High-Dimensional Asymptotics and Dataset Selection for Private Transfer Learning
External datasets can improve prediction, but distribution shifts and privacy noise can erase those gains. This work develops...
A theory lab forunderstandinglearning systems.
We use statistical physics, probability, and computer science to understand why learning systems generalise, fail, and develop bias, and how to build safer and more intelligent AI.
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Potency Prediction via Probabilistic Modeling of Dose–Response Curves
Drug discovery teams commonly summarise a dose–response experiment by the concentration at which a compound produces half its...
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How much can a single potency estimate tell us about a drug candidate? In a ChemRxiv preprint from his master’s project with AstraZeneca, Anton O’Nils worked with Stefano, Ola Engkvist, Jens Sjölund and Annie M. Westerlund to predict the full dose–response curve from molecular structure. They tested it on compounds screened for inhibition of hERG, a cardiac ion channel. Predicting the full curve gave more accurate potency estimates than the scalar models they compared.
Congratulations to Chenxiao Ma on being awarded a scholarship from the Adlerbertska Foundations!
A busy week for the lab at Chalmers! Stefano is co-organising the CHAIR Structured Learning workshop on 27–28 October, then moderating the AI Foundations panel at Chalmers AI Days on 29 October.
Three from the lab are heading to the Principia AI retreat! Frederick, Davide and Stefano will join colleagues to discuss the road to principled AI safety and the research needed to get there.
Meet the group.
We work across machine learning, statistical physics, cognitive science, and high-dimensional probability at Chalmers and the University of Gothenburg, with active links to Wits.
Chenxiao Ma
PhD Student
Flavio Nicoletti
Postdoc Researcher
Loek van Rossem
PhD student
Stefano Sarao Mannelli
Assistant Professor
Our group