Applied AI adoption
Translating frontier models, accelerated computing and agentic systems into clear architectures, adoption paths and measurable outcomes.
Applied AI · Life Sciences · Startups
From molecular systems to intelligent systems.
I work at the intersection of science, technology and company-building—helping founders and enterprise leaders turn ambitious AI research into products that reach patients, users and markets.
Research
Scale
20+years across science, biopharma and enterprise technology
PhDfoundation in computational science and structural biology
AIexperience spanning biopharma, cloud and accelerated computing
EMEAstartup and AI ecosystem leadership
Where I work
At the boundary where science meets scale.
My work connects technical depth with the commercial and organizational decisions required to make AI useful in the real world.
Life-sciences depth
Bringing first-hand understanding of molecular science, drug discovery, clinical technology and biopharma transformation to every conversation.
From founder to scale
Working with founders and C-suite teams on technical strategy, ecosystem leverage, go-to-market design and the move from research to production.
Featured essay · 6 min read
A molecule is not a medicine.
AI drug discovery cannot stop at a promising binding score, QSAR profile or early toxicity result. The harder test is whether a program can survive development, cross Phase 3 and become a medicine that creates value for patients and the people building it.
Read the essayWhat I’m exploring
Working preprint Ideas in development
What if disease is better understood as a position within a complex biological state?
And treatment as a navigable path back toward health?
Beyond the biomarker
Disease may be encoded in the relationships among genomics, cells, proteins, imaging and clinical history—not in any single signal.
Health as a reference landscape
Rather than defining one universal “normal,” I am exploring a healthy reference shaped by tissue, age, ancestry, sex and environment—and how far a patient’s biology has moved from it.
Treatment as navigation
Could therapy be designed as a sequence of measurable biological waypoints, with explicit moments to reassess progress, switch interventions or redraw the route?
The technical question
Can diffusion-based expert systems reason across incomplete multimodal evidence while explicitly representing what they do not know?
Early research direction. These concepts are still being developed and have not been clinically validated.
Interested in this direction? Let’s compare notesThe trajectory
One path. Four operating scales.
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01
Scientific foundations
Understanding systems at atomic resolution
PhD-trained at Heidelberg University, using molecular simulation to investigate the mechanics and energetics of biological systems.
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02
Biopharma transformation
Connecting science, data and enterprise change
At Roche, worked across science, data and enterprise technology on complex transformation programs in a global biopharma environment.
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03
Cloud and customer engineering
Turning technical possibility into adoption
At Google, helped HCLS organizations translate cloud architecture and AI into adoption, with experience spanning executive sponsorship, Google Cloud and DeepMind collaborations.
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04
AI and founder acceleration
Helping ambitious companies move from lab to launch
At NVIDIA, leads HCLS startup engagement across EMEA—supporting founders and working with product, solution architecture, sales and ecosystem teams around their path to scale.
Selected research
Before AI strategy, there was molecular motion.
Peer-reviewed work in computational structural biology continues to shape how I think: mechanisms matter, transitions matter and the path between states is often the real problem.
The Structural Coupling between ATPase Activation and Recovery Stroke in the Myosin II Motor
Simulations of the Myosin II Motor Reveal a Nucleotide-state Sensing Element that Controls the Recovery Stroke
A discriminative Ramachandran potential of mean force aimed at minimizing secondary structure bias
Start a conversation
Building where AI and life sciences meet?
I’m always interested in the hard questions behind turning breakthrough science into durable products and companies.
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