Anshul Kundaje

Assistant Professor
Dept. of Genetics, 

Dept. of Computer Science,
Stanford University

09/2013 - Present

Address: 300 Pasteur Drive, Lane L301B

Stanford, CA 94305-5120, USA

Email: akundaje@stanford.edu

Phone: (650)-723-2353


Research Scientist

Computer Science Dept. (Computational Biology Group)

Computer Science and Artificial Intelligence Lab (CSAIL)

Massachusetts Institute of Technology

Broad Institute of MIT and Harvard

Mentor: Prof. Manolis Kellis

02/2012 – 08/2013

Postdoctoral Research Associate
Computer Science Dept.

Stanford University
Mentors: Prof. Serafim Batzoglou, Prof. Arend Sidow

11/2008 – 01/2012

PhD in Computer Science

Columbia University in the City of New York
Advisor: Dr. Christina Leslie

09/2003 – 10/2008

M.S. in Electrical Engineering
Columbia University in The City of New York

CGPA 3.72

09/2001 – 02/2003

B.E. in Electrical Engineering

Veermata Jijabai Technological Institute (V. J. T. I.), Mumbai University, India

Passed with Distinction,  Rank 1 in Mumbai University

07/1997 – 06/2001


  • NIH Director's New Innovator Award (2016-2021)
  • Alfred Sloan Foundation Research Fellowship (2014-2016)
  • Prof. P.R Dandavate Memorial Award: Highest GPA in the Bachelor’s Program. (2001)
  • D.D. & L.H. Prize: Consistent Academic Career in the Bachelor’s Program. (2001)
  • M.B.P. Memorial Foundation Award: Highest GPA in final Year of Bachelors Program. (2001)
  • National Scholarship of Central Board Of Secondary Education India for highest score (100%) in Mathematics. (1995)


* Joint first-author/equal contribution

+ Corresponding author

  1. Yardimci G, Ozadam H, Sauria MEG, Ursu O, Yan KK, Yang T, Chakraborty A, Kaul A, Lajoie BR, Song F, Zhan Y, Ay F, Gerstein M, Kundaje A, Li Q, Taylor J, Yue F, Dekker J, Noble WS. Measuring the reproducibility and quality of Hi-C data (Accepted to Genome Biology)
  2. Greenside PG, Shimko T, Fordyce P, Kundaje A. Discovering epistatic feature interactions from neural network models of regulatory DNA sequences. Bioinformatics, Volume 34, Issue 17, 1 September 2018, Pages i629–i637 (Proceedings of ECCB 2018)
  3. Yang D, Denny SK, Greenside PG, Chaikovsky AC, Brady JJ, Ouadah Y, Granja JM, Jahchan NS, Lim JS, Kwok S, Kong CS, Berghoff AS, Schmitt A, Reinhardt HC, Park KS, Preusser M, Kundaje A, Greenleaf WJ, Sage J, Winslow MM. Intertumoral heterogeneity in SCLC is influenced by the cell type of origin. Cancer Discov. 2018 Sep 18 DOI: 10.1158/2159-8290.CD-17-0987
  4. Fu S, Wang Q, Moore JE, Purcaro MJ, Pratt HE, Fan K, Gu C, Jiang C, Zhu R, Kundaje A, Lu A, Weng Z. Differential analysis of chromatin accessibility and histone modifications for predicting mouse developmental enhancers. Nucleic Acids Res. 2018 Aug 22. doi: 10.1093/nar/gky753. (PMID: 30137428)
  5. Karimzadeh M, Ernst C, Kundaje A, Hoffman MH. Umap and Bismap: quantifying genome and methylome mappability. Nucleic Acids Research, Sept 1, 2018, gky677, https://doi.org/10.1093/nar/gky677
  6. Blumberg A, Danko CG, Kundaje A, Mishmar D. A common pattern of DNase-I footprinting throughout the human mtDNA unveils clues for a chromatin-like organization. Published in Advance July 12, 2018, doi:10.1101/gr.230409.117 Genome Res. 2018
  7. Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow PM, Zietz M, Hoffman MM, Xie W, Rosen GL, Lengerich BJ, Israeli J, Lanchantin J, Woloszynek S, Carpenter AE, Shrikumar A, Xu J, Cofer EM, Lavender CA, Turaga SC, Alexandari AM, Lu Z, Harris DJ, DeCaprio D, Qi Y, Kundaje A, Peng Y, Wiley LK, Segler MHS, Gitter A, Greene CS. Opportunities And Obstacles For Deep Learning In Biology And Medicine. J. R. Soc. Interface 2018 15 20170387; DOI: 10.1098/rsif.2017.0387. Published 4 April 2018 (PMID: 29618526)
  8. Ursu O, Boley N, Taranova M, Rachel WYX, Yardimci GG, Noble WS, Kundaje A. GenomeDISCO: A concordance score for chromosome conformation capture experiments using random walks on contact map graphs. Bioinformatics, March 2018 , bty164, https://doi.org/10.1093/bioinformatics/bty164 (PMID: 29554289)
  9. GK Marinov, A Kundaje. ChIP-ping the branches of the tree: functional genomics and the evolution of eukaryotic gene regulation. Briefings in Functional Genomics, Feb 2018. https://doi.org/10.1093/bfgp/ely004
  10. Carter AC, Chang HY, Church G, Dombkowski A, Ecker JR, Gil E, Giresi PG, Greely H, Greenleaf WJ, Hacohen N, He C, Hill D, Ko J, Kohane I, Kundaje A, Palmer M, Snyder MP, Tung J, Urban A, Vidal M, Wong W. Challenges and recommendations for epigenomics in precision health. Nat Biotechnol. 2017 Dec 8;35(12):1128-1132. doi: 10.1038/nbt.4030. (PMID: 29220033)
  11. Banovich NE, Li YI, Raj A, Ward MC, Greenside P, Calderon D, Tung PY, Burnett JE, Myrthil M, Thomas SM, Burrows CK, Romero IG, Pavlovic BJ, Kundaje A, Pritchard JK, Gilad Y. Impact of regulatory variation across human iPSCs and differentiated cells. Genome Research:gr.224436.117- (2017) doi:10.1101/gr.224436.117  (PMID: 29208628)
  12. Bien SA, Auer PL, Harrison TA, Qu C, Connolly CM, Greenside PG, Chen S, Berndt SI, Bézieau S, Kang HM, Huyghe J, Brenner H, Casey G, Chan AT, Hopper JL, Banbury BL, Chang-Claude J, Chanock SJ, Haile RW, Hoffmeister M, Fuchsberger C, Jenkins MA, Leal SM, Lemire M, Newcomb PA, Gallinger S, Potter JD, Schoen RE, Slattery ML, Smith JD, Le Marchand L, White E, Zanke BW, Abeçasis GR, Carlson CS, Peters U, Nickerson DA, Kundaje A*, Hsu L*; GECCO and CCFR. Enrichment of colorectal cancer associations in functional regions: Insight for using epigenomics data in the analysis of whole genome sequence-imputed GWAS data. PLoS One. 2017 Nov 21;12(11):e0186518. doi: 10.1371/journal.pone.0186518. eCollection 2017. (PMID: 29161273) 
  13. Daugherty AC, Yeo R, Buenrostro JD, Greenleaf WJ, Kundaje A, Brunet A. Chromatin accessibility dynamics reveal novel functional enhancers in C. elegans. Published in Advance November 15, 2017, doi:10.1101/gr.226233.117 Genome Res. 2017. (PMID: 29141961)
  14. Wang B, Huang L, Zhu Y, Kundaje A, Batzoglou S, Goldenberg A. Vicus: Exploiting local structures to improve network-based analysis of biological data. PLoS Comput Biol. 2017 Oct 12;13(10):e1005621. doi: 10.1371/journal.pcbi.1005621. eCollection 2017 Oct. (PMID: 29023470) 
  15. Mumbach MR, Satpathy AT, Boyle EA, Dai C, Gowen BG, Cho SW, Nguyen ML, Rubin AJ, Granja JM, Kazane KR, Wei Y, Nguyen T, Greenside PG, Corces MR, Tycko J, Simeonov DR, Suliman N, Li R, Xu J, Flynn RA, Kundaje A, Khavari PA, Marson A, Corn JE, Quertermous T, Greenleaf WJ, Chang HY. Enhancer connectome in primary human cells reveals target genes of disease-associated DNA elements. Nature Genetics (2017) doi:10.1038/ng.3963 (PMID: 28945252 )
  16. Corces MR, Trevino AE, Hamilton EG, Greenside PG, Sinnott-Armstrong NA, Vesuna S, Satpathy AT, Rubin AJ, Montine KS, Wu B, Kathiria A, Cho SW, Mumbach MR, Carter AC, Kasowski M, Orloff LA, Risca VI, Kundaje A, Khavari PA, Montine TJ, Greenleaf WJ, Chang HY. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat Meth. 2017. doi: 10.1038/nmeth.4396 (PMID: 28846090)
  17. Rubin AJ, Barajas BC, Furlan-Magaril M, Lopez-Pajares V, Mumbach MR, Howard I, Kim DS, Boxer LD, Cairns J, Spivakov M, Wingett SW, Shi M, Zhao Z, Greenleaf WJ, Kundaje A, Snyder M, Chang HY, Fraser P, Khavari PA. Lineage-specific dynamic and pre-established enhancer–promoter contacts cooperate in terminal differentiation. Nat Genet. 2017 Oct;49(10):1522-1528. doi: 10.1038/ng.3935. Epub 2017 Aug 14. (PMID: 28805829)
  18. PW Koh, E Pierson, A Kundaje. Denoising genome-wide histone ChIP-seq with convolutional neural networks. Bioinformatics 2017 33 (14), i225-i233
  19. Fu BXH, Wainberg M, Kundaje A+, Fire AZ+. High-Throughput Characterization of Cascade Type I-E CRISPR Guide Efficacy Reveals Unexpected PAM Diversity and Target Sequence Preferences. Genetics. 2017 Jun 20. pii: genetics.117.202580. doi: 10.1534/genetics.117.202580. (PMID: 28634160) 
  20. Morgens DW, Wainberg M, Boyle EA, Ursu O, Araya CL, Tsui CK, Haney MS, Hess GT, Han K, Jeng EE, Li A, Snyder MP, Greenleaf WJ, Kundaje A, Bassik MC. Genome-scale measurement of off-target activity using Cas9 toxicity in high-throughput screens. Nat Commun. 2017 May 5;8:15178. doi: 10.1038/ncomms15178. (PMID: 28474669)
  21. Kreimer A, Zeng H, Edwards MD, Guo Y, Tian K, Shin S, Welch R, Wainberg M, Mohan R, Sinnott-Armstrong NA, Li Y, Eraslan G, Amin TB, Goke J, Mueller NS, Kellis M, Kundaje A, Beer MA, Keles S, Gifford DK, Yosef N. Predicting gene expression in massively parallel reporter assays: a comparative study. Hum Mutat. 2017 Feb 21. doi: 10.1002/humu.23197 (PMID: 28220625)
  22. Chuang CH, Greenside PG, Rogers ZN, Brady JJ, Yang D, Ma RK, Caswell DR, Chiou SH, Winters AF, Grüner BM, Ramaswami G, Spencley AL, Kopecky KE, Sayles LC, Sweet-Cordero EA, Li JB, Kundaje A, Winslow MM. Molecular definition of a metastatic lung cancer state reveals a targetable CD109-Janus kinase-Stat axis. Nat Med. 2017 Feb 13. doi: 10.1038/nm.4285 (PMID: 28191885) 
  23. Blumberg A, Rice EJ, Kundaje A, Danko CG, Mishmar D. Initiation of mtDNA transcription is followed by pausing, and diverge across human cell types and during evolution. Genome Res. 2017 Jan 3. pii: gr.209924.116. doi: 10.1101/gr.209924.116. (PMID: 28049628) 
  24. Koh PW, Sinha R+, Barkal AA, Morganti RM, Chen A, Weissman IL, Ang LT, Kundaje A+, Loh KM. An atlas of transcriptional, chromatin accessibility, and surface marker changes in human mesoderm development. Sci. Data 2016 3:160109 doi: 10.1038/sdata.2016.109 (PMID: 27996962)
  25. Corces RM, Buenrostro J, Wu B, Greenside PG, Chan SM, Koenig JL, Snyder MP, Pritchard JK, Kundaje A, Greenleaf WJ, Majeti R, Chang HY. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nature Genetics (2016) doi:10.1038/ng.3646 (PMID: 27526324) 
  26. Loh KM, Chen A, Koh PW, Deng T, Sinha R, Tsai JM, Barkal AA, Shen KY, Jain R, Morganti RM, Ng SC, Morganti RM, Fernhoff NB, George BM, Wernig G, Salomon RAE, Chen Z, Vogel H, Epstein JA, Kundaje A, Talbot WS, Beachy PA, Ang LT, Weissman IL. Mapping the pairwise choices and molecular transitions leading from pluripotency to human bone, heart and other mesoderm cell-types. Cell. 2016 Jul 14;166(2):451-67. doi: 10.1016/j.cell.2016.06.011. (PMID: 27419872) 
  27. Kukurba KR, Parsana P, Balliu B, Smith KS, Zappala Z, Knowles DA, Favé MJ, Davis JR, Li X, Zhu X, Potash JB, Weissman MM, Shi J, Kundaje A, Levinson DF, Awadalla P, Mostafavi S, Battle A, Montgomery SB. Impact of the X Chromosome and sex on regulatory variation. Genome Res. 2016 Jun;26(6):768-77. doi: 10.1101/gr.197897.115. Epub 2016 Apr 21. (PMID: 27197214)
  28. Brady JJ, Chuang CH, Greenside PG, Rogers ZN, Murray CW, Caswell DR, Hartmann U, Connolly AJ, Sweet-Cordero EA, Kundaje A, Winslow MM. An Arntl2-Driven Secretome Enables Lung Adenocarcinoma Metastatic Self-Sufficiency. Cancer Cell. 2016 May 9;29(5):697-710. doi: 10.1016/j.ccell.2016.03.003. Epub 2016 Apr 14 (PMID: 27150038)
  29. Webb AE, Kundaje A, Brunet A. Characterization of the direct targets of FOXO transcription factors throughout evolution. Aging Cell. 2016 doi: 10.1111/acel.12479. (PMID: 27061590)
  30. Grubert F*, Zaugg J*, Kasowski M*, Ursu O*, Spacek DV, Greenside P, Srivas R, Martin A, Phanstiel D, Pekowska A, Heidari N, Euskirchen G, Huber W, Pritchard JP, Bustamante C, Steinmetz L, Kundaje A, and Snyder M. Genetic control of chromatin states in humans involves local and distal chromosomal interactions. Cell. 2015 Aug 19. pii: S0092-8674(15)00964-2. doi: 10.1016/j.cell.2015.07.048 (PMID: 26300125) 
  31. Sazonova O, Zhao Y, Nürnberg S, Miller C, Pjanic M, Castano VG, Kim JB, Salfati EL, Kundaje AB, Bejerano G, Assimes T, Yang X, Quertermous T. Characterization of TCF21 Downstream Target Regions Identifies a Transcriptional Network Linking Multiple Independent Coronary Artery Disease Loci. PLoS Genet. 2015 May 28;11(5):e1005202. doi: 10.1371/journal.pgen.1005202. eCollection 2015 May. (PMID: 26020271)
  32. Lin H, Chen M, Kundaje A, Valouev A, Yin H, Liu N, Neuenkirchen N, Zhong M, Snyder M. Reassessment of Piwi binding to the genome and Piwi impact on RNA polymerase II distribution. Dev Cell. 2015 Mar 23;32(6):772-4. doi: 10.1016/j.devcel.2015.03.004. (PMID: 25805139)
  33. Onengut-Gumuscu S, Chen WM, Burren O, Cooper NJ, Quinlan AR, Mychaleckyj JC, Farber E, Bonnie JK, Szpak M, Schofield E, Achuthan P, Guo H, Fortune MD, Stevens H, Walker NM, Ward LD, Kundaje A, Kellis M, Daly MJ, Barrett JC, Cooper JD, Deloukas P; Type 1 Diabetes Genetics Consortium, Todd JA, Wallace C, Concannon P, Rich SS. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat Genet. 2015 Mar 9. doi: 10.1038/ng.3245 (PMID: 25751624)
  34. Roadmap Epigenomics Consortium, Kundaje A*, Meuleman W*, Ernst J*, Bilenky M*, Yen A, Heravi-Moussavi A, Kheradpour P, Zhang Z, Wang J, Ziller MJ, Amin V, Whitaker JW, Schultz MD, Ward LD, Sarkar A, Quon G, Sandstrom RS, Eaton ML, Wu YC, Pfenning AR, Wang X, Claussnitzer M, Liu Y, Coarfa C, Harris RA, Shoresh N, Epstein CB, Gjoneska E, Leung D, Xie W, Hawkins RD, Lister R, Hong C, Gascard P, Mungall AJ, Moore R, Chuah E, Tam A, Canfield TK, Hansen RS, Kaul R, Sabo PJ, Bansal MS, Carles A, Dixon JR, Farh KH, Feizi S, Karlic R, Kim AR, Kulkarni A, Li D, Lowdon R, Elliott G, Mercer TR, Neph SJ, Onuchic V, Polak P, Rajagopal N, Ray P, Sallari RC, Siebenthall KT, Sinnott-Armstrong NA, Stevens M, Thurman RE, Wu J, Zhang B, Zhou X, Beaudet AE, Boyer LA, De Jager PL, Farnham PJ, Fisher SJ, Haussler D, Jones SJ, Li W, Marra MA, McManus MT, Sunyaev S, Thomson JA, Tlsty TD, Tsai LH, Wang W, Waterland RA, Zhang MQ, Chadwick LH, Bernstein BE, Costello JF, Ecker JR, Hirst M, Meissner A, Milosavljevic A, Ren B, Stamatoyannopoulos JA, Wang T, Kellis M. Integrative analysis of 111 reference human epigenomes. Nature. 2015 Feb 19;518(7539):317-30. doi: 10.1038/nature14248. (PMID: 25693563)
  35. Gjoneska E, Pfenning AR, Mathys H, Quon G, Kundaje A, Tsai LH, Kellis M. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease. Nature. 2015 Feb 19;518(7539):365-9. doi: 10.1038/nature14252. (PMID: 25693568)
  36. Cheng Y, Ma Z, Kim BH, Wu W, Cayting P, Boyle AP, Sundaram V, Xing X, Dogan N, Li J, Euskirchen G, Lin S, Lin Y, Visel A, Kawli T, Yang X, Patacsil D, Keller CA, Giardine B; Mouse ENCODE Consortium, Kundaje A, Wang T, Pennacchio LA, Weng Z, Hardison RC, Snyder MP. Principles of regulatory information conservation between mouse and human. Nature. 2014 Nov 20;515(7527):371-5. doi: 10.1038/nature13985. (PMID: 25409826)
  37. Yue F, Cheng Y, Breschi A, Vierstra J, Wu W, Ryba T, Sandstrom R, Ma Z, Davis C, Pope BD, Shen Y, Pervouchine DD, Djebali S, Thurman RE, Kaul R, Rynes E, Kirilusha A, Marinov GK, Williams BA, Trout D, Amrhein H, Fisher-Aylor K, Antoshechkin I, DeSalvo G, See LH, Fastuca M, Drenkow J, Zaleski C, Dobin A, Prieto P, Lagarde J, Bussotti G, Tanzer A, Denas O, Li K, Bender MA, Zhang M, Byron R, Groudine MT, McCleary D, Pham L, Ye Z, Kuan S, Edsall L, Wu YC, Rasmussen MD, Bansal MS, Kellis M, Keller CA, Morrissey CS, Mishra T, Jain D, Dogan N, Harris RS, Cayting P, Kawli T, Boyle AP, Euskirchen G, Kundaje A, Lin S, Lin Y, Jansen C, Malladi VS, Cline MS, Erickson DT, Kirkup VM, Learned K, Sloan CA, Rosenbloom KR, Lacerda de Sousa B, Beal K, Pignatelli M, Flicek P, Lian J, Kahveci T, Lee D, Kent WJ, Ramalho Santos M, Herrero J, Notredame C, Johnson A, Vong S, Lee K, Bates D, Neri F, Diegel M, Canfield T, Sabo PJ, Wilken MS, Reh TA, Giste E, Shafer A, Kutyavin T, Haugen E, Dunn D, Reynolds AP, Neph S, Humbert R, Hansen RS, De Bruijn M, Selleri L, Rudensky A, Josefowicz S, Samstein R, Eichler EE, Orkin SH, Levasseur D, Papayannopoulou T, Chang KH, Skoultchi A, Gosh S, Disteche C, Treuting P, Wang Y, Weiss MJ, Blobel GA, Cao X, Zhong S, Wang T, Good PJ, Lowdon RF, Adams LB, Zhou XQ, Pazin MJ, Feingold EA, Wold B, Taylor J, Mortazavi A, Weissman SM, Stamatoyannopoulos JA, Snyder MP, Guigo R, Gingeras TR, Gilbert DM, Hardison RC, Beer MA, Ren B; Mouse ENCODE Consortium. A comparative encyclopedia of DNA elements in the mouse genome. Nature. 2014 Nov 20;515(7527):355-64. doi: 10.1038/nature13992. (PMID: 25409824)
  38. Blumberg A, Sailaja BS, Kundaje A, Levin L, Dadon S, Shmorak S, Shaulian E, Meshorer E, Mishmar D. Transcription factors bind negatively-selected sites within human mtDNA genes. Genome Biol Evol, September 22, 2014 doi:10.1093/gbe/evu210 (PMID: 25245407)
  39. Araya CL, Kawli T, Kundaje A, Jiang L, Wu B, Vafeados D, Terrell R, Weissdepp P, Gevirtzman L, Mace D, Niu W, Boyle AP, Xie D, Ma L, Murray JI, Reinke V, Waterston RH, Snyder M. Regulatory analysis of the C. elegans genome with spatiotemporal resolution. Nature. 2014 Aug 28;512(7515):400-5. doi: 10.1038/nature13497. (PMID: 25164749)
  40. Ho JW, Jung YL, Liu T, Alver BH, Lee S, Ikegami K, Sohn KA, Minoda A, Tolstorukov MY, Appert A, Parker SC, Gu T, Kundaje A, Riddle NC, Bishop E, Egelhofer TA, Hu SS, Alekseyenko AA, Rechtsteiner A, Asker D, Belsky JA, Bowman SK, Chen QB, Chen RA, Day DS, Dong Y, Dose AC, Duan X, Epstein CB, Ercan S, Feingold EA, Ferrari F, Garrigues JM, Gehlenborg N, Good PJ, Haseley P, He D, Herrmann M, Hoffman MM, Jeffers TE, Kharchenko PV, Kolasinska-Zwierz P, Kotwaliwale CV, Kumar N, Langley SA, Larschan EN, Latorre I, Libbrecht MW, Lin X, Park R, Pazin MJ, Pham HN, Plachetka A, Qin B, Schwartz YB, Shoresh N, Stempor P, Vielle A, Wang C, Whittle CM, Xue H, Kingston RE, Kim JH, Bernstein BE, Dernburg AF, Pirrotta V, Kuroda MI, Noble WS, Tullius TD, Kellis M, MacAlpine DM, Strome S, Elgin SC, Liu XS, Lieb JD, Ahringer J, Karpen GH, Park PJ. Comparative analysis of metazoan chromatin organization. Nature. 2014 Aug 28;512(7515):449-52. doi: 10.1038/nature13415. (PMID: 25164756)
  41. Boyle AP, Araya CL, Brdlik C, Cayting P, Cheng C, Cheng Y, Gardner K, Hillier LW, Janette J, Jiang L, Kasper D, Kawli T, Kheradpour P, Kundaje A, Li JJ, Ma L, Niu W, Rehm EJ, Rozowsky J, Slattery M, Spokony R, Terrell R, Vafeados D, Wang D, Weisdepp P, Wu YC, Xie D, Yan KK, Feingold EA, Good PJ, Pazin MJ, Huang H, Bickel PJ, Brenner SE, Reinke V, Waterston RH, Gerstein M, White KP, Kellis M, Snyder M. Comparative analysis of regulatory information and circuits across distant species. Nature. 2014 Aug 28;512(7515):453-6. doi: 10.1038/nature13668. (PMID: 25164757)
  42. Benayoun BA, Pollina EA, Ucar D, Mahmoudi S, Karra K, Wong ED, Devarajan K, Daugherty AC, Kundaje AB, Mancini E, Hitz BC, Gupta R, Rando TA, Baker JC, Snyder MP, Cherry JM, Brunet A. H3K4me3 Breadth Is Linked to Cell Identity and Transcriptional Consistency. Cell. 2014 Jul 31;158(3):673-88. doi: 10.1016/j.cell.2014.06.027. (PMID: 25083876)
  43. Slattery M, Ma L, Spokony RF, Arthur RK, Kheradpour P, Kundaje A, Nègre N, Crofts A, Ptashkin R, Zieba J, Ostapenko A, Suchy S, Victorsen A, Jameel N, Grundstad AJ, Gao W, Moran JR, Rehm EJ, Grossman RL, Kellis M, White KP. Diverse patterns of genomic targeting by transcriptional regulators in Drosophila melanogaster. Genome Res. 2014 Jul;24(7):1224-35. doi: 10.1101/gr.168807.113. (PMID: 24985916)
  44. Kellis M, Wold BJ, Snyder MP, Bernstein BE, Kundaje A*, Marinov GK*, Ward LD*, Birney E, Crawford GE, Dekker J, Dunham I, Elnitski LL, Farnham PJ, Feingold EA, Gerstein M, Giddings MC, Gilbert DM, Gingeras TR, Green ED, Guigo R, Hubbard T, Kent J, Lieb JD, Myers RM, Pazin MJ, Ren B, Stamatoyannopoulos JA, Weng Z, White KP, Hardison RC. Defining functional DNA elements in the human genome. PNAS 2014 ; April 21, 2014, doi:10.1073/pnas.1318948111 (PMID: 24753594)
  45. Marinov GK, Kundaje A, Park PJ, Wold BJ. Large-scale quality analysis of published ChIP-seq data. G3 (Bethesda). 2013 Dec 17. pii: g3.113.008680v1. doi: 10.1534/g3.113.008680 (PMID: 24347632)
  46. Hardee JM, Ouyang Z, Zhang Y, Kundaje A, Lacroute P, Snyder M. STAT3 Targets Suggest Mechanisms of Aggressive Tumorigenesis in Diffuse Large B Cell Lymphoma. G3 (Bethesda). 2013 Oct 18. pii: g3.113.007674v1. doi: 10.1534/g3.113.007674. (PMID: 24142927)
  47. Kasowski M*, Kyriazopoulou-Panagiotopoulou S*, Grubert F*, Zaugg JB*, Kundaje A*, Liu Y, Boyle AP, Zhang QC, Zakharia Q, Spacek DV, Li J, Xie D, Olarerin-George A, Steinmetz LM, Hogenesch JB, Kellis M, Batzoglou S, Snyder M. Extensive Variation in Chromatin States Across Humans. Science. 2013 Oct 17; DOI:10.1126/science.1242510 (PMID: 24136358)
  48. Hoffman MM, Ernst J, Wilder SP, Kundaje A, Harris RS, Libbrecht M, Giardine B, Ellenbogen PM, Bilmes JA, Birney E, Hardison RC, Dunham I, Kellis M, Noble WS. Integrative annotation of chromatin elements from ENCODE data. Nucl. Acids Res. 2012 doi:10.1093/nar/gks1284 (PMID: 23221638)
  49. Dunham I, Kundaje A**, et al., ENCODE Project Consortium. An integrated Encyclopedia of DNA Elements in the human genome. Nature. 2012 Sep 6;489(7414):57-74. (PMID: 22955616) ** First  author (non-PI) amongst 594 authors
  50. Gerstein MB, Kundaje A**, Hariharan M, Landt SG, Yan KK, Cheng C, Mu XJ, Khurana E, Rozowsky J, Alexander R, Min R, Alves P, Abyzov A, Addleman N, Bhardwaj N, Boyle AP, Cayting P, Charos A, Chen DZ, Cheng Y, Clarke D, Eastman C, Euskirchen G, Frietze S, Fu Y, Gertz J, Grubert F, Harmanci A, Jain P, Kasowski M, Lacroute P, Leng J, Lian J, Monahan H, O'Geen H, Ouyang Z, Partridge EC, Patacsil D, Pauli F, Raha D, Ramirez L, Reddy TE, Reed B, Shi M, Slifer T, Wang J, Wu L, Yang X, Yip KY, Zilberman-Schapira G, Batzoglou S, Sidow A, Farnham PJ, Myers RM, Weissman SM, Snyder M. Architecture of the human regulatory network derived from ENCODE data. Nature. 2012 Sep 6;489(7414):91-100. (PMID: 22955619) ** First  author (non-PI)
  51. Kundaje A, Kyriazopoulou-Panagiotopoulou S, Libbrecht M, Smith CL, Raha D, Winters EE, Johnson SM, Snyder M, Batzoglou S, Sidow A. Ubiquitous heterogeneity and asymmetry of the chromatin environment at regulatory elements. Genome Res. 2012 Sep;22(9):1735-47. (PMID: 22955985)
  52. Landt SG, Marinov GK, Kundaje A*, Kheradpour P, Pauli F, Batzoglou S, Bernstein BE, Bickel P, Brown JB, Cayting P, Chen Y, DeSalvo G, Epstein C, Fisher-Aylor KI, Euskirchen G, Gerstein M, Gertz J, Hartemink AJ, Hoffman MM, Iyer VR, Jung YL, Karmakar S, Kellis M, Kharchenko PV, Li Q, Liu T, Liu XS, Ma L, Milosavljevic A, Myers RM, Park PJ, Pazin MJ, Perry MD, Raha D, Reddy TE, Rozowsky J, Shoresh N, Sidow A, Slattery M, Stamatoyannopoulos JA, Tolstorukov MY, White KP, Xi S, Farnham PJ, Lieb JD, Wold BJ, Snyder M. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 2012 Sep;22(9):1813-31. PMID: 22955991
  53. Schaub MA, Boyle AP, Kundaje A, Batzoglou S, Snyder M. Linking disease associations with regulatory information in the human genome. Genome Res. 2012 Sep;22(9):1748-59. (PMID: 22955986)
  54. Wang J, Zhuang J, Iyer S, Lin X, Whitfield TW, Greven MC, Pierce BG, Dong X, Kundaje A, Cheng Y, Rando OJ, Birney E, Myers RM, Noble WS, Snyder M, Weng Z. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res. 2012 Sep;22(9):1798-812. (PMID: 22955990)
  55. Bánfai B, Jia H, Khatun J, Wood E, Risk B, Gundling WE Jr, Kundaje A, Gunawardena HP, Yu Y, Xie L, Krajewski K, Strahl BD, Chen X, Bickel P, Giddings MC, Brown JB, Lipovich L. Long noncoding RNAs are rarely translated in two human cell lines. Genome Res. 2012 Sep;22(9):1646-57. (PMID: 22955977)
  56. Yip KY, Cheng C, Bhardwaj N, Brown JB, Leng J, Kundaje A, Rozowsky J, Birney E, Bickel P, Snyder M, Gerstein M. Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors. Genome Biol. 2012 Sep 26;13(9):R48. (PMID: 22950945)
  57. Dong X, Greven MC, Kundaje A, Djebali S, Brown JB, Cheng C, Gingeras TR, Gerstein M, Guigó R, Birney E, Weng Z. Modeling gene expression using chromatin features in various cellular contexts. Genome Biol. 2012 Jun 13;13(9):R53. (PMID: 22950368)
  58. ENCODE Project Consortium. A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 2011 Apr;9(4):e1001046. PMID: 21526222
  59. Kundaje A, Xin X, Lan C, Lianoglou S, Zhou M, Zhang L, Leslie C. A predictive model of the oxygen and heme regulatory network in yeast. PLoS Comput Biol. 2008 Nov;4(11):e1000224. (PMID: 19008939)
  60. Kundaje A, Lianoglou S, Li X, Quigley D, Arias M, Wiggins CH, Zhang L, Leslie C. Learning regulatory programs that accurately predict differential expression with MEDUSA. Ann N Y Acad Sci. 2007 Dec;1115:178-202. (PMID: 17934055)
  61. Kundaje A, Middendorf M, Shah M, Wiggins CH, Freund Y, Leslie C. A classification-based framework for predicting and analyzing gene regulatory response. BMC Bioinformatics. 2006 Mar 20;7 Suppl 1:S5. (PMID: 16723008)
  62. Kundaje A, Middendorf M, Gao F, Wiggins C, Leslie C. Combining sequence and time series expression data to learn transcriptional modules. IEEE/ACM Trans Comput Biol Bioinform. 2005 Jul-Sep;2(3):194-202. (PMID: 17044183)
  63. Stolovitzky GA, Kundaje A*, Held GA, Duggar KH, Haudenschild CD, Zhou D, Vasicek TJ, Smith KD, Aderem A, Roach JC. Statistical analysis of MPSS measurements: application to the study of LPS-activated macrophage gene expression. Proc Natl Acad Sci U S A. 2005 Feb 1;102(5):1402-7. (PMID: 15668391)
  64. Middendorf M, Kundaje A, Wiggins C, Freund Y, Leslie C. Predicting genetic regulatory response using classification. Bioinformatics. 2004 Aug 4;20. (PMID: 15262804) (Also in proceedings of ISMB 2004)
  65. Sussilo D, Kundaje A, Anastassiou D, Spectrogram Analysis of Genomes, EURASIP Journal of Applied Signal Processing, 2004.


  1. Avsec Z, Kreuzhuber R, Israeli J, Cheng J, Urban L, Banerjee A, Xu N, Shrikumar A, Ouwehand WH, Kundaje A+, Stegle O+, Gagneur J+. Kipoi: accelerating the community exchange and reuse of predictive models for regulatory genomics. ICML 2018 Workshop for Computational Biology
  2. Min J, Israeli J, Kundaje A. A Sequence-to-sequence Regression of Genome-wide Chromatin Data through Adversarial Training. ICML 2018 Workshop for Computational Biology
  3. Alexandri A, Shrikumar A, Kundaje A. Selective Classification via Curve Optimization. ICML 2018 Workshop for Computational Biology (Highlighted Paper)
  4. Avsec Z, Israeli J, Fropf R, Weilert M, Zeitlinger J, Kundaje A. BPNet: Learning single-nucleotide resolution predictive models of in vivo transcription factor binding from ChIP-nexus data. ICML 2018 Workshop for Computational Biology
  5. Shrikumar A, Israeli J, Kundaje A. TF-MoDISco: Learning High-Quality, Non-Redundant Transcription Factor Binding Motifs Using Deep Learning. NIPS 2017 Machine Learning in Computational Biology (MLCB) Workshop
  6. Alexandari A, Shrikumar A, Kundaje A. When the Model Is Wrong: Abstention Methods for Deep Neural Networks for Genomics. NIPS 2017 Machine Learning in Computational Biology (MLCB) Workshop
  7. Greenside PG, Shrikumar A, Kundaje A. Interpretable deep learning to decipher cis-regulatory sequence patterns and pleiotropic regulation of chromatin accessibility across cell types. NIPS 2017 Machine Learning in Computational Biology (MLCB) Workshop
  8. Greenside PG, Shimko T, Fordyce P, Kundaje A. DFIM: Deep Feature Interaction Maps uncover latent dependence structure encoded in deep learning models of regulatory DNA sequences. NIPS 2017 Machine Learning in Computational Biology (MLCB) Workshop
  9. Kim DS, Arivazhagan N, Khavari P, Kundaje A. A systematic evaluation of strategies for training deep neural network models of regulatory DNA sequence. NIPS 2017 Machine Learning in Computational Biology (MLCB) Workshop
  10. Banerjee A, Israeli J, Kaplow I, Kundaje A. Deep learning models reveal DNA methylation sensitivity of in vivo transcription factor binding. NIPS 2017 Machine Learning in Computational Biology (MLCB) Workshop
  11. Liu B, Hussami N, Shrikumar A, Shimko T, Bhate S, Longwell S, Montgomery S, Kundaje A. A multi-modal neural network for learning cis and trans regulation of stress response in yeast. NIPS 2017 Machine Learning in Computational Biology (MLCB) Workshop
  12. Greenside PG, Hillenmeyer M, Kundaje A. Prediction of protein-ligand interactions from paired protein sequence motifs and ligand substructures. Proceedings of the 2018 Pacific Symposium on Biocomputing (PSB)
  13. Shrikumar A, Greenside P, Kundaje A. Learning Important Features Through Propagating Activation Differences. Proceedings of the 34th International Conference on Machine Learning (ICML), PMLR 70:3145-3153, 2017
  14. Alexandari AM, Shrikumar A, Kundaje A. Separable Fully Connected Layers Improve Deep Learning Models For Genomics. bioRxiv 146431; doi: https://doi.org/10.1101/146431. 2017 ICML Workshop on Computational Biology (Best Poster Award)
  15. Koh PW, Pierson E, Kundaje A. Denoising genome-wide histone ChIP-seq with convolutional neural networks. Accepted at The 2017 Intelligent Systems in Molecular Biology (ISMB) Conference
  16. Wang, B, Zhu, J, Pourshafeie, A, Ursu, O, Batzoglou, S, Kundaje, A. Unsupervised Learning from Noisy Networks with Applications to Hi-C Data. In Advances In Neural Information Processing Systems. pp. 3297-3305. 2016
  17. Koh PW, Pierson E, Kundaje A. Denoising genome-wide histone ChIP-seq with convolutional neural networks. Proceedings of The ICML 2016 Workshop on Computational Biology. bioRxiv doi: http://dx.doi.org/10.1101/052118 (Preprint)
  18. Lianoglou S, Kundaje A, Leslie C. Inferring active signaling pathways by boosting the phosphorylation network. Proceeding of the NIPS Workshop on Computational Biology, 2006
  19. Middendorf M, Kundaje A, Shah M, Freund Y, Wiggins C, Leslie C, Motif Discovery through predictive modeling of gene regulation, Proceeding of RECOMB, 2005.
  20. Middendorf M, Kundaje A, Wiggins C, Freund Y, Leslie C, Predicting genetic regulatory response using classification: Yeast stress response, Lecture Notes in Computer Science, Vol. 3318(), pp. 1-13, 2005.


  1. Shrikumar A, Tian K, Shcherbina A, Avsec Z, Banerjee A, Sharmin M, Nair S, Kundaje A. TF-MoDISco v0.4.4.2-alpha: Technical Note. ArXiv e-prints:1811.00416, 2018 Nov 1
  2. Shrikumar A, Prakash E, Kundaje A. Gkmexplain: Fast and Accurate Interpretation of Nonlinear Gapped k-mer Support Vector Machines Using Integrated Gradients. bioRxiv 457606; doi: https://doi.org/10.1101/457606
  3. Movva R, Greenside PG, Shrikumar A, Kundaje A. Deciphering regulatory DNA sequences and non-coding genetic variants using neural network models of massively parallel reporter assays. bioRxiv 393926; doi: https://doi.org/10.1101/393926
  4. Shrikumar A, Su J, Kundaje A. Computationally Efficient Measures of Internal Neuron Importance. ArXiv e-prints [Internet]. 2018 Jul 26
  5. Avsec Z, Kreuzhuber R, Israeli J, Xu N, Cheng J, Shrikumar A, Banerjee A, Kim DS, Kundaje A*, Stegle O*, Gagneur J*. Kipoi: accelerating the community exchange and reuse of predictive models for regulatory genomics. bioRxiv 375345; doi: https://doi.org/10.1101/375345 
  6. Benayoun BA, Pollina E, Singh PP, Mahmoudi S, Harel I, Casey K, Dulken B, Kundaje A, Brunet A. Remodeling of epigenome and transcriptome landscapes with aging in mice reveals widespread induction of inflammatory responses. bioRxiv 336172; doi: https://doi.org/10.1101/336172
  7. Alexandri A, Shrikumar A, Kundaje A. Learning to Abstain via Curve Optimization. ArXiv e-prints [Internet]. 2018 Feb 20
  8. Wainberg M, Sinnott-Armstrong N, Knowles D, Golan D, Ermel R, Ruusalepp A, Quertermous T, Hao K, Bjorkegren JLM, Rivas MA, Kundaje A. Vulnerabilities of transcriptome-wide association studies. bioRxiv 206961; doi: https://doi.org/10.1101/206961
  9. Wang RYX, Sarkar P, Ursu O, Kundaje A, Bickel PJ. Network modelling of topological domains using Hi-C data. eprint arXiv:1707.09587 07/2017
  10. Greenside PG, Hussami N, Chang J, Kundaje A. PyBoost: A parallelized Python implementation of 2D boosting with hierarchies. bioRxiv 170803; doi: https://doi.org/10.1101/170803
  11. Shrikumar A, Greenside P,  Kundaje A. Reverse-complement parameter sharing improves deep learning models for genomics. bioRxiv 103663; doi: https://doi.org/10.1101/103663
  12. Karimzadeh M, Ernst C, Kundaje A, Hoffman MH. Umap and Bismap: quantifying genome and methylome mappability. bioRxiv 095463; doi: https://doi.org/10.1101/095463
  13. Blumberg A, Rice EJ, Kundaje A, Danko CG, Mishmar D. Initiation of mtDNA transcription is followed by pausing, and diverge across human cell types and during evolution. bioRxiv 054031; doi: https://doi.org/10.1101/054031
  14. Roberts EG, Mendez M, Viner C, Karimzadeh M, Chan RCW, Ancar R, Chicco D, Hesselberth JR, Kundaje A, Hoffman MH. Semi-automated genome annotation using epigenomic data and Segway. bioRxiv 080382; doi: https://doi.org/10.1101/080382


  1. Invited: Interpretable deep learning for genomics at The Molecules, Machines, and Medicines Workshop at Center for the Study of Inflammatory Bowel Disease (CSIBD) and the Center for Computational and Integrative Biology (CCIB) at Harvard University, Boston, MA, Nov 9 2018
  2. Invited: Interpretable deep learning for regulatory genomics and epigenomics at The Canadian Epigenetics Meeting, Esterel, Canada, Sept 29 2018
  3. Invited: Interpretable deep learning for regulatory genomics at McGill University, Montreal, Canada, Sept 28 2018
  4. Invited: Interpretable deep learning for regulatory genomics at The National Cancer Institute Workshop on Interpretable Deep Learning, Bethesda, MD, Sept 19 2018
  5. Invited: Deciphering genome function with functional genomics and machine learning at The IEEE Computational Intelligence Society, Santa Clara Valley Chapter, Santa Clara, CA, Aug 1 2018
  6. Invited: Deciphering genome function with functional genomics and machine learning at The NIH Workshop: Harnessing Artificial Intelligence and Machine Learning to Advance Biomedical Research, Bethesda, MD, July 23 2018
  7. Invited: Deep learning for genomics at Ancestry.com, San Francisco, CA, July 2 2018
  8. Invited: Machine learning approaches to decode the human genome at The 2018 Stanford AI4All Summer Program, Stanford, CA, June 29 2018
  9. Invited (Keynote): Predictive Models of Chromatin State to Understand Non-Coding Regulatory Genetic Variants at The 2018 Gordon Research Seminar on Human Genetic Variation and Disease, Biddeford, ME, June 9 2018
  10. Invited: Decoding non-coding regulatory elements and disease-associated genetic variants with deep learning at The Big Data, Deep Learning, and AI Methods for Cancer Analysis Education Session at 2018 American Association for Cancer Research (AACR) Annual Meeting, Chicago, IL, Apr 14 2018
  11. Selected: Enhanced interpretation and uncertainty estimation in neural network models of regulatory DNA at The 2018 CSHL Systems Biology: Global Regulation of Gene Expression Meeting, CSHL, NY, Mar 20 2018
  12. Selected: Deep learning a context-specific score for prioritizing regulatory non-coding variants associated with colorectal cancer at The GECCO Colorectal Cancer consortium meeting in Seattle, WA, Feb 14 2018
  13. Selected: Lessons from the ENCODE-DREAM Transcription Factor Binding Challenge at The 2018 ENCODE Consortium meeting, Stanford, CA Feb 5-7, 2018
  14. Invited: Learning the genetic and molecular basis of disease from a compendium of reference epigenomes at The 8th Annual California ALS Research Network Conference, Stanford, CA, Jan 20 2018
  15. Invited: Deep learning models for regulatory genomics at The Weizmann Institute of Science and Technology, Israel, Jan 15 2018
  16. Invited: Deep learning approaches to denoise, impute, integrate and interpret functional genomic data at The 2017 Computational and Genomic Biology Retreat, UC Berkeley, Nov 2017
  17. Invited: Deep learning approaches to decode the human genome at the 2017 Intelligence in Medicine Summit, Stanford, CA, Aug 2017
  18. Invited: Deep learning approaches to denoise, impute, integrate and interpret functional genomic data at the 2017 International Conference of Machine Learning (ICML) Workshop on Computational Biology, Sydney, Australia, Aug 2017
  19. Invited: Deep learning approaches to denoise, impute, integrate and interpret functional genomic data at the 2017 Cold Spring Harbor Labs Biology of Genomes Meeting, CSHL, NY, May 2017
  20. Invited: Deep learning approaches to decode the human genome at 2017 NVIDIA GTC conference, San Francisco, CA, May 2017
  21. Invited: Interpretable, Integrative Deep Learning for Decoding the Human Genome and Disease-Associated Genetic Variation at The 2017 Computer Forum AI Plenary Session, Stanford, CA, April 2017
  22. Invited: Deep learning approaches to denoise, impute, integrate and decode functional genomic data at The 2017 BIRS Meeting on Statistical and Computational Challenges in Large Scale Molecular Biology, Banff, Canada, Mar 2017
  23. Invited: Deep learning approaches to denoise, impute, integrate and decode functional genomic data at The CCMB Seminar Series, University of Michigan Ann Arbor, Ann Arbor, MI, Mar 2017
  24. Invited: The ENCODE DREAM in vivo transcription factor binding prediction challenge at The 2017 Cold Spring Harbor Systems Biology Meeting, CSHL, NY, Mar 2017
  25. Invited: Interpretable deep learning frameworks for regulatory genomics at 23&me, Mountain View, CA, Jan 2017
  26. Invited: Interpretable deep learning frameworks for regulatory genomics at The Bioinformatics Seminar, UCLA, Los Angeles, CA, Jan 2017
  27. Invited: Computational analysis of genomic data at The Center for Evolutionary Genomics and Medicine (EGM) at The Ben Gurion University of the Negev, Israel, Dec 2016
  28. Invited: The ENCODE DREAM in vivo transcription factor binding prediction challenge at 2016 RECOMB/ISCB Conference on Regulatory & Systems Genomics, Phoenix, AZ, Nov 2016
  29. Invited: Interpretable, integrative deep learning frameworks for regulatory genomics and epigenomics at The MIA Seminar Series, Broad Institute of MIT and Harvard, Boston, MA, Nov 2016
  30. Invited: Interpretable, integrative deep learning frameworks for regulatory genomics and epigenomics at Duke University CBB Seminar, Raleigh, NC, Nov 2016
  31. Invited: NHGRI Computational Genomics and Data Science Workshop at The NIH, Bethesda, MD, Sept 2016
  32. Invited: Deep learning models of transcription factor binding and chromatin accessibility in diverse human cell types and lineages at The Welcome Trust Genome Informatics Meeting, Hinxton, UK, Sept 2016
  33. Invited: Interpretable, integrative deep learning frameworks for regulatory genomics and epigenomics at ISMB 2016 Regulatory Genomics Special Interest Group, Orlando, FL, July 2016
  34. Invited: Interpretable, integrative deep learning frameworks for regulatory genomics and epigenomics at IBM TJ Watson Research Center, New York, NY, June 2016
  35. Invited: Decoding the human genome to decipher the genomic basis of disease at the Future of AI Meeting organized by Stanford AI and the White House Office of Science and Technology, Stanford, CA, June 2016
  36. Invited: How to train your DragoNN (Deep Regulatory genomic neural network) at the 2016 ENCODE Consortium Users Meeting, Stanford, CA, June 2016
  37. Invited: Interpretable, integrative deep learning frameworks for regulatory genomics and epigenomics at University of Massachusetts Medical School, Worcester, MA, April 2016
  38. Invited: Interpretable, integrative deep learning frameworks for regulatory genomics and epigenomics at Boston University Systems Biology Seminar, Boston, MA April 2016
  39. Invited: Interpretable, integrative deep learning frameworks for regulatory genomics and epigenomics at Microsoft Research New England, Boston, MA, April 2016
  40. Invited: Dynamics of 3D enhancer-gene associations across diverse human cell types and tissues at The Simons Institute Network Biology Workshop, Berkeley, CA, April 2016
  41. Invited: Interpretable, integrative deep learning frameworks for regulatory genomics and epigenomics at Illumina Inc, CA, April 2016
  42. Invited: Interpretable, integrative deep learning frameworks for regulatory genomics and epigenomics at The RIKEN Institute, Japan, March 2016
  43. Selected: Interpretable, integrative deep learning frameworks for regulatory genomics and epigenomics at The Cold Spring Harbor Meeting on Systems Biology: Global Regulation of Gene Expression, NY, March 2016
  44. Invited: Integrative deep learning frameworks for regulatory genomics and epigenomics at The Simons Institute Regulatory Genomics and Epigenomics Workshop, Berkeley, CA, March 2016
  45. Invited: Deep learning frameworks for regulatory genomics and epigenomics at Epigenomics 2016, Puerto Rico, Feb 2016
  46. Invited: Deep learning frameworks for regulatory genomics and epigenomics at Verily (Google Life Sciences), Mountain View, CA, Jan 2016
  47. Invited: Deep learning frameworks for regulatory genomics at Seminar Series of Computational Biology department at Memorial Sloan Kettering Cancer Center, NY, Dec 2015
  48. Invited: Deep learning frameworks for regulatory genomics at Seminar Series of the Systems Biology Department at Columbia University, NY, Dec 2015
  49. Invited: Deep learning frameworks for regulatory genomics at EMBL-Stanford Meeting on Personalised Health, Heidelberg, Germany, Nov 2015
  50. Invited: Deep learning frameworks for regulatory genomics at Calico Labs, San Francisco, CA, Nov 2015
  51. Selected: Deep learning the relationship between chromatin architecture, chromatin state and transcription factor binding at Annual meeting of The American Society of Human Genetics (ASHG) 2015., Baltimore, MD, Oct 2015
  52. Invited: Deep learning frameworks for regulatory genomics at the Genomics@JHU seminar at John's Hopkins University, Baltimore, MD, Sept 2015
  53. Invited: Leveraging reference epigenomes to decipher the genetic and molecular basis of complex diseases at CCSB Seminar at The Dana Farber Cancer Center, Boston, MA, April 1, 2015.
  54. Invited: Leveraging reference epigenomes to decipher the genetic and molecular basis of complex diseases at The Cornell-Weill Medical School, NYC, NY. Mar 18, 2015.
  55. Selected: Deep learning chromatin architecture, chromatin state and TF binding from a single assay at The 2015 ENCODE Consortium Meeting at The Cold Spring Harbor Labs, NY. Mar 16, 2015
  56. Invited: Regulatory Genomics and Epigenomics of Complex Disease at the La Jolla Institute of Allergy and Immunology in San Diego, CA. Feb 23. 2015
  57. Invited: Regulatory Genomics and Epigenomics of Complex Disease at The Epigenomics in Disease Session of the 2015 22nd Annual Molecular Medicine Tri-Conference in San Francisco, CA. Feb 16, 2015
  58. Invited: Learning and Visualizing Regulatory Landscapes across 100s of Human Tissue Types at the 2015 AAAS Meeting's session on Visualizing Biomedical Data and Processes Across Space and Time Scales, San Jose, CA. Feb 13, 2015
  59. Selected: Deep learning regulatory elements, chromatin architecture and chromatin state at The Cold Spring Harbor Systems Biology Meeting in Puerto Rico. Jan 28 2015
  60. Invited: Deciphering regulatory elements, factors, genes and pathways downstream of non-coding CRC GWAS variants at The GECCO Colorectal Cancer consortium meeting in Seattle. Jan 26 2015
  61. Invited: Three Dimensional Gene Regulation at the 2014 Neural Information Processing Systems (NIPS) workshop on Machine Learning in Computational Biology (MLCB) in Montreal, Canada. Dec 13 2015
  62. Invited: Learning three-dimensional regulatory interactomes at The Genetics/Bioinformatics and Systems Biology Colloquium at UCSD, CA. Nov 20, 2014
  63. Invited: Immune and neural epigenomics of Alzheimer’s disease in mouse and human at Big Ideas in Neuroscience Symposium. Stanford, CA. June 20 2014
  64. Invited: Integrative analysis of 111 Epigenomes at Symposium on Epigenetic Mechanisms in Cancer. Toronto, Canada, June 4 2014
  65. Invited: Learning Integrative Regulation Programs Across Diverse Human Cell Types at The 2014 Midwest Chromatin and Epigenetics meeting at The University of Wisconsin Madison. Wisconsin, MA. May 18 2014
  66. Invited: Interpreting non-coding genetic variants using functional genomic data at Novo Nordisk. Seattle, WA, April 18 2014
  67. Invited: Interpreting non-coding genetic variants using functional genomic data at the Genetic and Molecular Epidemiology (GAME) seminar series at The Fred Hutchinson Cancer Research Center. Seattle, WA. April 17 2014
  68. Invited: Integrative analysis of ENCODE data at ENCODE and Epigenomics Workshop organized by EBI-EMBL and Novartis. Boston, MA, April 15-16 2014
  69. Invited: Regulatory chromatin state variation across individuals, populations and cell types at UC Berkeley Statistics and Genomics Seminar. UC Berkeley, CA,  March 6 2014
  70. Invited: The dynamic regulatory architecture of the human genome at Stanford CS Computational Biology Dinner. Stanford, CA, January 2014
  71. Selected: Integrating Gene Expression and Sequence Data with Existing Biological Knowledge to Model Context-specific Gene Regulation at RECOMB/ISCB Conference on Regulatory and Systems Genomics 2013, Nov 2013 (Presented by Sofia KP)
  72. Selected: Epigenomic variation between species, tissues, populations and individuals. Platform Talk at American Society of Human Genetics (ASHG) 2013. Boston, MA, Oct 2013
  73. Invited: Dynamics of chromatin state and gene regulation across human cell-types and individuals. BIRS Statistical Data Integration Challenges in Computational Biology: Regulatory Networks and Personalized Medicine. Banff, Canada, Aug 2013
  74. Selected: Comparative analysis of chromatin state dynamics across organisms, cell types and individuals. Cold Spring Harbor Labs Biology of Genomes 2013. CSHL, NY. May 2013
  75. Invited: Heterogeneity of chromatin patterns are regulatory elements. American Society of Human Genetics (ASHG) 2012 - DNAnexus session. San Francisco, CA. Nov 2012
  76. Selected: Deciphering functional heterogeneity in the human genome using ENCODE data. ENCODE and modENCODE AWG/PI meeting. Cambridge, MA. May 2012
  77. Selected: Predictive models of transcription factor binding using ENCODE data. RECOMB Regulatory Genomics/Systems-Biology Meeting. New York, NY. Nov 2010
  78. Invited: Computational models for de-novo prediction of protein-ligand interactions. The Biopeople symposium on bioinformatics in the drug discovery value chain. University of Copenhagen, Denmark. Nov 2010
  79. Invited: Predictive Models of Gene Regulation- From Yeast to Humans. Stanford Biological Modeling Club, Stanford University. Oct 2010
  80. Selected: A supervised machine learning framework for integrative analysis of ENCODE data. ENCODE Consortium meeting. March 2009
  81. Invited: Predictive models of gene regulation. Broad Institute of MIT and Harvard. Cambridge, MA. 2008
  82. Selected: Hypoxia regulatory networks in yeast. Annual Yeast Molecular Biology meeting. 2006
  83. Selected: Predictive modeling of gene regulation. RECOMB Satellite Workshop on Regulatory Genomics. 2004
  84. Selected: A Classification based framework of learning gene regulatory networks. NIPS workshop on Methods in Computational Biology. 2004
  85. Invited: Using sequence and expression data to learn transcriptional modules. IBM T.J. Watson Research center. 2001


  • Department Admissions Committees
    • Dept. of Genetics PhD Admissions Committee member: 2014, 2015, 2016, 2017, 2018
    • Dept. of Computer Science PhD Admissions Committee member: 2014, 2017, 2018
    • Dept. of Computer Science M.S. Admissions Committee member: 2015, 2016
  • Journal Reviewer / Guest Editor (GE): 
    • Science: 2018
    • Nature Methods: 2014, 2016, 2017
    • Nature Communications: 2016,2018
    • Nature Genetics: 2016
    • Genetics: 2015
    • JAMA: 2014
    • Genome Research: 2012,2013,2014,2015,2016,2017,2018
    • Genome Biology: 2012,2013,2014, 2015
    • PLOS Computational Biology: 2010, 2012, 2013, 2014, 2016, 2017
    • PLOS One: 2012
    • PLOS Genetics: 2016 (GE)
    • TCBB: 2013
    • G3: 2013
    • Bioinformatics: 2012, 2015, 2016, 2017, 2018
    • Nucleic Acids Research: 2015
    • BMC Bioinformatics: 2014
    • In-silico Biology: 2011
    • IEEE Signal Processing Magazine: 2011
    • Columbia University Undergraduate Science Journal: 2008
    • Physiological Genomics: 2008
    • Molecular Systems Biology: 2016, 2017
    • eLife: 2016 (GE)
    • PeerJ: 2017,2018
  • Conference Reviewer / Program Committee (PC)
    • Neural Information Processing Systems (NIPS): 2007, 2012, 2013, 2014, 2015, 2016, 2017, 2018
    • NIPS Machine Learning in Computational Biology (MLCB): 2015 (PC), 2016 (PC)
    • Research in Computational Molecular Biology (RECOMB): 2010, 2011
    • RECOMB Regulatory and Systems Genomics Workshop: 2016 (PC)
    • Intelligent Systems in Molecular Biology (ISMB): 2012, 2013 (PC), 2014, 2015, 2016 (PC), 2017 (PC)
    • IEEE Computer Systems Bioinformatics: 2004
    • International Workshop on Machine Learning in Systems Biology (MLSB): 2016 (PC)
  • Grant/Award Reviewer
    •  Paul Allen Frontiers Group grants on in vivo epigenetic editing, assessment and visualization technology development: (2017)
  • Community Prediction Challenges and Summer Schools


Stanford Courses

  • GENE205 (Advanced Genetics) - Winter 2017
  • GENE200 (Genetics Training Camp) - Autumn 2017
  • GENE236/ CS273B / BIOMEDIN273B (Deep Learning for Genomics and Biomedicine) - Autumn 2017: 9/26/2017 - 12/13/2017
  • HUMBIO51 (Big Data for Biologists – Decoding genome function) - Autumn 2017: 9/26/2017 - 12/13/2017 (Awarded a VPTL Faculty College Program grant by the Vice Provost of Teaching and Learning for Human Biology Curriculum Development in Computational Biology)
  • GENE245 / BIO268 / STATS345 / CS373 / BIOMEDIN245 (Statistical and Machine Learning Methods for Genomics) - Spring 2017: 4/4/2017 - 6/6/2017
  • GENE200 (Genetics Training Camp) - Autumn 2016
  • GENE236/ CS273B / BIOMEDIN273B (Deep Learning for Genomics and Biomedicine) - Autumn 2016: 9/26/2016 - 12/13/2016
  • GENE245 / BIO268 / STATS345 / CS373 / BIOMEDIN245 (Statistical and Machine Learning Methods for Genomics) - Spring 2016: 3/29/2016 - 5/31/2016
  • GENE205 (Advanced Genetics) - Winter 2016: 1/28/2016
  • GENE245 / BIO268 / STATS345 / CS373 / BIOMEDIN245 (Statistical and Machine Learning Methods for Genomics) - Spring 2015: 3/30/2015 - 6/11/2015
  • GENE205 (Advanced Genetics) - Winter 2015: 1/6/2015 - 2/19/2015
  • GENE200 (Genetics Training Camp) - Autumn 2015
  • GENE205 (Advanced Genetics) - Winter 2014: 1/28/2014, 2/11/2014
  • GENE200 (Genetics Training Camp) - Autumn 2014: 9/8/2014 - 9/12/2014
  • GENE200 (Genetics Training Camp) - Autumn 2013: 9/9/2013 - 9/13/2013
Stanford Guest Lectures/Seminars

  • BIO209A (The Human Genome and Disease): 1/11/2016, 1/13/2016
  • CS300 (Computer Science): 10/24/2016
  • HUMBIO151R (Biology, Health and Big Data): 4/18/2017
  • SAILORS (AI FOR ALL): 7/12/2017

  • CS300 (Computer Science): 11/02/2015
  • HUMBIO151R (Biology, Health and Big Data): 4/20/2015
  • BIO209A (The Human Genome and Disease: 4/21/2015, 4/23/2015

  • BIOMEDIN 201 (Biomedical Informatics Seminar): 7/15/2014
  • BIO209A (The Human Genome and Disease): 4/1/2014, 4/3/2014
  • BioStatistics Workshop Seminar (Stanford): 2/27/2014

  • CS300 (Computer Science): 2013

                  External Teaching/Courses

                  Teaching Assistant

                  • Computational Genomics (Columbia University): Summer 2002, Spring 2004

                  • Machine Learning (Columbia University): Spring 2006

                  • Computer Networks (Columbia University): Spring 2002


                  Graduate Students

                  1. Daniel Kim (Biomedical Informatics, Stanford): Sept 2013 - Present (BioX Fellow). Co-advised by Paul Khavari
                  2. Avanti Shrikumar (Computer Science, Stanford): Sept 2014 - Present (BioX Fellow, HHMI International Student Fellow, Microsoft Women's Fellow)
                  3. Chris Probert (Genetics, Stanford): Sept 2015 - Present (NSF Fellow). Co-advised by Christina Curtis
                  4. Michael Wainberg (Computer Science, Stanford): Sept 2015 - Present (BioX Fellow). Co-advised by Michael Bassik
                  5. Anna Shcherbina (Biomedical Informatics, Stanford): Jan 2016 – Present. Co-advised by Euan Ashley.
                  6. Laksshman Sundaram (Computer Science, Stanford): June 2018 - Present
                  7. Abhimanyu Banerjee (Physics, Stanford): Sept 2016 - Present
                  8. Amr Mohammed (Computer Science, Stanford): Sept 2018 - Present
                  1. Akshay Balsubramani (From UCSD, Computer Science): Jan 2017-Present
                  2. Georgi Marinov (From Univ. of Indiana): May 2017 - Present (2018 Dean's Fellow)
                  3. Mahfuza Sharmin (From Univ. of Maryland College Park, Computer Science): Sept 2017 - Present
                  Alumni in Academia
                  1. Jianrong Wang (Postdoctoral Associate, March 2016 - Jan 2017): Assistant Professor at Dept. of Computational Mathematics, Science and Engineering, Michigan State University
                  2. Pang Wei Koh (Data Analyst, July 2015 - Aug 2016): Graduate Student, Dept. of Computer Science, Stanford University
                  3. Irene Kaplow (PhD, Computer Science, 2017): Now Postdoc in Andreas Pfenning's group, Carnegie Mellon University
                  4. Oana Ursu (Genetics, Stanford): Sept 2013-March 2018 (HHMI International Student Fellow). Co-advised by Michael Snyder - Now Postdoc in Aviv Regev's group, MIT and The Broad Institute
                  5. Peyton Greenside (Biomedical Informatics, Stanford): March 2014 - May 2018 (BioX SIGF Fellow, Schmidt Foundation Fellow) - 
                  Alumni in Industry
                  1. Maryna Taranova (Postdoctoral Associate): Sept 2014-Nov 2017 – Now Principal Scientist in Chemoinformatics, Roche, Santa Clara
                  2. Nathan Boley (Postdoctoral Associate, Jan 2015 - Sept 2016) -  Now Research Scientist at Freenome Inc.
                  3. Chuan-Sheng Foo (PhD, Computer Science, 2017): Now Scientist at Artificial Intelligence Department, Institute for InfoComm Research, Singapore
                  4. Nadine Hussami (Electrical Engineering, Stanford University): 2010-2011, 2016 – 2017. Co-advised by Rob Tibshirani - Now Data scientist in Healthcare division of Wolters Kluwer
                  5. Johnny Israeli (Biophysics, Stanford): Sept 2014 - June 2018 (BioX SIGF Fellow). Now Manager, Deep learning for Genomics, Nvidia Corp.
                  Qualifying exam Committee
                  1. David Yao (Genetics, Stanford)
                  2. Alex Bishara (CS, Stanford)
                  3. Bo Wang (CS, Stanford)
                  4. Bharath Ramsundar (CS, Stanford)
                  5. Johnny Israeli (Biophysics, Stanford)
                  6. Avanti Shrikumar (CS, Stanford)
                  7. Irene Kaplow (CS, Stanford)
                  8. Peyton Greenside (BMI, Stanford)
                  9. Jenny Hsu (Genetics, Stanford)
                  10. Oana Ursu (Genetics, Stanford) 
                  11. Alicia Schep (Genetics, Stanford)
                  12. Emily Tsang (BMI, Stanford)
                  13. Jessica Chang (Genetics, Stanford)
                  14. Joe Charalel (BMI, Stanford)
                  15. Ben Dulkan (MD-PhD, Stanford)
                  16. Diego Calderon (BMI, Stanford)
                  17. Natalie Telis (BMI, Stanford)
                  18. Kristin Muench (Neuroscience, Stanford)
                  19. Eli Moss (Genetics, Stanford)
                  20. Brayon Fremin (Genetics, Stanford)
                  21. Maxwell Mumbach (Genetics, Stanford)
                  22. Devin Alpoel King (Biology, Stanford)
                  23. Chris Probert (Genetics, Stanford)
                  Thesis Committee / Reader
                  1. Kristin Muench (Neuroscience, Stanford)
                  2. Ben Dulken (Stem Cell, Stanford):
                  3. Ragini Phansalkar (BMI/MSTP, Stanford)
                  4. Michael Wainberg (CS, Stanford): 2018
                  5. Bharath Ramsundar (CS, Stanford): 2017
                  6. Nadine Hussami (EE, Stanford): 2017
                  7. Peyton Greenside (BMI, Stanford): 2017
                  8. Oana Ursu (Genetics, Stanford): 2017
                  9. Awni Hannun (CS, Stanford): 2017
                  10. Alex Bishara (CS, Stanford): 2017
                  11. Volodymyr Kuleshov (CS, Stanford): 2017
                  12.  Irene Kaplow (CS, Stanford): 2017
                  13. Bo Wang (CS, Stanford): 2017
                  14. Alicia Schep (Genetics, Stanford): 2017
                  15. Chuan Sheng Foo (CS, Stanford): 2016
                  16. Lin Huang (CS, Stanford): 2016
                  17. Akosua Busia (MCS undergraduate, Stanford) - Served as Reader on Thesis: 2016
                  18. Sofia Kyriazopoulou-Panagiotopoulou (CS, Stanford): 2014
                  19. Cristina Pop (CS, Stanford): 2014
                  20. Dorna Kashef (CS, Stanford): 2015
                  21. Yoni Donner (CS, Stanford): 2015
                  22. Glenn Markov (Genetics, Stanford)
                  23. Oana Ursu (Genetics, Stanford) 
                  24. Alicia Schep (Genetics, Stanford)
                  25. Aaron Daugherty (Genetics, Stanford)
                  26. Joe Charalel (BMI, Stanford)
                  27. Wen Torng (Bioengg, Stanford)

                  Other graduate student mentees

                  1. Bo Wang (CS, Stanford): 2014-2017
                  2. Aaron Daugherty (Genetics, Stanford): 2015-2017
                  3. Azar Fazel (EE, Stanford): 2014
                  4. Nischay Kumar (CS, MIT): 2012 - 2013
                  5. Vinay Kartha (Bioinformatics, Boston University): 2012 – 2013
                  6. Steve Lianoglou (CS, Columbia University): 2006-2008
                  Undergraduate students mentees
                  1. Katherine Hufker (CS, Stanford): Senior Project: 2017-Present
                  2. Joyce Kang (CS, Stanford): Honor's thesis: 2017-Present
                  3. Brian Hie (CS, Stanford): Honor's thesis: 2015-2016
                  4. Akosua Busia (MCS, Stanford): 2015-2016
                  5. Kayla McCue (Applied Math, Caltech): Summer 2014
                  6. Kenneth Chang (CS, USC): Summer 2014
                  7. Eric Kofman (CS, Stanford): 2013
                  8. Sean Scott (CS, Stanford): 2013
                  9. Tyler Davis (CS, Stanford): 2013-2014
                  10. Hayden Metsky (CS, MIT): 2012 - 2013
                  11. Max Libbrecht (CS, Stanford University): 2009-2011
                  12. Jon Rodriguez (CS, Stanford University): 2010-2011
                  13. Sarah Xing (Biomedical Informatics, Stanford University): 2011
                  High school students mentees
                  1. Jocelin Su (2018)
                  2. Katherine Tian (2018)
                  3. Eva Prakash (2017, 2018)
                  4. Rajiv Movva (2017, 2018)
                  5. Arian Raje (2017)
                  6. Bryan Chiang (2017)
                  7. Nikhil Cheerla (Monta Vista High School): Summer 2016 (Seminfinalist in 2016 Regeneron Science Talent Show)
                  8. Rahul Mohan (Bellarmine College Preparatory, San Jose, CA): Summer 2015, Summer 2016
                  9. Pranav Reddy (Harker School, San Jose, CA): Summer 2014 (Semifinalist in 2014 Intel Science Talent Show)
                  10. Aniruddh Mathukumili (Mountain Vista School, Denver, CO): Summer 2014
                  11. Aathira Menon (Harker School, San Jose, CA): Summer 2014
                  12. Neha Sunil (Harker School, San Jose, CA): Summer 2014

                  INDUSTRY EXPERIENCE

                  Freenome Inc.


                  Cell-free DNA liquid biopsy technology for early cancer detection

                  07/2017 - Present

                  Epinomics Inc.


                  Clinical epigenomics startup

                  09/2015 – 05/2018

                  Deep Genomics
                  Scientific Advisory Board

                  11/2015 – 07/2017

                  Medical genomics startup

                  10/2012 – 07/2013

                  DNAnexus Inc., https://dnanexus.com
                  Cloud-based Next-generation sequencing data analysis and storage

                  06/2010 - 11/2010

                  IBM T.J. Watson Research Center, Functional Genomics and Systems Biology group.
                  Research software engineer
                  Published one of the first statistical noise models for massively parallel signature sequencing (MPSS) data in collaboration with Lynx Therapeutics and Institute for Systems biology (Alan Aderem lab) 
                  Manager: Gustavo Stolovitzky

                  01/2003 - 09/2003

                  OTHER ACTIVITIES

                  • Founder of the Indian Students Association (ISAC) at Columbia University (2003 – 2008). Organized charitable events UTSAV 2005, 2007 and 2008. Proceeds from the event were donated to victims of Pakistan earthquake (2005), micro-financing efforts in India (2007) and a secondary school in rural India (2008)