The team behind Experative
We bring together clinical research, computational biophysics and machine learning to pursue one shared ambition: make every experiment advance the next.
Clinical Researcher
Harvard Medical SchoolPostdoctoral Research Fellow
Daniel is a clinical data scientist focused on how evidence becomes better decisions. He has led multi-country research projects and earned a postdoctoral fellowship at Harvard University. At Experative, he brings this clinical and behavioural-science perspective to building AI that helps research and development learn from every experiment.
Computational Biophysicist
MITEMBO Postdoctoral Fellow · Fakhri Lab
Tom combines biophysical modeling, machine learning and biological data analysis to understand living systems. Alongside his research as an EMBO postdoctoral fellow at MIT, he is active in MIT Hacking Medicine and Nucleate Germany.
Machine Learning Researcher
MITM.Sc. (in progress), RWTH Aachen University
Raphael develops machine learning and reinforcement learning methods at MIT. His background spans multi-agent research at Harvard and earlier work on training multimodal models.
Machine Learning Researcher
University of CambridgeMPhil (in progress) in Advanced Computer Science
Maximilian develops trustworthy machine learning and reinforcement learning methods for biology and health, building on research at MIT’s HealthyML lab and graduate study at Cambridge.
A shared achievement
Winners · Global AI Hackathon 2026
Our team built AMBR, a research prototype that analyzes bacterial genomes for evidence of antibiotic resistance. The project won the overall global title at Hack-Nation’s 6th Global AI Hackathon.
The event brought together more than 2,000 builders across 60 countries. Our shared achievement reflects how we work: connecting biological questions with machine learning, and turning research ideas into working systems.
Why we are building Experative
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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