Kevin R. Moon

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

1. A. Duque, S. Morin, G. Wolf, K.R. Moon, "Geometry regularized autoencoders," IEEE Transactions on Pattern Analysis and Machine Intelligence, To appear, 2022.

2. 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)

3. 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)

4. 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)

5. 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)

6. 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)

7. 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)

8. 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