Workshops Organization:

  • Integration of Deep Learning Theories at NIPS 2018, Palais des Congrès de Montréal, Canada.
    — Co-organize with Professor Richard Baraniuk, Stephane Mallat, Anima Anandkumar, and Ankit Patel.

Conferences, Seminars, and Workshops Presentations:

  • On multilayer latent variable models: Computational and statistical perspectives. Mathematics of Data and Decisions Seminar, Department of Mathematics, UC Davis, 2019.

  • On optimal transport in machine learning and statistics: Computational, modeling, and theoretical perspectives. Research seminar, VinAI Research, Ha Noi, 2019.

  • Statistical and computational perspective of mixture and hierarchical models. BLISS Seminar, Department of EECS, UC Berkeley, 2019.

  • Singularity structures of mixture models: Statistical and computational perspective. Joint Statistical Meetings (JSM), Denver, Colorado, 2019.

  • On efficient optimal transport: an analysis of greedy and accelerated mirror descent algorithms. International Conference on Machine Learning (ICML), Long Beach, CA, 2019.

  • Singularity structures of mixture models: Statistical and computational perspective. Department Seminar, Department of Electrical Engineering and Computer Sciences, Rice, November, 2018, Houston, Texas.

  • Singularity structures of parameter estimation in nite mixtures of distributions. Joint Stanford and Berkeley Applied Math Event, November 2018, University of California, Berkeley.

  • Singularity Structure of Parameter Space and Posterior Contraction in Finite Mixture Models. Joint Statistical Meetings (JSM), August, 2017, Baltimore, Maryland.

  • Singularity structures and parameter estimation behavior in finite mixtures of distributions. Nonparametric Statistics Workshop: Integration of Theory, Methods, and Applications, October, 2016, Ann Arbor, Michigan.

  • Singularity structures and impacts on parameter estimation in finite mixtures of distributions. Shannon Centennial Symposium, September, 2016, Ann Arbor, Michigan.

  • Singularity structures and parameter estimation behavior in finite mixtures of distributions. Joint Statistical Meetings (JSM), August, 2016, Chicago, Illinois.

  • Singularity structures and parameter estimation behavior in finite mixtures of distributions. Conference on Statistical Learning and Data Science, June, 2016, University of North Carolina at the Chapel Hill.

  • Singularity structures and parameter estimation behavior in finite mixtures of distributions. Statistical Machine Learning Student Workshop, June, 2016, University of Michigan, Ann Arbor.

  • Singularity structures and parameter estimation in mixtures of skew normal distributions. Michigan Student Symposium for Interdisciplinary Statistical Sciences (MSSISS), March, 2016, Ann Arbor, MI.

  • Weak identifiability and convergence rate of mixing measures in over-fitted Gaussian mixture models. Student Seminar, Department of Statistics, University of Michigan, January, 2016, Ann Arbor, Michigan.

  • Intrinsic difficulties for the inference of mixing measures in finite mixtures of univariate skew normal distributions. From Industrial Statistics to Data Science, October, 2015, Ann Arbor, Michigan.

  • Posterior concentration of mixing parameters in some weakly identifiable finite mixture models. 10th Conference on Bayesian Nonparametrics, June, 2015, Raleigh, North Carolina.

  • Weak identifiability and optimal rate of convergence of mixing measures in over-fitted Gaussian mixture models. Statistical Machine Learning Student Workshop, June, 2015, University of Michigan, Ann Arbor.

  • Weak identifiability and optimal rate of convergence of mixing measures in over-fitted Gaussian mixture models. NSF Conference - Statistics for Complex Systems, June, 2015, Madison, Wisconsin.

  • Optimal convergence rate of parameter estimation in overfitted finite Gaussian mixture models. Michigan Student Symposium for Interdisciplinary Statistical Sciences (MSSISS), March, 2015, Ann Arbor, MI.

  • Identifiability and convergence rate of parameter estimations in exact-fitted finite mixture models. Statistical Machine Learning Student Workshop, June, 2014, University of Michigan, Ann Arbor.