Division of Computational Pathology, Brigham and Women's Hospital
Harvard Medical School
Email: icarmichael@bwh.harvard.edu
GitHub: idc9
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Harvard Medical School
Email: icarmichael@bwh.harvard.edu
GitHub: idc9
Google Scholar
Brief Bio
I am a postdoc in Computational Pathology at Harvard Medical School. Previously I was a postdoctoral fellow in the Department of Statistics at the University of Washington supported by the NSF Mathematical Sciences Postdoctoral Research Fellowship. I received a PhD in statistics from the University of North Carolina at Chapel Hill where I was advised by Shankar Bhamidi and J.S Marron.My research aims to solve challenging computational problems in biomedical sciences such as the analysis of very large scale medical images like whole slide images from Pathology. I’m broadly interested in developing statistical machine learning algorithms for data with a complex structure such as networks, images, and multi-view/modal data. The computational arm of my work weaves together statistics/probability, optimization, deep learning, and software engineering. The scientific arm of my work involves intensive collaboration with domain scientists/doctors and investigates basic scientific questions as well as clinical translational problems.
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. (2018). Fluctuation bounds for continuous time branching processes and nonparametric change point detection in growing networks. Under review.