Computational Biology Department
Pittsburgh, PA 15213
Note: I am now the Vice President of Molecular Design Technolgies at Danaher. Prior to that, I was Scientific Executive Director Amgen where I built and led a team focused on the development and application of AI/ML to the design of therapeutic proteins. I remain an adjunct professor at CMU, but my lab is no longer recruiting students or postdocs.
The Langmead lab is based in the Computational Biology Department in the School of Computer Science at Carnegie Mellon University in Pittsburgh, PA. Our research is focused on the development and application of Artificial Intelligence and Machine Learning methods to address challenges in three areas:
Computational Medicine. I also teach a course in Computational Medicine
Computational Biology
AI-driven Scientific Research (i.e., Robot Scientists). I also teach a course in AI-Driven Scientific Research
Most Recent Publications
Protein Language Models: Is Scaling Necessary? bioRxiv preprint
AI can help to speed up drug discovery—but only if we give it the right data M Mock, S Edavettal, C Langmead, A Russell Nature 621 (7979), 467-470
Identifying promising sequences for protein engineering using a deep transformer protein language model TS Frisby, CJ Langmead Proteins: Structure, Function, and Bioinformatics 91 (11), 1471-1486
Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment, T Nanayakkara, G Clermont, CJ Langmead, D Swigon (PLoS Digital Health)
Bayesian Optimization with Evolutionary and Structure-Based Regularization for Directed Protein Evolution, Algorithms for Molecular Biology, Algorithms for Molecular Biology 16(13), 2021
Asynchronous Parallel Bayesian Optimization for AI-driven Cloud Laboratories, ISMB/ECCB; Bioinformatics, 2021
A Novel Five Cytokine Panel Outperforms Conventional Predictive Markers of Persistent Organ Failure in Acute Pancreatitis Clinical and Translational Gastroenterology, 12(5), p. e00351, 2021
Harvestman: a framework for hierarchical feature learning and selection from whole genome sequencing data BMC Bioinformatics 2021 (in press)
Fold Family-Regularized Bayesian Optimization for Directed Protein Evolution WABI 2020, pp.1--18
All Publications