Empowering engineers with Active Learning

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 latest machine learning advances

Secondmind has published numerous award-winning papers in top machine learning journals and conferences. This research fuels our solutions, enabling us to explore innovative approaches to new and emerging problems.

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Our work

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.

Six 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

Our open-source toolboxes are some of the building blocks for the Secondmind Optimization Engine. Open-sourcing these frameworks enables us to contribute to the community and continue to advance applied machine learning.

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.

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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.

Our programme

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.

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Latest from Labs

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Seminar: Antonio Del Rio Chanona - Imperial College London

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Seminars

Neural Diffusion Processes

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Papers

Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes

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Papers