Matthew Reimherr

Biography

Matthew Reimherr is a Principal Research Scientist at Amazon and an Affiliate Professor of Statistics at Penn State.

Reimherr received his Ph.D. in Statistics from the University of Chicago in 2013. He received his M.S. in Statistics from the University of Utah in 2008 and his B.S. in Statistics in 2006.

His research interests include Functional Data Analysis, Longitudinal Data Analysis, and Data Privacy with applications to Omics data.

Honors and Awards

• Noether Young Scholar Award, 2019
• Simons-Berkeley Research Fellow, 2019
• Harper Dissertation Fellowship, 2012
• Canadian Journal of Statistics Award, 2010

Publications

• M. Reimherr and J. Awan. Elliptical perturbations for differential privacy. In Advances in Neural Information Processing Systems, pages 10185–10196, 2019a.
   
• M. Reimherr and J. Awan. Kng: The k-norm gradient mechanism. In Advances in Neural Information Processing Systems, pages 10208–10219, 2019b.
   
• A. Mirshani and M. Reimherr. Formal privacy for functional data with Gaussian pertur- bations. In Proceedings of the 36th International Conference on Machine Learning, 2019b.
   
• J. Awan, A. Kenney, M. Reimherr, and A. Slavkovi´ c. Benefits and pitfalls of the expo- nential mechanism with applications to hilbert spaces and functional pca. In International Conference on Machine Learning, pages 374–384, 2019.
   
• M. Reimherr, B. Sriperumbudur, and B. Taoufik. Optimal prediction for additive function- on-function regression. Electronic Journal of Statistics, 12(2):4571–4601, 2018.
   
• A. Parodi and M. Reimherr. Simultaneous variable selection and smoothing for high- dimensional function-on-scalar regression. Electronic Journal of Statistics, 12(2):4602– 4639, 2018.
   
• S. Craig, D. Blankenberg, A. Parodi, I. Paul, L. Birch, J. Savage, M. Marini, J. Stokes, A. Nekrutenko, M. Reimherr, F. Chiaromonte, and K. Makova. Infant weight gain trajec- tories linked to oral microbiome composition. Scientific Reports, 8(1), 2018.
   
• H. Choi and M. Reimherr. A geometric approach to confidence regions and bands for func- tional parameters. Journal of the Royal Statistical Society: Series B (Statistical Methodol- ogy), 80(1):239–260, 2018.
   
• P. Constantinou, P. Kokoszka, and M. Reimherr. Testing separability of space-time func- tional processes. Biometrika, 104(2):425–437, 2017.
   
• M. Reimherr and D. Nicolae. Estimating variance components in functional linear models with applications to genetic heritability. Journal of the American Statistical Association, 111(513):407–422, 2016.

Teaching

Stat 200 - Elementary Statistics
Stat 416 - Stochastic Modeling
Stat 440 - Statistical Computing
Stat 462 - Applied Linear Regression
Stat 505 - Applied Multivariate Statistical Analysis
Stat 515 - Stochastic Processes and Monte Carlo Methods
Stat 597 - Functional Data Analysis