01 About
01
Founder of VerySafe.ai
AI Safety Researcher & Engineer · Vice Chair, MLCommons Medical Working Group
AI researcher, engineer, and founder building trustworthy, end-to-end AI systems for regulated, high-stakes domains. I hold a Ph.D. in Computer Science from the Technical University of Munich (summa cum laude) and bring 11+ years of operationalizing AI from prototype to production.
I am the founder of VerySafe.ai, where I am building SafeCompute — a policy-aware compute platform that attaches cryptographic proof to every AI model run through remote attestation, supply-chain provenance, and signed audit lineage. The aim is to let organizations run frontier and open-source LLMs precisely where governance, privacy, and auditability are non-negotiable.
My work sits at the intersection of frontier AI capability and the safety, attestation, and governance infrastructure required to deploy it responsibly. I have led USD 9M+ in NIH/NCI-funded research, published in Nature, Nature Communications, and IEEE Transactions on Medical Imaging, and serve as Vice Chair for Algorithmic Development at the MLCommons Medical Working Group.
I believe open software fosters better science, which is why I stay deeply involved in the open-source community.
02 What I build
02
From research to production
I architect and ship AI systems end-to-end — concept, design, deployment — for regulated, high-stakes domains. Eleven years turning research into software that runs in production.
End-to-end AI systems
Architect and ship AI from prototype to production — multimodal data, low-code pipelines, clinical-grade workflows.
GaNDLF — 30% faster prototyping, now an MLCommons project
Confidential & federated compute
Privacy-preserving ML that trains and benchmarks across institutions without moving sensitive data.
USD 9M+ in NIH/NCI grants led · deployed on 6 continents
Optimization & deployment
Make models run where compute, latency, and cost are constrained — edge, HPC, and low-resource environments.
10–50% less compute · up to 70% lower inference latency
Benchmarking & evaluation
Trustworthy, reproducible evaluation of medical and enterprise AI at scale.
MedPerf — federated benchmarking across institutions
03 Open source
03
Selected Projects
Editor's Choice, Communications Engineering (Nature)
GaNDLF
Do-It-Yourself Deep Learning framework for everyone — low-code AI pipelines for healthcare.
MLCommons Working Group
MedPerf
Open platform for federated benchmarking of medical AI models across institutions.
Nature Communications
FeTS
Federated Tumor Segmentation — largest real-world federated learning study (71 sites, 6 continents).
securefederatedai
OpenFL
Open-source federated learning framework for healthcare and life sciences.
CBICA / UPenn
CaPTk
Cross-platform toolkit for medical image processing and analysis.
arXiv:2410.00173
GaNDLF-Synth
Synthetic data generation for medical imaging — training AI with artificial data.