# Selected papers

* [Sharp bounds for max-sliced Wasserstein distances](https://arxiv.org/abs/2403.00666)  
M. T. Boedihardjo

* [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

* [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)