My paper on Compositional Generalization in a Diffusion model (PDF) has been accepted at the NeurIPS 2026 On symmetry and geometry of neural representations workshop.
I study how complex neural systems represent information and implement computations, using methods from computational neuroscience and mechanistic interpretability.
My background is in systems and computational neuroscience, where I completed a PhD studying how spatial and contextual information is encoded by biological neurons. I am now focusing on mechanistic interpretability and representation learning in artificial neural networks, supported by Coefficient Giving funding.
Latest news
I will present a poster on collaborative work, led by Jai Bhagat, on SAEs for neural data at the Conference on the Mathematics of Neuroscience and AI in Rome.
I received funding from Coefficient Giving to support my independent research in AI safety.
Current interests
- Mechanistic interpretability
- Representation learning and generalization
- Computational neuroscience
- AI safety
Currently based in London, UK. Email: saramolas18@gmail.com