Elham Azizi

Biomedical Engineering

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.

  • Postdoctoral Fellow, Memorial Sloan Kettering Cancer Center, 2016-2019
  • Postdoctoral Fellow, Columbia University, 2014-2016
  • Microsoft Research, Redmond, 2014-2014
  • Assistant Professor of Biomedical Engineering, Columbia University, 2020-
  • Herbert & Florence Irving Assistant Professor of Cancer Data Research, Irving Institute for Cancer Dynamics, 2020-
  • American Association for Cancer Research 2017-
  • 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
  • Bachireddy P*, Azizi E*, Burdziak C, Nguyen VN, Ennis C, Choo Z-N, Li S, Livak K, 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”, submitted.
  • Price JC, Azizi E, Naiche LA, Parvani JG, Shukla P, Kim S, Slack-Davis JK, Pe’er D, Kitajewski JK, “Notch3 signaling promotes tumor cell adhesion and progression in a murine epithelial ovarian cancer model”, Plos one, 15(6): e0233962, 2020.
  • Burdziak C*, Azizi E*, Prabhakaran S, Pe’er D, “A Nonparametric Multi-view Model for Estimating Cell Type-Specific Gene Regulatory Networks”, arXiv 1902.08138, 2019.
  • 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 generation of regulatory T cells”, 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, 174 (5): 1293-1308, 2018 (featured as cover story).
  • Azizi E*, Prabhakaran* S, Carr A, Pe’er D, “Bayesian Inference for Single-cell Clustering and Imputing”, Genomics and Computational Biology. 3 (1), 46, 2017.
  • 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), PMLR. 48, 1070-1079, 2016.
  • Azizi E, Airoldi EM, Galagan JE, “Learning Modular Structures from Network Data and Node Variables”, Proceedings of the 31st International Conference on Machine Learning (ICML), PMLR. 32, 1440-1448, 2014.
  • Galagan JE, Minch K*, Peterson M*, Lyubetskaya A*, Azizi E*, Sweet L*, Gomes A*, Rustad T, Dolganov G, Glotova I, Abeel T, Mahwinney C, Kennedy AD, Allard R, Brabant W, Krueger A, Jaini S, Honda B, Yu WH, Hickey MJ, Zucker J, Garay C, Weiner B, Sisk P, Stolte C, Winkler JK, Van de Peer Y, Iazzetti P, Camacho D, Dreyfuss J, Liu Y, Dorhoi A, Mollenkopf HJ, Drogaris P, Lamontagne J, Zhou Y, Piquenot J, Park ST, Raman S, Kaufmann SH, Mohney RP, Chelsky D, Moody DB, Sherman DR, Schoolnik GK “The Mycobacterium tuberculosis regulatory network and hypoxia”, Nature. 2013 Jul 11 ; 499 (7457) : 178-183.