The Cambridge Ellis Unit is currently arranging our first Machine Learning Summer School from 11-15 July 2022 at the Department of Computer Science and Technology. 

The Cambridge Ellis Machine Learning Summer School is a distinguished course offered to graduate students, researchers and professionals, featuring engaging experts in their respective field and/or world-recognized professionals speaking about advanced machine learning concepts.

Important Dates

Application deadline: 22 May 2022
Admission notification by: 7 June 2022


  • Andreas Bender: Artificial Intelligence in Drug Discovery
  • Silvia Chiappa: Causal Inference and Fairness
  • Gabor Csanyi: Machine Learning and Atomistic Simulation
  • Carl Henrik Ek: Gaussian Processes and Bayesian Optimization
  • José Miguel Hernández-Lobato: Modern Bayesian Neural Networks
  • Neil Lawrence: Machine Learning
  • David Krueger:  AI safety and Alignment
  • Amanda Prorok: Multi-agent and Multi-robot Systems
  • Tom Rainforth: Bayesian Experimental Design and Active Learning
  • Rich Turner: Neural Processes
  • Mihaela van der Schaar: Machine Learning in Healthcare
  • Emily Shuckburgh: Machine Learning and Climate
  • Petar Veličković: Machine Learning and Graphs

Lecturers (Confirmed)

Andreas Bender
José Miguel Hernández-Lobato
Tom Rainforth
Rich Turner
Silvia Chiappa
Neil Lawrence
Mihaela van der Schaar
Gabor Csanyi
David Krueger
Emily Shuckburgh
Carl Henrik Ek
Amanda Prorok
Petar Veličković


Attendees not from the local area are advised to arrive the day before and schedule leaving after 8 pm on the Friday.

ELLIS Poster Session and Networking Event- Our goal is to bring students, researchers, and engineers from the greater Cambridge area (UK) together for an opportunity to meet and discuss recent machine learning research (recently published as well as ongoing work). Primarily, we wish to provide an opportunity for young researchers —some of whom have been doing research only during the duration of the pandemic— to promote their work and meet people from their local community. The event will feature poster sessions and a networking event.


The Summer School will be in person and held at the Department of Computer Science and Technology.

You can see travel information here.


All attendees will need to cover their own accommodation and travel costs. 


Travel awards will be given to students from under-represented backgrounds. Please email for more information.

There are no fees to attend the Summer School.

Lunch and tea/coffee will be provided.


Those wishing to attend the Cambridge Ellis Machine Learning Summer School will need to complete this form.

You will also have to supply:

  • 2 page CV
  • Letter of Reference: The writer should assess the qualities, characteristics, and capabilities of the person being recommended in terms of that individual’s abilities. The letter should address the applicant’s background and potential in Machine Learning, academic standing compared to other students, and how he or she would benefit from attending Cambridge Ellis Machine Learning Summer School. The referee should be a person that has some experience working together with the applicant. It can be for example a Ph.D. supervisor, a former employer or manager. 


José Miguel Hernández-Lobato
Markus Kaiser
Carl Rasmussen
Vincent Dutordoir
Ieva Kazlauskaite
Kim Cole