Spherical CNN's and DeepSurv for Psychosis Conversion and The Trick-or-Treat Index
About
Psychosis is a serious mental illness that involves altered perceptions of reality and symptoms such as hallucinations and delusions. Many of our questions regarding the disorder are still unanswered and here we'll discuss two projects taking computational approaches into investigating psychosis. First, a generative computational model that allows us to quantify the dysfunction in the brain's top-down expectations and bottom-up sensory signaling. Second, some ongoing work, a Spherical Convolutional Neural Network (SCNN) designed to analyze neuroimaging data across the brain - with an adapted integration of DeepSurv to predict conversion in Clinical High Risk (CHR) patients. The presentation will conclude with a few slides about the "Trick or Treat Index" and the best neighborhoods for trick or treating in Boston!
Speaker
Phillip Angelos
Phillip Angelos is a PhD student in the Faculty of Computing and Data Sciences at Boston University, advised by Dr. Joshua Peterson. He earned a Bachelor of Science in Psychology from Michigan State University and spent two years at Yale University researching positive symptom progression in psychosis. His research examines the intersection of artificial intelligence and psychology, with a focus on deep learning, decision-making, impulsivity, and related behavioral patterns.