Machine Learning Research Scientist

PSPG Grad student (2019-2022). Currently, clinicians practice medicine on a population level. The ability to molecularly characterize biological systems affords new opportunities in the personalization of patient treatment. Proper integration and interpretation of biological data types are necessary to deconvolute individual homeostatic imbalances. Network representation of this information most closely resembles our current models, and the application of machine learning methods to this data structure can help us understand complex interactions. Will was a student in the Pharmaceutical Sciences and Pharmacogenomics graduate degree program.

Lab papers

  1. Learning chemical sensitivity reveals mechanisms of cellular response.

    Connell W, Garcia K, Goodarzi H, Keiser MJ. bioRxiv. 2023 Aug 28.

  2. A single-cell gene expression language model.

    Connell W, Khan U, Keiser MJ. arXiv - NeurIPS LMRL. 2022 Oct 25.

  3. Predicting Cellular Drug Sensitivity using Conditional Modulation of Gene Expression.

    Connell W, Keiser MJ. bioRxiv - NeurIPS LMRL. 2020 Dec 11.