Publications

ArXiv and bioRxiv versions are linked when available. You can also visit my Google Scholar profile. Code for many of the methods is available on Github.

Journal Publications

1. S. Jarman, Z. Hampel-Arias, A. Carr, K.R. Moon, “Improved background estimation for gas plume identification in hyperspectral images,” IEEE Transactions on Geosciences and Remote Sensing, to appear 2025. 

2. T. White, J. Wheeler, C. Lindstrom, B. Bean, S. Jenkins, R. Christensen, K.R. Moon, "Inferring inertial navigation errors from SAR image distortions using a convolutional neural network," Navigation, to appear 2025.

3. E. Brunet-Ratnasingham, S. Morin, H.E. Randolph, M. Labrecque, J. Belair, R. Lima-Barbosa, A. Pagliuzza, L. Marchitto, M. Hultstrom, J. Nisessl, R. Cloutier, A.M. Sreng Flores, N. Brassard, M. Benlarbi, J. Prevost, S. Ding, S.P. Anand, G. Sannier, E. Bareke, H. Zeberg, M. Lipscey, R. Frithiof, A. LArsson, S. Zhou, T. Nakanishi, D. Morrison, D. Vezina, C. Bourassa, G. Bendron-Lepage, H. Medjaed, F. Point, J. Richard, C. Larochelle, A. Prat, N. Arbour, M. Durand, J.B. Richards, K.R. Moon, N. Chomont, A. Finzi, M. Tetreault, L. Barreiro, G. Wolf, D.E. Kaufmann, "Sustained IFN signaling is associated with delayed development of SARS-CoV-2-specific immunity," Nature Communications, vol. 15, no. 1, pp. 4177, May 2024. (Link, medRxiv)

4. J.S. Rhodes, A. Cutler, K.R. Moon, "Geometry- and accuracy-preserving random forest proximities," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 10947-10959, Mar. 2023. (Link, arXiv, code)

5. A. Duque*, S. Morin*, G. Wolf**, K.R. Moon**, "Geometry regularized autoencoders," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 6, pp. 7381-7394, Nov. 2022. (Link, code)

6. 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)

7. M.W. Moyle, K.M. Barnes, M. Kuchroo, A. Gonopolskiy, L. H. Duncan, T. Sengupta, L. Shao, M. Guo, A. Santella, R. Christensen, A. Kumar, Y. Wu, K.R. Moon, G. Wolf, S. Krishnaswamy, Z. Bao, H. Shroff, W. Mohler, D.A. Colón-Ramos, "Structural and developmental principles of neurophil assembly in C. elegans," Nature, vol. 591, pp. 99-104, Mar. 2021. (Link, bioRxiv, Video)

8. W. Zhang, J.S. Rhodes, K.R. Moon, B.S. Knudsen, L. Nokolova, A. Zhou, “Imaging of PD-L1 in single cancer cells by SERS-based hyperspectral analysis,” Biomedical Optics Express, vol. 11, no. 11, pp. 6197-6210, Oct. 2020. (Link)

9. W. Zhang*, J. Rhodes*, A. Garg, J. Takemoto, X. Qi, S. Harihar, C.T. Chang, K.R. Moon**, A. Zhou**, "Label-free discrimination and quantitative analysis of oxidative stress induced cytotoxicity and potential protection of antioxidants using Raman micro-spectroscopy and machine learning," Analytica Chimica Acta, vol. 1128, pp. 221-230, Sept. 2020. (Link)

10. Y. Zhao, M. Amodio, B. Vander Wyk, B. Gerritsen, M.M. Kumar, D. van Dijk, K.R. Moon, X. Wang, A. Malawista, M.M. Richards, M.E. Cahill, A. Desai, J. Sivadasan, M.M. Venkataswamy, V. Ravi, P. Kumar, S.H. Kleinstein, S. Krishnaswamy, R.R. Montgomery, "Single cell immune profiling of dengue virus patients reveals distinct immune signatures and intact immune responses to Zika virus," PLOS Neglected Tropical Diseases, vol. 14, no. 3, March 2020. (Link)

11. 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)

12. S. Yasaei Sekeh, M. Noshad, K.R. Moon, A.O. Hero, "Convergence Rates for Empirical Estimation of Binary Classification Bounds," Entropy (Special Issue on Robust Procedures for Estimating and Testing in the Framework of Divergence Measures), vol. 21, no. 12, pp. 1144, Nov. 2019. (Link, arXiv)

13. M. Amodio, D. van Dijk, K. Srinivasan, W. Chen, H. Mohsen, K.R. Moon, A. Campbell, Y. Zhao, X. Wang, M. Venkataswamy, A. Desai, V. Ravi, P. Kumar, R. Montgomery, G. Wolf, S. Krishnaswamy, "Exploring Single-Cell Data with Multitasking Deep Neural Networks," Nature Methods, vol. 16, pp. 1139-1145, Oct. 2019. (Link, bioRxiv, code)

14. M. Shin, K. Yim, K.R. Moon, H. Park. S. Mohanty, J. Kim. R. Montgomery, A. Shaw, S. Krishnaswamy, I. Kang, "Dissecting alterations in human CD8+ T cells with aging by high-dimensional single cell mass cytometry," Clinical Immunology, vol. 200, pp. 24-30, March 2019. (Link)

15. 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, code)

16. 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)

17. K.R. Moon, J. Stanley, D. Burkhardt, D. van Dijk, G. Wolf, S. Krishnaswamy, “Manifold Learning-based Methods for Analyzing Single-Cell RNA-Sequencing Data,” Current Opinion in Systems Biology, vol. 7, pp. 36-46, Feb. 2018. (Link)

18. 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," Journal of Space Weather and Space Climate (Topical Issue on Statistical Challenges in Solar Information Processing), vol. 6, A3, Jan. 2016. (Link, arXiv)

19. 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," Journal of Space Weather and Space Climate (Topical Issue on Statistical Challenges in Solar Information Processing), vol. 6, A2, Jan. 2016. (Link, arXiv)

20. K.R. Moon and D.G. Long, "Considerations for Ku-band scatterometer calibration using the dry snow zone of the Greenland ice sheet," IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 6, pp. 1344-1349, Feb. 2013. (Link)

21. G. Xiong, D.J. Lee, K.R. Moon, and R.M. Lane, "Shape similarity measure using turn angle cross-correlation for oyster quality evaluation," Journal of Food Engineering, vol. 100, no. 1, pp. 178-186, Sep. 2010. (Link)

Peer Reviewed Conference Proceedings

1. B. Shaw, J. Rhodes, S. Boubrahimi, K.R. Moon, "Forest proximities for time series," Intelligent Systems Conference (IntelliSys), to appear Aug. 2025. (arXiv)

2. H. Chen, A. Duque, G. Wolf, K.R. Moon, "Noisy data visualization using functional data analysis," Intertational Conference on Sampling Theory and Applications (SampTA), to appear July 2025. (arXiv)

3. S. Farokhi, H. Chen, K.R. Moon, H. Karimi, "Advancing tabular data classification with graph neural networks: A random forest proximity method," IEEE International Conference on Big Data, pp. 7011-7020, Dec. 2024. (Link)

4. B. Shaw, A. Magner, K.R. Moon, "Symmetry Discovery Beyond Affine Transformations," Advances in Neural Information Processing Systems (NeurIPS), vol. 37, pp. 112889-112918, Dec. 2024. (<26% acceptance rate), (Link, arXiv)

5. J. Mau, K.R. Moon, "Neural Network Ensembling with Random Features," International Conference on Machine Learning and Applications (ICMLA), pp. 748-752, Dec. 2024. (<25% acceptance rate), (Link)

6. M. Hossain, A. Wisler, K.R. Moon, "Nonparametric estimation of non-smooth divergences," Conference on Information and Knowledge Management (CIKM), pp. 3787-3791, Oct. 2024. (27% acceptance rate), (Link)

7. T. Kerby, T. White, K.R. Moon, "Learning local higher-order interactions with total correlation," IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Sep. 2024. (Link, arXiv)

8. S. Ni, A. Aumon, G. Wolf, K.R. Moon, J. Rhodes, "Enhancing supervised visualization through autoencoder and random forest proximities for out of sample extension," IEEE International Workshop on Machine Learning for Signal Processing (MLSP),  Sep. 2024. (arXiv)

9. S. Jarman, Z. Hampel-Arias, A. Carr, K.R. Moon, "Local background estimation for improved gas plume identification in hyperspectral images," IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 2024. (arXiv)

10. D. Eddington, A. Duque, G. Wolf, K.R. Moon, "Data imputation with an autoencoder and MAGIC," International Conference on Sampling Theory and Applications (SampTA), July 2023. (Link)

11. A. Duque, M. Lizotte, G. Wolf, K.R. Moon, "Manifold alignment with label information," International Conference on Sampling Theory and Applications (SampTA), July 2023. (Link, arXiv)

12. A. Duque, G. Wolf, K.R. Moon, "Diffusion transport alignment," International Symposium on Intelligent Data Analysis, pp. 116-129, April 2023. (Link, arXiv)

13. J.S. Rhodes, A. Cutler, G. Wolf, K.R. Moon, "Random Forest-based Diffusion Information Geometry for Supervised Visualization and Data Exploration," IEEE Statistical Signal Processing Workshop (SSP), July 2021. (Link, arXiv, code)

14. T. White, J. Wheeler, C. Lindstrom, R. Christensen**, K.R. Moon**, "GPS-denied navigation using SAR images and neural networks," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), June 2021. (Link, arXiv)

15. A. Duque*, S. Morin*, G. Wolf**, K.R. Moon**, "Extendable and invertible manifold learning with geometry regularized autoencoders," IEEE International Conference on Big Data, pp. 5027-5036, Dec. 2020. (<17% acceptance rate), (Link, arXiv, code)

16. N. Brugnone*, A. Gonopolskiy*, M.W. Moyle, M. Kuchroo, D. van Dijk, K.R. Moon, D. Colon-Ramos, G. Wolf**, M.J. Hirn**, S. Krishnaswamy**, "Coarse graining of data via inhomogeneous diffusion condensation," IEEE International Conference on Big Data, pp. 2624-2633, Dec. 2019. (<16% acceptance rate), (Link, arXiv)

17. A. Duque, G. Wolf, K.R. Moon, "Visualizing high dimensional dynamical processes," IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Oct. 2019. (Link, arXiv, code)

18. S. Gigante*, J.S. Stanley III*, N. Vu, D. van Dijk, K.R. Moon, G. Wolf**, S. Krishnaswamy**, "Compressed Diffusion," International Conference on Sampling Theory and Applications (SampTA), July 2019. (Link, arXiv)

19. S. Gigante*, D. van Dijk*, K.R. Moon, A. Strzalkowski, G. Wolf**, S. Krishnaswamy**, "Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks," International Conference on Sampling Theory and Applications (SampTA), July 2019. (Link, arXiv)

20. A. Wisler, K.R. Moon, V. Berisha, "Direct Ensemble Estimation of Density Functionals," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp 2866-2870, April 2018. (Link, arXiv)

21. 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)

22. M. Noshad, K.R. Moon, S. Yasaei Sekeh, A.O. Hero III, "Direct estimation of information divergences using nearest neighbor ratios," IEEE International Symposium on Information Theory (ISIT), pp. 903-907, June 2017. (Link, long version at arXiv)

23. K.R. Moon, M. Noshad, S. Yasaei Sekeh, A. O. Hero III, "Information theoretic structure learning with confidence,"  IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 6095-6099, Mar. 2017. (Invited Paper), (Link, long version at arXiv)

24. K.R Moon, K. Sricharan, K. Greenewald, A.O. Hero III, "Improving convergence of divergence functional ensemble estimators," IEEE International Symposium on Information Theory (ISIT), pp. 1133-1137, July 2016. (Link, long version at arXiv)

25. S.V. Gliske, W.C. Stacey,  K.R. Moon, and A.O. Hero III, "The intrinsic value of HFO features as a biomarker of epileptic activity," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 6290-6294, Mar. 2016. (Invited Paper),  (Link, arXiv)

26. K.R. Moon, V. Delouille, and A.O. Hero III, "Meta learning of bounds on the Bayes classifier error," IEEE Signal Processing and SP Education Workshop, pp. 13-18, Aug. 2015. (Best Student Paper Award: 3rd Place), (Link, arXiv)

27. K.R. Moon and A.O. Hero III, "Multivariate f-divergence estimation with confidence," Advances in Neural Information Processing Systems (NIPS), pp. 2420-2428, Dec. 2014. (<25% acceptance rate), (Link, arXiv)

28. K.R. Moon, J.J. Li, V. Delouille, F. Watson, and A.O. Hero III, "Image patch analysis and clustering of sunspots: A dimensionality reduction approach," IEEE  International Conference on Image Processing (ICIP), pp. 1623-1627, Oct. 2014. (Top 10% Paper Award), (Link, arXiv)

29. K.R. Moon and A.O. Hero III, "Ensemble estimation of multivariate f-divergence," IEEE International Symposium on Information Theory (ISIT), pp. 356-360, June 2014. (Link, long version at arXiv)

Dissertation

• K.R. Moon, "Nonparametric Estimation of Distributional Functionals and Applications," Dissertation, University of Michigan, 2016. (Link)

Master's Thesis

• K.R. Moon, "Investigations of the Dry Snow Zone of the Greenland Ice Sheet Using QuikSCAT," Master's Thesis, BYU, 2012. (Link)

Technical Reports

1. A. Aumon, S. Ni, M. Lizotte, G. Wolf, K.R. Moon, J.S. Rhodes, "Random forest autoencoders for guided representation learning," 2025. (arXiv)

2. B. Shaw, H. Burger, B. Bean, K.R. Moon, "Structure identification for high-dimensional data in the vicinity of Bear Lake," 2024. (Link)

3. K. Bladen, A. Cutler, D.R. Cutler, K.R. Moon, "Model agnostic local variable importance for locally dependent relationships," 2024. (arXiv)

4. T. Kerby, K.R. Moon, "Training-Free Guidance for Discrete Diffusion Models for Molecular Generation," 2024. (arXiv)

5. J. Mau, K.R. Moon, "Incorporating Taylor Series and recursive structure in neural networks for time series prediction," 2024. (arXiv)

6. J.S. Rhodes*, A. Aumon*, S. Morin, M. Girard, C. Larochelle, B. Lahav, E. Brunet-Ratnasingham, W. Zhang, A. Cutler, A. Zhou, D.E. Kaufmann, S. Zandee, A. Prat, G. Wolf**, K.R. Moon**, "Gaining biological insights through supervised data visualization," 2023. (bioRxiv, code)

7. T. Butler, A. Frandsen, R. Lightheart, B. Bargh, T.J. Bollerman, T. Kerby, K. West, G. Voronov, K.R. Moon, T. Kind, P. Dorrestein, A. Allen, V. Colluru, D. Healey, "MS2Mol: A transformer model for illuminating dark chemical space from mass spectra," 2023. (chemRxiv)

8. K.R. Moon, K. Sricharan, A.O. Hero III, "Ensemble Estimation of Distributional Functionals via k-Nearest Neighbors," 2017. (arXiv)

9. K.R Moon, K. Sricharan, K. Greenewald, A.O. Hero III, "Nonparametric ensemble estimation of distributional functionals," 2016. (arXiv)

Other Conference Papers or Abstracts

1. P. Gawade, A. Izdebski, M. Lizotte, K.R. Moon, J. Rhodes, G. Wolf, E. Szczurek, "Learning predictive and optimizable molecular embeddings from pretrained transformers via diffusion-based manifold learning," Munich Symposium on Generative AI in Molecule Discovery, July 2025.

2. H. Fluckiger, C. Swapp, M. Roberts, T. Kerby, K. Bladen, K.R. Moon, B. Bean, "River rapid detection pipeline using AI," Utah AI Summit, June 2025.

3. K. Bladen, A. Cutler, R. Cutler, K.R. Moon, "Conditional local importance by quantile expectation," Utah AI Summit, June 2025.

4. B. Shaw, A. Magner, K.R. Moon, "Continuous symmetry discovery and enforcement in machine learning," Utah AI Summit, June 2025.

5. H. Chen, K.R. Moon, "Functional Information Geometry: Visualizing and denoising high-dimensional dynamical systems," Utah AI Summit, June 2025.

6. R. May, A. Wisler, K.R. Moon, "Empirical evaluation of Bayes error rate bounds in binary classification," Utah AI Summit, June 2025.

7. T. Kerby, B. Fuller, K.R. Moon, "Nexarag: Accelerating research with AI-enhanced knowledge graphs," Utah AI Summit, June 2025.

8. H. Fluckiger, C. Swapp, M. Roberts, T. Kerby, K. Bladen, K.R. Moon, B. Bean, "River rapid detection pipeline using AI," Red Rock Data Science Conference, May 2025.

9. T. Kerby, B. Fuller, K.R. Moon, "Nexarag: Accelerating research with AI-enhanced knowledge graphs," Red Rock Data Science Conference, May 2025.

10. B. Shaw, A. Magner, K.R. Moon, "Continuous symmetry discovery and enforcement in machine learning," Red Rock Data Science Conference, May 2025.

11. B. Shaw, J. Rhodes, S. Boubrahimi, K.R. Moon, "Forest proximity graphs for time series," Graph Signal Processing Workshop, May 2025.

12. K. Bladen, A. Cutler, R. Cutler, K.R. Moon, "Conditional local importance by quantile expectation," Red Rock Data Science Conference, May 2025.

13. B. Lewis, S. Jones, C. Swenson, K.R. Moon, M. Harper, "Applying Machine Learning to Equatorial Plasma Bubble Now Casting on CubeSats," Small Satellite Conference, Aug. 2024.

14. A. Duque*, S. Morin*, G. Wolf**, K.R. Moon**, "Extendable and invertible manifold learning with geometry regularized autoencoders," Differential Geometry meets Deep Learning Workshop at NeurIPS, Dec. 2020.

15. A. Duque, G. Wolf, K.R. Moon, "Visualizing high dimensional and high frequency electrical biosignals," Machine Learning in Computational Biology (MLCB), Spotlight talk, Nov. 2020.

16. A. Duque*, S. Morin*, G. Wolf**, K.R. Moon**, "Extendable and invertible manifold learning with geometry regularized autoencoders," Deep Math, Nov. 2020.

17. A. Duque*, S. Morin*, G. Wolf**, K.R. Moon**, "Extendable and invertible manifold learning with geometry regularized autoencoders," Montreal AI Symposium, Sept. 2020.

18. J. Wheeler, B. Bean, S. Schwartz, R. Christensen, K.R. Moon, "GPS-denied navigation with artificial neural networks," Utah Conference on Undergraduate Research, Feb. 2020.

19. S. Gigante*, J.S. Stanley III*, N. Vu, D. van Dijk, K.R. Moon, G. Wolf**, S. Krishnaswamy**, "Compressed Diffusion," Signal Processing with Adaptive Sparse Structured Representations (SPARS), July 2019. (Link)

20. D. van Dijk*, S. Gigante*, K.R. Moon, A. Strzalkowski, K. Ferguson, J. Cardin, G. Wolf**, S. Krishnaswamy**, "Modeling Dynamics with Deep Transition-Learning Networks," in International Conference on Machine Learning (ICML) Workshop on Computational Biology, July 2018. (Link)

21. M. Amodio, K. Srinivasan, D. van Dijk, H. Mohsen, K. Yim, R. Muhle, K.R. Moon, R. Montgomery, J. Noonan, G. Wolf, S. Krishnaswamy, "SAUCIE: Sparse autoencoder for unsupervised clustering, imputation, and embedding," in Proceedings of the American Association for Cancer Research Annual Meeting, Apr. 2018. (Link)

22. K.R. Moon, V. Delouille, A.O. Hero III, “Image patch analysis of sunspots and active regions,” in AGU Fall Meeting, Dec. 2017.

23. K.R. Moon*, D. Van Dijk*, Z. Wang*, W. Chen, M. Hirn, R. Coifman, N.B. Ivanova**, G. Wolf**, S. Krishnaswamy**, “Visualization Beyond tSNE: PHATE for Visualizing and Analyzing Trajectory Structures in High-Dimensional Biological Data,” In Single Cell Genomics 2017, Oct. 2017.

24. K.R. Moon, K. Sricharan, A.O. Hero III, “Ensemble estimation of distributional functionals via k-nearest neighbors,” in 55th Annual Allerton Conference on Communication, Control, and Computing, Oct. 4, 2017. (Invited Talk)

25. V. Delouille, K.R. Moon, A.O. Hero III, S. Hofmeister, M. Reiss, M. Temmer, A. Veronig, “Supervised and non-supervised classification in solar physics using advanced techniques,” in Space Weather: A Multidisciplinary Approach, Sep. 26, 2017, Lorentz Center.

26. K.R. Moon*, D. Van Dijk*, Z. Wang*, W. Chen, M. Hirn, R. Coifman, N.B. Ivanova**, G. Wolf**, S. Krishnaswamy**, “Visualization Beyond tSNE: PHATE for Visualizing and Analyzing Trajectory Structures in High-Dimensional Biological Data,” In New York Area Meeting in Quantitative Biology: Making Use of Emerging Technologies, Aug. 2, 2017.

27. H. Mohsen, K. Srinivasan, K.R. Moon, G. Wolf, D. Van Dijk, S. Krishnaswamy, “Deep Neural Networks for Imputation, Clustering, and Embedding of Single-Cell Data,” In New York Area Meeting in Quantitative Biology: Making Use of Emerging Technologies, Aug. 2, 2017. (Link)

28. H. Mohsen, K. Srinivasan, K.R. Moon, G. Wolf, D. Van Dijk, S. Krishnaswamy, “Deep Neural Networks for Imputation, Clustering, and Embedding of Single-Cell Data,” In ISMB 2017: 25th conference on Intelligent Systems for Molecular Biology, July 2017. (Link)

29. S. Yasaei Sekeh, M. Noshad, K.R. Moon, A.O. Hero, "Estimation of Henze-Penrose Divergence Measures," In 2017 SIAM Annual Meeting, July 11, 2017. (Link)

30. K.R. Moon, D. van Dijk, Z. Wang, T. Welp, G. Wolf, R.R. Coifman, N. Ivanova, S. Krishnaswamy, "PHATE: Potential Heat-diffusion Affinity-based Trajectory Embedding for Visualization of Progression Structure," In 11th Annual Machine Learning Symposium, New York Academy of Science, March 3, 2017. (Link)

31. K.R. Moon, D. van Dijk, Z. Wang, T. Welp, G. Wolf, R.R. Coifman, N. Ivanova, S. Krishnaswamy, "PHATE: Potential Heat-diffusion Affinity-based Trajectory Embedding for Visualizing and Annotating Branching Trajectory Structure in Biological Data," In Systems Biology: Global Regulation of Gene Expression, Cold Spring Harbor Laboratory, Feb. 27, 2017. (Link)

32. K.R. Moon, K. Sricharan, A.O. Hero III, “Ensemble estimation of mutual information,” In Nonparametric Statistics Workshop: Integration of Theory, Methods, and Applications, Oct. 6, 2016. (Link)

33. K.R. Moon, V. Delouille, A.O. Hero III, “Meta learning of bounds on the Bayes classifier error,” In From Industrial Statistics to Data Science: A Conference in Honor of Vijay Nair, Oct. 1, 2015. (Link)

34. K.R. Moon, J.J. Li, V. Delouille, F. Watson, A.O. Hero III, “Image patch analysis and clustering of sunspots: A dimensionality reduction approach,” In Seventh Solar Information Processing Workshop, Aug. 20, 2014. (Link)

* = these authors contributed equally to this work

**= these authors contributed equally to this work