Vanessa Böhm
Principal Member Of Technical Staff at Salesforce
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolVanessa Böhm is a Principal Member of Technical Staff and machine learning researcher with nine years of experience building foundational AI models and applied ML systems across startups and research institutions. She holds a PhD in Physics from the Max Planck Institute and completed a postdoc at UC Berkeley, where she developed deep learning frameworks for scientific discovery, anomaly detection, and probabilistic inference. Vanessa has translated domain-heavy research into product impact—reducing maritime emissions with data-driven optimization, automating business processes with novel AI tools at Regrello, and now advancing next-generation foundational models at Salesforce. Comfortable at the intersection of academia and industry, she brings rigorous mathematical training and hands-on engineering to high-stakes problems like disaster assessment and complex physical simulations. Colleagues describe her work as both theoretically grounded and pragmatically focused, able to move models from prototype to production in real-world settings.
9 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD Physics, Doctor of Philosophy - PhD Physics at Ludwig-Maximilians-Universität MünchenMaster of Science - MS Theoretical and Mathematical Physics, Master of Science - MS Theoretical and Mathematical Physics at Heidelberg University
English, German, French, Portuguese
Github Skills (73)
probabilistic-programming10
mcmc10
bayesian-inference10
encoder10
variational-autoencoder10
autoencoder10
variational-inference10
information-theory10
bayesian10
probabilistic-models10
bayesian-statistics10
statistical-inference10
probabilistic10
generative-model10
modeling10
Programming languages (4)
Github contributions (5)
VMBoehm/MnuLFI
Mar 2019 - Oct 2019
throwing LFI on massiveNus sims
Contributions:76 commits, 5 PRs, 33 pushes in 7 months
simulationlfisims
VMBoehm/PAE
Jun 2020 - Dec 2022
Probabilistic Auto-Encoder
Contributions:161 commits, 2 PRs, 82 pushes in 2 years 6 months
information-theoryautoencodergenerative-modelmachine-learningincremental-learning
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