Biography
I am an applied mathematician operating at the intersection of machine learning, statistical inference, and complex networks. My work developing structural and probabilistic frameworks to extract insight from high-dimensional, complex systems fundamentally couples classical mathematical rigor with scalable computational algorithms.
Following a PhD in Mathematics focused on statistics and machine learning at Boston University, I transitioned into large-scale biomedical data science as an NIH-funded postdoctoral researcher at Dartmouth College’s Geisel School of Medicine. Driven by this dual orientation, my research portfolio spans both core disciplinary outlets and high-impact interdisciplinary journals.
Throughout my career, including my tenure as an Assistant Professor and graduate program coordinator, I have dedicated myself to advancing institutional curricula bridging theoretical foundations and programmatic implementations across statistics, data science, and computer science university curricula.
Core Expertise
- Mathematical Domains: Probability Theory, Mathematical Statistics, Numerical Analysis.
- Computational Focus: Machine Learning, Variational Inference, Structural Network Analysis.
- Application Frameworks: Computational Neuroscience, Gut Microbiome Epidemiology, Social Networks.
- Languages: Python, R, Matlab, C++
Academic History
- PhD in Mathematics (Network Data Analysis) | Boston University (2013)
- MA in Mathematics | University of Maine (2008)
- BS in Mathematics Education | University of Maine (2006)