Diversity in Life Sciences

Initiatives to Enhance Diversity, Equity & Inclusion in Science

 

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Stacey Finley, PhD
Gordon S. Marshall Early Career Chair and Associate Professor
University of Southern California

Stacey D. Finley, Ph.D., is an Associate professor in the Department of Biomedical Engineering at the University of Southern California. She is holder of the Gordon S. Marshall Early Career Chair and the Director of the Center for Computational Modeling of Cancer (http://modelingcancer.usc.edu). Dr. Finley has a joint appointment in the Mork Family Department of Chemical Engineering and Materials Science and the Department of Biological Sciences (Quantitative and Computational Biology section). She is also a member of the Norris Comprehensive Cancer Center.

Dr. Finley received her Bachelor's degree in Chemical Engineering from Florida A & M University in 2004. Her graduate studies were completed in 2009 in Chemical Engineering at Northwestern University and involved using computational tools to predict and estimate the feasibility of novel biodegradation pathways. Her postdoctoral studies at Johns Hopkins University focused on computational modeling of VEGF signaling pathways. She was awarded postdoctoral fellowships from the NIH National Research Service Award and the UNCF/Merck Science Initiative. Dr. Finley's current research applies a systems biology approach to develop molecular-detailed computational models of biological processes related to human disease. The main projects are focused on applying computational modeling to study angiogenesis, metabolism, and immunotherapy. Current projects study how these processes are exploited in cancer. The biochemical networks that regulate these processes involve numerous cell types, molecular species, and signaling pathways, and the dynamics occur on multiple timescales. Therefore, a systems biology approach, including experiment-based computational modeling, is required to understand these complex processes and their interconnectedness in cancer. Models can simulate biological processes under pathological conditions and predict interventions that restore normal physiology. Additionally, the models can identify which tumors will respond favorably to a particular therapy, aiding in the development and optimization of effective therapeutics.

Research Area(s):

Cancer

Mentor: Terrence J. Sejnowski, PhD

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