## 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:[75] On excess mass behavior in Gaussian mixture models with Orlicz Wasserstein distances. *Workshop on "Interpretable inference via principled Bayesian nonparametric approaches in biomedical research & beyond", National University of Singapore (NUS), 2024 (Invited talk)*.[74] On the multivariate Fourier integral theorem: Statistical and methodological perspectives. *International Conference on Econometrics and Statistics (EcoSta), Tokyo, Japan, 2023 (Invited talk)*.[73] Bayesian sieves and excess mass behaviors in Dirichlet Process mixture models. *Workshop on "Approximation Methods in Bayesian Analysis", Centre International de Rencontres Mathématiques, Marseille, France, 2023 (Invited talk)*.[72] On the multivariate Fourier integral theorem: Statistical and methodological perspectives. *Vietnam Institute for Advanced Study in Mathematics (VIASM), 2023 (Invited talk)*.[71] On the multivariate Fourier integral theorem: Statistical and methodological perspectives. *Summer School Series on Mathematical Statistics and Machine Learning: 2023 School on Bayesian Statistics and Computation, Ho Chi Minh, Viet Nam, 2023 (Invited talk)*.[70] Optimal transport in machine learning and data science. *Machine Learning Lunch Seminar, Vanderbilt University, 2023 (Invited talk)*.[69] Probabilistic frameworks for understanding self-attention mechanism in Transformer. *Applied Statistics Symposium, International Chinese Statistical Association (ICSA), 2023 (Invited talk)*.[68] Instability, statistical accuracy, and computational efficiency. *Applied Math Colloquium, University of California, Los Angeles (UCLA), 2022 (Invited talk)*.[67] Instability, statistical accuracy, and computational efficiency. *Workshop on Structured Optimization Models in High-Dimensional Data Analysis, National University of Singapore (NUS), 2022 (Invited talk)*.[66] Optimal transport in machine learning and data science. *Optimal Transport and Mean Field games Seminar, University of California, Los Angeles (UCLA), 2022 (Invited talk)*.[65] Statistical and computational perspectives on mixture models. *Department Seminar, INRIA, France, 2022 (Invited talk)*.[64] Instability, statistical accuracy, and computational efficiency. *Department Seminar, Department of Computer Science, Monash University, Australia, 2022 (Invited talk)*.[63] Statistical and computational perspectives on mixture models. *Department Seminar, Department of Mathematics and Statistics, University of Queensland, Australia, 2022 (Invited talk)*.[62] Instability, statistical accuracy, and computational efficiency. *Department Seminar, Department of Statistics, University of Sydney, Australia, 2022 (Invited talk)*.[61] Optimal transport in machine learning and data science. *Applied Artificial Intelligence Institute, Deakin University, Australia, 2022 (Invited talk)*.[60] Optimal transport in machine learning and data science. *Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2022 (Invited talk)*.[59] Bayesian sieves and excess mass behaviors in Dirichlet Process mixture models. *13th International Conference on Bayesian Nonparametrics, Pontificia Universidad Católica de Chile, 2022 (Invited talk)*.[58] Optimization in machine learning and data science. *Department Seminar, College of Engineering and Computer Science, VinUni, Ha Hoi, Vietnam, 2022 (Invited talk)*.[57] Optimal transport in large-scale machine learning applications. *FPT Software AI Center, Ha Noi, Viet Nam, 2022 (Invited talk)*.[56] Optimal transport in large-scale machine learning applications. *Viettel AI Center, Ha Noi, Viet Nam, 2022 (Invited talk)*.[55] On the multivariate Fourier integral theorem: Statistical and methodological perspectives. *International Conference on Econometrics and Statistics (EcoSta), Kyoto, Japan, 2022 (Invited talk)*.[54] Bayesian sieves and excess mass behaviors in Dirichlet Process mixture models. *49th Annual Meeting of the Statistical Society of Canada, Simon Fraser University, Canada, 2022 (Invited talk)*.[53] Bayesian sieves and excess mass behaviors in Dirichlet Process mixture models. *Optimal transport meets Bayes" workshop, 16th World Meeting of the International Society for Bayesian Analysis, 2022 (Invited talk)*.[52] On multimarginal partial optimal transport: equivalent forms and computational complexity. *SIAM Imaging conference, 2022 (Invited talk)*.[51] Instability, statistical accuracy, and computational efficiency. *BLISS Seminar, Department of Electrical Engineering and Computer Sciences, UC Berkeley, 2021 (Invited talk)*.[50] On optimal transport in machine learning and data science. *The 13th Asian Conference in Machine Learning, 2021 (Tutorial Talk)*.[49] On optimal transport in machine learning and data science. *The Online Asian Machine Learning School, 2021 (Invited talk)*.[48] On optimal transport in machine learning and data science: computational, modeling, and theoretical perspective. *INFORMS, 2021 (Invited talk)*.[47] Statistical efficiency of parameter estimation in generalized contaminated models. *International Indian Statistical Association (IISA) Conference, 2021 (Invited talk)*.[46] Statistical efficiency of parameter estimation in generalized contaminated models. *International Chinese Statistical Association (ICSA), Xi'an University, Xi’an, China, 2021 (Invited talk - Cancelled due to COVID-19)*.[45] Statistical efficiency of parameter estimation in generalized contaminated models. *International Conference on Econometrics and Statistics (EcoSta), Yonsei University, Seoul, South Korea, 2021 (Invited talk)*.[44] Statistical and computational perspectives on latent variable models. *Department of Decision Sciences at Bocconi University, Italy, 2020 (Invited talk)*.[43] Convergence rates for Gaussian mixtures of experts. *International Indian Statistical Association (IISA) Conference, University of Illinois at Chicago, 2020 (Invited talk - Cancelled due to COVID-19)*.[42] Statistical and computational perspectives on latent variable models. *Young Data Science Researcher Seminar, ETH Zurich, 2020 (Invited talk)*.[41] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Biostatistics, University of California, Berkeley, 2020 (Invited talk)*.[40] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Statistics and Data Science, CMU, 2020 (Invited talk)*.[39] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Statistics, University of California, Los Angeles, 2020 (Invited talk)*.[38] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Statistics and Data Science, Cornell University, 2020 (Invited talk)*.[37] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Statistics, Rutgers University, 2020 (Invited talk)*.[36] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Statistics, Purdue University, 2020 (Invited talk)*.[35] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Data Science, University of California, San Diego, 2020 (Invited talk)*.[34] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Data Science, John Hopskins University, 2020 (Invited talk)*.[33] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Statistics, University of Wisconsin, Madison, 2020 (Invited talk)*.[32] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Statistics, Duke University, 2020 (Invited talk)*.[31] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Statistical Science, University of Toronto, 2020 (Invited talk)*.[30] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Statistics and Data Sciences, University of Texas, Austin, 2020 (Invited talk)*.[29] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Statistics, University of Illinois, Urbana-Champaign, 2020 (Invited talk)*.[28] Statistical and computational perspectives on latent variable models. *Department Seminar, Booth School of Management, University of Southern California, 2020 (Invited talk)*.[27] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Data Science and Operation Research, University of Southern California, 2020 (Invited talk)*.[26] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Statistics, University of Southern California, 2020 (Invited talk)*.[25] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Statistics, North Carolina State University, 2020 (Invited talk)*.[24] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Statistics, University of Minnesota, Twin Cities, 2020 (Invited talk)*.[23] Statistical and computational perspectives on latent variable models. *Department Seminar, Krannert School of Management, Purdue University, 2020 (Invited talk)*.[22] Statistical and computational perspectives on latent variable models. *Department Seminar, Department of Statistics, Pennsylvania State University, 2020 (Invited talk)*.[21] On multilayer latent variable models: Computational and statistical perspectives. *Mathematics of Data and Decisions Seminar, Department of Mathematics, UC Davis, 2019 (Invited talk)*.[20] On optimal transport in machine learning and statistics: Computational, modeling, and theoretical perspectives. *Research seminar, VinAI Research, Ha Noi, 2019 (Invited talk)*.[19] Statistical and computational perspective of mixture and hierarchical models. *BLISS Seminar, Department of EECS, UC Berkeley, 2019 (Invited talk)*.[18] Singularity structures of mixture models: Statistical and computational perspective. *Joint Statistical Meetings (JSM), Denver, Colorado, 2019 (Invited talk)*.[17] On efficient optimal transport: an analysis of greedy and accelerated mirror descent algorithms. *International Conference on Machine Learning (ICML), Long Beach, CA, 2019*.[16] Singularity structures of mixture models: Statistical and computational perspective. *Department Seminar, Department of Electrical Engineering and Computer Sciences, Rice, November, 2018, Houston, Texas (Invited talk)*.[15] Singularity structures of parameter estimation in finite mixtures of distributions. *Joint Stanford and Berkeley Applied Math Event, November 2018, University of California, Berkeley (Invited talk)*.[14] Singularity Structure of Parameter Space and Posterior Contraction in Finite Mixture Models. *Joint Statistical Meetings (JSM), August, 2017, Baltimore, Maryland (Invited talk)*.[13] 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*.[12] Singularity structures and impacts on parameter estimation in finite mixtures of distributions. *Shannon Centennial Symposium, September, 2016, Ann Arbor, Michigan*.[11] Singularity structures and parameter estimation behavior in finite mixtures of distributions. *Joint Statistical Meetings (JSM), August, 2016, Chicago, Illinois*.[10] 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*.[9] Singularity structures and parameter estimation behavior in finite mixtures of distributions. *Statistical Machine Learning Student Workshop, June, 2016, University of Michigan, Ann Arbor*.[8] Singularity structures and parameter estimation in mixtures of skew normal distributions. *Michigan Student Symposium for Interdisciplinary Statistical Sciences (MSSISS), March, 2016, Ann Arbor, MI*.[7] 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*.[6] 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*.[5] Posterior concentration of mixing parameters in some weakly identifiable finite mixture models. *10th Conference on Bayesian Nonparametrics, June, 2015, Raleigh, North Carolina*.[4] 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*.[3] 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*.[2] 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*.[1] 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*.
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