Presentations

Invited Talks

1. “Visualizing the true structure of big data for data exploration,” Statistics Department Seminar Series, Jan. 23, 2020, Brigham Young University.

2. "Unsupervised Data Visualization for Big Data Exploratory Analysis," Salt Lake City Data Science Meetup, Nov. 14, 2018, Recursion Pharmaceuticals.

3. "Unsupervised Data Visualization for Big Data Exploratory Analysis," Nov. 14, 2018, Recursion Pharmaceuticals.

4. "Unsupervised Data Visualization for Biological Data Exploration," Biology Department Graduate Seminar Series, Mar. 15, 2018, Brigham Young University.

5. "Unsupervised Data Visualization for Big Data Exploratory Analysis," Computer Science Department Colloquium, Mar. 13, 2018, Brigham Young University.

6. "Unsupervised Data Visualization for Big Data Exploratory Analysis," Feb. 26, 2018, Utah State University Department of Mathematics and Statistics.

   1. "Unsupervised Data Visualization for Biological Data Exploration," Biostatistics and Medical Informatics Department Seminar, Feb. 8, 2018, University of Wisconsin-Madison.

7. "Nonparametric Estimation of Distributional Functionals in Machine Learning," Joint Stochastics/Statistics and Applied Math Seminar, Jan. 17, 2018, University of Utah.

8. "Unsupervised Data Visualization for Big Data Exploratory Analysis," Department of Mathematics Colloquium, Jan. 16, 2018, University of Utah.

9. “PHATE: Diffusion-based transition embedding for exploratory data analysis with applications in genomics,” Oct. 19, 2017, Tel Aviv University School of Computer Science.

10. “Ensemble estimation of distributional functionals via k-nearest neighbors,” 55th Annual Allerton Conference on Communication, Control, and Computing, Oct. 4, 2017, Allerton.

• 1. "Diffusion-based Representations for Revealing Trajectory Structure and Gene Interactions in Noisy Single Cell Data," New York Applied Topology Seminar Series, June 9, 2017, Columbia University.

  2. "Diffusion-based Representations for Revealing Trajectory Structure and Gene Interactions in Noisy Single Cell Data," May 4, 2017, Utah State University ECE Department.

  3. “Diffusion-Based Representations for Revealing Progressions, Multi-Scale Clusters, and Gene Interactions in Noisy Single Cell Data,” The Pittsburgh Conference and Exposition (PITTCON), Mar. 8, 2017, Chicago, IL.

  4. “Information Divergence Estimation: An Information Theoretic Approach to Machine Learning,” April 8, 2016, Presentation, Smita Krishnaswamy Lab, Yale University.

  5. "Information divergence estimation in signal processing and machine learning," Aug. 17, 2015, University of Utah ECE Department.

  6. "Information divergence estimation in signal processing and machine learning," Aug. 13, 2015, Brigham Young University ECEN Department.

  7. "Information divergence estimation in signal processing and machine learning," May 15, 2015, Utah State University ECE Department.

  8. “Applications of information divergence estimation,” Solar-Terrestrial Centre of Excellence Seminars, Aug. 26, 2014, Royal Observatory of Belgium.

Conferences and Workshops

1. “Image Patch Analysis of Sunspots and Active Regions,” AGU Fall Meeting 2017, Dec. 12, 2017, Poster, New Orleans.

2. “Visualization beyond t-SNE: PHATE for visualizing and analyzing trajectory structures in high-dimensional biological data,” Single Cell Genomics 2017, Oct. 16, 2017, Poster, Weizmann Institute of Science, Israel.

   1. "Information Theoretic Structure Learning with Confidence," IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Mar. 6, 2017, Presentation, New Orleans (Invited Paper).

   2. "PHATE: Visualizing Biological Progression via Heat-Diffusion Potential," 11th Annual Machine Learning Symposium, Mar. 3, 2017, Poster, The New York Academy of Sciences.

   3. "PHATE: Visualizing Biological Progression via Heat-Diffusion Potential," Systems Biology: Global Regulation of Gene Expression, Feb. 27, 2017, Poster, Cold Spring Harbor Laboratory.

   4. “Ensemble estimation of mutual information,” Nonparametric Statistics Workshop: Integration of Theory, Methods, and Applications, Oct. 6, 2016, Poster, Ann Arbor, MI.

   5. “Improving convergence of divergence functional ensemble estimators,” IEEE International Symposium on Information Theory, July 12, 2016, Presentation, Barcelona.

   6. “Meta learning of bounds on the Bayes classifier error,” From Industrial Statistics to Data Science: A Conference in Honor of Vijay Nair, Oct. 1, 2015, Poster, Ann Arbor, MI.

   7. “Meta learning of bounds on the Bayes classifier error,” IEEE Signal Processing and Signal Processing Education Workshop, Aug. 10, 2015, Poster, Snowbird, UT.

   8. “Multivariate f-divergence estimation with confidence,” Advances in Neural Information Processing Systems, Dec. 10, 2014, Poster, Montreal.

   9. “Image patch analysis and clustering of sunspots: A dimensionality reduction approach,” IEEE International Conference on Image Processing, Oct. 28, 2014, Poster, Paris.

   10. “Image patch analysis and clustering of sunspots: A dimensionality reduction approach,” Seventh Solar Information Processing Workshop, Aug. 20, 2014, Presentation, Belgium.

   11. “Ensemble estimation of multivariate f-divergence,” IEEE International Symposium on Information Theory, June 30, 2014, Presentation, Honolulu, HI.

   12. “Investigations of temperature and backscatter correlation in the dry snow zone of the Greenland ice sheet,” NASA Rocky Mountain Space Grant Fellowship Symposium 2012, May 2012, Presentation, Logan, UT.

Institutional Presentations

• 1. "Introduction to Machine Learning," Nov. 16, 2018, Presentation, HackUSU, Utah State University.

  2. "Visualizing Transitions and Structures for High Dimensional Data Exploration," Jan. 12, 2018, Presentation, Yale CyTOF Users' Meeting, Yale University.

  3. "Distributional Functional Estimation in Signal Processing and Machine Learning," Sep. 27, 2016, Presentation, Applied Math Seminar, Yale University.

  4. “Improving convergence of divergence functional ensemble estimators,” Statistical Machine Learning Student Workshop, June 1, 2016, Presentation, University of Michigan.

  5. “Information Theory in Machine Learning and Signal Processing,” Student Signal Processing in EECS seminar series (SPeecs), May 27, 2016, Presentation, University of Michigan.

  6. “Meta learning of bounds on the Bayes classifier error,” Engineering Graduate Symposium, Oct. 30, 2015, Poster, University of Michigan, (2nd place in Signal and Image Processing session).

  7. “Distributional functionals in signal processing and machine learning," SPeecs, Aug 21, 2015, Presentation, University of Michigan.

  8. “Information divergence estimation in signal processing and machine learning,” Physics Graduate Student Symposium (PGSS), July 1, 2015, Presentation, University of Michigan.

  9. “Meta learning of bounds on the Bayes classifier error,” Statistical Machine Learning Student Workshop, June 10, 2015, Presentation, University of Michigan.

  10. “Multivariate f-divergence estimation with confidence,” Michigan Student Symposium for Interdisciplinary Statistical Sciences (MSSISS), March 2015, Poster, University of Michigan.

  11. “Ensemble estimation of multivariate f-divergence: theory and applications,” Engineering Graduate Symposium, Nov. 14, 2014, Poster, University of Michigan.

  12. “Applications of information divergence estimation,”SPeecs, Sep. 12, 2014, Presentation, University of Michigan.

  13. “Introduction to measure theory,” SPeecs, Aug. 29, 2014, University of Michigan.

  14. “Ensemble estimation of multivariate f-divergence," Statistical Machine Learning Student Workshop, June 25, 2014, Presentation, University of Michigan.

  15. “Ensemble estimation of multivariate f-divergence,” MSSISS, March 2014, Poster, University of Michigan.

  16. “Image patch analysis of sunspot images: A dimensionality reduction approach,” SPeecs, Jan. 24, 2014, Presentation, University of Michigan.

  17. “Statistical distance and divergence,” SPeecs, Aug. 8, 2013, Presentation, University of Michigan.