Timothy D. Johnson is a Professor of Biostatistics. He received his PhD in Biostatistics from UCLA in 1997. He was a senior statistician in the UCLA department of Biomathematics from 1997-1998 and an adjunct assistant professor from 1998-2001. He also served as a consultant to the John Wayne Cancer Institute from 1997-2000. Dr.Johnson joined the faculty at the University of Michigan in 2001, where he is also a member of the biostatistics core of the Comprehensive Cancer Center.
- PhD, Biostatistics, University of California, Los Angeles, 1997
- M.S., Mathematics, University of California, Riverside, 1986
- B.S., Mathematics, University of California, Riverside, 1984
Research Interests & Projects
- Bayesian methods and MCMC. Statistical image analysis. Spatial point processes. Statistical modeling of biomedical data. Applications in neuroscience, cancer, radiology, radiation oncolocy, Psychology/Psychiatry and endocrinology.
- Samartsidis, P., Eickhoof, C. R., Eickhoff, S. B., Wager, T. D., Feldman Barrett, S., Atzil, S., Johnson, T. D., Nichols, T. E. (2018) Bayesian log-Gaussian Cox process regression: with applications to meta-analysis of neuroimaging working memory studies. JRSSC
- Teng, M., Johnson, T. D., Nathoo, F. S. (2018) Time Series Analysis of fMRI Data: Spatial Modelling and Bayesian Computation. Statistics in Medicine 37 2753–2770. DOI: 10.1002/sim.7680
- Liu, Z., Bartsch, A. J., Berrocal, V. J., Johnson, T. D. (2018) A mixed-effects, spatially varying coefficients model with application to multi-resolution functional magnetic resonance imaging data. Statistical Methods in Medical Research DOI: 10.1177/0962280217752378
- Montagna, S., Wager, T., Feldman Barrett, L., Johnson, T. D., Nichols, T. E. (2017) Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data. Biometrics PMCID: PMC5682245.
- Samartsidis, P., Montagna, S., Johnson, T. D., Nichols, T. E. (2017) The Coordinate-Based Meta- Analysis of Neuroimaging Data. Statistical Science 32(4) 580–599. DOI: 10.1214/17-STS624, PM- CID: PMC5849270.
- Teng, M., Nathoo, F., Johnson, T. D. (2017) Bayesian Computation for Log-Gaussian Cox Pro- cesses: A Comparative Analysis of Methods. Journal of Statistical Computation and Simulation 87:11, 2227–2252, DOI: 10.1080/00949655.2017.1326117, PMCID: PMC5708893.
- ∗‡Liu, Z., Berrocal, V. J., Bartsch, A. J., Johnson, T. D. (2016) Pre-surgical fMRI data analysis using a spatially adaptive conditionally autoregressive model. Bayesian Analysis 11(2), 599–625. PMCID: PMC4814103.
- Carlson, N. E., Grunwald, G. K., Johnson, T. D. (2016) Using Cox cluster processes to model the pulse generating mechanism driving time series of hormone data. Biostatistics 17(2), 320–333. doi:10.1093/biostatistics/kxv046.
- Wager, T. D., Kang, J., Johnson, T. D., Nichols, T. E., Satpute, A., Feldman Barrett, L., (2015) A Bayesian model of category-specific emotional brain responses. PLoS Computational Biology 11(4): e1004066. doi:10.1371/journal.pcbi.1004066. PMCID: PMC4390279
- ∗‡Kang, J., Nichols, T. E., Wager, T. D., Johnson, T. D. (2014) A Bayesian hierarchical spa- tial point process model for multi-type neuroimaging meta-analysis, AOAS 8, 1800–1824. PMCID: PMC4241351
∗—From my student’s dissertation, ‡—Stats
- Member, International Society of Bayesian Analysis
- Member, American Statistical Association
- Member, Delta Omega Society (scholarly society in public health)
- Member, Eastern North American Region of the International Biometrics Society