Dr. Reza Abbasi-Asl is a distinguished computational neuroscientist whose innovative work integrates machine learning, statistics, and neuroscience to unravel the complexities of brain function. As of 2024, Dr. Reza Abbasi-Asl is an Associate Professor in both the Department of Neurology and the Department of Bioengineering and Therapeutic Sciences at the University of California, San Francisco. As Director of Data Analytics and Visualization at the UCSF Weill Institute for Neuroscience, he spearheads initiatives that transform complex neural data into meaningful scientific insights. Dr. Abbasi-Asl earned his PhD in Electrical Engineering and Computer Sciences from UC Berkeley in 2018 under the mentorship of Professor Bin Yu, following earlier graduate studies at Sharif University of Technology.
His groundbreaking research has established novel frameworks for analyzing large-scale neurophysiological datasets using interpretable machine learning approaches that maintain scientific transparency while achieving computational sophistication. The Abbasi Lab has made significant contributions to understanding brain connectivity and visual sensory processing through innovative statistical modeling of single-cell neural data from large-scale recordings. His work has garnered over 3,400 citations according to Google Scholar, reflecting substantial impact across computational neuroscience and biomedical data science communities. By transforming unstructured neuroscience data into actionable knowledge through advanced computational approaches, his research has established new methodologies for characterizing neural function in relation to complex connectivity patterns.
Beyond his research achievements, Dr. Abbasi-Asl serves as a core faculty member at the UCSF Neuroscape Center and as a Weill Neurohub Investigator, where he fosters interdisciplinary collaborations between neuroscientists, clinicians, and data scientists. He actively mentors multiple PhD students and postdoctoral scholars across the UC Berkeley/UCSF Bioengineering and Computational Precision Health graduate programs, nurturing the next generation of computational neuroscience researchers. His leadership extends to developing projects that leverage large language models for analyzing unstructured biomedical data, advancing the field toward more comprehensive knowledge discovery systems. Currently, his laboratory continues to push boundaries through work on structure-based synthetic data augmentation for protein language models, maintaining his position at the forefront of computational neuroscience.
