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Vicky R Zhu

  • Assistant Professor
Academic Division: Mathematics, Analytics, Science, and Technology
Dr. Vicky Zhu earned her Ph.D. (2023) and M.S. (2020) in Applied and Computational Mathematics and Statistics from the University of Notre Dame, and her B.A. (2018) in Mathematics and Statistics from the University of California, Berkeley. She is an enthusiastic educator, an avid researcher, and an innovative entrepreneur. Before joining Babson, she taught classes on statistics for business, finite mathematics, probability, and statistical inference at Mendoza College of Business in Notre Dame for three years. Having been nominated for the Midwestern Excellent in Teaching awards in 2021, she continued to practice her teaching philosophy that prepares students with confidence, critical thinking, and collaboration skills.

As an applied mathematician and data scientist, Dr. Zhu is interested in developing mathematical and computational methods to study neural dynamics and learning in the brain. Her current research focuses on connecting neuroscience and machine learning through models of recurrent neuronal networks. Specifically, she is working on establishing direct, one-to-one analogs between artificial and biological neural networks. She is going to expand her machine learning research with a causal inference framework. Dr. Zhu is devoted to sharing her love for these subjects and their practical applications to diverse groups of people through classroom teaching, interdisciplinary research, and mentorship programs. In addition, she is passionate about discovering cutting-edge methods through technology to create business values. She is constantly seeking new ideas and tools from other resources and bringing them to her classroom and research. Dr. Zhu is excited to join the Babson community and contribute to Babson's superior education and innovative research.

Academic Degrees

  • Ph D, University of Notre Dame
  • BA, University of California, Berkeley


  • Degree Courses 2024

  • Degree Courses 2023



Journal Articles

  • Zhu, V.R., Rosenbaum, R.J. (2022). Evaluating the extent to which homeostatic plasticity learns to compute prediction errors in unstructured neuronal networks. Journal of Computational Neuroscience. Springer. link
  • Zhu, V.R., Baker, C., Rosenbaum, R. (2020). Nonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balance. Plos Computational Biology. PLOS. link