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.
Plenary and Special Talk/ Lecture:
[1] Foundation of mixture of experts in statistical machine learning. Vietnam Institute for Advanced Study in Mathematics, 2025 (Invited Lecture).
[2] Optimal transport on large-scale machine learning applications. Korean Artificial Intelligence Association (KAIA) conference, Seoul, Korea, 2023 (Special invited talk).
[3] Foundation of mixture of experts in complex and massive AI models. Conference on "Resonances in the Mathematical World", University of Science and at the Gifted High School, Vietnam National University, Ho Chi Minh City, Vietnam 2024 (Plenary Lecture).
Conferences, Seminars, and Workshops Presentations:
[104] Statistical foundation of mixture of experts. ASA Section on Statistical Learning and Data Science: Inference and Intelligence, New York, 2026 (Invited talk).
[103] Foundation of mixture of experts in complex and massive AI models. HORIZONS 2026: Learning and Optimization for a Sustainable Future, VinUniversity, Vietnam, 2026 (Invited talk).
[102] Bayesian nonparametrics meets data-driven robust optimization. ICERM Workshop on "Nonparametric Bayesian Inference - Computation Issues", Brown University, Providence, RI, 2026 (Invited talk).
[101] On mixture of experts in large-scale statistical machine learning applications. Department Seminar, Department of Mathematics, Penn State University, 2025 (Invited talk).
[100] On excess mass behavior in Gaussian mixture models with Orlicz Wasserstein distance. 19th International Joint Conference on Computational and Financial Econometrics (CFE) and Computational and Methodological Statistics (CMStatistics), King's College London, UK, 2025 (Invited talk).
[99] Foundation of mixture of experts in complex and massive AI models. International Conference on Next-Generation AI \& Machine Learning, San Francisco, CA, 2025 (Invited talk).
[98] Foundation of mixture of experts in complex and massive AI models. IFML/UT Math Workshop, Austin, 2025 (Invited talk).
[97] Foundation of mixture of experts in complex and massive AI models. Australian Statistical Conference (ASC), Australia, 2025 (Invited talk).
[96] Applied mathematics: The pillar of modern data science and decision-making thinking. Workshop on "Training in Applied Mathematics in the Era of Data and Smart Decision-Making", National Economics University, Ha Noi, 2025 (Invited talk).
[95] Bayesian nonparametrics meets data-driven robust optimization. 14th International Conference on Bayesian Nonparametrics, UCLA, 2025 (Invited talk).
[94] Foundation of mixture of experts in complex and massive AI models. Workshop on "Application of Optimal Transport in Control and Inverse Problems", SIAM Conference on Computational Science and Engineering (CSE25), 2025 (Invited talk).
[93] Bayesian nonparametrics meets data-driven robust optimization. BIRS Workshop on "Bayesian Uncertainty Quantification in Large Models", Chennai Mathematical Institute (CMI), India, 2025 (Invited talk).
[92] On mixture of experts in large-scale statistical machine learning applications. Department Seminar, Department of Mathematics and Statistics, McGill University, Montreal, Canada 2024 (Invited talk).
[91] On mixture of experts in large-scale statistical machine learning applications. Québec Mathematical Sciences Colloquium, Institut des sciences mathématiques (ISM) and Centre de recherches mathématiques (CRM), Canada 2024 (Invited talk).
[90] On mixture of experts in large-scale statistical machine learning applications. Department Seminar, Department of Statistical Science, Duke University, 2024 (Invited talk).
[89] On mixture of experts in large-scale statistical machine learning applications. Department Seminar, Department of Computer Science, Phenikaa University, Ha Noi, Vietnam, 2024 (Invited talk).
[88] On mixture of experts in large-scale statistical machine learning applications. Department Seminar, Department of Computer Science, Foreign Trade University, Ha Noi, Vietnam, 2024 (Invited talk).
[87] On mixture of experts in large-scale statistical machine learning applications. Department Seminar, Department of Computer Science, VNU University of Engineering and Technology, Ha Noi, Vietnam, 2024 (Invited talk).
[86] On mixture of experts in large-scale statistical machine learning applications. Department Seminar, Department of Computer Science, Ha Noi University of Industry, Vietnam, 2024 (Invited talk).
[85] On mixture of experts in large-scale statistical machine learning applications. Department Seminar, Department of Mathematics, National Economics University, Ha Noi, 2024 (Invited talk).
[84] On mixture of experts in large-scale statistical machine learning applications. Department Seminar, Department of Computer Science, Swinburne University of Technology Vietnam, Da Nang, 2024 (Invited talk).
[83] On mixture of experts in large-scale statistical machine learning applications. Department Seminar, Department of Electrical and Computer Engineering, University of Houston, 2024 (Invited talk).
[82] On foundation of mixture of experts and its applications to scalable AI. Department Seminar, Department of Computer Science, VinUni, Ha Noi, Vietnam 2024 (Invited talk).
[81] Optimal transport in large-scale machine learning applications. Workshop on "Algorithms & PDE", Austin, Texas, 2024 (Invited talk).
[80] Instability, statistical accuracy, and computational efficiency. DIMACS Workshop on "Modeling Randomness in Neural Network Training", Rutgers University, 2024 (Invited talk).
[79] Optimal transport in large-scale machine learning applications. AI Winter School, Quy Nhon City, Vietnam 2024 (Invited talk).
[78] Instability, statistical accuracy, and computational efficiency. DIMACS Workshop on "Modeling Randomness in Neural Network Training", Rutgers University, 2024 (Invited talk).
[77] Optimal transport in large-scale machine learning applications. Special Session on Machine Learning, Data Science and Related Fields, American Mathematical society (AMS) Fall Central Sectional, University of Texas, San Antonio, Texas 2024 (Invited talk).
[76] Demystifying softmax gating mixture of experts. International Society for Business and Industrial Statistics (ISBIS) Conference, Indonesia, 2024 (Invited talk).
[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 Chicago, 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.