Portrait of Aaron Wolfe Scheffler

Aaron Wolfe Scheffler

Associate Professor · Department of Epidemiology & Biostatistics
University of California, San Francisco

Statistical methods for highly structured biomedical data — disease progression models, multimodal neuroimaging, and functional data analysis.

I am an Associate Professor in Residence in the Department of Epidemiology & Biostatistics at the University of California, San Francisco. My research program addresses the statistical challenges that arise in highly structured biomedical data — disease progression models, joint models of multi-modal brain images, high-dimensional regressions, functional data analysis, and curve registration and warping.

I am a core statistician for several centers at the UCSF Memory and Aging Center, including the Alzheimer’s Disease Research Center and the ALBA Language Neurobiology Laboratory, and I hold faculty affiliations in Computational Precision Health, the Bakar Computational Health Sciences Institute, and the Center for Intelligent Imaging. I maintain a wide set of collaborations with clinical and public health researchers at UCSF in neurology, orthopedics, and HIV/AIDS.

My methodological research is supported by an NIH/NINDS R01 on Bayesian object-oriented modeling of multi-modal imaging data, and I serve as a Multiple Principal Investigator and Associate Program Director for Biostatistics and AI on the UCSF CTSA K12 program.

Prior to UCSF I received a doctorate from the Department of Biostatistics at UCLA under the advisement of Dr. Damla Senturk, and a BA in Biochemistry from Columbia University.

News

  1. July 2026Announcement
    I have been promoted to Associate Professor in Residence in the Department of Epidemiology & Biostatistics at UCSF.
  2. July 2026Funding
    We’re funded! I am part of Team ATLAS, awarded a Dementia Frontiers Fund grant from Alzheimer’s Research UK and Gates Ventures. The team is led by Professor Duygu Tosun (University of California, San Francisco) and Dr Oliver Robinson (Imperial College London), and brings together researchers from the US, UK and Spain to study the amyloid-to-tau interval and the factors that accelerate or delay the onset of symptoms. More information can be found here.
  3. July 2026Funding
    We’re funded! We were awarded an NIH/NCATS K12 grant titled “CTSA K12 Program at UCSF.” I serve as a Multiple Principal Investigator and Associate Program Director for Biostatistics and AI.
  4. January 2026PositionFilled
    We’re hiring! Dr. Rajarshi Guhaniyogi and I are seeking a postdoctoral research associate for an NIH-funded research program, beginning September 2026. The research relates to one or more of the following areas: Bayesian learning with heterogeneous objects (e.g. tensor and functional data); Bayesian interpretable deep learning with heterogeneous objects; distributed Bayesian computation and federated learning with Gaussian processes and their variants; and data sketching with random sketching matrices for efficient Bayesian inference with massive structured data. Please e-mail me directly for more information.
  5. 2025Publication
    Our paper “Sketching in high-dimensional regression with big data using Gaussian scale mixture priors” is published in the Journal of Machine Learning Research. This is joint work with Dr. Rajarshi Guhaniyogi. The manuscript can be viewed here.
  6. 2025Publication
    Our paper “Multi-object data integration in the study of primary progressive aphasia” is published in The Annals of Applied Statistics. The manuscript can be viewed here.
  7. April 2024Funding
    We’re funded! I am a Co-Investigator on an NIH/NIA P30 grant titled “New Approaches to Dementia Heterogeneity” (PI: Rabinovici).
  8. 2024Publication
    Our paper “A Bayesian covariance based clustering for high-dimensional tensors” is accepted in Technometrics.
  9. January 2024PositionFilled
    We’re hiring! Dr. Rajarshi Guhaniyogi and I are seeking a postdoctoral research associate for an NIH-funded research program at the Department of Statistics, Texas A&M University, starting as early as May 2024. The research relates to one or more of the following areas: Bayesian learning with heterogeneous objects (e.g. tensor and functional data); Bayesian interpretable deep learning with heterogeneous objects; distributed Bayesian computation and federated learning with Gaussian processes and their variants; and data sketching with random sketching matrices for efficient Bayesian inference with massive structured data. Please e-mail me directly for more information.
  10. August 2023Funding
    We’re funded! I am a Co-Investigator on an NIH/NIA P01 grant titled “Frontotemporal Dementia: Genes, Images, and Emotions” (PI: Gorno-Tempini).
  11. June 2023Funding
    We’re funded! I am a Co-Investigator on an NIH/NIAMS R01 grant titled “Mechanistic Structure-Function Relationships for Paraspinal Muscle Fat Infiltration in Chronic Low Back Pain Patients” (PI: Bailey).
  12. April 2023Funding
    We’re funded! I was awarded an NIH/NINDS R01 grant titled “Bayesian Object-Oriented Modeling of Multi-Modal Imaging Data.” This is joint work with Dr. Rajarshi Guhaniyogi.
  13. 2023Publication
    Our paper “Bayesian adaptive design for covariate-adaptive historical control information borrowing” is published in Statistics in Medicine. The manuscript can be viewed here.
  14. 2023Publication
    Our book chapter “Modeling longitudinal trends in event-related potentials” is published.
  15. June 2022Funding
    We’re funded! I was awarded an NSF DMS grant titled “Use of Random Compression Matrices for Scalable Inference in High Dimensional Structured Regressions.” The full project description can be found here.
  16. 2022Publication
    Our paper “Multilevel hybrid principal components analysis for region-referenced functional electroencephalography data” is published in Statistics in Medicine. The manuscript can be viewed here.
  17. 2022Publication
    Our paper “Covariate-adjusted hybrid principal components analysis for region-referenced functional EEG data” is published in Statistics and its Interface. The manuscript can be viewed here.
  18. 2020Publication
    Our paper “Hybrid principal components analysis for region-referenced longitudinal functional EEG data” is published in Biostatistics. The manuscript can be viewed here.
  19. 2019Publication
    Our paper “Covariate-adjusted region-referenced generalized functional linear model for EEG data” is published in Statistics in Medicine. The manuscript can be viewed here.
  20. 2017Publication
    Our paper “A multi-dimensional functional principal components analysis of EEG data” is published in Biometrics. The manuscript can be viewed here.