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Kevin R. Moon
Director, Data Science and AI Center
Associate Professor, Department of Mathematics & Statistics
Utah State University
Email: kevin.moon "at" usu.edu
I am interested in advising students in theoretical or application-focused projects at the advanced undergraduate, masters, and PhD levels.
For consulting services, go here.
About Me
I am the Director of the Data Science and AI Center at Utah State University and an associate professor in the Department of Mathematics and Statistics focusing on data science and machine learning. Prior to coming to Utah State, I was a postdoctoral associate at Yale University under Dr. Smita Krishnaswamy in the Genetics and Computer Science departments and Dr. Ronald Coifman in the Mathematics and Computer Science departments. I completed my PhD in Electrical Engineering and Computer Science (EECS) under Dr. Alfred Hero at the University of Michigan in Ann Arbor in 2016. I completed an MS (2016) in Mathematics at Michigan and an MS (2012) and BS (2011) in electrical engineering from Brigham Young University in Provo, Utah. During my undergraduate studies, I spent two years giving service in Puebla, Mexico and studied abroad in China for six weeks.
Research
This video gives an overview of some of the projects in my research program.
My general research interests are in the development of theory and applications in machine learning, deep learning, information theory, manifold learning, big data, statistical signal processing, statistical learning theory, estimation, graphical models, and random matrix theory. I am strongly interested in biological and medical applications.
My main projects currently focus on data visualization, data denoising, and nonparametric estimation of information theoretic measures such as entropy, mutual information, and information divergence. Specific applications I am involved in include sunspot and active region images, biological data, financial data, ecological data, and navigation data. My work has been published in prestigious journals including Nature, Cell, and Nature Biotechnology, as well as competitive machine learning conferences such as NeurIPS. See my research page for more details.
See my github page for publicly available code: https://github.com/KevinMoonLab
Selected Publications
A. Duque, S. Morin, G. Wolf, K.R. Moon, "Geometry regularized autoencoders," IEEE Transactions on Pattern Analysis and Machine Intelligence, To appear, 2022.
K.R. Moon, K. Sricharan, A.O. Hero III, "Ensemble estimation of generalized mutual information with applications to genomics," IEEE Transactions on Information Theory, vol. 67, no. 9, pp. 5963-5996, Sept. 2021. (Link, arXiv)
K.R. Moon, D. van Dijk, Z. Wang, S. Gigante, D. Burkhardt, W. Chen, K. Yim, A. van den Elzen, M.J. Hirn, R.R. Coifman, N.B. Ivanova, G. Wolf, S. Krishnaswamy, "Visualizing Transitions and Structure for Biological Data Exploration," Nature Biotechnology, vol. 37, no. 12, pp. 1482-1492, Dec. 2019. (Link, bioRxiv, code)
K.R. Moon, K. Sricharan, K. Greenewald, A.O. Hero III, "Ensemble Estimation of Information Divergence," Entropy (Special Issue on Information Theory in Machine Learning and Data Science), vol. 20, no. 8, pp. 560, July 2018. (Link, arXiv)
K.R. Moon, K. Sricharan, A.O. Hero III, "Ensemble estimation of mutual information," IEEE International Symposium on Information Theory (ISIT), pp. 3030-3034, June 2017. (Link, long version at arXiv)
D. van Dijk, R. Sharma, J. Nainys, K. Yim, P. Kathail, A. Carr, C. Burdsiak, K.R. Moon, C. Chaffer, D. Pattabiraman, B. Bierie, L. Mazutis, G. Wolf, S. Krishnaswamy, D. Pe'er, "Recovering Gene Interactions from Single-Cell Data Using Data Diffusion," Cell, vol. 174, no. 3, pp. 716-729, July 2018. (Link, bioRxiv)
K.R. Moon, V. Delouille, J.J. Li, R. De Visscher, F. Watson, and A.O. Hero III, "Image patch analysis of sunspots and active regions. II. Clustering via matrix factorization," Topical Issue on Statistical Challenges in Solar Information Processing, Journal of Space Weather and Space Climate, vol. 6, A3, Jan. 2016. (Link, arxiv)
K.R. Moon, J.J. Li, V. Delouille, R. De Visscher, F. Watson, and A.O. Hero III, "Image patch analysis of sunspots and active regions. I. Intrinsic dimension and correlation analysis," Topical Issue on Statistical Challenges in Solar Information Processing, Journal of Space Weather and Space Climate, vol. 6, A2, Jan. 2016. (Link, arxiv)
A full list is found here or Google Scholar.
Awards and Honors
AI Utah 100, AI Utah, 2024
Faculty Researcher of the Year, Department of Mathematics and Statistics, USU, 2023
Graduate Faculty Mentor of the Year, College of Science, USU, 2022
Graduate Faculty Mentor of the Year, Department of Mathematics and Statistics, USU, 2022
2nd Place in Signal and Image Processing poster competition at 2015 Engineering Graduate Symposium at the University of Michigan
3rd Place Best Student Paper Award at SPW 2015
Top 10% Paper Award at ICIP 2014
NSF Graduate Research Fellowship, 2012-2016
NSF Graduate Research Fellowship Honorable Mention, 2011
NASA Rocky Mountain Space Grant, 2011
Distinguished Student Award from BYU Math Department, 2011
Gordon B. Hinckley Presidential Scholar at BYU, 2005-2011
National Merit Scholar, 2005