Tightening the gap between machine learning theory and value delivered to the real world.

About Secondmind labs

Our origins lie in academic research, which is the foundation on which our products are built. By combining years of expertise in machine learning research with hands-on commercial experience, we are identifying and addressing one by one the parts of the puzzle that are missing to build an active learning framework that addresses the needs of the industry.

The mission of Secondmind labs is to provide the machine learning expertise and the know-how that fuels our product, and to explore innovative ideas to address currently open questions. Our main areas of expertise are probabilistic modelling and Bayesian optimisation, which are the two main ingredients required in active learning. Our work in these areas covers a large spectrum that goes from theoretical contributions to developing practical tools: We are both known for our very strong scientific publication record and for the open source toolboxes that we make available to the world.

The team is led by our Chairman, Carl Edward Rasmussen, Professor of machine learning at Cambridge University. Under his leadership, our team of researchers and engineers uses proven mathematical principles to build scalable tools that solve problems in a range of businesses and a variety of sectors.

Publications

We have published more than 70 papers in top machine learning journals and conferences over the past years. More importantly, the quality of the work that we are pushing forward has been recognised by three best paper awards: ICML in 2019 and AISTATS in 2020-2021.

Open source toolboxes

The benefit we get from the widely available libraries for scientific computing, visualisation and machine learning deployment is invaluable. Making our own machine learning frameworks available open source is our way to contribute back to the community.

Secondmind has been the home of the GPflow project for a few years now, and we have more recently open sourced our Bayesian optimisation framework Trieste as well as our Deep Gaussian process library GPflux.

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GPflow

GPflow has become the standard library for Gaussian process models in Python / Tensorflow. It covers classic GP regression models, but also the modern approaches based on variational inference and MCMC.

Check it out on github

Careers

Interested in helping automotive engineers achieve greater sustainability through machine learning? Join us.

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Seminar: Noémie Jaquier - Karlsruhe Institute of Technology

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Seminar: François Bachoc - Toulouse Mathematics Institute

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Seminar: Frank Hutter - University of Freiburg

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Seminar: Javier González Hernández - Microsoft Research Cambridge

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Seminar: Emtiyaz Khan - RIKEN Centre

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Seminar: Ciara Pike-Burke - Imperial College London

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GPflux: A Library for Deep Gaussian Processes

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Seminar: José Miguel Hernández Lobato - University of Cambridge

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Seminar: Carl Henrik Ek - University of Cambridge

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