Check out our new paper ‘AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Mode

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Published: October 19, 2016

Check out our new paper “AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models” - joint work with K. Krauth, K. Cutajar, and E. V. Bonilla (link)

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Check out our new book on ‘Bayesian Deep Learning’

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Published: August 25, 2026

Our new “Handbook of Bayesian Deep Learning” is now available online (link)

Talk at the SMILES summer schools ‘Bayesian Deep Learning’

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Published: July 19, 2026

Invited talk at the ‘SMILES summer school of machine learning’ in Nanjing University: “Bayesian Deep Learning” (link)

Talk at ISBA ‘Model Selection for Over-Parameterized Models’

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Published: July 02, 2026

Invited talk at the ‘Calibrated Bayes: Model Design and Adaptation Under Limited Resources’ session at ISBA in Nagoya: “Model Selection for Over-Parameterized Models” (link)

Paper ‘Review of timescale distributions for electrochemical impedance spectroscopy analysis: Advantages, synergies, and future directions’ accepted in DeCarbon

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Published: June 18, 2026

Our paper “Review of timescale distributions for electrochemical impedance spectroscopy analysis: Advantages, synergies, and future directions” has been accepted for publication in the journal DeCarbon (link)

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Paper ‘Review of timescale distributions for electrochemical impedance spectroscopy analysis: Advantages, synergies, and future directions’ accepted in DeCarbon