Iain Carmichael

Brief Bio

I am a Neyman Visiting Assistant Professor in the Department of Statistics at UC Berkeley. Previously, I completed a postdoc in the Division of Computational Pathology at Brigham and Women's Hospital/Harvard Medical School and a NSF Mathematical Sciences Postdoctoral Research Fellowship in the Department of Statistics at the University of Washington. I received my PhD in Statistics from the University of North Carolina at Chapel Hill.

I’m broadly interested in developing statistical/machine learning algorithms for data with a complex structure such as networks, images, and multi-modal data. My research aims to solve challenging computational and data analytic problems in biomedical sciences such as the analysis of very large scale medical images like whole slide images from Pathology. The computational arm of my work weaves together statistics, probability, optimization, deep learning, and software engineering. The scientific arm of my work investigates both basic science questions and clinical translational problems through intensive collaborations with doctors and domain scientists.

Representative work

• Carmichael, I., Song, A.H., Chen, R.J., Williamson, D.F.K., Chen, T.Y., Mahmood, F. (2022). Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling. The International Conference on Medical Image Computing and Computer Assisted Intervention. To appear.


• Carmichael, I. (2021). The folded concave Laplacian spectral penalty learns block diagonal sparsity patterns with the strong oracle property. Under review.


• Carmichael, I., Keefe, T., Giertych, N., Williams, J.P. (2021). yaglm: a Python package for fitting and tuning generalized linear models that supports structured, adaptive and non-convex penalties.


• Carmichael, I., Calhoun, B.C., Hoadley, K.A., Troester, M.A., Geradts, J., Couture, H.D., Olsson, L,. Perou, C.M., Niethammer, M., Hannig, J., Marron, J.S. (2021). Joint and individual analysis of breast cancer histologic images and genomic covariates. The Annals of Applied Statistics, 15(4), pp.1697-1722.


• Carmichael, I. (2020). Learning sparsity and block diagonal structure in multi-view mixture models. Under review.


• Banerjee, S., Bhamidi, S., Carmichael, I. (2022). Fluctuation bounds for continuous time branching processes and nonparametric change point detection in growing networks. The Annals of Applied Probability. To appear.