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Jascha Achterberg
Jascha did his PhD at the University of Cambridge, in collaboration with Google DeepMind and Intel. His work focuses on efficient learning and problem solving in brains and artificial neural networks. By combining approaches from animal electrophysiology, computational neuroscience, and AI, he aims to build theories that help us to understand both biological and artificial intelligent systems. Part of his ongoing work is specifically targeting the nature of distributed computations, to show which joint principles underly the efficient and robust distributed computations in brains, large scale artificial neural networks, and modern AI hardware accelerators.
Faredin Alejevski
Michał Wójcik
Michał did his PhD at Oxford, exploring the dimentionality of prefrontal cortex representations over learning. His research focuses on using a machine-learning oriented framework to understand how biological circuits give rise to higher cognition. He is especially interested in the processes of abstraction and generalisation and how their learning dynamics can be explained and predicted by neural networks models. To capture how neural activity changes over the course of learning, he uses human electroencephalography (EEG) and high resolution neural population recordings from non-human primates.