Discovery & research leadership
Daniel Hofmann
Daniel has built his research career around accelerating discovery, leading international research projects and earning a postdoctoral fellowship at Harvard University.
The team behind Experative
Four researchers.
One vision.
With research experience at MIT and Harvard, we bring together expertise in AI, biophysics and experimental science to pursue one shared ambition: make every experiment advance the next.
AI & interpretable reasoning
Maximilian von Klinski
Maximilian has conducted AI research at MIT and published first-author work at WACV on reinforcement learning for interpretable reasoning. He is pursuing an MPhil in Advanced Computer Science at Cambridge.
Machine learning & industry
Raphael Karmalker
Raphael has conducted research in machine learning and reinforcement learning at Harvard and MIT. He brings industry experience from Mercedes-Benz and Porsche.
Biophysics & representation learning
Tom Burkart
Tom holds a PhD in biophysics and is an EMBO postdoctoral fellow at MIT, studying representation learning and biological data. He is also active in MIT Hacking Medicine and Nucleate Germany.
A shared achievement
Different disciplines.
Proven teamwork.
Winners · Global AI Hackathon 2026
Our team won the Global AI Hackathon 2026, one of the world’s largest AI hackathons, bringing together more than 2,000 participants in person across hubs including MIT, Harvard and Stanford.
That achievement reflects how we work: combining complementary research perspectives, moving quickly from ideas to implementation, and building together around a shared goal.
Why we are building Experative
Discovery should compound.
Every experiment produces evidence. Too often, that knowledge stays scattered across instruments, spreadsheets and individual memory. We are building the operating memory for experimental work, so each result becomes a foundation for what comes next.
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