# Selected papers
* [Sharp bounds for max-sliced Wasserstein distances](https://arxiv.org/abs/2403.00666)
M. T. Boedihardjo
Foundations of Computational Mathematics (to appear)
* [Private measures, random walks, and synthetic data](https://link.springer.com/content/pdf/10.1007/s00440-024-01279-z.pdf)
M. Boedihardjo, T. Strohmer, R. Vershynin
Probability Theory and Related Fields (2024)
* [Matrix Concentration Inequalities and Free Probability](https://web.math.princeton.edu/~rvan/mconcfree-230228.pdf)
A. S. Bandeira, M. T. Boedihardjo, R. van Handel
Inventiones Mathematicae (2023)
* [Covariance's Loss is Privacy's Gain: Computationally Efficient, Private and Accurate Synthetic Data](https://link.springer.com/article/10.1007/s10208-022-09591-7)
M. Boedihardjo, T. Strohmer, R. Vershynin
Foundations of Computational Mathematics (2022)
* [C*-algebras isomorphically representable on $l^p$](https://arxiv.org/abs/1812.11165)
M. T. Boedihardjo
Analysis & PDE (2020)
# Other papers
* [Embedding $C^{*}$-algebras into the Calkin algebra of $\ell^{p}$](https://arxiv.org/abs/2409.07386)
M. T. Boedihardjo
Journal of Functional Analysis (2024)
* [Metric geometry of the privacy-utility tradeoff](https://arxiv.org/abs/2405.00329)
M. Boedihardjo, T. Strohmer, R. Vershynin
* [Covariance loss, Szemeredi regularity, and differential privacy](https://arxiv.org/abs/2301.02705)
M. Boedihardjo, T. Strohmer, R. Vershynin
Proceedings of the American Mathematical Society (to appear)
* [Privacy of synthetic data: a statistical framework](https://arxiv.org/abs/2109.01748)
M. Boedihardjo, T. Strohmer, R. Vershynin
IEEE Transactions on Information Theory (2022)
* [Private sampling: a noiseless approach for generating differentially private synthetic data](https://arxiv.org/abs/2109.14839)
M. Boedihardjo, T. Strohmer, R. Vershynin
SIAM Journal on Mathematics of Data Science (2022)
* [A Performance Guarantee for Spectral Clustering](https://arxiv.org/abs/2007.05627)
M. Boedihardjo, S. Deng, T. Strohmer
SIAM Journal on Mathematics of Data Science (2021)
* [Estimation of expected value of function of i.i.d. Bernoulli random variable](https://arxiv.org/abs/1909.05837)
M. T. Boedihardjo
* [Multiplication operators on $L^p$](https://arxiv.org/abs/1707.04798)
M. T. Boedihardjo
Studia Mathematica (2019)
* [Similarity of operators on $l^p$](https://arxiv.org/abs/1706.08582)
M. T. Boedihardjo
* [Calkin representations for $L^p$](https://arxiv.org/abs/1707.09658)
M. T. Boedihardjo
Integral Equations Operator Theory (2019)
* [On algebra-valued R-diagonal elements](https://arxiv.org/abs/1512.06321)
M. Boedihardjo and K. Dykema
Houston Journal of Mathematics (2018)
* [Asymptotic *-moments of some random Vandermonde matrices](https://www.sciencedirect.com/science/article/pii/S0001870816302870)
M. Boedihardjo and K. Dykema
Advances in Mathematics (2017)
* [Remarks on “Weak limits of almost invariant projections” by Foias, Pasnicu and Voiculescu](https://arxiv.org/abs/1411.3343)
M. T. Boedihardjo
Operators and Matrices (2017)
* [A coordinate free characterization of certain quasidiagonal operators](https://arxiv.org/abs/1401.5829)
M. T. Boedihardjo
Indiana University Mathematics Journal (2015)
* [On mean ergodic convergence in the Calkin algebras](https://www.ams.org/journals/proc/2015-143-06/S0002-9939-2015-12432-X/S0002-9939-2015-12432-X.pdf)
M. T. Boedihardjo and W. B. Johnson
Proceedings of the American Mathematical Society (2015)
# Selected seminar talks
* Max-sliced Wasserstein distances
Stochastics Seminar, Georgia Institute of Technology, 25 April 2024
* Covariance Loss and privacy
One World MINDS Seminar, 14 September 2023
* Spectral norm and strong freeness
Probabilistic Operator Algebra Seminar, University of California, Berkeley, 21 August 2023
* Spectral norm of random matrices
Data Science Seminar, University of Minnesota, 24 January 2023
* Sharp Matrix Concentration
Department Seminar, Department of Statistics and Data Science, Yale University, 7 November 2022
* Spectral norms of Gaussian matrices with correlated entries
Probability Seminar, University of California, San Diego, 29 April 2021
# Selected conference talks
* Covariance loss and privacy
ICERM, Connecting Higher-Order Statistics and Symmetric Tensors, 9 January 2024
* Private synthetic data
FOCM 2023, Computational Harmonic Analysis and Data Science, 16 June 2023
* Sharp Matrix Concentration
Simons Institute, Deep Learning Theory Symposium, 6 December 2021
* Non-Commutative Concentration Inequalities (cont.)
Oberwolfach, Applied Harmonic Analysis and Data Science, 1 December 2021
* Brown-Douglas-Fillmore theorem for l^p
Great Plains Operator Theory Symposium, 28 May 2019 (Plenary Speaker)
# Employment
2023-present: Assistant Professor, Michigan State University
2022-2023: Postdoctoral Fellow, ETH Zurich
2021-2022: Visiting Assistant Professor, University of California, Irvine
2019-2021: Assistant Adjunct Professor, University of California, Los Angeles
2016-2019: Hedrick Assistant Professor, University of California, Los Angeles
# Education
2012-2016: Ph.D. in Mathematics, Texas A&M University
Advisor: [Bill Johnson](https://en.wikipedia.org/wiki/William_B._Johnson_(mathematician)), Co-Advisor: [David Kerr](https://www.uni-muenster.de/FB10srvi/persdb/MM-member.php?id=1539)
Other committee members: [Ciprian Foias](https://en.wikipedia.org/wiki/Ciprian_Foias), [Ron Douglas](https://en.wikipedia.org/wiki/Ronald_G._Douglas), [Thomas Schlumprecht](https://people.tamu.edu/~t-schlumprecht), [Tony Cahill](https://engineering.tamu.edu/civil/profiles/acahill.html)
2011-2012: Visiting Scholar, Texas A&M University
2007-2011: B.Sc. and M.Phil in Mathematics, Hong Kong Baptist University
Advisor: [Hermann Brunner](https://www.mun.ca/math/our-people/faculty/professores-emeriti/hermann-brunner)