Summary
Vincent Valton is a Senior Machine Learning Data Scientist with nine years of experience applying ML/AI to wearable health and computational psychiatry, now based in San Francisco and currently driving ML work at ŌURA. He has moved models from Bayesian hierarchical and reinforcement-learning prototypes into production-grade systems at All.Health, combining deep learning, probabilistic modelling and efficient deployment (ONNX, MLFlow, Docker) to improve continuous patient monitoring. Previously he led an NIHR-funded postdoctoral fellowship at UCL where he developed computational cognitive fingerprints—latent parameters from hierarchical models—that predict individual risk trajectories for mental health disorders. Comfortable across research and engineering, he blends HMC sampling and Stan with PyTorch/TensorFlow engineering and production observability (Prometheus, Grafana) to close the loop from discovery to deployment. He also designs and teaches advanced tutorials in Bayesian inference and decision models, translating complex methods into reproducible code used in large international courses. A subtle strength is his ability to quantify individual decision-making strategies and turn those insights into scalable clinical predictors for early intervention.
9 years of coding experience
9 years of employment as a software developer
Ph.D., Computational Neuroscience (ML/AI applied to mental health), Awarded with no corrections, Ph.D., Computational Neuroscience (ML/AI applied to mental health), Awarded with no corrections at The University of Edinburgh
Heriot-Watt University Edinburgh Campus
DUT, Computer Science & Software Engineering, DUT, Computer Science & Software Engineering at Institut Universitaire Technologique (IUT) de Nantes