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Will Cipolli

Assistant Professor of Mathematics
Mathematics, 323 McGregory Hall
p 315-228-6118
Website: www.cipolli.com

Research Interests

My methodological research focuses on Bayesian Nonparametric approaches to a variety of problems including multiple testing, density estimation, and supervised learning. More specifically, I aim to address key questions related to assumptions in parametric approaches by envisioning new, flexible solutions that are computationally efficient and widely available. My work has been published in journals like Statistics and Computing, and Computational Statistics and Data Analysis.

My future work in supervised learning includes a nonparametric classification schema which has been submitted to Advances in Classification and Data Analysis. I am currently working with collaborators to extend this methodology for prediction and feature selection using Householder reflections to make the approaches computationally efficient. Due to the success of these approaches in applications to real data, I'm currently working on implementing these methods into R packages which would make these theoretical approaches widely available.

I also complete many collaborations by providing simulation techniques to other statisticians and quantitative expertise to those in other sciences including Sociology, Psychology, Biology and the Medical field. These interdisciplinary collaborations involve translating qualitative hypotheses to a quantitative, actionable solutions through careful experimental design and model building. These solutions provide precise estimates and compelling evidence for such impactful theories about the world. These works have been published in top journals of statistical simulation, sociology, psychology, biology and Medicine.


2012–2016 Ph.D Statistics, University of South Carolina, Columbia, SC. 
2012–2014 MS Statistics, University of South Carolina, Columbia, SC. 
2007–2011 BA Mathematics, Quinnipiac University, Hamden, CT. 
2007–2011 BS Computer Science, Quinnipiac University, Hamden, CT. 


[1] W. Cipolli and T. Hanson, “Supervised learning via smoothed Polya trees.” Submitted to Advances in Data Analysis and Classification, 2018.

[2] A. Robertson W. Cipolli and M. Dascalu, “On the distribution of monochromatic complete subgraphs and arithmetic progressions.” Submitted to Experimental Math, 2018.

[3] R. Bower, J. Hussey, J. Zhang, J. Quattro, M. Muhling, W. Cipolli, and J. Hardin, “The score test for independence of two marginal Poisson variables.” Submitted to Communications in Statistics - Case Studies and Data Analysis, 2018.

[4] R. Bower, J. Hussey, J. Zhang, J. Quattro, W. Cipolli, and J. Hardin, “A copula approach for testing independence using Poisson cumulative distribution functions.” Accepted at Communications in Statistics - Case Studies and Data Analysis, 2018.

[5] A. Jimenez, J. Winward , U. Beattie and W. Cipolli, “Cellular metabolism and oxidative stress as a possible determinant for longevity in small breed and large breed dogs.” Under revision with PLOS ONE., 2018.

[6] D. M. Silva, R. Bower and W. Cipolli, “In search of a five-star: The centrality of body
discourses in the scouting of high school football athletes.” 2017.

[7] E. Cooley, B. Payne, W. Cipolli, C. Cameron, A. Berger, and K. Gray, “The paradox of group mind: “people in a group" have more mind than “a group of people",” Journal of
Experimental Psychology: General, vol. 146, pp. 1691–699, May 2017.

[8] K. Flory, B. A. Bell, K. Burgess, E. R. Siceloff, W. Cipolli, and R. Bower, “Bifactor models of the strengths and difficulties questionnaire in a large U.S. community sample,” Presented at the annual meeting of the American Psychological Association, Denver, CO, 2016.

[9] W. Cipolli and T. Hanson, “Computationally tractable approximate and smoothed Polya trees,” Statistics and Computing, pp. 1–13, April 2016.

[10] W. Cipolli, T. Hanson, and A. McLain, “Bayesian nonparametric multiple testing,”
Computational Statistics and Data Analysis, vol. 101, pp. 64–79, September 2016.

[11] J. Fowler, W. Cipolli, and T. Hanson, “A comparison of three diagnostic tests for diagnosis of carpal tunnel syndrome using latent class analysis,” Journal of Bone & Joint Surgery, vol. 97, pp. 1958–1961, December 2015.