Scholar
Danny Jesus Diaz
Google Scholar ID: lVD0CNEAAAAJ
Institute for Foundations of Machine Learning
Citations & Impact
Citations
1,644
H-index
12
i10-index
15
Publications
20
Co-authors
0
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Published multiple papers on applying machine learning to protein engineering, such as 'Distilling structural representations into protein sequence models' (BioRxiv, 2024) and 'A Systematic Evaluation of The Language-of-Viral-Escape Model Using Multiple Machine Learning Frameworks' (BioRxiv, 2024).
Research Experience
- During my PhD, I was the primary developer of MutCompute: a machine learning as a service tool for structure-based ML-guided protein engineering. Currently, I lead the Deep Proteins Groups at the Institute for Foundations of Machine Learning (IFML). Co-founded Intelligent Proteins, LLC where we use machine learning-guided protein engineering to develop protein-based biotechnologies for nutraceutical, therapeutic, and biomanufacturing applications.
Education
- PhD in Chemistry from the University of Texas at Austin, under the supervision of Dr. Andrew Ellington and Dr. Eric Anslyn.
Background
- I am a computational protein engineer. My research interests include developing sequence- and structure-based machine learning frameworks for identifying stabilizing and functional mutations in proteins. I collaborate extensively with experimental protein engineers to accelerate the developability and functionality of proteins for biotechnology applications.
Miscellany
- Interested in protein engineering, machine learning, computer vision, biocatalysis, cancer metabolism, rare metabolic diseases, automation, and startup/entrepreneurship.
Co-authors