About

I am an Assistant Professor in the Department of Statistics at George Mason University.

Prior to joining George Mason, I obtained a PhD in Statistics in 2020 from the University of British Columbia where I was advised by Dr. Gabriela Cohen Freue. Before that, I obtained a Master of Science in Statistics from the Vienna University of Technology under supervision by Prof. Peter Filzmoser.

My research agenda comprises methodological and computational aspects of robust estimation in high-dimensional problems as well as their application to Biomedical Sciences. I am working on statistical methods with reliable performance under the presence of adverse contamination anywhere in the numerous features of the data.

For regression problems, for instance, I work on estimators which are resilient to outliers in the response but also to unusual values in the (potentially) explanatory variables. If not handled appropriately, unusual values in the explanatory variables can have a much more detrimental affect on the analysis than outliers in the response alone.

Selected projects

Cherry trees in peak bloom.

Improving phenological modeling via participatory science

Can we guide the wisdom of the crowds to solve complex scientific problems?

We aim at improving phenological modeling by sourcing models from citizen scientists. The starting point for the project was our First International Cherry Blossom Prediction Competition, where we asked participants to predict the peak bloom date of cherry trees in four locations: Washington, D.C., Vancouver, BC, Kyoto, Japan, and Liestal-Weideli in Switzerland.

In the news

The 2021 edition of the prediction competition has been featured prominently in the news. For example, The Weather Network, CBC Radio, Public Radio's The World, the Vancouver Sun, and the Daily Hive reported on the competition and how it can inform future research.

Find out more about the competition
Observed log-concentration of P2O5 versus the predicted values from adaptive PENSE.

Robust estimation and variable selection in high-dimensional models

Outliers and contamination in data sets with many variables make common tasks like variable selection and parameter estimation a very daunting task. We are working on statistical methods and computational strategies for estimation, variable selection, and hyper-parameter selection to leverage as much information from the data set as possible, without being affected by misleading values.

Publications

Book Chapters

2022
D. Kepplinger and G. V. Cohen Freue, “Robust prediction and protein selection with adaptive PENSE,” in Statistical analysis of proteomic data: Methods and tools, T. Burger, Ed., in Methods in molecular biology., Springer US, 2022. doi: 10.1007/978-1-0716-1967-4. Author copy (PDF)
[B1]

Refereed Journal Papers

2025
D. Kepplinger and S. Wei, “Information sharing for robust and stable cross-validation,” Technometrics, 2025, doi: 10.1080/00401706.2025.2540970.
[R1]
Y. Long, G. Cao, D. Kepplinger, and L. Wang, “Robust mean signal estimation and inference for imaging data,” Statistica Sinica, 2025, doi: 10.5705/ss.202024.0402.
[R2]
R. Zahir, O. Gould, D. Kepplinger, R. Dave, and T. Lateef, “Clinical factors associated with opioid use in abortive headache treatment among adolescents in the emergency department: Who gets them and why,” Clinical Neurology and Neuroscience, pp. 13–19, 2025, doi: 10.11648/j.cnn.20250901.12.
[R3]
2024
A. Minciunescu, C. Rosner, D. Kepplinger, T. Del Castillo, D. Overbeck, W. S. Levy, C. M. O’Connor, and T. M. Haddad, “Novel initiative increasing GDMT use among patients with heart failure with reduced ejection fraction,” JACC: Heart Failure, vol. 12, no. 8, pp. 1487–1493, 2024, doi: 10.1016/j.jchf.2024.03.022.
[R4]
V. Vasas, M. C. Lowell, J. Villa, Q. D. Jamison, A. G. Siegle, P. K. R. Katta, P. Bhagavathula, P. G. Kevan, D. Fulton, N. Losin, D. Kepplinger, M. K. Yetzbacher, S. Salehian, R. E. Forkner, and D. Hanley, “Recording animal-view videos of the natural world using a novel camera system and software package,” PLOS Biology, vol. 22, no. 1, 2024, doi: 10.1371/journal.pbio.3002444.
[R5]
J. Leonard, D. Kepplinger, E. Torres, C.-H. Hu, R. Veneziano, and C. D. Hoemann, “Comparative analysis of Lox-1 and CD36 expression in human platelets and on circulating microparticles during ARDS-induced coagulopathy,” Thrombosis Research, 2024, doi: 10.1016/j.thromres.2024.109202.
[R6]
J. Auerbach, D. Kepplinger, and N. Rios, “What is data science? A closer look at science’s latest priority dispute,” Significance, vol. 21, pp. 11–14, 2024, doi: 10.1093/jrssig/qmae030.
[R7]
J. J. Chang, D. Kepplinger, E. J. Metter, Y. Kim, C. T. Trankiem, D. R. Felbaum, J. C. Mai, R. B. Mason, R. A. Armonda, and E. F. Aulisi, “Time thresholds for using pressure reactivity index in neuroprognostication for patients with severe traumatic brain injury,” Neurosurgery, pp. 297–304, 2024, doi: 10.1227/neu.0000000000002876.
[R8]
B. N. Tehrani, M. W. Sherwood, A. A. Damluji, K. C. Epps, H. Bakhshi, L. Cilia, I. Dassanayake, M. Eltebaney, R. Gattani, E. Howard, D. Kepplinger, A. Ofosu-Somuah, and W. B. Batchelor, “A randomized comparison of radial artery intimal hyperplasia following distal vs. Proximal transradial access for coronary angiography: PRESERVE RADIAL trial,” Journal of the American Heart Association, 2024, doi: 10.1161/JAHA.123.031504.
[R9]
2023
J. Leonard, D. Kepplinger, V. Espina, P. Gillevet, Y. Ke, K. G. Birukov, A. Doctor, and C. D. Hoemann, “Whole blood coagulation in an ex vivo thrombus is sufficient to induce clot neutrophils to adopt a myeloid-derived suppressor cell signature and shed soluble Lox-1,” Journal of Thrombosis and Haemostasis, pp. 1031–1045, 2023, doi: 10.1016/j.jtha.2023.12.014.
[R10]
J. J. Chang, D. Kepplinger, E. J. Metter, D. R. Felbaum, J. C. Mai, R. A. Armonda, and E. F. Aulisi, “Pressure reactivity index for early neuroprognostication in poor-grade subarachnoid hemorrhage,” Journal of the Neurological Sciences, no. 450, 2023, doi: 10.1016/j.jns.2023.120691.
[R11]
D. Kepplinger, “Robust variable selection and estimation via adaptive elastic net S-estimators for linear regression,” Computational Statistics & Data Analysis, vol. 183, 2023, doi: 10.1016/j.csda.2023.107730.
[R12]
2019
G. V. Cohen Freue, D. Kepplinger, M. Salibián-Barrera, and E. Smucler, “Robust elastic net estimators for variable selection and identification of proteomic biomarkers,” Annals of Applied Statistics, vol. 13, no. 4, pp. 2065–2090, 2019, IF: 1.80.
[R13]
2017
D. Kepplinger, M. Takhar, M. Sasaki, Z. Hollander, D. Smith, B. McManus, W. R. McMaster, R. T. Ng, and G. V. C. Freue, “PGCA: An algorithm to link protein groups created from MS/MS data,” PLoS ONE, vol. 12, no. 5, p. e0177569, 2017, doi: 10.1371/journal.pone.0177569.
[R14]
2013
D. Kepplinger, M. Templ, and S. Upadhyaya, “Analysis of energy intensity in manufacturing industry using mixed-effects models,” Energy, vol. 59, pp. 754–763, 2013, doi: 10.1016/j.energy.2013.07.003.
[R15]

Patents/Patent Applications

2024
C. D. Hoemann, J. A. Leonard, V. A. Espina, and D. Kepplinger, “Method for inducing and detecting soluble Lox-1 (sLox-1) in cultured blood clots,” U.S. Patent Application No. 18/517,066, 2024.
[P1]

Software

2024
D. Kepplinger, examinr: R package to create online exams from R markdown documents. (2024). Latest version 0.4.2. Available: https://dakep.github.io/examinr/
[S1]
D. Kepplinger, M. Salibián-Barrera, and G. V. Cohen Freue, pense: R package for fast computation of penalized elastic net S/MM-estimators of linear regression. (2024). Latest version 2.2.2; Downloads from CRAN since 01/01/2021: 29,505. doi: 10.32614/CRAN.package.pense.
[S2]
2022
D. Kepplinger, M. Salibián-Barrera, and G. V. Cohen Freue, pyinit: R package to compute peña-yohai initial estimates for robust S-regression. (2022). Latest version 1.1.3; Downloads from CRAN since 01/01/2021: 72,550. doi: 10.32614/CRAN.package.pyinit.
[S3]
2021
G. V. Cohen Freue and D. Kepplinger, pgca: R package implementing the PGCA algorithm to link protein groups created from MS/MS data. (2021). Latest version 1.28.0; Downloads from BioConductor since 01/01/2021: 10,832. doi: 10.18129/B9.bioc.pgca.
[S4]
2023
D. Kepplinger, gaselect: R package implementing genetic algorithms for variable selection using partial least squares. (2023). Latest version 1.0.22; Downloads from CRAN since 01/01/2021: 18,068. doi: 10.32614/CRAN.package.gaselect.
[S5]
2019
D. Kepplinger, complmrob: R package for robust linear regression with compositional covariates. (2019). Latest version 0.7.0; Downloads from CRAN since 01/01/2021: 17,364. doi: 10.32614/CRAN.package.complmrob.
[S6]

Lab members

Photo of Yang Long.

Yang Long

PhD Candidate at Mason

Yang Long is a member of my lab since 2021. His primary focus is on improving computation of robust regularized regression estimators by direct minimization of the non-convex objective function.

Personal website

Photo of Siqi Wei in Dubrovnik, Croatia.

Siqi Wei

PhD Candidate at Mason

Siqi Wei joined my lab in 2021 and currently works on improving hyper-parameter selection for robust regularized regression estimators and general non-convex estimators.

Personal website

Code

I am maintaining several stable R packages on CRAN and Bioconductor as well as a few experimental software tools available on my GitHub and GitLab pages.

pense

Implementation of penalized adaptive Elastic Net S/MM-Estimators of Regression

Robust penalized adaptive elastic net S- and MM-estimators for linear regression.

More info View on CRAN

examinr

Create online exams from R markdown documents.

Write online exams as R markdown documents and publish them as shiny app. Allows for randomized exams, different question types (including R coding questions), and grading of submissions.

More info

pyinit

Peña-Yohai Initial Estimator for Robust S-Regression

Fast and deterministic procedure to compute initial estimates for robust S-estimators of regression using as described in Peña-Yohai (1999).

View on Github View on CRAN

gaselect

Genetic algorithms for variable selection.

Multi-threaded genetic algorithms applicable to a wide range of variable selection methods, but particularly suited for Partial Least Squared Regression.

View on CRAN

complmrob

Robust Linear Regression with Compositional Covariates

Methods for robustly fitting regression models where the explanatory variables are compositional. Includes bootstrap methods for classical robust regression and compositional robust regression.

View on CRAN

nsoptim

Algorithms for non-smooth optimization

C++ template library, wrapped in an R package, providing modern and fast algorithms for optimizing non-smooth functions (e.g., L1 regularized objective functions).

View on GitLab

PGCA

Link Protein Groups Created from MS/MS Data

Protein Group Code Algorithm (PGCA) is a computationally inexpensive algorithm to merge protein summaries from multiple experimental quantitative proteomics data.

View on Bioconductor

Teaching

George Mason University

University of British Columbia