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Assistant Professor
Dept. of Genetics
Dept. of Computer Science
Stanford University
Address
Department of Genetics
300 Pasteur Dr., Lane Building, L301
Stanford, CA 94305-5120, USA [Map]

E-mail: anshul @ kundaje .net ; akundaje @ stanford .edu 
Phone: (650)-723-2353 ; Fax: (650)-725-1534



BIO

Anshul Kundaje is an Assistant Professor of Genetics and Computer Science at Stanford University. The Kundaje lab develops statistical and machine learning methods for large-scale integrative analysis of functional genomic data to decode regulatory elements and pathways across diverse cell types and tissues and understand their role in cellular function and disease. Anshul completed his Ph.D. in Computer Science in 2008 from Columbia University. As a postdoc at Stanford University from 2012-2014 and a research scientist at MIT and the Broad Institute from 2012-2014, he led the integrative analysis efforts for two of the largest functional genomics consortia - The Encyclopedia of DNA Elements (ENCODE) and The Roadmap Epigenomics Project. Dr. Kundaje is a recipient of the 2016 NIH Director’s New Innovator Award and The 2014 Alfred Sloan Foundation Fellowship.

WHAT WE DO

My primary research interests are computational biology and applied machine learning with a focus on gene regulation. Our research focusses on development of statistical and machine learning methods for integrative analysis of diverse functional genomic and genetic data to learn models of gene regulation. We have led the analysis efforts of the Encyclopedia of DNA Elements (ENCODE) and The Roadmap Epigenomics Projects with the development of novel methods for 
  1. Denoising and normalization of large-scale functional genomic data
  2. Dissecting combinatorial transcription factor co-occupancy within and across cell-types
  3. Predicting cell-type specific enhancers from chromatin state profiles
  4. Modeling 3D genome architecture and predicting cell-type specific enhancer-promoter interactions
  5. Learning transcriptional regulatory networks that integrate proximal and distal cis and trans signals.
  6. Improving the detection and interpretation of potentially causal disease-associated variants from genome-wide association studies
More recently, we have also been developing
  1. Interpretable deep learning frameworks for functional genomics and epigenomics
  2. Causal regulatory models by integrating functional genomic data from temporal (e.g. differentiation/reprogramming) and perturbation (e.g. drug response, knockdown, genome-editing) experiments
  3. Methods to understand the relationships between genetic variation, regulatory chromatin variation and expression variation in healthy and diseased individuals
For more details see our Projects. Also check out some of the lectures/talks from our lab.

Talks Playlist


RECENT NEWS

BACKGROUND

2012-2013, I was a Research Scientist in Manolis Kellis' lab at MIT and The Broad Institute studying epigenomic and chromatin state dynamics across organisms, cell-types and individuals as part of the Roadmap Epigenomics Project and the mod/ENCODE (Encyclopedia for DNA elements) consortium. 

2008 - 2012, I was a Postdoctoral Research Associate with Serafim Batzoglou and Arend Sidow in the Computer Science Dept. at Stanford University. I served as one of the lead data coordinators and computational analysts for the ENCODE consortium. My primary focus was on deciphering heterogeneity of regulatory interactions in the human genome. I also developed ENCODE's ChIP-seq statistical data analysis pipeline.

2003-2008, I was a graduate student (PhD.) in Christina Leslie's lab in the Computer Science Dept. at Columbia University in New York. I developed Machine Learning methods for modeling transcriptional gene regulation in yeast and worm. 

2002-2003, I briefly worked at the IBM T.J. Watson Research Center in the Functional Genomics and Systems Biology group under Gustavo Stolovitzky. I developed one of the first statistical noise models for massively parallel sequencing data (MPSS) in a collaboration with The Institute of Systems Biology and Lynx Therapeutics.

In a past life, I was an Electrical Engineer (B.E from Mumbai University, 2001 and M.S. from Columbia University, 2002) and worked on computer networks and voice over IP with Henning Schulzrinne.

REPRESENTATIVE PUBLICATIONS

  • Learning Important Features Through Propagating Activation Differences [Code], [Videos], [Preprint]
    Shrikumar A, Greenside P,  Kundaje A
    Proceedings of the 34th International Conference on Machine Learning (ICML), PMLR 70:3145-3153, 2017

  • Denoising genome-wide histone ChIP-seq with convolutional neural networks [Code] [Preprint]
  • Koh PW, Pierson E, Kundaje A
    Bioinformatics (2017) 33 (14): i225-i233

  • Proceedings of the 2017 Intelligent Systems in Molecular Biology (ISMB) Conference

  • An integrated encyclopedia of DNA elements in the human genome
    Dunham I, Kundaje A, ENCODE Project Consortium
    Nature. 2012 Sep 6;489(7414):57-74. doi: 10.1038/nature11247.

  • Integrative analysis of 111 reference human epigenomes
    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.
    Nature. 2015 Feb 19;518(7539):317-30. doi: 10.1038/nature14248. (PMID: 25693563)
  • Architecture of the human regulatory network derived from ENCODE data. [Website]
    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. 
    Nature. 2012 Sep 6;489(7414):91-100. doi: 10.1038/nature11245.
    *Joint First Author

  • Ubiquitous heterogeneity and asymmetry of the chromatin environment at regulatory elements. [Website] [Code]
    Kundaje A*, Kyriazopoulou-Panagiotopoulou S*, Libbrecht M*, Smith CL, Raha D, Winters EE, Johnson SM, Snyder M, Batzoglou S, Sidow A. 
    Genome Res. 2012 Sep;22(9):1735-47. doi: 10.1101/gr.136366.111.
    Subpages (1): Lab Photos
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