​​I am a Postdoctoral Research Fellow in the Dana Pe'er Lab, Computational & Systems Biology Program at Memorial Sloan Kettering Cancer Center (MSKCC). I joined the Pe'er Lab in Columbia University in October 2014, prior to its move to MSKCC.
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I received a PhD in Bioinformatics from Boston University in 2014, advised by James Galagan (Biomedical Engineering, BU) and in close collaboration with Edoardo Airoldi (Statistics, Harvard). I completed an MS degree in Electrical Engineering also from Boston University in 2010 and a BS in Electrical Engineering with a minor in Industrial Engineering from Sharif University of Technology in 2008.​ I gained additional experience at Microsoft Research (Redmond) in 2014.

Update: I will be starting my lab as Assistant Professor in Biomedical Engineering and Herbert and Florence Irving Professor of Cancer Data Research in Irving Institute for Cancer Dynamics, ​Columbia University in Jan 2020. Please see azizilab.com for more information.

Research Interests

My research is focused on developing novel statistical machine learning techniques for systems-level characterization of the tumor microenvironment and the underlying regulatory circuitry. Through integrating single-cell and multi-omics technologies with Bayesian and probabilistic modeling approaches, I aim to infer dysregulated programs driving cancer stem cells as well as the reprogramming of immune cells leading to immune dysfunction. 

My PhD research was on developing computational models for regulatory programs in microbial organisms, through integrating gene expression and epigenetic data.  

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News!

• May 2019: Honored to be selected as a winner of the Tri-Institutional Breakout Prize for Junior Investigators.
• May 2019: Invited to present at the New York Genome Center Computational Cancer Genomics Evening Lecture.
• Inviting submissions to our 4th Workshop on Computational Biology at ICML2019 due April 30th!
• Mar 2019: Presented at Harvard Medical School BWH Center for Data Sciences.
• Feb 2019: Presented at MIT Biology & Broad Institute.
• Feb 2019: Presented at Biomedical Engineering Department, Columbia University. 
• Feb 2019: Presented at Immunobiology Department, Yale School of Medicine.
  
• Feb 2019: Presented at Rockefeller University. 
• Jan 2019: Presented at Departments of Cancer Biology and Pathology, University of Pennsylvania.
• Jan 2019: Presented at Symposium of Mathematical Genomics, Columbia University.
• Jan 2019: Presented at ICM Computational Cancer Seminar Series, NYU School of Medicine.

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Awards & Honors

• Tri-Institutional Breakout Prize for Junior Investigators, 2019.
  
• ​​NIH Pathway to Independence Award (K99/R00), National Cancer Institute, 2018-2023. 
• Finalist, Burroughs Wellcome Fund Career Awards at the Scientific Interface, 2018. 
• American Cancer Society Postdoctoral Fellowship, 2017. 
• Best Poster Presentation Award, Memorial Sloan Kettering Postdoctoral Research Symposium, 2016.
• Dataminr Poster Presentation Award, 10th Annual Machine Learning Symposium, The New York Academy of Sciences, 2016.
• Ford Fund Travel Award, Drawing Causal Inference from Big Data, National Academy of Sciences, 2015. 
• IBM Best Student Paper Award, New England Statistics Symposium (NESS), Harvard University, 2014. 
• TEDMED Front Line Scholarship, 2014. 
• Travel Grant, Women In Machine Learning Workshop, 2013. 
• Student Travel Award, Virginia Bioinformatics Institute, Virginia Tech, 2013.
• Best Poster Presentation Award, Boston Bacterial Meeting, Harvard University, 2013. 
• Best Poster Presentation Award, Bioinformatics Student Organized Symposium, Boston University, 2013. 
• Travel Fellowship, Intl Society of Computational Biology (ISCB), 2012. 
• Travel Fellowship, Bioinformatics Program, Boston University, 2012. 
• Presidential Award for Exceptional Students, Iran, 2004. 
• Silver Medal in 16th National Physics Olympiad, Iran, 2003. ​
• Prize in 11th Intl. Competition First Step to Nobel Prize in Physics, Polish Academy of Sciences, 2003 (first student from Iran to win the Award). 
• Silver Medal in National Khwarizmi Student Research Contest, Iran, 2002. 

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Selected Publications 

Cassandra Burdziak*, Elham Azizi*, S. Prabhakaran, D. Pe'er, ​A Nonparametric Multi-view Model for Estimating Cell Type-Specific Gene Regulatory Networks, arXiv, 2019. (*Equal Contribution) (pdf)

Jessica Price, Elham Azizi, L.A. Naiche, Jill K. Slack-Davis, Dana Pe'er, Jan K. Kitajewski, Notch3 signaling promotes tumor cell adhesion and progression in a murine epithelial ovarian cancer model, submitted. 

​Elham Azizi*, Ambrose J. Carr*, George Plitas*, Andrew E. Cornish*, Catherine Konopacki, Sandhya Prabhakaran, Juozas Nainys, Kenmin Wu, Vaidotas Kiseliovas, Manu Setty, Kristy Choi, Rachel M. Fromme, Phuong Dao, Peter T. McKenney, Ruby C. Wasti, Krishna Kadaveru, Linas Mazutis, Alexander Y. Rudensky, Dana Pe'er, Single-cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment, Cell 174 (5): 1293-1308, 2018 (*Equal Contribution) (Featured as Cover Story) (outreach piece by NCI Cancer Systems Biology Consortium)

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Elham Azizi*, Sandhya Prabhakaran*, Ambrose Carr, Dana Pe'er, Bayesian Inference for Single-cell Clustering and Imputing, Genomics and Computational Biology 3 (1), 46, 2017. (*Equal Contribution)

​Sandhya Prabhakaran*, Elham Azizi*, Ambrose Carr, Dana Pe'er, 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 (Acceptance rate: 24%) (*Equal Contribution) (pdf, supplementary, R code) (Recipient of Dataminr Poster Presentation Award, NYAS Machine Learning Symposium 2016).

Rigzin Dekhang, Cheng Wu, Kristina Smith, Teresa Lamb, Matthew Peterson, Erin Bredeweg, Oneida Ibarra, Jillian Emerson, Nirmala Karunarathna, Anna Lyubetskaya, Elham Azizi, Jennifer Hurely, Jay Dunlap, James Galagan, Michael Freitag, Matthew Sachs, Deborah Bell-Pedersen The Neurospora Transcription Factor ADV-1 Transduces Light Signals and Temporal Information to Control Rhythmic Expression of Genes Involved in Cell-Fusion,G3: Genes| Genomes| Genetics, 2016.

Elham Azizi, Edoardo M. Airoldi, James E. Galagan, Learning Modular Structures from Network Data and Node Variables, Proceedings of the 31st International Conference on Machine Learning (ICML), PMLR 32(2):1440-1448, 2014 (Acceptance rate: 22%)(Recipient of IBM Best Student Paper Award, NESS 2014) (pdf, Extended version).

Antonio L. C. Gomes, Thomas Abeel, Matthew Peterson, Elham Azizi, Anna Lyubetskaya, Luís Carvalho & James E. Galagan, Decoding ChIP-Seq peaks with a double-binding signal refines binding peaks to single-nucleotide and predicts cooperative interaction, Genome Research 2014: gr. 161711.113 (pdf) 

James E. Galagan, Kyle Minch*, Matthew Peterson*, Anna Lyubetskaya*, Elham Azizi*, Linsday Sweet*, Antonio Gomes*, Tige Rustad, Gregory Dolganov, Irina Glotova, Thomas Abeel, Chris Mahwinney, Adam D Kennedy, René Allard, William Brabant, Andrew Krueger, Suma Jaini, Brent Honda, Wen-Han Yu, Mark J Hickey, Jeremy Zucker, Christopher Garay, Brian Weiner, Peter Sisk, Christian Stolte, Jessica K Winkler, Yves Van de Peer, Paul Iazzetti, Diogo Camacho, Jonathan Dreyfuss, Yang Liu, Anca Dorhoi, Hans-Joachim Mollenkopf, Paul Drogaris, Julie Lamontagne, Yiyong Zhou, Julie Piquenot, Sang Tae Park, Sahadevan Raman, Stefan HE Kaufmann, Robert P Mohney, Daniel Chelsky, D Branch Moody, David R Sherman, Gary K Schoolnik The Mycobacterium tuberculosis regulatory network and hypoxia, Nature. 2013 Jul 11; 499 (7457): 178-183. doi: 10.1038/nature12337 (pdf) (*Equal Contribution).

Elham Azizi, Joint Learning of Modular Structures from Multiple Data Types, NIPS 2013 Workshop of Frontiers of Network Analysis: Methods, Models, and Applications, Lake Tahoe, NV, USA. 

Sarah Kianfar, Elham Azizi, Farhad Kianfar, A Comparison of Two Estimators for Solutions to Greedy Algorithm in Scheduling Depletable Sources, Proc. of International Conference on Risk Management & Engineering Management (RMEM 2008), 80-85, University of Toronto, Ontario, Canada. 

Elham Azizi, G Hossein Mohimani, Masoud Babaie-Zadeh, Adaptive Sparse Source Separation with Application to Speech Signals, Proc. of IEEE International Conference on Signal Processing and Communications (ICSPC 2007), 640-643, Dubai, UAE (pdf). 

Sarah Kianfar, Elham Azizi, Farhad Kianfar, An Approximation Approach to Numerical Solution of Convex Cost Algorithm in Production Planning, Proc. of International Management Conference (IMC 2007), Tehran, Iran. 

Elham Azizi, Modeling gene regulatory networks through data integration, PhD Thesis, Boston University, 2014.

Abstracts

Vincent-Philippe Lavallee, Elham Azizi, Vaidotas Kiseliovas, Ignas Masilionis, Linas Mazutis, Ross L Levine, Dana Pe'er, Comprehensive Single-Cell RNA-Sequencing Mapping of Primary Acute Myeloid Leukemias and Profiling of NPM1-Mutated Cells, Blood 132 (Suppl 1), 995-995.
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Pavan Bachireddy, Elham Azizi, Vinhkhang N Nguyen, Shuqiang Li, Donna S Neuberg, Robert J Soiffer, Jerome Ritz, Edwin P Alyea, Dana Pe'er, Catherine J Wu, Mapping the Evolution of T Cell Transcriptional States during DLI Response and Resistance Using Single-Cell Data, Blood 132 (Suppl 1), 821-821.

Jessica Price, Elham Azizi, Nathaniel Jones, Jan Kitajewski, Notch3 signal activation promotes peritoneal metastasis in a mouse model of epithelial ovarian cancer, Clinical Cancer Research 22 (2 Supplement), B69-B69.

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Presentations

​​Invited Talks

• "Bayesian hierarchical modeling of immune phenotypes in the breast tumor microenvironment​", Next Generation in Biomedicine Symposium (Nov 2018), Broad Institute of MIT and Harvard.​​
  
• "Characterization of tumor microenvironment in breast carcinoma using scRNA-seq"​, ASHG Session on Understanding tumor-immune heterogeneity from single cell sequencing of genomes, transcriptomes and epigenomes, (Oct 2018), San Diego, CA (preview by Cell Press).
• "Computational Approaches to Understanding Cellular Heterogeneity in the Tumor-Immune Microenvironment", Keystone Symposia on Translational Systems Immunology (Jan 2018), Snowbird, UT. 
• "Bayesian Inference of Cell Type-Specific Gene Regulatory Networks", Challenges and Synergies in the Analysis of Large-Scale Population-Based Biomedical Data (Nov 2017), Oaxaca, Mexico. 
• "Bayesian Inference for Single-cell Clustering and Imputing", Bioconductor Conference BioC 2017: Where Software and Biology Connect (July 2017), Boston, MA. 
• "Bayesian Inference for Single-cell Clustering and Imputing", 2nd Challenges in Computational Biology meeting (Dec 2016), Mainz, Germany.
• “Reconstructing the Regulatory Network of TB: Deconstruction of the Hypoxic Response”, ISMB Conference: Special Session on Modeling Infectious Disease Processes (July 2012) Long Beach, CA (Video) 

Contributed Talks

• “Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data”, International Conference on Machine on Machine Learning (June 2016), New York, NY. (Video)
• “Modeling the Neurospora Clock Regulatory Network”, Neurospora Meeting at Asilomar (March 2014), Pacific Grove, CA.
• “Predictive models of gene regulation for Mycobacterium tuberculosis”, International Conference on Computational Cell Biology (August 2013), Blacksburg, VA. 
• “Regulatory and Metabolic Modeling in MTB”, 4th Annual Systems Biology Programmatic Meeting (Nov 2012), Richland, WA (Slides) 
• “Predictive models of gene regulation for Mycobacterium tuberculosis”, International Workshop on Bioinformatics and Systems Biology (IBSB 2012), Boston, MA. 
• “Adaptive Sparse Source Separation with Application to Speech Signals”, IEEE International Conference on Signal Processing and Communications (ICSPC 2007), Dubai, UAE. 
• “Genomic Signal Processing”, ACRI Seminars, Sharif University (2007), Tehran, Iran. 

Poster Presentations

• American Cancer Society Jiler Professors & Fellows Conference in Minneapolis, MN.​, Sep 2018.
• Memorial Sloan Kettering Postdoctoral Research Symposium, Nov 2016 (Best Poster Award). 
• International Conference on Machine on Machine Learning, June 2016.
• Systems Biology: Global Regulation of Gene Expression, Cold Spring Harbor Lab Meeting, March 2016. 
• 10th Annual Machine Learning Symposium, New York Academy of Sciences, March 2016 (Best Poster Award). 
• New England Machine Learning Day, Camrbidge, MA, May 2014. 
• SAMSI Computational Methods for the Social Sciences Transition Workshop, Research Triangle Park, NC, May 2014. 
• New England Statistics Symposium, Boston MA, Aril 2014 (Best Paper Award).
• NIPS Workshop of Frontiers of Network Analysis: Methods, Models, and Applications, Lake Tahoe NV, Dec 2013.
• Women in Machine Learning Workshop, Lake Tahoe NV, Dec 2013.
• Boston Bacterial Meeting, Harvard University, June 2013 (Best Poster Award).
• Bioinformatics Student Organized Symposium, Boston University, June 2013 (Best Poster Award).
• 4th Annual Systems Biology Programmatic and SBWG Meeting, Richland WA, Nov. 2012.
• 20th Intl Conference on Intelligent Systems for Molecular Biology (ISMB), Long Beach CA, July 2012.
• 3rd Annual Systems Biology Programmatic and SBWG Meeting, Seattle WA, Nov. 2011.
• Bioinformatics Student Organized Symposium, Boston University, May 2012.
• International Workshop on Bioinformatics and Systems Biology (IBSB), Berlin, 2011.

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Work Experience

Memorial Sloan Kettering Cancer Center, Dana Pe'er Lab (moved from Columbia): Postdoctoral Research Fellow (Oct 2016-present). 
Columbia University, Dana Pe'er Lab: Postdoctoral Research Scientist (Oct 2014-Sep 2016). 
Microsoft Research Redmond, Bill Bolosky's Group, Cancer Genomics: Intern (Summer 2014). 
Harvard University, Department of Statistics, Edoardo Airoldi Lab : Visiting Researcher (2013-2014).
Boston University, Biomedical Engineering Department, James Galagan Lab: Research Assistant (2010-2014).
Boston University, Electrical & Computer Engineering Department: Research Assistant (2009).
Sharif University of Technology, Advanced Communications Research Institute: Undergraduate Research Assistant (2007-2008).

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Teaching Experience

ENG BE 562 Computational Biology: Genomes, Networks, Evolution: Guest Lecturer, Boston University, Fall 2012, Fall 2013. 
Lecture on modeling regulatory networks.

ENG ME 310 Instrumentation and Theory of Experiments: Teaching Fellow, Boston University, Spring 2010. 
Supervised weekly lab experiments and helped in design and improvement of experiments, graded lab reports. 

ENG EK 307/317 Circuits Theory: Teaching Fellow, Boston University, Fall 2009. 
Taught weekly tutorials, problem-solving sessions, held weekly office hours.

Farzanegan High School: Physics Instructor and Project Mentor, Tehran, Iran, Sep 2004 - Sep 2005. 
Trained students for high school Physics Olympiads; supervised research activities for high school students

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Services

• Moderator in ASHG Session on Understanding tumor-immune heterogeneity from single cell sequencing of genomes, transcriptomes and epigenomes, Oct 2018, San Diego.  
• ​Initiated and co-organized the annual Workshop on Computation Biology at the International Conference in Machine Learning (ICML) held for 3 years to date: 
  • Joint ICML and IJCAI Workshop on Computational Biology, July 2018, Stockholm, Sweden.
  • Workshop on Computational Biology at ICML 2017, August 2017, Sydney, Australia.
  • Workshop on Computational Biology at ICML 2016, June 2016, NYC.​
• Mentor in 1000 Girls, 1000 Futures Program, Global STEM Alliance, 2016. 
• ​Reviewer and Event Coordinator for 1st Annual Women in Science @ Columbia (WISC) Graduate Research Symposium, April 2016. 
• Chair of Contributed Session on Bayesian Applications in Genetics, NESS 2014. 
• Mentored multiple undergraduate and graduate rotation students at Memorial Sloan Kettering, Columbia University and Boston University.