For universities & research institutions
From bench to bedside — and built to stay there.
Academic labs produce the breakthroughs. Vaiyu is the engineering team that turns a published model into sustainable, validated, deployable software — the work that rarely fits inside a grant’s scope or a postdoc’s tenure.
The translation gap
Papers don’t run in production.
Research code is written to prove a point and publish it — by students and postdocs optimizing for novelty, not maintenance, validation, or deployment. When they graduate, the institutional knowledge leaves with them. Meanwhile reviewers, journals, and IRBs increasingly demand reproducibility, sustainability, and audit-ready validation.
We close that gap. Vaiyu is the professional engineering layer alongside your lab — not competing with your people, but doing the part they were never staffed to do. We have spent a decade building AI that runs in hospitals, and we bring that discipline to the tools and models your science depends on.
What we do for labs
Built for how research gets funded — and used.
01
Research software sustainability
Turn grant-funded code into maintained, citeable, reviewer-proof infrastructure: packaging, CI/CD, documentation, and test coverage — with a maintainership plan that outlives any single hire.
02
Clinical translation
Take a validated research model to deployment on hospital infrastructure: harmonization, retraining, inference optimization for clinical hardware, and validation protocols that satisfy clinical review.4
03
Federated & multi-site learning
Train and harmonize across institutions without moving patient data — the approach behind a 71-site, six-continent study, hardened into infrastructure your consortium can run.3
04
Reproducibility & benchmarking
Reproducible pipelines, experiment lineage, and federated benchmarking, so results hold up to peer review and replicate across sites and scanners.
05
Data harmonization
Reconcile imaging and tabular data across scanners, sites, formats, and legacy systems, so models generalize beyond the cohort they were trained on.
06
Proposal & grant support
Co-develop the software, translation, and sustainability aims of your next submission — with defensible scope and effort estimates that evaluators trust.a
Built in the open
Our frameworks already run in research labs worldwide.
We don’t arrive with slideware. The open-source tools our team created and co-leads are used by research hospitals and consortia on six continents — and we maintain them to a production standard.
- GaNDLFLow-code, reproducible deep learning for clinical workflows.Communications Engineering (Nature) Editor’s Choice · an MLCommons project · Created and led by our founderGitHub ↗
- MedPerfFederated benchmarking of medical AI at global scale.Nature Machine Intelligence · an MLCommons project · Core teamGitHub ↗
- FeTSReal-world federated tumor segmentation across 71 sites on 6 continents.Nature Communications · covered by The Wall Street Journal · Co-led by our founderGitHub ↗
- OpenFLAn open framework for federated learning, hardened in healthcare.Physics in Medicine & Biology · Core contributorGitHub ↗
- CaPTkQuantitative cancer-imaging platform for radiomics and ML phenotyping.Journal of Medical Imaging · Co-led by our founderGitHub ↗
- GaNDLF-SynthDemocratizing generative AI for medical imaging — autoencoders to diffusion.MLCommons ecosystem · Created by our founderGitHub ↗
Plus 40+ conda-forge packages maintained for reproducible scientific computing.
Credibility
Peer-reviewed, cited, and standard-setting.
Our team’s research appears in Nature Communicationsand Nature Machine Intelligence, and has been covered by The Wall Street Journal. We’ve led more than$9M in NIH/NCI-funded R&D, and we hold the Vice Chair for Algorithmic Development of theMLCommons Medical Working Group— helping write the standards medical AI is measured against.
Publications & standards work →- Vice Chair, Algorithmic Development — MLCommons Medical Working Group
- Organizers of leading benchmarks: the BraTS and FeTS challenges (MICCAI)
- Contributors to imaging standards: IBSI and AI-RANO
- Reviewers for Nature Communications, IEEE Transactions on Medical Imaging, and Radiology
- Tutorial faculty at MICCAI, AAAI, ISBI, and RSNA
How we engage
Ways in that fit a grant budget.
We meet research budgets where they are — from a fixed-fee pilot to a named role on your next award.
01
Discovery sprint
2–4 weeks, fixed scope and fee: framing, feasibility, and a costed plan. The low-risk way to start.
02
Subaward or consultant
A defined scope on an active award — software maintenance, federated infrastructure, deployment — as a subcontract or consulting line.
03
Co-developed proposal
We help author the engineering, translation, or sustainability aims of a new submission and join as a named partner.
04
SBIR / STTR partnership
For commercialization-ready tools: Vaiyu as the small-business partner, your lab as the academic collaborator.
05
Build & handover
Scoped delivery with documentation and training, so the capability stays with your students and staff.
Bring us into your next proposal — or your next sprint.
Start with a 30-minute call or a fixed-scope discovery sprint. If software sustainability, federated learning, or clinical deployment is on your roadmap, we should talk.
Sources & attribution
- 1 — Led by our founder across NIH/NCI-funded programs at the University of Pennsylvania and Indiana University.
- 2 — Vaiyu client engagements: pre-training optimization with model accuracy maintained or improved.
- 3 — Pati, S. et al. “Federated learning enables big data for rare cancer boundary detection.” Nature Communications 13 (2022).doi:10.1038/s41467-022-33407-5 — 71 sites across 6 continents, the largest real-world federated learning study to date.
- 4 — Founder track record at Indiana University: inference latency reduced by up to 70%, compute requirements by 10–50%, in clinical research environments.
- a — The “data as IP” approach our founder led at Indiana University contributed to a $3.5M NIH/NCI award.