Research

• develop cutting-edge machine learning methods for large datasets
• high-performance and cloud computing, statistical software
• Gaussian processes, Bayesian inference, neural networks, deep learning
• image processing for remote sensing, climate model, and medical applications

I. Use neural networks for fast covariance parameter estimation

• reduces the computing time for covariance parameter estimation of Gaussian process models by at least a factor of 100
• the TensorFlow implementation features a customized data generator defined using the Python API
• data illustration using climate model output from the LENS project

Gerber F., Nychka D. W. Fast covariance parameter estimation of spatial Gaussian process models using neural networks, Stat, 2022

II. Parallel computing for Gaussian process models

• a Gaussian process model is fitted to a spatial dataset with 5 million observations
• high-performance computing with 512 CPUs in parallel is used
• uses cross-validation and the division of the domain in overlapping subsets

Gerber F., Nychka D. W. Parallel cross-validation: a scalable fitting method for Gaussian process models, Computational Statistics & Data Analysis, 2021

III. Predicting missing values in spatio-temporal remote sensing data

• predictions are obtained by selecting subsets, sorting procedures, and quantile regression
• the workload can be distributed to several computers
• the method is available in the R package gapfill available on CRAN

Gerber F., de Jong R., Schaepman M. E., Schaepman-Strub G., Furrer R. Predicting Missing Values in Spatio-Temporal Remote Sensing Data, IEEE Transactions on Geoscience and Remote Sensing, 2018

IV. 64-bit sparse matrices with the R package spam

• the R packages spam and spam64 provide methods for sparse matrices with more than 2^31 non-zero elements
• the R package dotCall64 provides an enhanced interface to link R with compiled code
• the new functionality of the R packages is illustrated with a non-stationary spatial model

Gerber F., Mösinger K., Furrer R. Extending R Packages to Support 64-bit Compiled Code: An Illustration with spam64 and GIMMS NDVI3g Data, Computers & Geoscience, 2016

Gerber F., Mösinger K, Furrer R. dotCall64: An Efficient Interface to Compiled C/C++ and Fortran Code Supporting Long Vectors, SoftwareX, 2018

V. Bayesian inference and medical applications

• assessment of inference methods for Bayesian hierarchical models
• spatial models for areal count data
• R package gsbDesign for operating characteristics of Bayesian clinical trial designs

Gerber F., Furrer R. Pitfalls in the Implementation of Bayesian Hierarchical Modeling of Areal Count Data: An Illustration Using BYM and Leroux Models, Journal of Statistical Software, 2015
Gerber F., Gsponer T. gsbDesign: An R Package for Evaluating the Operating Characteristics of a Group Sequential Bayesian Design, Journal of Statistical Software, 2016
Gsponer T., Gerber F., Bornkamp B., Ohlssen D., Vandemeulebroecke M., Schmidli H. Bayesian Group Sequential Designs for Clinical Trials and their Operating Characteristics, Pharmaceutical Statistics, 2014

Software

Publications

[16] Gerber, F. and Nychka, D. W. Fast covariance parameter estimation of spatial Gaussian process models using neural networks. Stat, 2022. doi: 10.1002/sta4.382

[15] Roman, F. Gerber, F., Schmid, B, and Furrer, R. Identification of dominant features in spatial data. Spatial Statistics, 2021. doi: 10.1016/j.spasta.2020.100483

[14] Gerber, F., and Nychka, D. W. Parallel cross-validation: a scalable fitting method for Gaussian process models. Computational Statistics & Data Analysis, 2021. doi: 10.1016/j.csda.2020.107113

[13] Gerber, F., and Furrer, R. optimParallel: An R package providing a parallel version of the L-BFGS-B optimization method. The R Journal, 2019. doi: 10.32614/RJ-2019-030

[12] Heaton, M. J., Datta, A., Finley, A., Furrer, R., Guinness, J., Guhaniyogi, R., Gerber, F., Gramacy, R. B., Hammerling, D., Katzfuss, M., Lindgren, F., Nychka, D. W., Sun, F., and Zammit-Mangion, A. A case study competition among methods for analyzing large spatial data. Journal of Agricultural, Biological and Environmental Statistics, 2018. doi: 10.1007/s13253-018-00348-w

[11] Warren, B. H., Hagen, O., Gerber, F., Thébaud, C., Paradis, E., and Conti, E. Evaluating alternative explanations for an association of extinction risk and evolutionary uniqueness in multiple insular lineages. Evolution, 2018. doi: 10.1111/evo.13582

[10] Gerber, F., Mösinger, K., and Furrer, R. dotCall64: An efficient interface to compiled C/C++ and Fortran code supporting long vectors. SoftwareX, 2018. doi: 10.1016/j.softx.2018.06.002

[9] Gerber, F., de Jong, R., Schaepman, M. E., Schaepman-Strub, G., and Furrer, R. Predicting missing values in spatio-temporal remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 2018. doi: 10.1109/TGRS.2017.2785240

[8] Gerber, F., Mösinger, K., and Furrer, R. Extending R packages to support 64-bit compiled code: An illustration with spam64 and GIMMS NDVI_3g data. Computers & Geosciences, 2017. doi: 10.1016/j.cageo.2016.11.015

[7] Gerber, F. and Gsponer, T. gsbDesign: An R package for evaluating the operating characteristics of a group sequential Bayesian design. Journal of Statistical Software, 2016. doi: 10.18637/jss.v069.i11

[6] Bratulic, S., Gerber, F., and Wagner, A. Mistranslation drives the evolution of robustness in tem-1 β-lactamase. Proceedings of the National Academy of Sciences, 2015. doi: 10.1073/pnas.1510071112

[5] Gerber, F. and Furrer, R. Pitfalls in the implementation of Bayesian hierarchical modeling of areal count data: An illustration using BYM and Leroux models. Journal of Statistical Software, 2015. doi: 10.18637/jss.v063.c01

[4] Bashir, T., Sailer, C., Gerber, F., Loganathan, N., Bhoopalan, H., Eichenberger, C., Grossniklaus, U., and Ramamurthy, B. Hybridization alters spontaneous mutation rates in a parent-of-origin-dependent fashion in Arabidopsis. Plant Physiology, 165(1):424–437, 2014. doi: 10.1104/pp.114.238451

[3] Gsponer, T., Gerber, F., Bornkamp, B., Ohlssen, D., Vandemeulebroecke, M., and Schmidli, H. A practical guide to Bayesian group sequential designs. Pharmaceutical Statistics, 2014. doi: 10.1002/pst.1593

[2] Gerber, F., Marty, F., Eijkel, G. B., Basler, K., Brunner, E., Furrer, R., and Heeren, R. M. A. Multiorder correction algorithms to remove image distortions from mass spectrometry imaging data sets. Analytical Chemistry, 2013. doi: 10.1021/ac402018e

[1] Wandeler, G., Gerber, F., Rohr, J., Chi, B., Orrell, C., Chimbetete, C., Prozesky, H., Boulle, A., Hoffmann, C., Gsponer, T., Fox, M., Zwahlen, M., Egger, M., and IeDEA S. A. Tenofovir or Zidovudine in second-line antiretroviral therapy after Stavudine failure in Southern Africa. Antiviral Therapy, 2013. doi: 10.3851/imp2710

Family

Angela's illustrations