Jeffrey A. Ruffolo
Member of Profluent's machine learning team; co-author of the Profluent-E1 and ProGen3 protein model preprints
Jeffrey A. Ruffolo is a member of the machine learning team at Profluent, which builds AI models for protein design. Profluent's team page says that while studying molecular biophysics at Johns Hopkins, Ruffolo developed machine learning models for antibody structure prediction and antibody-specific language models. Ruffolo is a co-author of the Profluent-E1 protein encoder preprint and the ProGen3 scaling preprint.124
Research
At Johns Hopkins, Ruffolo worked on machine learning for antibodies. In December 2021 Ruffolo was first author, with Jeffrey J. Gray and Jeremias Sulam, of a paper on deciphering antibody affinity maturation with language models and weakly supervised learning.15
At Profluent, Ruffolo co-authored the April 2025 preprint accompanying the ProGen3 protein generation models and the November 2025 preprint introducing E1, a family of retrieval-augmented protein encoders released for research and commercial use.423
Work
Profluent-E1 600MOpen weights
Co-author of the E1 preprint describing Profluent's retrieval-augmented protein encoder family, released in 150M, 300M and 600M parameter sizes.23
Profluent-E1 300MOpen weights
Co-author of the E1 preprint describing the retrieval-augmented protein encoder family that includes the 300M-parameter model.2
Profluent-E1 150MOpen weights
Co-author of the E1 preprint describing the retrieval-augmented protein encoder family that includes the 150M-parameter model.2
Career timeline
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