Scholar
Bruno Kacper Mlodozeniec
Google Scholar ID: kGPBRy8AAAAJ
University of Cambridge
Citations & Impact
Citations
469
H-index
7
i10-index
6
Publications
20
Co-authors
4
list available
Publications
Resume (English only)
Academic Achievements
- Published several papers, including: 'Probabilistic Modelling is Sufficient for Causal Inference' (ICML 2025), 'Influence Functions for Scalable Data Attribution in Diffusion Models' (ICLR 2025), 'Implicitly Bayesian Prediction Rules in Deep Learning' (AABI 2024), and more.
Research Experience
- Worked on a variety of research projects, including understanding intelligence, learning, and generalization, as well as AI applications in science, such as molecular dynamics simulation and modeling in synthetic biology.
Education
- PhD student at the University of Cambridge, supervised by Richard Turner, David Krueger, and Bernhard Schölkopf.
Background
- Currently a PhD student at the University of Cambridge, co-supervised by Richard Turner, David Krueger, and Bernhard Schölkopf. Broadly interested in understanding how intelligence, learning, and generalization work, and how deep learning succeeds or fails at those. This has led to work on a range of topics from tools for interpreting neural networks (influence functions), generative modelling and symmetries, through to causality and probabilistic modelling. Also interested in AI for science, and has worked on applications to molecular dynamics simulation and modelling in synthetic biology.
Miscellany
- Blog, Twitter, GitHub, Google Scholar, and LinkedIn profiles.
