I am a Neyman Visiting Assistant Professor in the Department of Statistics at UC Berkeley and an Adjunct Visiting Assistant Professor in the Department of Pathology at UCSF. I completed a postdoc in the Division of Computational Pathology
at Brigham and Women's Hospital/Harvard Medical School and an NSF Mathematical Sciences
postdoctoral 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, multi-modal data integration, and high-resolution medical imgaging data. The major focus of my research is computational pathology
- 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.