Biography

I completed my B.S in Molecular Biology (2010) and his M.Sc. in Neuroscience (2012) at Scuola Normale Superiore of Pisa, Italy. My neuroscience research started with an Armenise-Harvard Summer Fellowship in the Datta lab at Harvard Medical School, and further blossomed during a master thesis in the Ratto Lab at Scuola Normale. I was then awarded a Wellcome Trust Fellowship to complete my doctoral training University College London (2018). At UCL, working under the mentorship of Matteo Carandini, and in collaboration with Kenneth Harris and the Kullmann lab, I pioneered novel strategies to record the function and connectivity of neurons and deployed them to understand the brain mechanisms of vision and of epilepsy. Finally, to transition to independence, I secured a Sir Henry Wellcome fellowship, sponsored by Michael Hausser, focusing on the mechanisms of visual and motor computations both in the neocortex and in the cerebellum.

In 2023, supported by an Armenise-Harvard Career Development Award and by a Human Technopole Early Career Fellowship, I launched the Functional Architecture of Neural Circuits Laboratory, based at the Italian Institute of Technology - Center for Neuroscience and Cognitive Systems.

Biography

I obtained my master’s degree in Software Engineering from the University of Carthage. During my studies, I held several industry roles in which I developed computer vision algorithms and built data-fusion pipelines and cloud infrastructure. My experience working with production AI systems gradually shifted my interests toward alternative computing paradigms and more fundamental questions about computation. For my master’s thesis, I collaborated with the Danish Research Centre for Magnetic Resonance (DRCMR) to develop a novel Bayesian method for mapping parameters onto fMRI data. I later joined the Nuclear Physics and Quantum Computing group at the University of Trento, where I worked on quantum-computing algorithms for simulating many-body systems. I am currently a Research Fellow in the Rossi Lab at the Italian Institute of Technology (IIT), where I investigate how visual circuits distinguish self-generated motion from externally generated motion. To address this question, I combine calcium imaging with electrophysiological recordings from the superior colliculus.

Biography

Mranmay Shetty is an M.Sc. candidate in Modeling for Neuronal and Cognitive Systems at Université Côte d’Azur. With a background in engineering and machine learning, his research has evolved from explainable AI to neural decoding, biologically grounded models of working memory, and cortical circuit analysis. His interests lie at the intersection of systems neuroscience and Neuro-AI, with a particular emphasis on using computational methods to understand brain function. In the lab, he uses the MICrONS dataset to investigate how visual information and synaptic connectivity are organized across cortical circuits. His long-term goal is to combine circuit-level experiments, large-scale neural data analysis, and mechanistic modeling to better understand the principles underlying neural computation.