I am an Assistant Professor in the Department of Neuroscience at the Université de Montréal and an Associate Member of Mila (Quebec Artificial Intelligence Institute). My research fuses AI and computational neuroscience with experimental neurophysiology and neural engineering to study how biological brains throughout the evolutionary tree coordinate behavior. Previously: Icahn School of Medicine at Mount Sinai / University of Geneva / Northwestern University / EPFL / University of Pittsburgh

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(co-supervisor: Guillaume Lajoie) I am a post-doctoral researcher in computational neuroscience interested in unraveling the algorithmic basis of learning in neural control of movements. Particularly, I focus on fundamental motion such as reaching movements and tabula rasa learning that occurs in infancy, as opposed to sequential and compositional skills that require re-arrangement of pre-acquired movements. I currently work on disentangling which learning rules best match neural dynamics of learning, and what are the most efficient and stable ways to improve performance in complex control problems, including non-linear and over-determined (redundant) control. To that end, I leverage biomechanical modeling, deep recurrent neural networks, and reinforcement learning一particularly policy gradient methods, which are best suited for continuous control problems.

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(co-supervisor: Numa Dancause) I am a post-doctoral fellow in neurosciences at the Université de Montréal and associated with Mila as of Fall 2024. Here, I am studying the neurophysiologic basis of movement production in preclinical models for the ultimate goal of developing rehabilitative strategies after acquired injury. I am specifically interested in the coordination of neural activity across different cortical areas with an emphasis on interhemispheric dynamics and how they are integrated downstream at the level of the spinal cord resulting in muscle activation. Towards this end, I aim to probe these circuits using both electrical and optogenetic stimulation and relate it to the motor network during naturalistic behavior.

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I joined the sinthlab lab as a Ph.D. candidate in January 2025. Having a mixed background in biology and mathematics, I am interested in using different approaches to study the complexity of cognitive functions. More precisely, I aim to understand the computations underlying the integration of sensory-motor feedback from both computational and physiological approaches.

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I'm a Ph.D. candidate navigating the wondrous realms of Neuroscience at Université de Montréal and Mila. In this exciting journey, I'm exploring the fascinating blend of artificial intelligence and neuroscience. My mission involves using powerful AI tools to unravel the secrets hidden within neurons and physiological data. I'm particularly captivated by AI models that mimic brain circuits involved in sensory-motor loops to decode the brain's mysterious language, uncovering how it processes information and choreographs our movements. We unearth the fundamental neural mechanisms behind sensation and motor control.

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I am a PhD student at Mila and Université de Montréal, where my research intersects artificial intelligence and neuroscience, focusing on the brain’s exceptional abilities in motor control, data and energy efficiency, learning dynamics, and adaptation. I am fascinated by how the brain integrates information from various regions to produce complex behaviors and adapt to novel environments. Inspired by these biological processes, I incorporate inductive biases into deep learning models, leveraging insights from the brain’s adaptation and learning mechanisms. By embedding these biologically inspired principles, I aim to enhance the efficiency, scalability, and adaptability of AI systems. My goal is to draw parallels between neural mechanisms and AI architectures, advancing our understanding of the brain while developing more robust and versatile AI models.

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I am a mystery...

(co-supervisor: Guillaume Lajoie) I am a graduate student interested in bridging the gap between natural and artificial intelligence. Primarily, I am driven by the fact that certain abilities, notably efficient multimodal perception and compositional reasoning, are natural for humans but fickle for our most powerful AI systems. I wish to investigate the conditions under which these capabilities emerge for neural networks, and then use these insights to design more robust, interpretable, and sample-efficient generative frameworks — a crucial step in mitigating the widening resource disparity in an era of increasingly large models. My other interests include deep generative models, neural decoding, and the Toronto Raptors.

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