PhD
Efficient Learning and Probabilistic Inference for Science (ELPIS) at TUM and Helmholtz AI
Ingolstädter Landstraße 1
85764 Oberschleißheim
Biosketch
PhD candidate in Bayesian Machine Learning. Previously in Finance. Academic background Maths and Computer Science (St. John’s College, Oxford)
relAI Research
Advances in Bayesian Meta- and Continual Learning
Meta- and Continual Learning are some of the most important open problems standing in the way of achieving artificial general intelligence. The human-like ability to generalize across tasks and over time is essential for adaptaibility in the real world. We utilize the Bayesian framework to contribute to the fields exploring various related open problems. In particular, we rely on transformers and in-context learning as a base framework for our experiments.