Bruce Wittmann
Scientific Advisor - Machine Learning-Assisted Directed Evolution at Microsoft
Redmond, Washington, United States
Summary
👤
Senior🎓
Top SchoolBruce Wittmann is a scientist-engineer who bridges bioengineering and machine learning to accelerate protein and enzyme design, currently advising ENZIDIA on machine learning-assisted directed evolution and serving as Principal Applied Scientist at Microsoft. With a PhD from Caltech and eight years of experience across industry and academia, he pioneered semi-supervised and NLP-inspired in silico directed evolution methods and built accessible software (MLDE) that enables wet-lab scientists to use ML for sparse-data protein engineering. His background includes scaling high-throughput molecular workflows and automation at Intrexon, designing LIMS and computational infrastructure, and mentoring teams to high operational success rates. Known for translating advanced computational models into practical experimental pipelines, he repeatedly reduces experimental iteration time and cost while improving discovery yield. Based in Redmond, WA, he combines deep wet-lab expertise with production-grade software development—an uncommon dual fluency that speeds deployment of ML-guided biology.
9 years of coding experience
5 years of employment as a software developer
California Institute of TechnologyBachelor’s Degree Major in Chemistry/Biochemistry Minor in Anthropology, Bachelor’s Degree Major in Chemistry/Biochemistry Minor in Anthropology at Washington University in St. Louis
Spanish
Github Skills (27)
folding10
sequencing10
protein-structure10
bioinformatics9
language-model8
genomics8
phylogenetics8
semi-supervised-learning8
sequence-alignment8
pytorch8
protein-sequences8
gpu7
language-modeling7
computational-biology7
machine-learning6
Programming languages (3)
Github contributions (5)
fhalab/MLDE
Dec 2020 - Aug 2021
A machine-learning package for navigating combinatorial protein fitness landscapes.
Contributions:2 releases, 12 commits, 6 pushes in 8 months
machine-learning
fhalab/evSeq
Aug 2019 - Mar 2022
Computational tools for extremely low-cost, massively parallel amplicon-based sequencing of every variant in protein mutant libraries.
Contributions:1 release, 194 commits, 4 pushes in 2 years 7 months
sequencing
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