About Secondmind Labs
Secondmind Labs is our R&D team, combining decades of machine learning research and expertise with hands-on automotive experience. We apply the very latest machine learning thinking to solve the most acute problems in automotive design and development.
At the forefront of the latest machine learning advances
Secondmind has published numerous award-winning papers in top machine learning journals and conferences. This research fuels our products, enabling us to explore innovative approaches to new and emerging problems.
At the forefront of the latest machine learning advances
Secondmind has published numerous award-winning papers in top machine learning journals and conferences. This research fuels our products, enabling us to explore innovative approaches to new and emerging problems.
At the forefront of the latest machine learning advances
Secondmind has published numerous award-winning papers in top machine learning journals and conferences. This research fuels our products, enabling us to explore innovative approaches to new and emerging problems.
At the forefront of the latest machine learning advances
Secondmind has published numerous award-winning papers in top machine learning journals and conferences. This research fuels our products, enabling us to explore innovative approaches to new and emerging problems.
Our work
Award-winning
The quality of our work has been recognised by three best paper awards: ICML (2019) and AISTATS (2020, 2021)
Award-winning
The quality of our work has been recognised by three best paper awards: ICML (2019) and AISTATS (2020, 2021)
Award-winning
The quality of our work has been recognised by three best paper awards: ICML (2019) and AISTATS (2020, 2021)
Award-winning
The quality of our work has been recognised by three best paper awards: ICML (2019) and AISTATS (2020, 2021)
Over 80 papers published
To date, we have published over 80 papers in top machine learning journals and conferences.
Over 80 papers published
To date, we have published over 80 papers in top machine learning journals and conferences.
Over 80 papers published
To date, we have published over 80 papers in top machine learning journals and conferences.
Over 80 papers published
To date, we have published over 80 papers in top machine learning journals and conferences.
Seven patents
We are continuing to drive innovation in the application of ML to solve the most complex engineering challenges.
Seven patents
We are continuing to drive innovation in the application of ML to solve the most complex engineering challenges.
Seven patents
We are continuing to drive innovation in the application of ML to solve the most complex engineering challenges.
Seven patents
We are continuing to drive innovation in the application of ML to solve the most complex engineering challenges.
Collaborating with the machine learning community
GPflow
Secondmind is the home of GPflow - the standard library for Gaussian process models in Python/Tensorflow. It covers classic GP regression models, and the modern approaches based on variational inference and MCMC.
GPflow
Secondmind is the home of GPflow - the standard library for Gaussian process models in Python/Tensorflow. It covers classic GP regression models, and the modern approaches based on variational inference and MCMC.
GPflow
Secondmind is the home of GPflow - the standard library for Gaussian process models in Python/Tensorflow. It covers classic GP regression models, and the modern approaches based on variational inference and MCMC.
GPflux
GPflux is our Deep GP library. It is built on top of GPflow and Keras and allows users to quickly build models with complex input/output relationships.
GPflux
GPflux is our Deep GP library. It is built on top of GPflow and Keras and allows users to quickly build models with complex input/output relationships.
GPflux
GPflux is our Deep GP library. It is built on top of GPflow and Keras and allows users to quickly build models with complex input/output relationships.
Trieste
Trieste is our active learning library. It is extremely data-efficient and compatible with advanced probabilistic models from GPflow and GPflux. It supports constrained optimization, noisy data and multi-objective optimization.
Trieste
Trieste is our active learning library. It is extremely data-efficient and compatible with advanced probabilistic models from GPflow and GPflux. It supports constrained optimization, noisy data and multi-objective optimization.
Trieste
Trieste is our active learning library. It is extremely data-efficient and compatible with advanced probabilistic models from GPflow and GPflux. It supports constrained optimization, noisy data and multi-objective optimization.
Research paper
Neural Diffusion Processes
This paper proposes Neural Diffusion Processes (NDPs), a novel approach that learns to sample from a rich distribution over functions through its finite marginals.
Research paper
Neural Diffusion Processes
This paper proposes Neural Diffusion Processes (NDPs), a novel approach that learns to sample from a rich distribution over functions through its finite marginals.
Get involved
Learn with Labs
Our virtual research seminar programme supports our culture of continuous learning. We exchange ideas with our guest speakers and explore how emerging academic theories could be applied to our customers’ problems.
Learn with Labs
Our virtual research seminar programme supports our culture of continuous learning. We exchange ideas with our guest speakers and explore how emerging academic theories could be applied to our customers’ problems.
Learn with Labs
Our virtual research seminar programme supports our culture of continuous learning. We exchange ideas with our guest speakers and explore how emerging academic theories could be applied to our customers’ problems.
Careers
Our team is led by our Chief Science Officer, Carl Edward Rasmussen, Professor of Machine Learning at Cambridge University. Under his leadership, our team uses proven mathematical principles to build scalable tools to solve the latest and most complex optimization problems.
Careers
Our team is led by our Chief Science Officer, Carl Edward Rasmussen, Professor of Machine Learning at Cambridge University. Under his leadership, our team uses proven mathematical principles to build scalable tools to solve the latest and most complex optimization problems.
Resources
Stay ahead in a rapidly evolving industry. Access our latest resources, insights, and tools to equip you with the knowledge you need to stay competitive.
Resources
Stay ahead in a rapidly evolving industry. Access our latest resources, insights, and tools to equip you with the knowledge you need to stay competitive.
Resources
Stay ahead in a rapidly evolving industry. Access our latest resources, insights, and tools to equip you with the knowledge you need to stay competitive.
Resources
Stay ahead in a rapidly evolving industry. Access our latest resources, insights, and tools to equip you with the knowledge you need to stay competitive.
Products
Resources
Get in touch
© Secondmind 2025
Products
Resources
Get in touch
© Secondmind 2025
Products
Resources
Get in touch
© Secondmind 2025
Products
Resources
Get in touch
© Secondmind 2025