Research Interests

  • Multi-view data/data integration
  • Networks
  • Applied probability
  • Machine learning interpretability
  • Applications coming from: the law, computational neuroscience and breast cancer pathology


Publications

In Preparation
  • Iain Carmichael. (2020). Multi-view mixture models with sparsity and block diagonal constraints.

  • Iain Carmichael. (2020). Subspace geometry of multi-view dimensionality reduction algorithms.

  • Iain Carmichael, Jung Meilei, Marron, J.S. (2020). Python, R and Matlab packages for angle-based joint and individual variation explained. (python, R, Matlab)
Under Review
Published


Conference Presentations and Posters




Talks

Slides which are not linked below are available upon request.
  • Joint and individual analysis of breast cancer histologic images and genomic covariates, Harvard Medical School. Boston, MA. December, 2019. (Slides)

  • Joint and individual analysis of histopathology images and genetic covariates. Computational Medicine group, UNC Chapel Hill. May, 2019.

  • Angle-based Joint and Individual Variation Explained with Applications to Image and Genetic Data. University of Illinois Urbana-Champaign, Department of Statistics. Urbana, IL. February, 2019.

  • Angle-based Joint and Individual Variation Explained with Applications to Image and Genetic Data. University of Wisconsin-Madison, Department of Statistics. Madison, WI. January, 2019.

  • Angle-based Joint and Individual Variation Explained with Applications to Image and Genetic Data. Harvard University, Department of Biostatistics. Boston, MA. January, 2019.

  • Joint analysis of H&E stained images and genetic covariates using deep learning and AJIVE. GenStat group, UNC Chapel Hill. September, 2018.

  • Data Science and the Undergraduate Curriculum. UNC STOR Department Colloquium. Chapel Hill, NC. October, 2017. (Slides)

  • Open Data, Networks and the Law by Iain Carmichael and Michael Kim. PyData Carolinas, 2016.


Past Projects


  • Probabilistic Programming (summer 2016)
    Summer internship at Gamalon Machine Intelligence. Worked on theoretical problems in probabilistic programming and contributed to the development of Chimpy, a probabilistic programming language in Python.

  • Regional Coverage from Space Systems (summer 2012)
    Developed algorithms for satellite regional coverage problems at the NSF sponsored RIPS program. Collaboration with Institute for Pure and Applied Mathematics and the Aerospace Corporation.