Agostina Palmigiano studied Physics at the University of Buenos Aires before completing her PhD at the Max Planck Institute for Dynamics and Self-Organization under the supervision of Theo Geisel and Fred Wolf. Her doctoral work was recognised with the Max Planck Society’s Otto Hahn Medal in 2018. She then joined the Center for Theoretical Neuroscience at Columbia University, where she worked with Ken Miller, first as a Swartz Postdoctoral Fellow and later as a Simons BTI Fellow.

Matthew Tilley studied Physics at Imperial College London, completing a PhD in High Energy Physics under Professor Mitesh Patel as part of the LHCb collaboration. He later transitioned into neuroscience and machine learning, conducting postdoctoral research with Professor David Freedman at the University of Chicago. He joined the Lab in March 2025.

Samuel Eckmann obtained a PhD at the Max Planck Institute for Brain Research (MPIBR) and the Frankfurt Institute for Advanced Studies (FIAS), working with Julijana Gjorgjieva and Jochen Triesch, respectively. He then became a Royal Society Newton International Fellow at the Computational and Biological Learning Lab (CBL) at the University of Cambridge, collaborating with Máté Lengyel and Yashar Ahmadian. He joined the Lab in June 2026.

Pratyusha Chowdhury completed her PhD in Theoretical High Energy Physics at the University of Southampton, following her undergraduate and master’s studies in India. She is currently interested in using tools from statistical field theory and information theory to study neural computation and learning dynamics in both biological and artificial systems.

Jan Bauer is interested in the core mechanisms behind learning in neural networks, in particular how stability and flexibility are balanced. He studied Physics at RWTH Aachen University, Germany, where he worked with Moritz Helias. Now as part of the Gatsby Unit, he is also in active collaboration with Jonathan Kadmon at ELSC at the Hebrew University.

Mateo Amortegui Cifuentes is motivated by identifying which computations are useful models of how the brain learns about value, makes value-based decisions and controls motivated behaviours, such as approaching or avoiding rewards and punishments, and by finding biologically plausible circuit-level implementations of those computations. He joined the Gatsby Unit after completing the French–Italian Master’s in Physics of Complex Systems.

Jake Laherty studied Neuroscience and Data Science at the University of Sydney before joining the Lab. He is interested in the theoretical foundations of learning and the necessary conditions for intelligence.

Alessandro Breccia studied Physics and Machine Learning at the University of Padua, completing his Master’s thesis in collaboration with ETH Zurich. He works at the intersection of Theoretical Neuroscience and Machine Learning, using tools from Statistical Physics. His main research interests are feature learning dynamics in deep learning, learning theory for RNNs using Dynamical Mean Field Theory and Continual Learning.

Hantao Zhang studied Statistics at the University of Warwick and Yale University. He is interested in the computational foundations of biological and machine intelligence. His recent work focuses on the mechanisms underlying flexible working-memory control and decision-making.

Ravi Thakrar studied Mathematics and Economics at the London School of Economics and is currently pursuing an MSc in Mathematical Modelling at UCL. He is interested in the geometry of learned representations, and in what latent dynamical models reveal about structure in high-dimensional data.

Mikolaj Sobieralski is a final-year MSci Neuroscience student at UCL. His research interests are neural representations during learning, and biologically plausible, interpretable neural networks.