Machine Learning Dynamics in the Tumor Microenvironment
Data Science Seminar Series
May 22, 2024 | 11:00 AM – 12:00 PM
Virtual
Are you a cancer researcher trying to tackle the statistical and computational challenges of analyzing and integrating data types in the tumor microenvironment (TME)? Recent genomic technologies that measure cell features present exciting opportunities to study the heterogeneity of cells and characterize complex interactions in the TME.
Join Columbia University’s Dr. Elham Azizi as she presents a set of statistical machine learning methods for inferring the temporal and spatial dynamics of cells in the TME. She will show their application in the characterization of spatial dynamics in aggressive metaplastic breast cancer, revealing how metabolic reprogramming is shaping immunosuppressive niches. Additionally, she will present a systematic dissection of coordinated immune cell networks in an established adoptive cellular therapy, donor lymphocyte infusion in relapsed leukemia.
About the Speaker
Elham Azizi, Ph.D.
Dr. Azizi is a Herbert and Florence Irving Associate Professor of Cancer Data Research and Associate Professor of Biomedical Engineering at Columbia University. Her research focuses on developing machine learning and statistical methods to analyze single-cell genomic data, characterizing cell interactions in the tumor microenvironment to guide personalized cancer treatments.
About the Data Science Seminar Series
CBIIT’s Data Science Seminar Series is dedicating its 2026 events to spotlighting the use of AI in cancer research and care. Brought to you by CBIIT and NCI's Division of Cancer Treatment and Diagnosis AI working group, the upcoming webinars will explore a variety of questions, such as the following:
- How can AI be used for diagnosis, treatment, or omics research?
- What are the related laws and ethical considerations for AI?
- How can we empower an AI-ready cancer research community through workforce development, collaborations, and funding?
To view upcoming speakers or recordings of past presentations, visit the Data Science Seminar Series page.