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Elham Azizi

ASSISTANT PROFESSOR OF BIOMEDICAL ENGINEERING & HERBERT AND FLORENCE IRVING ASSISTANT PROFESSOR OF CANCER DATA RESEARCH (IN THE HERBERT AND FLORENCE IRVING INSTITUTE FOR CANCER DYNAMICS AND IN THE HERBERT IRVING COMPREHENSIVE CANCER CENTER)

351 Engineering Terrace

Tel(212) 851-0271
Fax(212) 854-8725
Email[email protected]

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

Links

AZIZI LABCVVISIT BIOMEDICAL ENGINEERINGVISIT CANCER DYNAMICS

 

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

Professional Affiliations
  • American Association for Cancer Research 2017-

Honors & Awards
  • 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
  • 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.

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