My current research interests focus on machine learning and statistical methodologies using theories and techniques from Functional Analysis and related mathematical fields. In particular, I have been working on theories and methods involving *reproducing kernel Hilbert spaces (RKHS)*, *Riemannian geometry*, *Matrix and Operator Theory*, *Information Geometry*, and *Optimal Transport*, especially in the *Infinite-Dimensional* setting.

I received my PhD in mathematics from Brown University (Providence, RI, USA) and wrote my dissertation under the supervision of Stephen Smale. Before joining RIKEN, I was a researcher at the Pattern Analysis and Computer Vision group at the Italian Institute of Technology (Istituto Italiano di Tecnologia) in Genoa (Genova), Italy. Prior to Italy, I was a postdoctoral researcher at the University of Vienna, Austria, and the Humboldt University of Berlin, Germany.

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H.Q. Minh Fisher-Rao Riemannian Geometry of Equivalent Gaussian Measures on Hilbert Space, Geometric Science of Information, 2023, extended arxiv version

H.Q. Minh. Convergence and finite sample approximations of entropic regularized Wasserstein distances in Gaussian and RKHS settings, Analysis and Applications, 2023

H.Q. Minh. Entropic regularization of Wasserstein distance between infinite-dimensional Gaussian measures and Gaussian processes, Journal of Theoretical Probability, 2022

H.Q. Minh. Finite sample approximations of exact and entropic Wasserstein distances between covariance operators and Gaussian processes, SIAM/ASA Journal on Uncertainty Quantification, 2022

H.Q. Minh Alpha Procrustes metrics between positive definite operators: A unifying formulation for the Bures-Wasserstein and Log-Euclidean/Log-Hilbert-Schmidt metrics, Linear Algebra and Its Applications, 2022

H.Q. Minh. Regularized Divergences Between Covariance Operators and Gaussian Measures on Hilbert Spaces, Journal of Theoretical Probability, 2020

H.Q. Minh. Infinite-dimensional Log-Determinant divergences between positive definite Hilbert–Schmidt operators, Positvity, 2020

H.Q. Minh. Alpha-Beta Log-Determinant Divergences Between Positive Definite Trace Class Operators, Information Geometry, 2019

(**Book**) H.Q. Minh and V. Murino. Covariances in Computer Vision and Machine Learning, Springer Synthesis Lectures in Computer Vision, 2018

H.Q. Minh. Infinite-dimensional Log-Determinant divergences between positive definite trace class operators, Linear Algebra and Its Applications, 2017

H.Q. Minh, L. Bazzani, V. Murino. A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning, Journal of Machine Learning Research, 2016

ICLR 2024: Area Chair

QTML 2023 (Quantum Techniques in Machine Learning): Steering Committee and Program Committee member

August 2023: I gave an invited talk at the minisymposium *Geometric methods in machine learning and data analysis* at the 10th International Congress on Industrial and Applied Mathematics (ICIAM 2023), in Tokyo, Japan

June 2023: I gave an invited talk at the workshop DEEP LEARNING: Theory, Algorithms, and Applications (DL 2023), in Trento, Italy

May 2023: I gave a tutorial talk at the Second European Summer School on Quantum AI (EQAI 2023), in Udine, Italy

NeurIPS 2023: Area Chair

ICML 2023: Area Chair

September 2022: I gave an invited talk at IG4DS (International Conference on Information Geometry for Data Science)

NeurIPS 2022: Area Chair

QTML 2022: Steering Committee and Program Committee member

ICML 2022: Meta-reviewer (Area Chair)

ICLR 2022: Area Chair

AISTATS 2022: Area Chair

QTML 2021 (Quantum Techniques in Machine Learning): Main Organizer

I can be reached by email at minh dot haquang at the domain riken.jp