Elham Azizi

Herbert & Florence Irving Assistant Professor of Cancer Data Research, Irving Institute for Cancer Dynamics, Assistant Professor of Biomedical Engineering, Affiliated Faculty of Computer Science, Affiliated Member of Data Science Institute

351 Engineering Terrace

Tel(212) 851-0271
Fax(212) 854-8725

Elham Azizi’s multidisciplinary research utilizes novel machine learning techniques and cutting-edge genomic technologies to study the composition and circuitry of cells in tumors.

Research Interests

Computational Biology, Machine Learning, Genomics, Cancer Biology, Cancer Immunology


Characterizing various interacting cell types in the tumor microenvironment, and unraveling their underlying mechanisms can guide the development of improved and personalized cancer treatments. Azizi’s approach involves leveraging genomic profiling at single-cell resolution and developing machine learning and statistical method to analyze and integrate high-dimensional genomic data.

Azizi holds a BSc in Electrical Engineering from Sharif University of Technology (2008), and an MSc in Electrical Engineering (2010) and a PhD in Bioinformatics (2014) from Boston University. She was a postdoctoral fellow at Columbia University and Memorial Sloan Kettering Cancer Center (2014-2019). She joined the faculty of Columbia Biomedical Engineering and Irving Institute of Cancer Dynamics in 2020. She is also affiliated with the Department of Computer Science, Data Science Institute, and the Herbert Irving Comprehensive Cancer Center. 

Research Experience

  • Postdoctoral Fellow, Memorial Sloan Kettering Cancer Center, 2016-2019
  • Postdoctoral Fellow, Columbia University, 2014-2016
  • Microsoft Research, Redmond, 2014-2014

Professional Experience

  • Herbert & Florence Irving Assistant Professor of Cancer Data Research, Irving Institute for Cancer Dynamics, 2020-
  • Assistant Professor of Biomedical Engineering, Columbia University, 2020-
  • Affiliated Faculty of Computer Science, 2020-
  • Affiliated Member of Data Science Institute, 2020-

Professional Affiliations

  • American Association for Cancer Research 2017-

Honors & Awards

  • Early-Career Innovator in Science Award in Cancer Immunology, Takeda and the New York Academy of Sciences, 2024.Allen
  • Distinguished Investigator Award, Allen Institute, 2023. 
  • NHGRI Award for Supporting Talented Early Career Researchers in Genomics (R01), 2023. 
  • CZI Science Diversity Leadership Award, Chan Zuckerberg Initiative and the National Academies of Sciences, Engineering, and Medicine, 2022. 
  • NSF CAREER Award, 2022. 
  • Provost’s Grant for junior faculty contributing to the diversity goals of Columbia University, 2022. 
  • Columbia Research Initiatives in Science & Engineering (RISE) Award, 2021. 
  • Irving Endowed Assistant Professorship in Cancer Data Research, Columbia University, 2020.
  • Tri-Institutional Breakout Prize for Junior Investigators, 2019
  • NIH NCI Pathway to Independence Award K99/R00), 2018
  • American Cancer Society Postdoctoral Fellowship, 2017
  • IBM Best Student Paper Award, New England Statistics Symposium (NESS), 2014
  • TEDMED Front Line Scholarship, 2014

Selected Publications

He S*, Jin Y*, Nazaret A*, Shi L, Chen X, Rampersaud R, Dhillon BS, Valdez I, Friend LE, Fan JL, Park CY, Mintz Y-H, Carrera D, Fang KW, Mehdi K, Rohde M, McFaline-Figueroa JL, Blei D, Leong KW, Rudensky AY^, Plitas G^, Azizi E^Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor–immune hubs. Nature Biotechnology, 2024.

Nazaret A*^, Hong J*^, Azizi E^, Blei D^. Stable Differentiable Causal Discovery, Proceedings of The 33rd International Conference on Machine Learning (ICML). 2024. 

Maurer K*, Park CY*, Mani S, Borji M, Penter L, Jin Y, Zhang JY, Shin C, Brenner JR, Southard J, Krishna S, Lu W, Lyu H, Abbondanza D, Mangum C, Olsen LR, Neuberg DS, Bachireddy P, Garhi SL, Li S, Livak KL, Ritz J, Coiffeur RJ, Wu CJ^, Azizi E^Coordinated Immune Cell Networks in the Bone Marrow Microenvironment Define the Graft versus Leukemia Response with Adoptive Cellular TherapyBiorxiv. 2024

Park C*, Mani S*, Beltran-Velez N, Maurer K, Gohil S, Li S, Huang T, Knowles DA, Wu CJ, Azizi E^DIISCO: A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data. Proceedings of Conference on Research in Computational Molecular Biology (RECOMB)2024. 

Boyeau P*, Hong J*, Gayoso A, Kim M, McFaline-Figueroa JL, Jordan MI, Azizi E, Ergen C^, Yosef N^, Deep generative modeling of sample-level heterogeneity in single-cell genomicsbioRxiv. 2024.

Nazaret A*, Fan JL*, Lavallée V-P*, Cornish AE, Kiseliovas V, Masilionis I, Chun J, Bowman RL, Eisman SE, Wang J, Shi L, Levine RL, Mazutis L, Blei D, Pe'er D^, Azizi E^Deep generative model deciphers derailed trajectories in acute myeloid leukemia. bioRxiv. 2023. 

Liu Y, Jin Y, Azizi E^, Blumberg AJ^.  CellStitch: 3D Cellular Anisotropic Image Segmentation via Optimal Transport. BMC Bioinformatics. 24, 480. 2023.

Wang Y*, Fan JL*, Melms JC*, Amin AD, Georgis Y, Barrera I, Ho P, Tagore S, Abril-Rodriguez G, He S, Jin Y, Biermann J, Hofree M, Caprio L, Berhe S, Khan S, Henick BS, Ribas A, Macosko EZ, Chen F, Taylor AM, Schwartz GK, Carvajal RD, Azizi E^, Izar B^, Multi-modal single-cell and whole-genome sequencing of small, frozen clinical specimens. Nature Genetics, 2023. 

Bachireddy P*^, Azizi E*^, Burdziak C, Nguyen VN, Ennis C, Maurer K, Park CY, Choo Z-N, Li S, Gohil, SH, Ruthen NG, Ge Z, Keshin D, Cieri N, Livak K, Kim HT, Neuberg DS, Soiffer RJ, Ritz J, Alyea E, Pe'er D^, Wu CJ^. Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy. Cell Reports, 2021. (Featured in top 10 Best of Cell Reports 2021-2022).  

Hemmers S, Schizas M, Azizi E, Dikiy S, Zhong Y, Feng Y, Altan-Bonnet G, Rudensky AY. IL-2 production by self-reactive CD4 thymocytes scales regulatory T cell generation in the thymus. Journal of Experimental Medicine. 2019.

Azizi E*, Carr AJ*, Plitas G*, Cornish AE*, Konopacki C, Prabhakaran S, Nainys J, Wu K, Kiseliovas V, Setty M, Choi K, Fromme, R.M., Dao P, McKenney P.T., Wasti, R.C., Kadaveru, K., Mazutis L, Rudensky AY^, Pe'er D^, Single-cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment, Cell. 2018 (Featured as Cover Story).

Prabhakaran S*, Azizi E*, Carr A, Pe'er D, Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data, Proceedings of The 33rd International Conference on Machine Learning (ICML),  2016.


* denotes equal contributions
^ denotes corresponding author(s)
Underlined authors are lab members

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