Consulting for Statistics, Computing and Analytics Research (CSCAR) provides individualized support and training to University of Michigan researchers in a variety of areas relating to the management, collection, and analysis of data. CSCAR also supports the use of technical software and advanced computing in research. Researchers from nearly all disciplines at U-M have made effective use of our services.

Our scope is broad, including formal statistical analysis, management of large data sets, development and optimization of computing code, data visualization, predictive modeling, geographic information systems, and text analysis, among other areas.  See our areas of expertise page for more details.

Many of our services are free to the U-M research community.  Contact us at with administrative questions.  Technical questions can be sent to the email addresses listed on our contacts page.

The CSCAR Team

Chris Andrews

CSCAR Consultant

Expertise: Statistical modeling, particularly survival analysis, all forms of regression, programming in R and SAS; applications to clinical trials, administrative claims data, and some “omics” areas.

Jesse Cordoba

CSCAR Administrative Assistant

Expertise: Administration, event planning.

Josh Errickson

CSCAR Consultant

Expertise: Statistical modeling and computation; causal inference, data imputation, basic to advanced regression analysis, resampling approaches, multivariate analysis, complex surveys, programming in Stata and R.

Alexander Gaenko

CSCAR Consultant

Expertise: Software development and software engineering (build systems, version control, test-driven development, continuous integration), parallelization and performance analysis, multiple-language development (e.g., Fortran-C-Python integration); C, C++, Fortran, Perl, Make, MPI, OpenMP, OpenACC.

Sharon Broude Geva

Director for Innovation and Computational Research


Brenda Gillespie

CSCAR Associate Director, Research Associate Professor of Biostatistics

Expertise: Survival analysis, including interval/left-censored data, designed experiments; generalized linear models; mixed and multilevel models; multiple imputation; applications to clinical trials, programming in SAS and R.

James Henderson

CSCAR Consultant

Expertise: Statistical modeling, statistical computation including parallel and high performance computing, programming in R and Python; applications to physical and biological sciences, health services research, administrative claims data.

Andrew Hlynka

CSCAR Consultant

Expertise: Software development in C#, Java, and C, Android mobile app development, 3D graphics animation / interactive games and programming (OpenGL, Blender3D, Unity3D, Unreal Engine), agent-modeling for artificial intelligence, machine learning, genetic algorithms.

Hyungjin Myra Kim

CSCAR Research Scientist, Adjunct Professor of Biostatistics

Expertise: Advanced biostatistics, clinical trials, basic to advanced regression, multilevel modeling, marginal structure modeling, missing data methods, longitudinal data, causal inference, programming in Stata, R, and SAS; applications to administrative claims data, pharmaco-epidemiology.

Corey Powell

CSCAR Consultant

Expertise: Basic to intermediate statistics, all forms of regression analysis, multivariate methods, programming in R, SAS, and Perl; applications to biostatistics and bioinformatics.

Kerby Shedden

CSCAR Director, Professor of Statistics and Biostatistics

Expertise: Statistical methods for analyzing complex data, including basic to advanced regression, multivariate methods, large-scale data analysis using HPC, development of statistical software in Python and Go, applications to genomics and human biology.

Gregory Teichert

CSCAR Consultant

Expertise: Scientific computing using the Finite Element Method, software programming (C++, Python), machine learning with Deep Neural Networks, high performance computing, applications to mechanical engineering.

Manish Verma

CSCAR Consultant

Expertise: Spatial data analysis, GIS, terrestrial remote sensing, image processing, spatial statistics, programming in Python, R, Arc/Gis, and Matlab; applications to earth science, natural resources, and public health.