Join us for a one-day workshop bringing together computational and experimental researchers who utilize large-scale models, machine learning, and data-driven strategies in neuroscience . This event will include expert presentations, interactive discussions, and hands-on technical sessions focused on implementing large-scale computational and AI-driven models within the field. 

Participants will also have the hands-on opportunity to explore computational and ML/AI models, learning to build and run realistic simulations, and implement ML/AI models on various neuroscience datasets. By the end of the workshop, attendees will have both the practical skills and the computational resources necessary to integrate these advanced methods into their own research workflows. 

This workshop is supported by the IDEaS ARTISAN group , which provides cyberinfrastructure for neuroscience research. Through the NSF-funded Cybershuttle project ( Cybershuttle ), participants will have access to advanced computational tools and data-sharing platforms to enhance their research capabilities.

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Anton Arkhipov

Abstract: TBA.

About: Anton Arkhipov applies high-performance computing to simulate neuronal activity in the mouse visual system. Using experimentally observed neuronal morphologies, connectivity patterns, and electrophysiological properties, he constructs highly detailed, biophysically accurate models of neuronal circuits. The main aim is to build powerful models that, on the one hand, reproduce major features of in vivo experimental recordings from anesthetized and awake mice and, on the other, allow for testable predictions about mechanisms involved in processing of the visual information in the cortex.

 Maxim Bazhenov

Abstract: TBA

 About:The ultimate goal of my research is to understand how the brain processes and learns, the underlying mechanisms behind brain activities in norm and pathology. My main research interests are: Brain inspired AI algorithms for memory and learning, Role of sleep in memory consolidation, Reinforcement learning and decision making, Robotics, Neuronal mechanisms of cognitive deficiencies in epilepsy and schizophrenia. To address these questions, I use a broad spectrum of approaches including computational neuroscience, machine learning and electrophysiology.

Eva Dyer

Abstract: TBA

 About: Eva Dyer’s research combines machine learning and neuroscience to understand the brain, its function, and how neural circuits are shaped by disease. Her lab, the Neural Data Science (NerDS) Lab, develops new tools and frameworks for interpreting complex neuroscience datasets and building machine intelligence architectures inspired by the brain. Through a synergistic combination of methods and insights from both fields, Dr. Dyer aims to advance the understanding of neural computation and develop new abstractions of biological organization and function that can be used to create more flexible AI systems.

Anqi Wu

Abstract: TBA

About: Anqi Wu is an Assistant Professor at the School of Computational Science and Engineering (CSE), Georgia Institute of Technology. She was a Postdoctoral Research Fellow at the Center for Theoretical Neuroscience, the Zuckerman Mind Brain Behavior Institute, Columbia University. She received her Ph.D. degree in Computational and Quantitative Neuroscience and a graduate certificate in Statistics and Machine Learning from Princeton University. Anqi was selected for the 2018 MIT Rising Star in EECS, 2022 DARPA Riser, and 2023 Alfred P. Sloan Fellow. Her research interest is to develop scientifically-motivated Bayesian statistical models to characterize structure in neural data and behavior data in the interdisciplinary field of machine learning and computational neuroscience. She has a general interest in building data-driven models to promote both animal and human studies in the system and cognitive neuroscience.

Hannah Choi

Abstract: TBA

About: Hannah Choi is an Assistant Professor in the School of Mathematics at Georgia Tech. Her research focuses on mathematical approaches to neuroscience, with primary interests in linking structures, dynamics, and computation in data-driven brain networks at multiple scales. Before coming to Georgia Tech, she was a postdoctoral fellow at the University of Washington and also a visiting scientist at the Allen Institute for Brain Science, and spent one semester at the Simons Institute for the Theory of Computing at the University of California, Berkeley as a Patrick J McGovern Research Fellow. She received her Ph.D. in Applied Mathematics from Northwestern University and her BA in Applied Mathematics from the University of California, Berkeley.

Lu Mi

Abstract: TBA

About: Lu Mi comes to Georgia Tech from the Allen Institute for Brain Science and the University of Washington where she was a Shanahan Foundation Fellow. She completed her Ph.D. at MIT in 2022 with Professor Nir Shavit. Mi’s research lies at the intersection of natural intelligence and artificial intelligence. She mainly focuses on building fast and scalable automatic brain imaging pipelines. By leveraging artificial neural networks, Mi aims to understand the mechanisms of coding, computation, and learning within biological neural networks, using multi-modal neural data. She is also interested in developing brain-inspired machine intelligence frameworks.

Ratan Murty

Abstract: TBA

About: Research in the Murty Lab aims to uncover the neural codes and algorithms that enable us to see. The central theme of our work is to integrate biological vision with artificial models of vision. Our work combines the benefits of closed-loop experimental testing (using 3T/7T human functional-MRI) with cutting-edge computational methods (like deep neural networks, generative algorithms, and AI interpretability) toward a new computationally precise understanding of human vision. Our research also guides the development of neurally mechanistic biologically constrained models aimed to uncover a better understanding of the neurobiological changes that underlie perceptual abnormalities such as agnosias.

Nabil Imam

Abstract: TBA

About: Nabil Imam works on topics in machine learning and theoretical neuroscience with the goal of understanding general principles of neural coding and computation, and their technological applications. Prof. Imam joined Georgia Tech faculty in January 2022.