Archana Venkataraman

Associate Professor (ECE)

Office 8 St. Mary’s Street Boston, MA 02215, Room 422
Email archanav@bu.edu

Honors and Awards

2022 Program Chair, Medical Imaging with Deep Learning (MIDL)
2021- Associate Editor, Frontiers in Neuroimaging – Analysis Methods
2021 Best Paper Award, SPIE Medical Imaging (Image Processing Conference)
2020 Best Paper Award, Machine Learning for Clinical Neuroimaging Workshop
2020 Elected to Full Membership in the Sigma Xi Honor Society
2020 Invited Participant, National Academy of Engineering FOE Symposium
2020 Invited Scholar, World Leader’s Forum
2019- Editorial Board Member, Medical Image Analysis
2019 Best Paper Award, Connectomics for NeuroImaging Workshop
2019 MIT Technology Review: 35 Innovators Under 35
2019 NSF CAREER Award
2017 John C. Malone Assistant Professorship @ Johns Hopkins University
2016 Council of Early Career Investigators in Imaging (CECI2) Travel Award
2011-2012 NIH Advanced Multimodal Neuroimaging Training Program
2007-2010 National Defense Science and Engineering Graduate Fellowship (NDSEG)
2007-2008 Siebel Scholarship
2007 Morris Joseph Levin Award, Best Thesis Presentation (M.Eng.)
2006-2007 MIT Provost Presidential Fellowship
2006 Association of MIT Alumnae, Senior Academic Achievement Award

Research Areas

Professor Venkataraman’s research lies at the intersection of medical imaging, artificial intelligence, and clinical neuroscience. As PI of the Neural Systems Analysis Laboratory (NSA Lab), Professor Venkataraman develops new computational models and algorithms to characterize complex processes associated with brain dysfunction. Her current projects span the gamut from foundational computational neuroscience to translational applications of neuroimaging data to exploratory directions for probing neural circuitry. Each of these projects has yielded a new window into debilitating neurological and psychiatric disorders, such as autism, epilepsy, and schizophrenia, with the long-term goal of improving patient care. On the modeling front, our strategy has been to combine hypothesis-driven insights about the brain with data-driven learning techniques. This combination provides interpretable and actionable information while retaining predictive power. Our analytical frameworks also represent innovations in machine learning that can be adapted to problems outside of the medical realm.

Publications

View a complete list of publications chronologically by year.
View a complete list of publications by citation.

Department or Division: Electrical & Computer Engineering

Affiliations: Intelligent, Autonomous & Secure Systems, Primary & Affiliated Faculty

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