Stacey Finley

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Nichole A. and Thuan Q. Pham Professor and Associate Professor of Biomedical Engineering, Chemical Engineering and Materials Science, and Quantitative and Computational Biology


  • Doctoral Degree, Chemical Engineering, Northwestern University
  • Bachelor's Degree, Chemical Engineering, Florida A & M University


Dr. Stacey Finley is holder of the Nichole A. and Thuan Q. Pham Professorship and Associate Professor of Biomedical Engineering, as well as Quantitative and Computational Biology, at the University of Southern California. Dr. Finley received her B.S. in Chemical Engineering from Florida A & M University and obtained her Ph.D. in Chemical Engineering from Northwestern University. She completed postdoctoral training at Johns Hopkins University in the Department of Biomedical Engineering. Dr. Finley joined the faculty at USC in 2013, and she leads the Computational Systems Biology Laboratory. Dr. Finley has a joint appointment in the Department of Chemical Engineering and Materials Science, and she is a member of the USC Norris Comprehensive Cancer Center. Dr. Finley is also a standing member of the MABS Study Section at NIH. Her research has been supported by grants from the NSF, NIH, and American Cancer Society.

Selected honors. 2016 NSF Faculty Early CAREER Award; 2016 Young Innovator by the Cellular and Molecular Bioengineering journal; Leah Edelstein-Keshet Prize from the Society of Mathematical Biology; Junior Research Award from the USC Viterbi School of Engineering; the Hanna Reisler Mentorship Award; 2018 AACR NextGen Star; 2018 Orange County Engineering Council Outstanding Young Engineer; 2021 Elected Fellow of American Institute for Biological and Medical Engineering; 2022 Fellow of the Biomedical Engineering Society

Research Summary

The Computational Systems Biology Laboratory at USC develops mechanistic models of biological processes and utilize the models to:

- gain insight into the dynamics and regulation of biological systems
- synthesize and interpret experimental and clinical observations
- provide a quantitative framework to test biological hypotheses
- contribute to the development of novel therapeutics for pathological conditions

We collaborate closely with experimental and clinical researchers, in order to construct experimentally validated computational models that increase our understanding of specific biological processes. These fruitful collaborations enable experimental testing of the model predictions.

The main projects are focused on applying computational modeling to study immune cell signaling, metabolism, and angiogenesis 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.


  • 2015 Diverse: Issues in Higher Education Emerging Scholar Award
  • 2014 National Academy of Engineering Participant in Frontiers in Engineering Education Symposium
  • 2010 National Institutes of Health Ruth L. Kirschstein National Research Service Award
  • 2010 Elmer Gaden Jr. Award
  • 2008 Merck Poster Award
  • 2006 National Science Foundation Graduate Research Fellowship
  • Director of Program, Center or Institute, Director of the Center of Computational Modeling of Cancer
  • Alfred E. Mann Department of Biomedical Engineering
  • Mork Family Department of Chemical Engineering and Materials Science
  • Molecular and Computational Biology

  • Michelson Center for Convergent Bioscience
  • 1002 West Childs Way
  • USC Mail Code: 1111

Contact Information
  • (213) 740-8788


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