2nd Annual Computational Data Neuroscience Symposium:

Virtually Connecting the Brain, Big Data, and Outcomes

Friday Oct 23, 2020 Virtual - 8 am-4 pm EST

Hosted by the Brigham Health/Harvard Medical School Computational Neuroscience Outcomes Center at the Dept of Neurosurgery  & the Harvard School of Public Health Onnela Lab. Made possible with generous support from Care New England Health System.


Thank you for attending the 2nd Annual Computational Data Neuroscience Symposium. Representing the first annual series of its kind, the symposium brings together leading data science experts in the clinical neurosciences for a day of keynote & plenary talks on state-of-the-art advances, abstract presentations of cutting-edge work, & sessions to foster innovative collaborations. The audience includes the spectrum of computational neuroscience: both clinicians & scientists in neurosurgery, neurology, neuroradiology, neuropathology, psychiatry, neuroonc, rad onc, neuroengineering, biostatistics, & data science.


Due to public health concerns, the Symposium will take place virtually this year.


Registration is FREE

Registration is open to all clinicians, researchers, trainees, & students. 


Abstract submission is closed

Abstract submissions, especially from medical/graduate students and residents/postdocs, are welcome. Top-scoring abstracts will be eligible for awards and oral presentation.


Thank you for attending, the abstracts and talks will be posted soon.


The program and talks from last year's inaugural 2019 Symposium are now also available (accessed from the links above), including from our Keynote speakers: Dr. Anil Nanda (Senior Vice President and Chair of Neurosurgery, Rutgers and RWJ Medical School), Dr. Jianying Hu (Global Science Leader, AI for Healthcare, IBM Research), and Dr. Neil Martin (Chief Quality Officer, Geisinger; Director of Geisinger Neuroscience Institute)

Symposium Topics Include:

National Outcomes and Health Services Research


Patient Health Tracking and Digital Phenotyping


Precision Medicine - Genomics, Pathomics, and Radiomics


Big Data and AI: Implications, Ethics, and Innovation