Hi! π
I am a last-year PhD student at the University of Oxford working on Data-Efficiency and Uncertainty in Large Scale Vision and Language Models. My supervisors are Yarin Gal in OATML and Tom Rainforth in OxCSML.
I am on the industry job market this year. Please feel free to reach out with interesting machine learning research opportunities.
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. With Yarin and Tom in Oxford, I have worked on hallucinations and in-context learning in large language models, non-parametric transformer architectures, and active model evaluation.
I received an MSc in Physics from Heidelberg University and have spent time studying in Bremen, Darmstadt, Padova, and at University College London.
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 as a data scientist intern at Bosch.
π° News
01/24 β Our work on how in-context learning in LLMs learns label relationships has been accepted to ICLR 2024.
09/23 β Work from my internship at Google on contrastive learning with pre-trained models has been accepted to NeurIPS 2023.
08/23 β New preprint on how in-context learning in large language models learns label relationships.
07/23 β Work from my internship at Google on contrastive learning with pre-trained models has been accepted at ES-FoMo Workshop at ICML.
07/23 β Work from my internship at DeepMind on Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task has been accepted at Transactions on Machine Learning Research.
05/23 β New Preprint vom my time as student rearcher at Google: Three Towers: Flexible Contrastive Learning with Pretrained Image Models.
11/22 β Our work on Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation has been accepted as an oral to NeurIPS 2022.
11/22 β Work from my internship at DeepMind on Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task has been accepted at the Foundation Models for Decision Making NeurIPS 2022 Workshop.
