Matthew Reimherr

Associate Professor of Statistics
Matt Reimherr
Matthew
Reimherr
Associate Professor of Statistics
Interests
Functional Data Analysis
Longitudinal Data Analysis
Statistical Genetics
Address
Phone
(814) 865-2544
Website
Personal Site

Biography

Matthew Reimherr is an Associate 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.

Reimherr is an Associate Editor for Statistical Modelling, Journal of Multivariate Analysis, and Annals of of Applied Statistics . He is affiliated with the CBIOS Training program, the B2D2K Training program, the Center for Computational Biology and Bioinformatics , the Center for Mathematical Biology , the Center for Medical Genomics, and the Center of Machine Learning and Applications. He joined Penn State as an Assistant Professor in 2013 before being promoted to Associate Professor in 2019.

 

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