Event Details

BayesAI is a workshop on probabilistic and Bayesian aspects of artificial intelligence to be held at Lancaster University from midday Monday, 23 September to midday Thursday, 26 September.

The 4 day workshop seeks to catalyse research at the interface of Bayesian statistics and artificial intelligence. Themes include:

• Generative modeling.
• Physics-informed machine learning models.
• Monte Carlo methods in machine learning.
• Applications of the above.

Confirmed speakers include:

• Theo Damoulas (Warwick)
• Daniela De Angelis (Cambridge)
• Vincent Dutordoir (DeepMind)
• Arthur Gretton (UCL)
• José Miguel Hernández-Lobato (Cambridge)
• Sidharth Jaggi (Bristol)
• Katerina Karoni (Edinburgh)
• Samuel Kaski (Manchester)
• Jonas Latz (Manchester)
• Henry Moss (Cambridge)
• Patricia Murrieta-Flores (Lancaster)
• Hao Ni (UCL)
• Nikolas Nüsken (KCL)
• Chris Oates (Newcastle)
• Angus Phillips (Oxford)
• Ciara Pike-Burke (Imperial)
• Sam Power (Bristol)
• Theresa Smith (Bath)
• Kate Smith-Miles (Melbourne)
• Jared Tanner (Oxford)
• Mark van der Wilk (Oxford)

Registration is open until 7 September 2024, see bayesaiworkshop.github.io for further details and to register

Participants are invited to submit posters for the evening session on Monday 23 September.

BayesAI is co-organized by the Prob_AI Hub and the STOR-i Centre for Doctoral Training. Prob_AI is an £8.5M research hub which aims to develop a better understanding of AI methods by bringing together researchers from across the mathematical disciplines. The STOR-i CDT is an established centre for doctoral training, committed to methodological advancement through solving cutting-edge industrial challenges.