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Kevin Moon

Profile Picture

Mathematics and Statistics

Assistant Professor

Assistant Professor

Contact Information

Office Location: AnSci 312
DialPhone: (435) 797-0749

Educational Background

MS, Mathematics, University of Michigan, 2016
PhD, Electrical Engineering: Systems, (Signal Processing), University of Michigan, 2016
Nonparametric estimation of distributional functionals and applications


Dr. Moon earned a bachelor's and master's degree in Electrical Engineering at Brigham Young University, focusing on signal processing. He then obtained an M.S. degree in Mathematics and a Ph.D. in Electrical Engineering at the University of Michigan where his research focused on nonparametric estimation of distributional functionals. Prior to joining Utah State University in 2018, he was a postdoctoral scholar in the Genetics Department and the Applied Math Program at Yale University where he developed methods for exploratory data analysis with a focus in biomedical applications.

Teaching Interests

Machine learning and data science

Research Interests

Development of theory and applications in machine learning, big data, information theory, deep learning, manifold learning, statistical learning theory, estimation, graphical models, and random matrix theory.

Publications - Abstracts

    Publications - Books & Book Chapters

      * Has not been peer reviewed

      Publications - Fact Sheets

        * Has not been peer reviewed

        Publications - Curriculum

          * Has not been peer reviewed

          Publications - Journal Articles

            Academic Journal

          • Moon, K., van Dijk, D., Wang, Z., Gigante, S., Burkhardt, D., Chen, W., Yim, K., van den Elzen, A., Hirn, M., Coifman, R., Ivanova, N., Wolf, G., Krishnaswamy, S., (2019). Visualizing Structure and Transitions in High-Dimensional Biological Data. Nature Biotechnology, 37:12, 1482-1492.
          • Amodio, M., van Dijk, D., Srinivasan, K., Chen, W., Mohsen, H., Moon, K., Campbell, A., Zhao, Y., Wang, X., Venkataswamy, M., Desai, A., V, R., Kumar, P., Montgomery, R., Wolf, G., Krishnaswamy, S., (2019). Exploring single-cell data with deep multitasking neural networks. Nature Methods, 16, 1139-1145.
          • Yasaei Sekeh, S., Noshad, M., Moon, K., Hero, A.O, (2019). Convergence Rates for Empirical Estimation of Binary Classification Bounds. Entropy, 21:12, 1144.
          • Shin, M., Yim, K., Moon, K., Park, H., Mohanty, S., Kim, J., Montgomery, R., Shaw, A., Krishnaswamy, S., Kang, I., (2019). Dissecting alterations in human CD8+ T cells with aging by high-dimensional single cell analysis. Clinical Immunology, 200, 24-30.
          • Moon, K., Sricharan, K., Greenewald, K., Hero, A., (2018). Ensemble Estimation of Information Divergence. Entropy, 20:8, 560.
          • Van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A., Burdziak, C., Moon, K., Chaffer, C.L, Pattabiraman, D., Bierie, B., Mazutis, L., Wolf, G., Krishnaswamy, S., Pe'er, D., (2018). Recovering Gene Interactions from Single-Cell Data Using Data Diffusion. Cell, 174:3, 716-729.
          • Moon, K., Stanley, J., Burkhardt, D., van Dijk, D., Wolf, G., Krishnaswamy, S., (2018). Manifold Learning-based Methods for Analyzing Single-Cell RNA-Sequencing Data. Current Opinion in Systems Biology, 7, 36-46.
          • Moon, K., Li, J.J, Delouille, V., De Visscher, R., Watson, F., Hero, III, A.O, (2016). Image patch analysis of sunspots and active regions. I. Intrinsic dimension and correlation analysis. Journal of Space Weather and Space Climate, 6, A2.
          • Moon, K., Delouille, ., Li, J., De Visscher, R., Watson, F., Hero, III, A.O, (2016). Image patch analysis of sunspots and active regions. II. Clustering via matrix factorization. Journal of Space Weather and Space Climate, 6, A3.

          * Has not been peer reviewed

          Publications - Literary Journal

            * Has not been peer reviewed

            Publications - MultiMedia

              * Has not been peer reviewed

              Publications - Technical Reports

                * Has not been peer reviewed

                Publications - Translations & Transcripts

                  Publications - Other

                    * Has not been peer reviewed

                    Scheduled Teaching

                    STAT 6910, 7810 - Deep Learning Theory and Applications, Spring 2019

                    STAT 6910 - Statistical Learning and Data Mining II, Fall 2018

                    Graduate Students Mentored

                    Teresa White, Mathematics & Statistics, September 2019
                    Jake Rhodes, Mathematics & Statistics, December 2018
                    Andres Duque, Mathematics & Statistics, November 2018
                    Thomas Brower, Mathematics & Statistics, October 2018
                    Ronak Tali, Mathematics & Statistics, September 2018
                    Jared Hansen, Mathematics & Statistics, September 2018