I hold a B.Sc. in Electronic Engineering from the University of Aleppo, an M.Sc. in Computational Science from the University of Frankfurt, and a Doctorate in Cognitive Science (summa cum laude) from the University of Osnabrück. Before joining the University of Nottingham, I held postdoctoral research positions, first at the Central Institute of Mental Health in Heidelberg University, later at the Institute of Neuroinformatics in the University of Zurich and ETH Zurich. I was a visiting lecturer on theoretical neuroscience twice at the Department of Cognitive Psychology and Neuroscience of Jacobs University, Bremen.
I develop computational models and statistical tools for relating neural dynamics to complex behaviours. My areas of expertise include dynamical systems, neural and circuit-level models, statistical neuroscience, reinforcement learning, decision making, prefrontal cortex, songbird, neuronal plasticity and bio-inspired robotics. I recently started developing behavioural experiments for investigating computations underlying human decision making and learning.
I am currently investigating the computations underlying learning in both songbirds and humans. Those two lines of research are a continuation of my work in the Institute of Neuroinformatics in… read more
I am currently investigating the computations underlying learning in both songbirds and humans. Those two lines of research are a continuation of my work in the Institute of Neuroinformatics in Zurich. In songbirds, through data-guided computational modelling, I aim at probing the subjective metrics by which a juvenile bird evaluates the quality of its own song to drive learning in absence of external reinforcement. In humans, I am developing experimental paradigms and computational models, aiming to identify the behavioural strategies that guide reward-driven learning in perceptually ambiguous environments.
My postdoctoral work at the Central Institute of Mental Health of Heidelberg University focused on developing efficient model-based analysis methods for relating high-dimensional neural data to behaviour. I applied those methods to data acquired from rodent mPFC recordings during extinction and rule learning. Prior to that, I investigated during my doctoral research how associative and homeostatic plasticity mechanisms interact to orchestrate complex nonlinear computations and behaviours, including sequence learning in recurrent neural networks and stable locomotion in a neuro-controlled insect-like robot.
My ultimate goal is to identify key computations that underlie complex behaviors involving perception, action, and reward that are conserved across species. To this end, and in collaboration with experimental colleagues, I plan to develop an experimental paradigm that can probe such computations concurrently in songbirds, rodents and humans, and to apply a data-guided computational modelling approach to the resulting data.