Chuong (Tom) Do

Software Engineer
Coursera, Inc.
E-mail: cdo@coursera.org

I lead the Analytics group at Coursera, an educational startup based in Mountain View, CA. We're out to change the world by making high-quality online educational resources available for free to everyone.

Previously, I was a research scientist at 23andMe, Inc., a personal genetics company, and completed a Ph.D. in Computer Science at Stanford University, where I did research in machine learning with Andrew Ng and computational biology with Serafim Batzoglou.

Selected publications

Retention and intention in massive open online courses: in depth [link]
Koller, D., Ng, A., Do, C., Chen, Z. 2013. EDUCAUSE Review Online.
Genome-wide analysis points to roles for extracellular matrix remodeling, the visual cycle, and neuronal development in myopia [pdf]
Kiefer, A.K., Tung, J.Y., Do, C.B., Hinds, D.A., Mountain, J.L., Francke, U., Eriksson, N. 2013. PLOS Genet 9(2): e1003299. doi:10.1371/journal.pgen.1003299
Serum iron levels and the risk of Parkinson disease: a Mendelian randomization study [pdf]
Pichler, I., Del Greco M., F., Gogele, M., Lill, C.M., Bertram, L., Do, C.B., Eriksson, N, Foroud, T., Myers, R.H., PD GWAS Consortium, Nalls, M., Keller, M.F., International Parkinson's Disease Genomics Consortium, et al. 2013. PLOS Med 10(6): e1001462. doi:10.1371/journal.pmed.1001462
A scalable pipeline for local ancestry inference using thousands of reference individuals [pdf]
Do, C.B., Durand, E.Y., Macpherson, J.M., Naughton, B., Mountain, J.L. 2012. Poster presented at ASHG.
Comparison of family history and SNPs for predicting risk of complex disease [pdf]
Do, C.B., Hinds, D.A., Francke, U., Eriksson, N. 2012. PLOS Genet 8(10): e1002973. doi:10.1371/journal.pgen.1002973
Comprehensive research synopsis and systematic meta-analyses in Parkinson's disease genetics: the PDGene Database [pdf]
Lill, C.M., Roehr, J.T., McQueen, M.B., et al. 2012. PLOS Genet 8(3): e1002548. doi:10.1371/journal.pgen.1002548
A genetic variant near olfactory receptor genes influences cilantro preference [pdf]
Eriksson, N., Wu, S., Do, C.B., Kiefer, A.K., Tung, J.Y., Mountain, J.L., Hinds, D.A., Francke, U. 2012 Flavour 1:22. doi:10.1186/2044-7248-1-22
Genetic variants associated with breast size also influence breast cancer risk [pdf]
Eriksson, N., Benton, G.M., Do, C.B., Kiefer, A.K., Mountain, J.L., Hinds, D.A., Francke, U., Tung, J.Y. 2012. BMC Medical Genetics 13:53. doi:10.1186/1471-2350-13-53
Novel associations for hypothyroidism include known autoimmune loci [pdf]
Eriksson, N., Tung, J.Y., Kiefer, A.K., Hinds, D.A., Francke, U., Mountain, J.L., Do, C.B. 2012. PLOS ONE 7(4): e34442. doi:10.1371/journal.pone.0034442
Web-based genome-wide association study identifies two novel loci and a substantial genetic component for Parkinson's disease [pdf]
Do, C.B., Tung, J.Y., Dorfman, E., Kiefer, A.K., Drabant, E.M., Francke, U., Mountain, J.L., Goldman, S.L., Tanner, C.M., Langston, J.W., Wojcicki, A., Eriksson, N. 2011. PLOS Genet 7(6): e1002141. doi:10.1371/journal.pgen.1002141
Efficient replication of over 180 genetic associations with self-reported medical data [pdf]
Tung, J.Y., Do, C.B., Hinds, D.A., Kiefer, A.K., Macpherson, J.M., Chowdry, A.B., Francke, U., Naughton, B.T., Mountain, J.L., Wojcicki, A., Eriksson, N. 2011. PLOS ONE 6(8): e23473. doi:10.1371/journal.pone.0023473
Proximal regularization for online and batch learning [pdf]
Do, C.B., Le, Q., Foo, C.S. 2009. In Proceedings of the 26th International Conference on Machine Learning.
(extended version with proofs)
A majorization-minimization algorithm for (multiple) hyperparameter learning [pdf]
Foo, C.S., Do, C.B., Ng, A.Y. 2009. In Proceedings of the 26th International Conference on Machine Learning.
A classifier-based approach to identify genetic similarities between diseases [pdf]
Schaub, M.A., Kaplow, I.M., Sirota, M., Do, C.B., Butte, A.J., Batzoglou, S. 2009. Bioinformatics, 25(12): i21-i29. doi:10.1093/bioinformatics/btp226
What is the expectation maximization algorithm? [pdf]
Do, C.B., Batzoglou, S. 2008. Nature Biotechnology, 26:897-899.
Tighter bounds for structured estimation [pdf]
Do, C.B., Le, Q., Teo, C.H., Chapelle, O., Smola, S. 2008. In Advances in Neural Information Processing Systems 21.
A max-margin model for efficient simultaneous alignment and folding of RNA sequences [pdf]
Do, C.B., Foo, C.S., Batzoglou, S. 2008. Bioinformatics, 24(13):i68-i76. (source code)
Protein multiple sequence alignment [pdf]
Do, C.B., Katoh, K. 2008. Methods in Molecular Biology 484:379-413.
Effect of genetic divergence in identifying ancestral origin using HAPAA [pdf]
Sundquist, A., Fratkin, E., Do, C.B., Batzoglou, S. 2008. Genome Research, 18:676-682.
(website, source code).
Automatic parameter learning for multiple network alignment [pdf]
Flannick, J., Novak, A., Do, C.B., Batzoglou, S. 2008. In Proceedings of the 12th Annual International Conference on Computational Molecular Biology (RECOMB 2008), 214-231.
(website)
CONTRAST: a discriminative, phylogeny-free approach to multiple informant de novo gene prediction [pdf]
Gross, S.S., Do, C.B., Sirota, M., Batzoglou, S. 2008. Genome Biology 8:R269.
Efficient multiple hyperparameter learning for log-linear models [pdf]
Do, C.B., Foo, C.-S., Ng, A.Y. 2007. In Advances in Neural Information Processing Systems 20.
Evolution of genes and genomes on the Drosophila phylogeny [pdf]
Drosophila 12 Genomes Consortium. 2007. Nature 450, 203-218.
Training conditional random fields for maximum labelwise accuracy [pdf]
Gross, S.S., Russakovsky, O., Do, C.B., Batzoglou, S. 2006. In Advances in Neural Information Processing Systems 19.
(derivation of recurrences)
CONTRAfold: RNA secondary structure prediction without physics-based models [pdf]
Do, C.B., Woods, D.A., Batzoglou, S. 2006. Bioinformatics, 22(14):e90-e98.
(Best Paper, ISMB 2006) (webserver, manual, recurrences, source code)
Multiple alignment of protein sequences with repeats and rearrangements [pdf]
Phuong, T.M., Do, C.B., Edgar, R.C., Batzoglou, S. 2006. Nucleic Acids Res, 34(20):5932-5942.
(manual, source code)
Evidence for intelligent (algorithm) design [pdf]
Srinivasan, B.S., Do, C.B., Batzoglou, S. 2006. In Genome Biology, 7:322.
CONTRAlign: Discriminative training for protein sequence alignment [pdf]
Do, C.B., Gross, S.S., Batzoglou, S. 2006. In Proceedings of the 10th Annual International Conference on Computational Molecular Biology (RECOMB 2006), 160-174.
(Best Poster, BCATS 2005) (webserver, source code)
Transfer learning for text classification [pdf]
Do, C.B., Ng, A.Y. 2006. In Advances in Neural Information Processing Systems 18, 299-306.
PROBCONS: Probabilistic consistency-based multiple sequence alignment [pdf]
Do, C.B., Mahabhashyam, M.S.P., Brudno, M., and Batzoglou, S. 2005. Genome Research, 15(2):330-340.
(Best Paper, ISMB/ECCB 2004; Best Master's Thesis, Stanford Computer Science Department 2004) (webserver, source code)
Glocal Alignment: Finding rearrangements during alignment [pdf]
Brudno, M., Malde, S., Poliakov, A., Do, C.B., Couronne, O., Dubchak, I., and Batzoglou, S. 2003. Bioinformatics, 19S1:i54-i62.
LAGAN and Multi-LAGAN: Efficient tools for large-scale multiple alignment of genomic DNA [pdf]
Brudno, M., Do, C.B., Cooper, G.M., Kim, M.F., Davydov, E., Green, E.D., Sidow, A., and Batzoglou, S; NISC Comparative Sequencing Program. 2003. Genome Research, 13(4):721-731.
(webserver, source code)

Comments to Chuong Do (cdo@coursera.org)