Jannik Kossen

Jannik Kossen

Active Learning, Bayesian Deep Learning, Transformers

I am a PhD student at the University of Oxford supervised by Tom Rainforth in OxCSML and Yarin Gal in OATML. I am interested in contrastive learning of vision-language models, uncertainties in large language models and computer vision, (vaguely) Bayesian deep learning, active data selection, and deep non-parametric models. I received an MSc in Physics from Heidelberg University and have spent time studying in Bremen, Darmstadt, Padova, and at University College London.

I was a Student Researcher at Google Research, working on large scale contrastive learning, and a Research Scientist Intern at Deepmind, exploring active feature acquisition for temporal multimodal data.

I am interested in the societal and ethical implications of AI: I have co-authored a book explaining machine learning to a broad audience, discussed the ethics of AI at the Berlin-Brandenburg Academy of Sciences, and gathered real-world field experience at Bosch.



  • J. Kossen , S. Farquhar , Y. Gal , T. Rainforth , Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation, in Advances in Neural Information Processing Systems, 2022.


  • J. Kossen , N. Band , C. Lyle , A. N. Gomez , T. Rainforth , Y. Gal , Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning, Advances in Neural Information Processing Systems, 2021.
  • J. Kossen , S. Farquhar , Y. Gal , T. Rainforth , Active Testing: Sample-Efficient Model Evaluation, International Conference on Machine Learning, 2021.