Welcome!

I am an Assistant Professor in the Department of Epidemiology & Biostatistics at the University of California, San Francisco (UCSF). My independent research program addresses the statistical challenges that arise in highly structured data generated in biomedical activities such as imaging. This includes 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. In addition, I maintain a wide set of collaborations with clinical and public health researchers at UCSF in the areas neurology, orthopedics, and HIV/AIDS.

My methodological research is sponsored by extramural grants from the National Institutes of Health Institute of Neurological Disorders and Stroke and the National Science Foundation (Division of Mathematical Sciences).

Prior to my arrival at UCSF, I received a doctorate from the Department of Biostatistics at the University of California, Los Angeles (UCLA) under the advisement of Dr. Damla Senturk and a BA in Biochemistry from Columbia University.

For more information, please see my CV.

News

January 2024: We’re hiring! Dr. Rajarshi Guhaniyogi and I are are seeking a postdoctoral research associate position at the Department of Statistics at Texas A&M University (starting as early as May 2024) for a NIH-funded research program. The research is related to one or more of the following areas: Bayesian learning with heterogeneous objects (e.g., tensor, 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.
December 2023: We’re funded! I was awarded a NIH/NINDS R01 grant titled “Bayesian Object-Oriented Modeling of Multi-Modal Imaging Data”. This is joint work with Dr. Rajarshi Guhaniyogi.
September 2023: Our paper “A bayesian covariance based clustering for high-dimensional tensors” is accepted with minor revisions in Technometrics.
September 2023: Our paper “Bayesian adaptive design for covariate-adaptive historical control information borrowing” is published in Statistics and Medicine. The manuscript can be viewed here.
June 2022: 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.
April 2022: We’re funded! I was awarded a NSF DMS grant titled “Use of Random Compression Matrices For Scalable Inference in High Dimensional Structured Regressions”. This is joint work with Dr. Rajarshi Guhaniyogi. The full project description can be found here.