My research focuses on the development and analysis of dynamical systems models representing complex biological phenomena. I am especially interested in applications to human disease where inter- and intra-individual heterogeneity can have a profound impact on patient responses to targeted interventions. I am also working in the closely related fields of uncertainty quantification and parameter identifiability. Such issues arise from imperfect data and permeate virtually all models that are data-driven. Specifically, I am interested in developing quantitative frameworks for connecting computationally complex mathematical models with noisy, sparse real-world data.

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My teaching philosophy centers on helping students become independent thinkers and confident professionals. I focus on creating an engaging, active learning environment that fosters critical thinking and exploration. Whether through project-based learning in my upper-level modeling courses or using 3D printed solids to enhance understanding in multivariable calculus, my goal is to provide students with hands-on, authentic learning experiences that deepen their knowledge and analytical skills. I engage students at all levels—from high school through graduate—in my research, and I am always looking for curious, motivated students to join my group.

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I have been supported with grants from Eli Lilly & Co, NIH, NSF and the Simons Foundation.