Caleb N. Ellington

Ph.D. Student @ SAILING Lab in CMU-Pitt Computational Biology, advised by Eric P. Xing
Previously: UW IPD / Indeed / Klavins Lab / Amazon

prof_pic.jpg

GHC 7603

Gates Hillman Complex, 4902 Forbes Ave

Pittsburgh, PA 15213

Check out my research on contextualized.ml, a statistical machine learning toolbox for estimating powerful biological models at per-patient or per-cell resolution, enabling truly personalized modeling for healthcare and precision diagnostics.

My research interests span meta-learning, multi-task learning, graphical models, and biological modeling. I primarily work on adaptive and sample-efficient machine learning systems that respond to new challenges by using knowledge gathered across many different environments. I’m particularly interested in using these tools to study heterogeneous diseases, like cancer and COVID-19, where disease behavior and pathological mechanisms vary from patient to patient. Meta-learning helps us understand how contexts like genetics and environmental factors create patient-specific disease behaviors and pathologies.

I also enjoy rock climbing, guitar, learning languages, my cats Mimi and Goma, and my rats Boogie and Disco. I’ve lived in Austin TX, Seattle WA, and Pittsburgh PA and I call them all home.

news

Apr 30, 2022 Our study on the variability of the effects COVID-19 treatment effectiveness is published in JBI.
Apr 26, 2022 The first version of contextualized.ml, our open-source machine learning toolbox for personalized modeling, has been released!
Nov 3, 2021 MultiDAG: Multi-task learning of order-consistent causal graphs is in NeurIPS.
Nov 2, 2021 Our pre-print (and my first publication) for estimating sample-specific Bayesian networks is online here!

selected publications

  1. arXiv
    NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters
    Lengerich, Ben,  Ellington, Caleb,  Aragam, Bryon and 2 more authors