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Weston Viles

Teaching Assistant Professor

Weston Viles is a teaching assistant professor of computer science at the Roux Institute. With a focus on mathematics, he teaches courses in the core Data Science and Computer Science Align program curriculum. Viles has developed programs for data science, including coursework specifically for introducing core mathematical and statistical methodology to aspiring data scientists.

He graduated with a PhD in mathematics from Boston University and subsequently completed his postdoctoral training in the Department of Biomedical Data Science at Dartmouth College. Viles has held teaching positions at Mt. Holyoke College, Boston University, Colby College, and the University of Southern Maine.

Viles grew up in Maine and, after a nearly decade-long absence from it, was gratefully afforded the opportunity to return to his home state.  When his laptop is closed and his pen is down, he most enjoys conversation by campfire and happens to know of some of the best sites west of the Penobscot River.

Research and Academic Overview

Viles’ research objective is to further develop the theory and methods of network data analysis from its statistical foundations to applications in natural systems. His attention toward network inference and characterization aims to quantify and distinguish aspects of the statistical associations among entities in complex systems to gain knowledge of the system as a whole. His recent work has focused on developing network-based predictors in the context of health-related applications in areas of neuroscience and epidemiology.

Viles is typically an instructor for DS 5020 and CS 5002.  He is generally associated with the mathematics-based components of the Align program.  Otherwise, his research interests are focused in areas of network data analysis.

Areas of Expertise

  •  Mathematical Statistics and Probability
  •  Network Analysis
  •  Numerical Algorithms


  • D. Viles, J. C. Madan, H. Li, M. R. Karagas, and A. G. Hoen. Information content of high-order associations of the human gut microbiota network. Annals of Applied Statistics, 15(4):1788–1807,2021. https://arxiv.org/abs/2005.08107L.-E. Martinet, M. A. Kramer, W. D. Viles, L. N. Perkins, E. Spencer, S. S. Chu, C. J. Cash, and
  • D. Kolaczyk. Robust dynamic community detection with applications to human brain functional networks. Nature Communications, 11:2785, 2020. https://doi.org/10.1038/s41467-020-16285-7
  • Balachandran, E. D. Kolaczyk, and W. D. Viles. On the propagation of low-rate measurement errorto subgraph counts in large networks. Journal of Machine Learning Research, 18(61):1–33, 2017. http://jmlr.org/papers/v18/16-497.html
  • D. Viles, C. E. Ginestet, A. Tang, M. A. Kramer, and E. D. Kolaczyk. Percolation under noise: Detecting explosive percolation using the second-largest component. Phys. Rev. E, 93:052301, May 2016 https://link.aps.org/doi/10.1103/PhysRevE.93.052301