2nd Annual Computational Data Neuroscience Symposium:

Virtually Connecting the Brain, Big Data, and Outcomes

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

Sponsored by the Brigham Health/Harvard Medical School Computational Neuroscience Outcomes Center at the Dept of Neurosurgery  & the Harvard School of Public Health Onnela Lab

 

You are cordially invited to the 2nd Annual Computational Data Neuroscience Symposium. Representing the first annual event 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 by Oct 10th.

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

 

Submit Your Abstract

Abstract submissions, especially from medical/graduate students and residents/postdocs, are welcome. Top-scoring abstracts will be eligible for awards and oral presentation.  Submission deadline is Sept 25.

 

We look forward to having you join us for this exciting event!

 

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 Sessions 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 Policy