Students under my supervision are underlined.

C. Lee and M. Wang. Statistical and computational rates in high rank tensor estimation. [arXiv]

This work wins the 2023 American Statistical Association Best Student Paper Award from the Statistical Learning and Data Science Section.

C. Lee and M. Wang. Smooth tensor estimation with unknown permutations. [arXiv] [Software]

This work wins the 2022 New England Statistical Society Best Student Paper Award.

An earlier version of this work wins 2022 Hannan graduate student travel award from Institute of Mathematical Statistics (IMS).

Part of the work is selected as Oral Presentation at NeurIPS 2021 Workshop on Quantum Tensor Networks in Machine Learning.

J. Hu and M. Wang. Multiway spherical clustering via degree-corrected tensor block models. Short version published in AISTATS 2022. Journal version under review. [arXiv] [AISTATS version] [Software]

This work wins the 2022 American Statistical Association Best Student Paper Honorable Mention Award from the Statistical Learning and Data Science Section.

Part of the work is presented at NeurIPS 2021 Workshop on Quantum Tensor Networks in Machine Learning.

C. Lee and M. Wang. Beyond the Signs: Nonparametric tensor completion via sign series. Short version published in 2021 Advances in Neural Information Processing Systems 34 (NeurIPS). Journal version under review. [arXiv] [NeurIPS version] [Software]

Invited conference talk at Eastern North American Region (ENAR) and American Physical Society's March Meeting, 2022. Invited talk at University of Pittsburgh, UC Irvine, University of Haifa in Israel, 2022. Brown bag talk at IFDS UW-Madison, 2021.

C. Lee, L. Li, H. Zhang, and M. Wang. Nonparametric trace regression in high dimensions via sign series representation. 2021. [arXiv] [Software]

Invited department talks at UC-Berkeley (EECS), Penn State (Stats), Fred Hutchinson Cancer Research Center (Biostats), Tsinghua University (Stats), London School of Economics (Stats), 2022.

R. Han, Y. Luo, M. Wang, and A. R. Zhang. Exact clustering in tensor block model: Statistical optimality and computational limit. Journal of the Royal Statistical Society: Series B. 2022. [arXiv] [Software]

This work wins the 2021 American Statistical Association Best Student Paper Award from the Statistical Learning and Data Science Section.

J. Hu, C. Lee, and M. Wang. Generalized Tensor Decomposition with features on multiple modes. Journal of Computational and Graphical Statistics, Vol. 31, No. 1, 204-218, 2021. [Journal] [Conference version] [arXiv] [Software]

This work wins the 2021 American Statistical Association Best Student Paper Award from the Statistical Computing and Graphics Section.

Part of the work is accepted into Advances in Neural Information Processing Systems 33 (NeurIPS) Second Workshop on Machine Learning and the Physical Sciences, 2020.

C. Lee and M. Wang. Tensor denoising and completion based on ordinal observations. Proceedings of International Conference on Machine Learning (ICML), PMLR 119:5778-5788, 2020. [Journal] [arXiv] [Software]

Invited conference talk at Eastern North American Region (ENAR), International Biometric Society, 2020. Invited conference talk at ICSA Applied Statistics Symposium, 2020.

M. Wang and L. Li. Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and Its Statistical Optimality. Journal of Machine Learning Research, 21(154): 1−38, 2020. [Journal] [arXiv] [Software]

Invited department talks at UChicago (Stats), Stanford (Stats), Society for Industrial and Applied Mathematics (SIAM) annual meeting, Potland; Bosch Center for Artificial Intelligence, Sunnyvale, California; Systems, Information, Learning and Optimization (SILO) Talk at UW-Madison, 2019

M. Wang and Y. Zeng. Multiway clustering via tensor block models. Advances in Neural Information Processing Systems 32 (NeurIPS), 715-725, 2019. [Journal] [Preprint] [Software]

Invited department talk at Columbia, 2020. Institute of Foundation of Data Science (IFDS) brown-bag at UW-Madison. Invited talks at 2019 International Conference on Frontiers of Data Science, 2019 Institute of Mathematical Statistics (IMS) meeting at China.

M. Wang, J. Fischer, and Y. S. Song. Three-way Clustering of Multi-tissue Multi-individual Gene Expression Data Using Semi-nonnegative Tensor Decomposition. Annals of Applied Statistics. Vol. 13, No. 2, 1103-1127, 2019. [Journal] [arXiv] [Supplementary] [Software: Multi-Cluster]

Invited department talks at Stanford (Stats), UC Berkeley (Biostats). Platform Talk at Computational and Genomic Biology Retreat at UC Berkeley. Computation and Informatics in Biology and Medicine (CIBM) at UW-Madison, 2019.

M. Wang, F. Roux, C. Bartoli, C. H.-Chauveau, C. Meyer, H. Lee, D. Roby, M. S. McPeek, and J. Bergelson. Two-way Mixed-Effects Methods for Joint Association Analyses Using Both Host and Pathogen Genomes. Proc. Natl. Acad. Sci. (direct submission). Vol. 115 (24), E5440-E5449, 2018. [Journal] [Typesetting error in the published version] [Supplementary] [Software: ATOMM]

Attention score in the top 5% of all research articles ever tracked by Altmetric (as of June 2018) ; Higher than 89% of the research articles published in PNAS.

Platform talk (top 13%) at the 2nd Probabilistic Modeling in Genomics, Cold Spring Harbor Laboratory, NY.

Invited talk at the 2019 European Society for Evolutionary Biology-Special Topic Networks workshop at Germany.

M. Wang and Y. S. Song. Tensor Decomposition via Two-Mode Higher-Order SVD (HOSVD). Journal of Machine Learning Research W & CP (AISTATS track), Vol. 54, 614-622, 2017. [Journal] [Supplementary] [Preprint] [Software: TM-HOSVD]

Invited department talks at CMU (Stats & Data Science), UW-Madison (Stats), Columbia (Stats), UToronto (Stats), Johns Hopkins (Biostats).

M. Wang, K. Dao Duc, J. Fischer, and Y. S. Song. Operator Norm Inequalities Between Tensor Unfoldings on the Partition Lattice. Linear Algebra and its Applications, Vol. 520, 44-66, 2017. [Journal] [Preprint]

Invited Department Colloquium Talk at Duke (Stats), Boston University (Math), UMass-Amherst (Applied Math), Queen's (Math), and JSM16.

M. Wang, J. Jakobsdottir, A. V. Smith, and M. S. McPeek. G-STRATEGY: Optimal Selection of Individuals for Sequencing in Genetic Association Studies. Genetic Epidemiology, Vol. 40, No. 6, 446–460, 2016. [Journal] [Software: G-STRATEGY]

Highlighted as Editor’s Pick Paper of this issue.

For part of this work, I was awarded the ASHG Charles J. Epstein Trainee Semifinalist Award (27 predoctoral recipients out of 550 candidates) for Excellence in Human Genetics Research; Also invited to give a platform talk (top 8%) in 2014 Annual Meeting of American Society of Human Genetics.

For a different part of this work, I was awarded the IGES Williams Finalist Award (3 out of 156) for Best Platform Presentation by Graduate Students; Also invited to give one of the six talks in Neels and Williams Awards Session in 2013 Annual Meeting of the International Genetic Epidemiology Society.

National Science Foundation (NSF) CAREER DMS-2141865, $400K, sole PI, 2022-2026.

National Science Foundation (NSF) EF-2133740, UW-Madison PI with Daniel Bolnick (UConn PI) and Tina Eliassi-Rad (Northeastern PI), $3 million, 2022-2026.

National Science Foundation (NSF) DMS-1915978, $180K, sole PI, 2019-2023.

National Science Foundation (NSF) DMS-2023239, $4.5 million, Senior Personal, 2020-2025.

U.S. Department of Defense (DOD), W911NF2010051, $150K, co-I, 2020-2021.

Fall Research Competition, Wisconsin Alumni Research Foundation, $40K/year, sole PI, 2020-2022 (three times).

ICML Diversity and Inclusion Fellowship, 2020.

Institute of Mathematical Statistics (IMS) New Researcher Travel Award, 2020-2021.

NeurIPS Junior Researcher Travel Award, 2019.

Google Cloud Platform Education Grants. Free clound computing credits to students in class, 2019.

Vice Chancellor for Research and Graduate Education (VCRGE) Travel Award, UW-Madison, 2019.

Madison Teaching and Learning Excellence Fellow, UW-Madison, 2019.