Bahareh TolooshamsView profile
Assistant Professor
Bahareh Tolooshams is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Alberta, where she leads the NeuBahar Lab (Neuro–Bayesian AI for Human-interpretable Abstractions and Representation Learning). She is also a Fellow at the Alberta Machine Intelligence Institute (Amii), a world-leading AI institute, and a member of the Neuroscience and Mental Health Institute (NMHI). Her educational background includes a PhD from Harvard University (School of Engineering and Applied Sciences, May 2023), where she was affiliated with the Center for Brain Science, and a BASc with distinction from the University of Waterloo's Department of Electrical and Computer Engineering. Prior to joining the University of Alberta, she completed a postdoctoral fellowship at Caltech's AI for Science Lab, supported by the Swartz Foundation Fellowship for Theoretical Neuroscience. Dr. Tolooshams' research bridges machine learning, neuroscience, and signal processing, with a focus on developing interpretable AI systems that can provide mechanistic insights into neural computation. Her work centers on sparse representations, Bayesian methods, neural operators, and developing methods for analyzing neural signals and biological data. She has made significant contributions to dictionary learning, unrolled optimization networks, and physics-informed deep learning for inverse problems. Her recent publications demonstrate a strong trend toward developing interpretable deep learning models for neuroscience applications, particularly focusing on sparse coding frameworks that provide mechanistic insights into neural computation. She has pioneered approaches that bridge theoretical neuroscience with practical deep learning techniques, creating models that are both powerful and interpretable for understanding brain function and processing biological signals. Scientific Awards and Recognition Tianqiao and Chrissy Chen Brain-Machine Interface Grant Award at Caltech (2025) Rising Stars Award in Conference on Parsimony and Learning (2023) Rising Star in UChicago Data Science (2023) Swartz Foundation Fellowship for Postdoctoral Research in Theoretical Neuroscience (2023) AWS Machine Learning Research Awards (2019) QBio Student Fellowship (2019) QBio Student Award Competition Fellowship (2018) Dr. Tolooshams is actively involved in mentoring and community building, having participated in the Women in STEM Mentorship program at Harvard and the InTouch peer-to-peer support network for graduate students. She has co-initiated the NeurReps Global Speaker Series and served on the organizing team for the NeurReps workshop at NeurIPS. Her current research at the University of Alberta focuses on developing neuro-Bayesian AI approaches for creating human-interpretable representations that can provide insights into both artificial and biological intelligence. The NeuBahar Lab, under her leadership, is developing cutting-edge methodologies that combine Bayesian inference with deep learning to create models that not only perform well but also provide interpretable insights into the underlying data structures, with applications spanning neuroscience, medical imaging, and computational biology.
