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


GHC 7405

Gates Hillman Complex, 4902 Forbes Ave

Pittsburgh, PA 15213

Check out my research on, 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.


Nov 16, 2022 Check out my talk from CSHL Biological Data Science this past week: Contextualized Graphical Models Reveal Sample-Specific Transcriptional Networks for 7000 Tumors
Oct 20, 2022 Our extended abstract Sample-Specific Contextualized Graphical Models Using Clinical and Molecular Data Reveal Transcriptional Network Heterogeneity Across 7000 Tumors has been selected for a talk at CSHL Biological Data Science and was also accepted to ML4H and GLIndA.
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, 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.

selected publications

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