01About
Founder of VerySafe.ai
AI Safety Researcher & Engineer · Vice Chair, MLCommons Medical Working Group
I build AI systems for settings where a wrong answer is expensive: hospitals, regulated industries, anywhere the output has to be trusted before it can be used. I hold a Ph.D. in Computer Science from the Technical University of Munich (summa cum laude) and have spent the last 15+ years taking AI from prototype to production.
Most of that work now happens at VerySafe.ai, where I'm building SafeCompute, a policy-aware compute platform that attaches cryptographic proof to every AI model run, using remote attestation, supply-chain provenance, and signed audit lineage. The goal is to let organizations run frontier and open-source LLMs in exactly the places where governance, privacy, and auditability are non-negotiable.
Along the way I've led USD 9M+ in NIH/NCI-funded research, published in Nature, Nature Communications, and IEEE Transactions on Medical Imaging, and I serve as Vice Chair for Algorithmic Development at the MLCommons Medical Working Group.
I think open software makes for better science, so most of my code lives in the open.
02What I build
From research to production
I design and ship AI systems end to end, from first concept to deployment, in domains where the stakes are high and the regulators are paying attention. Most of the last 15 years has gone into turning research into software that runs in production.
End-to-end AI systems
I take AI from first prototype to clinical-grade production, multimodal data and all.
GaNDLF: 30% faster prototyping, now an MLCommons project
Confidential & federated compute
Models train and benchmark across institutions while the sensitive data stays put.
USD 9M+ in NIH/NCI grants led · deployed on 6 continents
Optimization & deployment
I get models running where compute is scarce and latency budgets are real, from edge hardware to HPC.
10–50% less compute · up to 70% lower inference latency
Benchmarking & evaluation
Evaluation of medical and enterprise AI that other people can actually reproduce and check.
MedPerf: federated benchmarking across institutions
03Open source
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.
Trying to ship AI in a regulated domain?
That's my favorite kind of problem. The first call is on me.
sarthak@verysafe.ai