research Drive, LSRC D239, Durham NC 27708
office location: LSRC Building, Room D239

tel: (919) 660-6514
fax: (919) 660-6519
email: amink at cs.duke.edu

Education

• 2001: Ph.D., Electrical Engineering and Computer Science, MIT
• 1997: S.M., Electrical Engineering and Computer Science, MIT
• 1996: M.Phil., Economics, Oxford University
• 1994: B.S., Mathematics, Duke University
• 1994: B.S., Physics, Duke University
• 1994: A.B., Economics, Duke University

Scholarships, Fellowships, Awards, and Prizes

Post-Faculty

• Senior Member of the International Society for Computational Biology (ISCB, 2015)
• Alfred P. Sloan Fellowship (116 awarded, 2005)
• NSF Faculty Early Career Development Award (CAREER, 2004)
• David and Janet Vaughn Brooks Distinguished Teaching Award (4 named awards, 2007)
• DARPA Computer Science Study Panel (11 panelists, 2008)
• ORAU Ralph E. Powe Junior Faculty Enhancement Award (24 awarded, 2002)

Pre-Faculty

• Rhodes Scholarship (32 awarded, 1994)
• Presidential Scholarship, White House Commission (141 awarded, 1990)
• National Kalin Award for Mathematics (1 awarded, 1990)
• NSF Graduate Research Fellowship (~300 awarded, 1994)
• Hertz Foundation Graduate Research Fellowship Grant (30 awarded, 1998)
• Barry M. Goldwater Memorial Scholarship (~250 awarded, 1992)
• Angier B. Duke Memorial Scholarship (~20 awarded, 1990)
• Karl Menger Award for Mathematics (3 awarded, 1993)
• Julia Dale Prize for Mathematics (4 awarded, 1994)
• Merck/MIT Graduate Fellowship in Informatics (~10 awarded, 1999)
• NIH Genomics Training Grant Fellowship (~20 awarded, 2000)
• Rothermere Fellowship (~20 awarded, 1993)
• Dannenberg Mentorship (3 awarded, 1991)
• National Merit Scholarship (~2000 awarded, 1990)
• National Honor Society Scholarship (100 awarded, 1990)
• Tandy Technology Scholarship (100 awarded, 1990)
• Century III Leaders Scholarship (200 awarded, 1990)

Grants

• “Elucidating the Molecular Basis of Cellular Metal Stress by using Mass Spectrometry-Based Proteomic Methods,” NIH-NIGMS R01 (PA-20-185), PI: Katherine Franz, Role: Collaborator, $1,769,236, 4/5/2022–1/31/2026, R01-GM145035-01A1.
• “Methods to Elucidate the Dynamics of Transcriptional Regulation and Chromatin,” NIH-NIGMS R35 (PAR-GM-19-367), PI: Alexander Hartemink, $2,143,132, 6/1/2021–5/31/2026, R35-GM141795-01.
• “IRTG Engaged in Dissecting and Reengineering the Regulatory Genome,” NSF-OISE IRES Track I (NSF 18-505), PI: Greg Wray, Role: Mentor, $299,626, 5/15/2019–4/30/2022, NSF 1854254.
• “Dissecting and Reengineering the Regulatory Genome,” DFG IRTG Program, PIs: Uwe Ohler and Greg Wray, Role: Co-PI, 1/1/2019–6/30/2023, DFG IRTG 2403.
• “Exploring the Role of Dynamic Chromatin Occupancy in Transcriptional Regulation,” NIH-NIGMS R01, PI: Alexander Hartemink, $1,576,671, 4/1/2016–3/31/2021, R01-GM118551-01.
• “Fundamental Properties of Reservoir Computers,” DARPA-DSO (BAA 15-39), PI: Dan Gauthier, Role: Co-PI, $130,000, 8/10/2016–5/13/2017, supplement to ARO W911NF-12-1-0099.
• “Computational Biology and Bioinformatics Training Program,” NIH-NIGMS T32, PI: Alexander Hartemink (later, Paul Magwene), ~$1,198,756, 7/1/2016–6/30/2021, T32-GM071340-11.
• “Decoding and Reprogramming the Corticosteroid Transcriptional Regulatory Network,” NIH-NHGRI GGR: Genomics of Gene Regulation U01 (RFA-HG-13-012), PI: Tim Reddy, Role: Co-Investigator, ~$5,996,505, 1/5/2015–11/30/2017, U01-HG007900-01.
• “Modular Microbial Strains for the Rapid Scale up of Living Foundry Molecules,” DARPA Living Foundries: 1000 Molecules (BAA 13-37), PI: Michael Lynch, Role: Co-PI, $1,585,812, 5/29/2014–5/9/2016, HR011-14-C-0075.
• “Bioinformatics and Computational Biology Training Program,” NIH-NIGMS T32, PI: Alexander Hartemink (earlier, John Harer), $959,227, 7/1/2011–6/30/2016, T32-GM071340-06A1.
• “High Performance Computing System for Bioinformatics,” NIH-NCRR SIG: Shared Instrumentation Grant S10, PI: Hunt Willard, Role: Co-Investigator, $461,402, 6/1/2009–5/31/2010, S10-RR025590-01.
• “CSSG Phase II: New Computational Methods for Elucidating Transcriptional Regulation During the Eukaryotic Cell Cycle,” DARPA CSSG (BAA 08-22), PI: Alexander Hartemink, $500,000, 5/7/2009–5/6/2012, HR0011-09-1-0040.
• “High Performance Computing System for Bioinformatics,” North Carolina Biotechnology Center Institutional Development Grant, PI: Hunt Willard, Role: Co-PI, ~$117,000.
• “2008 Computer Science Study Group (CSSG),” DARPA CSSG (RA 07-43), PI: Alexander Hartemink, $100,000, 1/1/2008–12/31/2008, HR0011-08-1-0023.
• “Duke Center for Systems Biology,” NIH-NIGMS National Centers for Systems Biology P50 (RFA-GM-07-004), PI: Philip Benfey, Role: Co-Investigator, $14,498,123, 7/10/2007–6/30/2012, P50-GM081883-01.
• “Clinico-Molecular Predictors of Presymptomatic Infectious Disease,” DARPA-SPAWAR (BAA 06-19), PI: Geoff Ginsburg, Role: Co-PI, $6,018,678, 7/1/2007–2/28/2009, N66001-07-C-2024.
• “Identification and Characterization of Epigenetically Labile Genes,” NIH-NIEHS R01, PI: Randy Jirtle, Role: Co-Investigator, $2,421,408, 9/25/2006–6/30/2010, R01-ES015165-01.
• “Integration of IBM Management Software with Campus Blade Clusters in Support of Duke Academic Infrastructure,” IBM SUR: Shared University Research Program, PI: Richard Lucic, Role: Co-PI, ~$250,000.
• “Integrated Systems: Integrative Sciences,” Howard Hughes Undergraduate Biological Sciences Education Program, PI: Dean Robert Thompson, Role: member of steering committee, VIP team leader, ~$1,900,000, 8/1/2006–7/31/2010.
• “CRCNS: Neural Flow Networks in Songbirds,” NIH/NSF CRCNS: Collaborative Research in Computational Neuroscience (NSF 04-514), PI: Alexander Hartemink, $2,023,005, 8/1/2005–7/31/2012, R01-DC007996-01.
• “Alfred P. Sloan Research Fellowship,” Alfred P. Sloan Research Fellowship Program, PI: Alexander Hartemink, $45,000, 9/16/2005–9/15/2007, BR-4493.
• “Discovery of Biomarkers for Lung Cancer Metastasis,” NIH-NCI R01, PI: Ned Patz, Role: Co-Investigator, ~$1,378,000, 04/01/2005–03/31/2009, R01-CA19384-01A1.
• “Cluster Computing Infrastructure for Life Sciences Computing,” IBM SUR: Shared University Research Program, PI: Richard Lucic, Role: Co-PI, $249,965.
• “CAREER: Computational Methods for Learning Dynamic Networks of Biological Regulation and Control,” NSF CAREER: Faculty Early Career Development Award (NSF 02-111), PI: Alexander Hartemink, $487,344, 2/1/2004–1/31/2009, NSF-IIS 0347801.
• “Computational Functional Genomics: Discovering Genetic Regulatory Networks,” ORAU Ralph E. Powe Junior Faculty Enhancement Award, PI: Alexander Hartemink, $10,000, 6/1/2002–5/31/2003.
• “Making Meaning of Genomic Information,” Howard Hughes Undergraduate Biological Sciences Education Program, PI: Dean Robert Thompson, Role: member of steering committee, ~$1,800,000, 8/1/2002–7/31/2006.

Publications

1. Mitra, S., Malik, R., Wong, W., Rahman, A., Hartemink, A., Pritykin, Y., Dey, K., & Leslie, C. (2023) “Single-cell multiome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis.” Nature Genetics, 56, April 2024. pp. 627–636. Online Access.
2. Chen, B., MacAlpine, H., Hartemink, A., & MacAlpine, D. (2023) “Spatiotemporal kinetics of CAF-1–dependent chromatin maturation ensures transcription fidelity during S-phase.” Genome Research, 33, December 2023. pp. 2108–2118. [Supp. Info.]
3. Luo, K., Zhong, J., Safi, A., Hong, L., Tewari, A., Song, L., Reddy, T., Ma, L., Crawford, G., & Hartemink, A. (2022) “Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data.” Genome Research, 32, June 2022. pp. 1183–1198. [Supp. Info.]
4. Li, Y., Hartemink, A., & MacAlpine, D. (2021) “Cell-cycle–dependent chromatin dynamics at replication origins.” Genes, 12, December 2021. pp. 1998:1–13.
5. Mitra, S., Zhong, J., Tran, T., MacAlpine, D., & Hartemink, A. (2021) “RoboCOP: Jointly computing chromatin occupancy profiles for numerous factors from chromatin accessibility data.” Nucleic Acids Research, 49, August 2021. pp. 7925–7938. [Supp. Info.]
6. Tran, T., MacAlpine, H., Tripuraneni, V., Mitra, S., MacAlpine, D., & Hartemink, A. (2021) “Linking the dynamics of chromatin occupancy and transcription with predictive models.” Genome Research, 31, June 2021. pp. 1035–1046. [Supp. Info.]
7. Tripuraneni, V., Memisoglu, G., MacAlpine, H., Tran, T., Zhu, W., Hartemink, A., Haber, J., & MacAlpine, D. (2021) “Local nucleosome dynamics and eviction following a double-strand break are reversible by NHEJ-mediated repair in the absence of DNA replication.” Genome Research, 31, May 2021. pp. 775–788. [Supp. Info.]
8. Mitra, S., Zhong, J., MacAlpine, D. & Hartemink, A. (2020) “RoboCOP: Multivariate state space model integrating epigenomic accessibility data to elucidate genome-wide chromatin occupancy.” Research in Computational Molecular Biology 2020 (RECOMB20), Lecture Notes in Bioinformatics, Schwartz, R., ed. 12074, May 2020. pp. 136–151. [Supp. Info.]
9. McDowell, I., Barrera, A., D'Ippolito, A., Vockley, C., Hong, L., Leichter, S., Bartelt, L., Majoros, W., Song, L., Safi, A., Koçak, D., Gersbach, C., Hartemink, A., Crawford, G., Engelhardt, B., & Reddy, T. (2018) “Glucocorticoid receptor recruits to enhancers and drives activation by motif-directed binding.” Genome Research, 28, September 2018. pp. 1272–1284.
10. Welch, J., Hartemink, A., & Prins, J. (2017) “MATCHER: Manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics.” Genome Biology, 18, 24 July 2017. 138 (pp. 1–19). [Supp. Info.]
11. Welch, J., Hartemink, A., & Prins, J. (2017) “E pluribus unum: United states of single cells.” Research in Computational Molecular Biology 2017 (RECOMB17), Lecture Notes in Bioinformatics, Sahinalp, S.C., ed. 10229, May 2017. pp. 400–401.
12. Mayhew, M., Iversen, E., & Hartemink, A. (2017) “Characterization of dependencies between growth and division in budding yeast.” Journal of the Royal Society Interface, 14, February 2017. 20160993 (pp. 1–12).
13. Sparks, E., Drapek, C., Gaudinier, A., Li, S., Ansariola, M., Shen, N., Hennacy, J., Zhang, J., Turco, G., Petricka, J., Foret, J., Hartemink, A., Gordân, R., Megraw, M., Brady, S., & Benfey, P. (2016) “Establishment of expression in the SHORTROOT-SCARECROW transcriptional cascade through opposing activities of both activators and repressors.” Developmental Cell, 39, 5 December 2016. pp. 585–596.
14. Welch, J., Hartemink, A., & Prins, J. (2016) “SLICER: Inferring branched, nonlinear cellular trajectories from single cell RNA-seq data.” Genome Biology, 17, 23 May 2016. 106 (pp. 1–15).
15. Welch, J., Liu, Z., Wang, L., Lu, J., Lerou, P., Purvis, J., Qian, L., Hartemink, A., & Prins, J. (2016) “SLICER: Inferring branched, nonlinear cellular trajectories from single cell RNA-seq data.” Research in Computational Molecular Biology 2016 (RECOMB16), Lecture Notes in Bioinformatics, Singh, M., ed. 9649, April 2016. pp. 239–240.
16. Zhong, J., Luo, K., Winter, P., Crawford, G., Iversen, E., & Hartemink, A. (2016) “Mapping nucleosome positions using DNase-seq.” Genome Research, 26, March 2016. pp. 351–364.
17. Zhang, Y., Henao, R., Carin, L., Zhong, J., & Hartemink, A. (2016) “Learning a hybrid architecture for sequence regression and annotation.” AAAI Conference on Artificial Intelligence 2016 (AAAI16), February 2016. pp. 1415–1421. Also available at arXiv, arXiv:1512.05219.
18. Scholl, Z., Zhong, J., & Hartemink, A. (2015) “Chromatin interactions correlate with local transcriptional activity in Saccharomyces cerevisiae.” bioRxiv, bioRxiv:021725.
19. Belsky, J., MacAlpine, H., Lubelsky, Y., Hartemink, A., & MacAlpine, D. (2015) “Genome-wide chromatin footprinting reveals changes in replication origin architecture induced by pre-RC assembly.” Genes and Development, 29, 15 January 2015. pp. 212–224.
20. Pfenning, A., Hara, E., Whitney, O., Rivas, M., Wang, R., Roulhac, P., Howard, J., Wirthlin, M., Lovell, P., Ganapathy, G., Mouncastle, J., Moseley, M., Thompson, J., Soderblom, E., Iriki, A., Kato, M., Gilbert, M., Zhang, G., Bakken, T., Bongaarts, A., Bernard, A., Lein, E., Mello, C., Hartemink, A., & Jarvis, E. (2014) “Convergent transcriptional specializations in the brains of humans and song-learning birds.” Science, 346, 12 December 2014. pp. 1256846-1–13. [Author Summary] [Kavli Biggest Science Stories 2014] [Nature] [Science] [New Scientist] [Washington Post] [MIT News]
21. Whitney, O., Pfenning, A., Howard, J., Blatti, C., Liu, F., Ward, J., Wang, R., Audet, J.-N., Kellis, M., Mukherjee, S., Sinha, S., Hartemink, A., West, A., & Jarvis, E. (2014) “Core and region-enriched networks of behaviorally regulated genes and the singing genome.” Science, 346, 12 December 2014. pp. 1256780-1–11. [Author Summary]
22. Zhong, J., Wasson, T., & Hartemink, A. (2014) “Learning protein-DNA interaction landscapes by integrating experimental data through computational models.” Bioinformatics, 30, 15 October 2014. pp. 2868–2874.
23. Zhong, J., Wasson, T., & Hartemink, A. (2014) “Learning protein-DNA interaction landscapes by integrating experimental data through computational models.” Research in Computational Molecular Biology 2014 (RECOMB14), Lecture Notes in Bioinformatics, Sharan, R., ed. 8394, April 2014. pp. 433–447.
24. Meyer, P., Siwo, G., Zeevi, D., Sharon, E., Norel, R., DREAM6 Promoter Prediction Consortium, Segal, E., & Stolovitsky, G. (2013) “Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach.” Genome Research, 23, November 2013. pp. 1928–1937.
25. Mordelet, F., Horton, J., Hartemink, A., Engelhardt, B., & Gordân, R. (2013) “Stability selection for regression-based models of transcription factor-DNA binding specificity.” Intelligent Systems in Molecular Biology 2013 (ISMB13), Bioinformatics, 29, July 2013. pp. i117–i125.
26. Mayhew, M. & Hartemink, A. (2013) “Cell-cycle phenotyping with conditional random fields: A case study in Saccharomyces cerevisiae.” IEEE International Symposium on Biomedical Imaging 2013: From Nano to Macro (ISBI 2013), April 2013. pp. 1062–1065.
27. Guo, X., Bernard, A., Orlando, D., Haase, S., & Hartemink, A. (2013) “Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program.” PNAS, 110, 5 March 2013. pp. E968–E977. [Deconvolution Website] [Author Summary] [Supp. Info.]
28. Perez-Pinera, P., Ousterout, D., Brunger, J., Farin, A., Glass, K., Guilak, F., Crawford, G., Hartemink, A., & Gersbach, C. (2013) “Synergistic and tunable gene activation in human cells by combinations of synthetic transcription factors.” Nature Methods, 10, 3 February 2013. pp. 239–242. [Supp. Info.]
29. Luo, K. & Hartemink, A. (2013) “Using DNase digestion data to accurately identify transcription factor binding sites.” In Pacific Symposium on Biocomputing 2013 (PSB13), Altman, R., Dunker, A.K., Hunter, L., Murray, T., & Klein, T., eds. World Scientific: New Jersey. pp. 80–91. [Supp. Info.] [Code]
30. Landt, S., Marinov, G., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., Bernstein, B., Bickel, P., Brown, B., Cayting, P., Chen, Y., DeSalvo, G., Epstein, C., Euskirchen, G., Fisher-Aylor, K., Gerstein, M., Gertz, J., Hartemink, A., Hoffman, M., Iyer, V., Jung, Y., Karmakar, S., Kellis, M., Kharchenko, P., Li, Q., Liu, T., Liu, X., Ma, L., Milosavljevic, A., Myers, R., Park, P., Pazin, M., Perry, M., Raha, D., Reddy, T., Rozowsky, J., Shoresh, N., Sidow, A., Slattery, M., Stammatoyonnopoulous, J., Tolstorukov, M., White, K., Xi, S., Farnham, P., Lieb, J., Wold, B., & Snyder, M. (2012) “ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia.” Genome Research, 22, September 2012. pp. 1813–1831.
31. Mayhew, M., Guo, X., Haase, S., & Hartemink, A. (2012) “Close encounters of the collaborative kind.” IEEE Computer, Special Issue on Computationally Driven Experimental Biology, 45, March 2012. pp. 24–30. [Cover Feature]
32. Guo, X., Bulyk, M., & Hartemink, A. (2012) “Intrinsic disorder within and flanking the DNA-binding domains of human transcription factors.” In Pacific Symposium on Biocomputing 2012 (PSB12), Altman, R., Dunker, A.K., Hunter, L., Murray, T., & Klein, T., eds. World Scientific: New Jersey. pp. 104–115.
33. Meyer, P., Alexopoulos, L., Bonk, T., Califano, A., Cho, C., de la Fuente, A., de Graaf, D., Hartemink, A., Hoeng, J., Ivanov, N., Koeppl, H., Linding, R., Marbach, D., Norel, R., Peitsch, M., Rice, J., Royyuru, A., Schacherer, F., Sprengel, J., Stolle, K., Vitkup, D., & Stolovitzky, G. (2011) “Verification of systems biology research in the age of collaborative competition.” Nature Biotechnology, 29, September 2011. pp. 811–815.
34. Mayhew, M., Robinson, J., Jung, B., Haase, S., & Hartemink, A. (2011) “A generalized model for multi-marker analysis of cell cycle progression in synchrony experiments.” Intelligent Systems in Molecular Biology 2011 (ISMB11), Bioinformatics, 27, July 2011. pp. i295–i303.
35. Miller, H., Robinson, T., Gordân, R., Hartemink, A., & Garcia-Blanco, M. (2011) “Identification of Tat-SF1 cellular targets by exon array analysis reveals dual roles in transcription and splicing.” RNA, 17, April 2011. pp. 665–674.
36. Robinson, J. & Hartemink, A. (2010) “Learning non-stationary dynamic Bayesian networks.” Journal of Machine Learning Research, 11, December 2010. pp. 3647–3680.
37. Gordân, R., Narlikar, L., & Hartemink, A. (2010) “Finding regulatory DNA motifs using alignment-free evolutionary conservation information.” Nucleic Acids Research, 38, April 2010. p. e90. [Supp. Info.]
38. MacAlpine, H., Gordân, R., Powell, S., Hartemink, A., & MacAlpine, D. (2010) “Drosophila ORC localizes to open chromatin and marks sites of cohesin complex loading.” Genome Research, 20, February 2010. pp. 201–211.
39. Orlando, D., Iversen, E., Hartemink, A., & Haase, S. (2009) “A branching process model for flow cytometry and budding index measurements in cell synchrony experiments.” Annals of Applied Statistics, 3, December 2009. pp. 1521–1541.
40. Wasson, T. & Hartemink, A. (2009) “An ensemble model of competitive multi-factor binding of the genome.” Genome Research, 19, November 2009. pp. 2101–2112.
41. Gordân, R., Hartemink, A., & Bulyk, M. (2009) “Distinguishing direct versus indirect transcription factor-DNA interactions.” Genome Research, 19, November 2009. pp. 2090–2100. [Supp. Info.]
42. Guo, X. & Hartemink, A. (2009) “Domain-oriented edge-based alignment of protein interaction networks.” Intelligent Systems in Molecular Biology 2009 (ISMB09), Bioinformatics, 25, 15 June 2009. pp. i240–i246.
43. Robinson, J. & Hartemink, A. (2009) “Non-stationary dynamic Bayesian networks.” In Advances in Neural Information Processing Systems 21 (NIPS08), Koller, D., Schuurmans, D., Bengio, Y., & Bottou, L., eds. MIT Press: Cambridge, MA. pp. 1369–1376. [Appendix]
44. Orlando, D., Lin, C., Bernard, A., Wang, J., Socolar, J., Iversen, E., Hartemink, A., & Haase, S. (2008) “Global control of cell-cycle transcription by coupled CDK and network oscillators.” Nature, 453, 12 June 2008. pp. 944–947. [Supp. Info.]
45. Gordân, R., Narlikar, L., & Hartemink, A. (2008) “A fast, alignment-free, conservation-based method for transcription factor binding site discovery.” Research in Computational Molecular Biology 2008 (RECOMB08), Lecture Notes in Bioinformatics, Vingron, M. & Wong, L., eds. 4955, April 2008. pp. 98–111. [Supp. Info.]
46. Gordân, R. & Hartemink, A. (2008) “Using DNA duplex stability information for transcription factor binding site discovery.” In Pacific Symposium on Biocomputing 2008 (PSB08), Altman, R., Dunker, A.K., Hunter, L., Murray, T., & Klein, T., eds. World Scientific: New Jersey. pp. 453–464. [Supp. Info.]
47. Lüdi, P., Dietrich, F., Weidman, J., Bosko, J., Jirtle, R., & Hartemink, A. (2007) “Computational and experimental identification of novel human imprinted genes.” Genome Research, 17, December 2007. pp. 1723–1730. [Supp. Info.] [Cover] [Nature Reviews Genetics] [Science] [AP] [Wired]
48. Narlikar, L., Gordân, R., & Hartemink, A. (2007) “A nucleosome-guided map of transcription factor binding sites in yeast.” PLoS Computational Biology, 3, November 2007. pp. 2199–2208.
49. Bernard, A., Vaughn, D., & Hartemink, A. (2007) “Reconstructing the topology of protein complexes.” Research in Computational Molecular Biology 2007 (RECOMB07), Lecture Notes in Bioinformatics, Speed, T. & Huang, H., eds. 4453, April 2007. pp. 32–46.
50. Narlikar, L., Gordân, R., & Hartemink, A. (2007) “Nucleosome occupancy information improves de novo motif discovery.” Research in Computational Molecular Biology 2007 (RECOMB07), Lecture Notes in Bioinformatics, Speed, T. & Huang, H., eds. 4453, April 2007. pp. 107–121. [Supp. Info.]
51. Orlando, D., Lin, C., Bernard, A., Iversen, E., Hartemink, A., & Haase, S. (2007) “A probabilistic model for cell cycle distributions in synchrony experiments.” RECOMB Satellite Conference on Systems Biology 2006, Cell Cycle, 6, February 2007. pp. 478–488.
52. Smith, V., Yu, J., Smulders, T., Hartemink, A., & Jarvis, E. (2006) “Computational inference of neural information flow networks.” PLoS Computational Biology, 2, November 2006. pp. 1436–1449. [Supp. Info.] [Code] [Most Viewed Research Article at PLoS Computational Biology]
53. Bernard, A. & Hartemink, A. (2006) “Evaluating algorithms for learning biological networks.” DREAM Workshop, September 2006.
54. Narlikar, L., Gordân, R., Ohler, U., & Hartemink, A. (2006) “Informative priors based on transcription factor structural class improve de novo motif discovery.” Intelligent Systems in Molecular Biology 2006 (ISMB06), Bioinformatics, 22, July 2006. pp. e384–e392. [Supp. Info.] [Code] [Input Data]
55. Hartemink, A. (2006) “Bayesian networks and informative priors: Transcriptional regulatory network models.” In Bayesian Inference for Gene Expression and Proteomics, Do, K.-A., Müller, P., & Vannucci, M., eds. Cambridge University Press: Cambridge, UK. pp. 401–424.
56. Narlikar, L. & Hartemink, A. (2006) “Sequence features of DNA binding sites reveal structural class of associated transcription factor.” Bioinformatics, 22, January 2006. pp. 157–163.
57. Pratapa, P., Patz, E., & Hartemink, A. (2006) “Finding diagnostic biomarkers in proteomic spectra.” In Pacific Symposium on Biocomputing 2006 (PSB06), Altman, R., Dunker, A.K., Hunter, L., Murray, T., & Klein, T., eds. World Scientific: New Jersey. pp. 279–290. [Larger Figs.]
58. Krishnapuram, B., Williams, D., Xue, Y., Carin, L., Figueiredo, M., & Hartemink, A. (2005) “Active learning of features and labels.” Learning with Multiple Views Workshop at ICML05, August 2005.
59. Lüdi, P., Hartemink, A., & Jirtle, R. (2005) “Genome-wide prediction of imprinted murine genes.” Genome Research, 15, June 2005. pp. 875–884.
60. Krishnapuram, B., Figueiredo, M., Carin, L., & Hartemink, A. (2005) “Sparse multinomial logistic regression: Fast algorithms and generalization bounds.” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 27, June 2005. pp. 957–968. [Code]
61. Hartemink, A. (2005) “Reverse engineering gene regulatory networks.” Nature Biotechnology, 23, May 2005. pp. 554–555.
62. Yin, P. & Hartemink, A. (2005) “Theoretical and practical advances in genome halving.” Bioinformatics, 21, April 2005. pp. 869–879.
63. Bernard, A. & Hartemink, A. (2005) “Informative structure priors: Joint learning of dynamic regulatory networks from multiple types of data.” In Pacific Symposium on Biocomputing 2005 (PSB05), Altman, R., Dunker, A.K., Hunter, L., Jung, T., & Klein, T., eds. World Scientific: New Jersey. pp. 459–470. [Supp. Info.]
64. Krishnapuram, B., Williams, D., Xue, Y., Hartemink, A., Carin, L., & Figueiredo, M. (2005) “On semi-supervised classification.” In Advances in Neural Information Processing Systems 17 (NIPS04), Saul, L., Weiss, Y., & Bottou, L., eds. MIT Press: Cambridge, MA. pp. 721–728.
65. Yu, J., Smith, V., Wang, P., Hartemink, A., & Jarvis, E. (2004) “Advances to Bayesian network inference for generating causal networks from observational biological data.” Bioinformatics, 20, December 2004. pp. 3594–3603.
66. Krishnapuram, B., Hartemink, A., Carin, L., & Figueiredo, M. (2004) “A Bayesian approach to joint feature selection and classifier design.” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 26, September 2004. pp. 1105–1111.
67. Krishnapuram, B., Carin, L., & Hartemink, A. (2004) “Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data.” Journal of Computational Biology, 11, March 2004. pp. 227–242.
68. Krishnapuram, B., Carin, L., & Hartemink, A. (2004) “Gene expression analysis: Joint feature selection and classifier design.” In Kernel Methods in Computational Biology, Schölkopf, B., Tsuda, K., & Vert, J.-P., eds. MIT Press: Cambridge, MA. pp. 299–318.
69. Liu, Q., Krishnapuram, B., Pratapa, P., Liao, X., Hartemink, A., & Carin, L. (2003) “Identification of differentially expressed proteins using MALDI-TOF mass spectra.” ASILOMAR Conference: Biological Aspects of Signal Processing, November 2003.
70. Krishnapuram, B., Carin, L., & Hartemink, A. (2003) “Joint classifier and kernel design.” Kernel Methods in Bioinformatics Workshop at RECOMB03, April 2003.
71. Krishnapuram, B., Carin, L., & Hartemink, A. (2003) “Joint classifier and feature optimization for cancer diagnosis using gene expression data.” In Research in Computational Molecular Biology 2003 (RECOMB03), Vingron, M., Pevzner, P., Istrail, S. & Waterman, M., eds. ACM: New York. pp. 167–175.
72. Smith, V., Jarvis, E., & Hartemink, A. (2003) “Influence of network topology and data collection on network inference.” In Pacific Symposium on Biocomputing 2003 (PSB03), Altman, R., Dunker, A.K., Hunter, L., Jung, T., & Klein, T., eds. World Scientific: New Jersey. pp. 164–175.
73. Yu, J., Smith, V., Wang, P., Hartemink, A., & Jarvis, E. (2002) “Using Bayesian network inference algorithms to recover molecular genetic regulatory networks.” International Conference on Systems Biology 2002 (ICSB02), December 2002.
74. Jarvis, E., Smith, V., Wada, K., Rivas, M., McElroy, M., Smulders, T., Carninci, P., Hayashisaki, Y., Dietrich, F., Wu, X., McConnell, P., Yu, J., Wang, P., Hartemink, A., & Lin, S. (2002) “A framework for integrating the songbird brain.” Journal of Comparative Physiology A, 188, December 2002. pp. 961–980.
75. Krishnapuram, B., Hartemink, A., & Carin, L. (2002) “Applying logistic regression and RVM to achieve accurate probabilistic cancer diagnosis from gene expression profiles.” GENSIPS: Workshop on Genomic Signal Processing and Statistics, October 2002.
76. Smith, V., Jarvis, E., & Hartemink, A. (2002) “Evaluating functional network inference using simulations of complex biological systems.” Intelligent Systems in Molecular Biology 2002 (ISMB02), Bioinformatics, 18:S1. pp. S216–S224.
77. Hartemink, A., Gifford, D., Jaakkola, T., & Young, R. (2002) “Bayesian methods for elucidating genetic regulatory networks.” IEEE Intelligent Systems, special issue on Intelligent Systems in Biology, 17, March/April 2002. pp. 37–43.
78. Hartemink, A., Gifford, D., Jaakkola, T., & Young, R. (2002) “Combining location and expression data for principled discovery of genetic regulatory networks.” In Pacific Symposium on Biocomputing 2002 (PSB02), Altman, R., Dunker, A.K., Hunter, L., Lauderdale, K., & Klein, T., eds. World Scientific: New Jersey. pp. 437–449.
79. Hartemink, A. (2001) “Principled Computational Methods for the Validation and Discovery of Genetic Regulatory Networks.” Massachusetts Institute of Technology, Ph.D. dissertation.
80. Hartemink, A., Gifford, D., Jaakkola, T., & Young, R. (2001) “Maximum likelihood estimation of optimal scaling factors for expression array normalization.” SPIE International Symposium on Biomedical Optics 2001 (BiOS01). In Microarrays: Optical Technologies and Informatics, Bittner, M., Chen, Y., Dorsel, A., & Dougherty, E., eds. Proceedings of SPIE, 4266. pp. 132–140.
81. Hartemink, A., Gifford, D., Jaakkola, T., & Young, R. (2001) “Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks.” In Pacific Symposium on Biocomputing 2001 (PSB01), Altman, R., Dunker, A.K., Hunter, L., Lauderdale, K., & Klein, T., eds. World Scientific: New Jersey. pp. 422–433.
82. Hartemink, A., Mikkelsen, T., & Gifford, D. (2000) “Simulating biological reactions: A modular approach.” DNA Based Computers V. Winfree, E. & Gifford, D., eds. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 54, American Mathematical Society. pp. 111–121.
83. Schechter, S., Parnell, T., & Hartemink, A. (1999) “Anonymous authentication of membership in dynamic groups.” Financial Cryptography '99. Franklin, M., ed. Lecture Notes in Computer Science, 1648, Springer-Verlag. pp. 184–195.
84. Hartemink, A., Gifford, D., & Khodor, J. (1999) “Automated constraint-based nucleotide sequence selection for DNA computation.” Biosystems, 52, October 1999, Elsevier Press. pp. 227–235.
85. Hartemink, A. & Gifford, D. (1999) “Thermodynamic simulation of deoxyoligonucleotide hybridization for DNA computation.” DNA Based Computers III. Rubin, H. & Wood, D., eds. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 48, American Mathematical Society. pp. 25–38.

Software

Banjo

Banjo (Bayesian Network Inference with Java Objects) is a highly efficient, configurable, and cluster-deployable Java package for the inference of static or dynamic Bayesian networks. Banjo is currently limited to discrete variables; however, it can discretize continuous data for you, and is modular and extensible so that new components can be written to handle continuous variables if you wish. The modular design also allows you to mix and match various inference algorithm components to implement different learning procedures, ranging from simulated annealing with random local moves to greedy hillclimbing with all local moves, as well as create new ones.

SMLR

SMLR (Sparse Multinomial Logistic Regression) is an efficient implementation of a true multiclass probabilistic classifier based on the well-studied multinomial logistic regression framework. Within this framework, we adopt a Bayesian perspective, enabling us to incorporate a Laplacian prior (related to LASSO) which promotes the learning of a sparse weight vector. The result is a classifier that can operate either directly on input features and perform automatic feature selection (embedded, not filter or wrapper), or with a kernel and perform automatic sample selection (much like the SVM). The objective function is convex so it has a unique global optimum. SMLR software implements a suite of bound-optimization algorithms that we have developed to find this unique optimum efficiently, even when the number of samples or features is large (at least tens of thousands).

PRIORITY

PRIORITY is a tool for de novo motif discovery in the context of transcription factor (TF) binding sites. It implements a new approach to motif discovery in which informative priors over sequence positions are used to guide the search. Although this approach will work for any motif model and any search/optimization strategy, the initial version of PRIORITY adopts a PSSM model and collapsed Gibbs sampling. PRIORITY is packaged with priors designed to measure how likely each sequence position is to be bound by three specific structural classes of TFs: basic leucine zipper, forkhead, and basic helix loop helix. In addition to discovering TF binding sites and a motif model for those binding sites, PRIORITY also predicts the structural class of the TF recognizing the binding sites.

COMPETE

COMPETE predicts the quantitative occupancy level of DNA binding factors—including transcription factors, nucleosomes, and the origin recognition complex—that compete to bind along the genome. The prediction reflects the quantitative occupancy of each factor at each genomic position, and is computed as a weighted average over the entire thermodynamic ensemble of all potential binding configurations. Each of those configurations has a certain probability, which itself depends on the different sequence affinities and concentrations of the various factors in the model. The goal of the COMPETE software package is to be high performance, flexible, and extensible.

RoboCOP

RoboCOP produces chromatin occupancy profiles (COPs) automatically from any combination of chromatin accessibility data, ideally fragment data from paired-end MNase-seq, but alternatively accessiblity data from ATAC-seq or DNase-seq. RoboCOP uses a multivariate hidden Markov model (HMM) to compute a probabilistic occupancy landscape of nucleosomes and hundreds of TFs genome-wide at single-nucleotide resolution. The link above is to a GitHub repository for RoboCOP, which is primarily implemented in Python and C, but calls some functions in R.

TOP

TOP predicts the quantitative occupancy of hundreds of transcription factors (TFs) from a single DNase- or ATAC-seq experiment, allowing one to efficiently study how TF binding changes genome-wide across cell types, over time, or across varying genetic backgrounds. TOP, which stands for TF occupancy profiler, uses a Bayesian hierarchical regression framework and is implemented in R. The link above is to a GitHub repository; precomputed tracks of quantitative predictions of genome-wide occupancy for hundreds of TF × cell type combinations are being uploaded and will be made available shortly.

MILLIPEDE

MILLIPEDE uses DNase-seq data to predict whether a given site in the genome is likely to be bound by a transcription factor. Potential binding sites are determined based on low-threshold TF motif matching, and then DNase data at the site and at its upstream and downstream flanking regions are provided to a trained logistic regression classifier to predict whether the site is indeed bound in this experiment. The model benefits from supervision, but semi-supervised and unsupervised variants work nearly as well. MILLIPEDE bins the DNase data to reduce the size of the parameter space and mitigate against overfitting. Its name is a pun on the fact that its number of parameters is at least an order of magnitude smaller than the popular CENTIPEDE model that preceded it.

NucID

NucID (Nucleosome Identification using DNase) is Python software that uses DNase-seq data to map nucleosome positions genome-wide. Nucleosome positions are identified on the basis of nucleosome scores that are computed from single-end DNase-seq read counts using a Bayes-factor–based method. The nucleosome scores reflect the relative posterior probability that a given nucleosome-sized window (147 bp) is occupied by a nucleosome versus not. The link above is to a GitHub repository that also contains a Jupyter notebook demonstrating how to use the software. Pre-computed tracks of genome-wide nucleosome scores are also available here.

CLOCCS

CLOCCS (Characterizing Loss of Cell Cycle Synchrony) is a branching process model that precisely characterizes how a population of synchronized cells lose synchrony as they repeatedly progress through the cell division cycle. Parameters of the model capture imperfections in the initial synchrony, gradual loss of synchrony due to cell-to-cell variation in cell cycle progression, and losses of synchrony due to potential asymmetric cell division. The model produces precise estimates of the fraction of the cell population at any stage of the cell cycle at any point in time (including times not measured). These estimates are determined by an MCMC fit to observational data, which can be flow cytometric measurements of DNA content and/or counts of binary markers of progression, for example budding index or other measures arising from fluorescence microscopy. The model can also account for the fact that some small but non-negligible number of cells may be dead or halted during the experiment (and thus not progressing through the cell cycle like the other cells, muddying the observational data).

DECONV

Our DECONV algorithm takes the parameter estimates from CLOCCS software, described above, and uses them to deconvolve time-course data collected from a population of cells during a cell cycle synchrony-release experiment. In so doing, the algorithm learns a more accurate view of dynamic cell-cycle processes, free from the convolution effects associated with imperfect cell synchronization. Through wavelet-basis regularization, our DECONV method sharpens signal without sharpening noise, and can remarkably increase both the dynamic range and the temporal resolution of time-series data. The link above is to a website sharing the results of this algorithm applied to a yeast cell-cycle transcription time course.

Teaching

• Spring 2025: Sabbatical Leave
• Fall 2024: Sabbatical Leave
• Spring 2024: Introduction to Computational Genomics (COMPSCI 260)
• Fall 2023: Sophomore Seminar: Human Flourishing in a Digital Age (ETHICS/COMPSCI 247S)
• Spring 2023: Senior Seminar: What Should I Do With My Life? (ETHICS 450S)
• Fall 2022: Introduction to Computational Genomics (COMPSCI 260)
• Spring 2022: Introduction to Computational Genomics (COMPSCI 260)
• Fall 2021: Research Leave
• Spring 2021: Introduction to Computational Genomics (COMPSCI 260)
• Fall 2020: Introduction to Computational Genomics (COMPSCI 260)
• Spring 2020: Freshman Seminar: The Examined Life (ETHICS 89S)
• Fall 2019: Introduction to Computational Genomics (COMPSCI 260)
• Spring 2019: Freshman Seminar: The Examined Life (ETHICS 89S)
• Fall 2018: Introduction to Computational Genomics (COMPSCI 260)
• Spring 2018: Freshman Seminar: The Examined Life (ETHICS 111S)
• Fall 2017: Introduction to Computational Genomics (COMPSCI 260)
• Spring 2017: Freshman Seminar: The Examined Life (ETHICS 111S)
• Fall 2016: Introduction to Computational Genomics (COMPSCI 260)
• Spring 2016: Sabbatical Leave
• Fall 2015: Sabbatical Leave
• Spring 2015: Computational Systems Biology (COMPSCI/CBB 662)
• Fall 2014: Introduction to Computational Genomics (COMPSCI 260)
• Spring 2014: Research Leave
• Fall 2013: Introduction to Computational Genomics (COMPSCI 260)
• Spring 2013: Computational Systems Biology (COMPSCI/CBB 662)
• Fall 2012: Introduction to Computational Genomics (COMPSCI 260)

Before this point, courses at Duke had different numbers, which were lower (below 200 indicated undergraduate and above 200 indicated graduate):

• Spring 2012: Research Leave
• Fall 2011: Introduction to Computational Genomics (CPS 160)
• Spring 2011: Computational Systems Biology (CPS/CBB 262)
• Fall 2010: Introduction to Computational Genomics (CPS 160)
• Spring 2010: Sabbatical Leave (teaching in Kenya)
• Fall 2009: Sabbatical Leave (teaching in Kenya)
• Spring 2009: Computational Systems Biology (CPS/CBB 262)
• Fall 2008: Introduction to Computational Genomics (CPS 160)
• Spring 2008: Introduction to Computational Genomics (CPS 160)
• Fall 2007: Systems Biology and Machine Learning (CPS 296)
• Spring 2007: Introduction to Computational Genomics (CPS 160)
• Spring 2007: Algorithms in Computational Biology (CPS 260/CBB 230)
• Fall 2006: Research Leave
• Spring 2006: Introduction to Computational Genomics (CPS 160)
• Spring 2006: House Course: Patterns (HOUSE 79)
• Fall 2005: Junior Research Leave
• Spring 2005: Introduction to Computational Genomics (CPS 160)
• Fall 2004: Algorithms in Computational Biology (CPS 260/BGT 204)
• Spring 2004: Computational Functional Genomics (CPS 262/BGT 211)
• Fall 2003: Introduction to Computational Genomics (CPS 160)
• Spring 2003: Computational Functional Genomics (CPS 296/BGT 208)
• Fall 2002: Algorithms in Computational Biology (CPS 260/BGT 204)
• Spring 2002: Computational Functional Genomics (CPS 296/BGT 208)
• Fall 2001: Introduction to Research in Computer Science (CPS 300)

Students

Current Students

Doctoral

Supervising

• Nhat Duong (CBB)
• Kevin Moyung (CBB), co-supervised with Dave MacAlpine
• Trung Tran (CS)

Committees

• Leo Biral (CBB, Sandeep Dave)
• Jack Goffinet (CS, David Carlson)
• Kuei-Yueh Ko (CBB, Tim Reddy)
• Mikie Phan (Max Planck Institute for Molecular Genetics, Daniel Ibrahim)
• Kyle Pinheiro (CS, Raluca Gordân)
• Mario Rubio (Max Planck Institute for Molecular Genetics, Andreas Mayer)
• Harshit Sahay (CBB, Raluca Gordân)
• Andrew Soborowski (CBB, Amy Schmid)
• Devang Thakkar (CBB, Sandeep Dave)
• Sarah van Dierdonck (CBB, Phil Benfey)
• Changxin Wan (CBB, Jason Ji)
• Yingfan Wang (CS, Cynthia Rudin)
• Hana Wasserman (CBB, Raluca Gordân)
• Pascal Wetzel (Max Delbrück Center for Molecular Medicine, Uwe Ohler)

Masters

Committees

• Boqian Shi (CS, Tan Songdechakraiwut)

Undergraduate

Supervising

• Kash Sreeram (Program II, Andrew West*)

         * indicates that another faculty member is the primary supervisor

Committees

• Yuxi (Jaden) Long (CS, Bruce Donald)
• Helen Xu (CS/Biology, Lingchong You)

Former Students

Postdoctoral

Supervising

• Dr. Yulong Li (CS)
  [joined GeneCast Biotechnology in China]
• Dr. Fantine Mordelet (CS)
  [started a postdoc with Barbara Engelhardt at Duke; later joined Showtime Analytics in Ireland]
• Dr. Victoria Anne Smith (Neurobiology), co-supervised with Erich Jarvis
  [joined the faculty at University of St. Andrews, Scotland]

Doctoral

Supervising

• Jason Belsky (CBB), co-supervised with Dave MacAlpine
  [joined BASF Plant Science; later joined OmicSoft, a QIAGEN company]
• Allister Bernard (CS)
  [joined AlphaSimplex Group]
• Raluca Gordân (CS)
  [started a postdoc with Martha Bulyk at Harvard Medical School/MIT;
  later joined the faculty at Duke; Alfred P. Sloan Fellow]
• Xin Guo (CS)
  [joined Gilead Sciences; later founded Apostle, a biotech startup]
• Yezhou Huang (CS)
  [joined Facebook]
• Balaji Krishnapuram (ECE), co-supervised with Larry Carin
  [joined Siemens Medical Solutions; winner of 2019 ACM SIGKDD Service Award]
• Philippe Lüdi (CBB), co-supervised with Fred Dietrich and Randy Jirtle
  [joined AlphaSimplex Group]
• Kaixuan (Kevin) Luo (CBB)
  [started a postdoc with Matthew Stephens at Chicago]
• Michael Mayhew (CBB)
  [joined Lawrence Livermore National Lab; later joined Inflammatix]
• Sneha Mitra (CS)
  [started a postdoc with Christina Leslie at Memorial Sloan Kettering Cancer Center]
• Leelavati Narlikar (CS)
  [started a postdoc with Ivan Ovcharenko at NCBI (National Center for Biotechnology Information);
  later named a Ramanujan Fellow at the National Chemical Laboratory in Pune, India]
• David Orlando (CBB), co-supervised with Philip Benfey and Steve Haase
  [started a postdoc with Rick Young at MIT/Whitehead Institute; later joined Syros Pharmaceuticals]
• Andreas Pfenning (CBB), co-supervised with Erich Jarvis
  [started a postdoc with Manolis Kellis at MIT/Broad Institute;
  later joined the faculty at Carnegie Mellon; Alfred P. Sloan Fellow]
• Josh Robinson (CS)
  [joined Signal Innovations Group, a machine learning startup; later joined BAE Systems]
• Todd Wasson (CBB)
  [joined Lawrence Livermore National Lab; later joined Netflix]
• Jing Yu (ECE), co-supervised with Erich Jarvis and Paul Wang
  [joined Novartis Institute for Biomedical Research]
• Jianling Zhong (CBB)
  [joined Groupon; later joined LinkedIn; later joined Apple; later joined Google]

Committees

• Alan Boyle (CBB, Terry Furey and Greg Crawford)
  [started a postdoc with Mike Snyder at Stanford;
  later joined the faculty at University of Michigan]
• Bonnie Chen (Cancer Biology, Dave MacAlpine)
  []
• Peng Dong (CBB, Bernard Mathey-Prevot and Lingchong You)
  [started a postdoc with James Liu Lan at HHMI Janelia]
• Yanting Dong (ECE, Larry Carin)
  [joined Guidant (acquired by Boston Scientific)]
• Matt Eaton (CBB, David MacAlpine)
  [started a postdoc with Manolis Kellis at MIT/Broad Institute; later joined Syros Pharmaceuticals]
• Lee Elizabeth Edsall (UPGG, Greg Crawford)
  [starting a postdoc with Matt Weirauch at Cincinnati Children's Hospital]
• Pablo Gainza-Cirauqui (CS, Bruce Donald)
  [started a postdoc with Bruno Correia at EPFL]
• Ashish Gehani (CS, Gershon Kedem)
  [started a postdoc with Surendar Chandra at Notre Dame; later joined SRI International]
• Ivelin Georgiev (CS, Bruce Donald)
  [joined the NIH Vaccine Research Center as a research fellow;
  later joined the faculty at Vanderbilt]
• Stoyan Georgiev (CBB, Uwe Ohler and Sayan Mukherjee)
  [started a postdoc with Jonathan Pritchard at Chicago (moved to Stanford); later joined Alphabet/Google]
• Monica Gutierrez (UPGG, Dave MacAlpine)
  [started a postdoc with Debbie Winter at Northwestern]
• Rylee Hackley (UPGG, Amy Schmid)
  []
• Dina Hafez (CS, Uwe Ohler)
  [joined Natera]
• Lanie Happ (CBB, Sandeep Dave)
  [joined Data Driven Bioscience, a cancer genomics startup]
• Jonathan Jesneck (BME, Joseph Lo)
  [started a postdoc with Jill Mesirov and Todd Golub at Broad Institute;
  later joined the Field Intelligence Lab at MIT as a Research Scientist; later joined Firefly]
• Shihao Ji (ECE, Larry Carin)
  [joined Yahoo; later joined Microsoft]
• Jonathan (JJ) Jou (CS, Bruce Donald)
  [started a postdoc with Bruce Donald]
• Laura Kavanaugh (UPGG, Fred Dietrich)
  [joined Syngenta Biotechnology]
• Mitch Levesque (UPGG, Philip Benfey)
  [started a postdoc at Max Planck Institute for Developmental Biology;
  later joined the faculty at University of Zurich]
• Qiuhua Liu (ECE, Larry Carin)
  [started a postdoc at Schlumberger-Doll Research]
• Jiuliu Lu (ECE, Larry Carin)
  [joined Beckman Coulter, maybe?]
• Dan Mace (CBB, Uwe Ohler)
  [started a postdoc with Bob Waterston at University of Washington; later joined Microsoft]
• Jeff Martin (CS, Bruce Donald)
  [joined Scalgo, a computational geometry startup]
• Vincentius Martin (CS, Raluca Gordân)
  [joined GlaxoSmithKline]
• Firas Midani (CBB, Lawrence David)
  [started a postdoc with Robert Britton at Baylor College of Medicine]
• Melyssa Minto (CBB, Anne West)
  [joined Research Triangle Institute]
• Ian McDowell (CBB, Tim Reddy)
  [joined Element Genomics (acquired by UCB)]
• Nabil Mustafa (CS, Pankaj Agarwal)
  [started a postdoc at Max Planck Institute for Informatics and later EPFL;
  later joined the faculty at ESIEE Paris]
• Johannes Norrell (Physics, Josh Socolar)
  [joined a government agency]
• Constantin Pistol (CS, Alvy Lebeck and Chris Dwyer)
  [joined Apple]
• Sudheer Sahu (CS, John Reif)
  [joined Microsoft; later joined AT&T Interactive]
• Jungkyun Seo (CBB, Tim Reddy)
  [started a T32-funded postdoc with Kari North and Dana Hancock of the CHARGE Consortium]
• Nathan Sheffield (CBB, Greg Crawford and Terry Furey)
  [started a postdoc with Christoph Bock at Center for Molecular Medicine in Vienna;
  later joined the faculty at Virginia]
• Ning Shen (Cancer Biology, Raluca Gordân)
  [joined Fulcrum Therapeutics]
• Tianqi Song (CS, John Reif)
  [started a postdoc with Lulu Qian at CalTech]
• Jason Stajich (UPGG, Fred Dietrich)
  [Miller Fellowship; started a postdoc with John Taylor at UC Berkeley;
  later joined the faculty at UC Riverside; winner of the Alexopoulos Prize]
• Peter Tonner (CBB, Amy Schmid)
  [joined NIST (National Institute of Standards and Technology); NRC Research Assistantship]
• Florian Wagner (CBB, Sandeep Dave)
  [started a postdoc with Itai Yanai at NYU]
• Hanghang Wang (CBB, Svati Shah)
  [continued medical residency in cardiac surgery at Duke]
• Rui Wang (CBB, Erich Jarvis)
  [joined Beijing Prosperous Biopharm as CEO and President]
• Jennifer Weidman (UPGG, Randy Jirtle)
  [started a postdoc with Randy Jirtle at Duke; later joined Mallinckrodt Pharmaceuticals]
• Joshua Welch (UNC CS, Jan Prins)
  [started a postdoc with Evan Macosko at the Broad Institute;
  later joined the faculty at University of Michigan]
• Ya Xue (ECE, Larry Carin)
  [joined Centice, a sensor technology startup; later joined GE Global Research]
• Gürkan Yardımcı (CBB, Greg Crawford and Uwe Ohler)
  [started a postdoc with Bill Noble at Washington;
  later joined the faculty at Oregon Health and Science University]
• Peng Yin (CS, John Reif)
  [Outstanding Ph.D. Dissertation; started a postdoc with Niles Pierce and Erik Winfree at CalTech;
  later joined the faculty at Harvard]
• Jianyang (Michael) Zeng (CS, Bruce Donald)
  [joined the faculty at Tsinghua University in China]
• Carolyn Zhang (BME, Lingchong You)
  [joined GlaxoSmithKline]
• Jenny Zhang (UPGG, Sandeep Dave)
  [joined Immuneering Corporation]
• Yizhe Zhang (CBB, Larry Carin)
  [joined Microsoft Research]
• Zhihong (Joe) Zhang (UPGG, Fred Dietrich)
  [started a postdoc with Stan Fields at University of Washington; later joined Illumina]
• Shiwen Zhao (CBB, Sayan Mukherjee and Barbara Engelhardt)
  [joined FeatureX, a machine learning startup]

Research Initiation Project Committees

• Austin Alexander (CS, Barbara Engelhardt)
• Alan Davidson (CS, Carlo Tomasi)
• Mark Fashing (CS, Carlo Tomasi)
• Abhijit Guria (CS, Herbert Edelsbrunner)
• Charles (Chip) Killian (CS, Amin Vahdat)
• Branka Lakic (CS, Carlo Tomasi)
• Urmi Majumder (CS, John Reif)
• Mac Mason (CS, Ron Parr)
• Rajiv Nagipogu (CS, John Reif)
• Wenbin Pan (CS, Herbert Edelsbrunner)

Rotations

• Rachel Ballantyne (MD/PhD CS)
• Diana Fusco (CBB)
• Karthik Jayasurya (CBB)
• Ziqi (Alvin) Lu (CMB)
• Samuel Ramirez (CBB)
• George Tretyakov (CBB)

Masters

Supervising

• Abrita Chakravarty (CS)
  [joined Wolfram Research]
• Yasunori Hongo (CS)
  [joined Bank of Japan]
• Pallavi Pratapa (CS)
  [Outstanding Master's Thesis; joined UBS Warburg; later joined Lenovo]
• David Vaughn (CS)
  [joined Modality, a software startup; later joined Measurement Incorporated]

Committees

• Kanishk Asthana (BME, Lingchong You)
• Avik Bhattacharya (CS, Terry Furey)
• Anaghe Gupta (CS, Carlo Tomasi)
• Nicholas Haynes (Physics, Dan Gauthier)
• Kuan-ming Lin (CS, Larry Carin)
• Zachery Mielko (CS, Raluca Gordân)
• Arvind Sastry (CS, Carlo Tomasi)
• Paul Shealy (CS, Carlo Tomasi)
• Rumen Stamatov (CBB, Raluca Gordân)
• Jie Xu (CS, Uwe Ohler)
• Ming Yang (CS, John Reif)
• Farica Zhuang (CS, Raluca Gordân)

Undergraduate

Supervising

• Alexandra (Tally) Balaban (Math)
  [joined NESCent (National Evolutionary Synthesis Center); later entered grad school in Biostatistics at UNC]
• Kshipra Bhawalkar (Math/CS)
  [entered grad school in Computer Science at Stanford; later joined Google Research]
• Jason Bosko (ECE/CS)
  [joined SAS]
• Scott Brothers (CS/Math)
  [Alex Vasilos Award; joined Microsoft]
• Brian Bullins (Math/CS)
  [Graduation with Distinction; entered grad school in Computer Science at Princeton;
  later joined the faculty at Purdue]
• Jer-Yee (John) Chuang (ECE/CS)
  [Graduation with High Distinction; entered grad school in Bioinformatics at UCSF]
• Forest Cummings-Taylor (CS/Philosophy)
  [joined Microsoft]
• Matt Edwards (CS/Math)
  [Graduation with High Distinction; Alex Vasilos Award; joined IonTorrent;
  later entered grad school in Computational and Systems Biology at MIT; later joined Verily]
• Daphne Ezer (CS/Biology)
  [Marshall Scholar; Graduation with Highest Distinction; Duke Faculty Scholar; Alex Vasilos Award;
  entered grad school in Genetics at Cambridge; later named a Turing Research Fellow]
• Eric Fountain (Math)
  [entered grad school in Physics at Princeton]
• Mike Gloudemans (CS/Biology)
  [Alex Vasilos Award; Graduation with Highest Distinction; entered grad school in Biomedical Informatics at Stanford]
• Daniel Greenblatt (CS)
  [entered grad school in Human Computer Interaction at Georgia Tech]
• Kelvin Gu (post-baccalaureate), co-supervised with David Dunson
  [entered grad school in Statistics at Stanford; later joined Google AI]
• Paul Heymann (CS/Philosophy)
  [Graduation with High Distinction; entered grad school in Computer Science at Stanford]
• Boyoun (Sarah) Jung (CS/Biology)
  [interned for the Office of the President of the Republic of Korea at Cheong Wa Dae ("Blue House");
  later entered medical school at Dartmouth]
• Brody Kellish (ECE/CS/Math)
  [joined Google]
• Newton Kwan (Physics)
  [joined Zume, a food supply chain analytics company]
• Abigail Lin (CS/Biology)
  [entered medical school at Johns Hopkins]
• Charles Lin (Biology)
  [entered grad school in Computational and Systems Biology at MIT;
  later joined the faculty at Baylor College of Medicine]
• Jonathan Mathew (CS)
  [entered medical school at UNC]
• Jackson (Jack) Michuda (CS/Biology)
  [joined Tempus]
• Kyle Moran (CS/Biology)
  [Graduation with Distinction; entered grad school in Computational Biology at Duke]
• Jimmy Mu (CS)
  [Graduation with Distinction; joined Microsoft]
• Hayley Pearson (CS)
• Nikhil Saxena (BME/ECE)
  [joined Yelp]
• Bella Smith (Public Policy)
  [joined MuddyWater, a film production company]
• Michael Vogelsong (BME)
  [joined Amazon]
• Pranav Warman (CS/Biology)
  [entered medical school at Duke]
• Austin Weiss (Computational Biology)
  [entered medical school at Yale]
• Aaron Wise (ECE/CS)
  [joined Google; later entered grad school in Computational Biology at CMU; later joined Illumina]
• Albert Xue (Math/CS)
  [entered grad school in Bioinformatics at UCLA]
• Alex Yearley (CS)
  [started a research position at Brigham and Women's Hospital]
• Jia Zeng (CS/ECE)
  [joined LinkedIn]
• Derek Zhou (CS/Biology)
  [Graduation with Distinction; joined Citigroup]

Committees

• Clay Baker (CS, Diego Bohórquez)
  [Graduation with Distinction; applying to medical schools]
• Andrew Declercq (CS, Ron Parr)
  [joined IBM]
• Brian Du (Program II, Zhicheng Ji)
  [Graduation with Distinction; entered medical school at Baylor College of Medicine]
• Rachel Freedman (CS/Psychology, Vincent Conitzer)
  [Graduation with High Distinction; joined Galatea Associates;
  later entered grad school in Computer Science at UC Berkeley]
• JT Galla (Neuroscience, Jonathan Young)
  [Graduation with Distinction; applying to medical schools]
• Rita Glazer (CS, Jason Somarelli)
  [Graduation with Distinction; applying to medical schools]
• Sid Gopinath (CS, Robert Calderbank)
  [Graduation with Distinction; joined Namely]
• Jerry Huang (CS, David Banks and Adele Quigley-McBride)
  [Graduation with Distinction]
• Zanele Munyikwa (CS, Jeff Forbes)
  [Graduation with Distinction; started a research fellowship at Stanford School of Business;
  later entered grad school at MIT Sloan School of Business]
• Patrick Paczkowski (CS, Terry Furey)
  [Graduation with Distinction; entered grad school in Computer Science at Yale]
• Aditya Sridhar (CS/ECE, Krish Chakrabarty)
  [Graduation with Highest Distinction; entered grad school in Computer Science at Columbia]
• Katherine (Beth) Trushkowsky (CS, Jeff Forbes)
  [Graduation with High Distinction; entered grad school in Computer Science at UC Berkeley;
  later joined the faculty at Harvey Mudd]
• Caleb Watson (CS/Biology, Bruce Donald)
  [Graduation with High Distinction; entered medical school at University of Pittsburgh]
• Barbara Xiong (CS, Sandeep Dave)
  [Graduation with Highest Distinction; entered medical school at UPenn]
• David Zhou (CS/ECE/BME, Bruce Donald)
  [Graduation with High Distinction; joined Google]