Please join the Johns Hopkins Department of Electrical and Computer Engineering for the Distinguished Lecture, titled “Deep Learning based Prediction for Autism and Cognitive Development,” presented by Dr. Martin Styner, Director of the UNC Neuro-Image Research and Analysis Laboratory.
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Title: Defining Patient Specific Functional Parcellations in Lesional Cohorts via Markov Random Fields
Niharika Shimona D’Souza’s Paper: A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data
Jeff Craley’s Paper: A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models
Please join the Whiting School of Engineering and the Johns Hopkins Department of Electrical and Computer Engineering for the William B. Kouwenhoven Memorial Lecture, titled “The ENIGMA Consortium: Mapping Human Brain Diseases with Imaging and Genomics in 50,000 Individuals from 35 Countries,” presented by Dr. Paul Thompson, Director of the ENIGMA Center for Worldwide Medicine, Imaging & Genomics.
Jacob will being working with Prof. Venkataraman on research to understand how manipulating a basic signal character can alter the perception of emotion contained in speech. He will receive a $4,000 fellowship and a $500 stipend for research supplies.
April 14, 2017 @ the Johns Hopkins Club, Homewood Campus
Dr. Venkataraman’s research lies at the intersection of multimodal integration, network modeling, and clinical neuroscience. Her goal is to develop a comprehensive and system-level understanding of the brain by strategically combining analytical tools, such as matrix factorization, signal processing, and probabilistic inference, with application-driven hypotheses. This approach promises to yield novel insights into debilitating neurological disorders, with the long-term goal of improving patient care.
April 5, 2017 @ 5:30pm, Rochester Institute of Technology
Title: An Adaptable Framework to Extract Abnormal Brain Networks
Abstract: There is increasing evidence that complex neurological disorders reflect distributed impairments across multiple brain systems. These findings underscore the importance of network-based approaches for functional data. However, network analyses in clinical neuroimaging is largely limited to aggregate measures, which do not pinpoint a concrete etiological mechanism. In contrast, I will present a novel Bayesian framework that captures the underlying topology of the altered functional pathways. I will also highlight some exciting future directions for our methodology that revolve around clinical understanding and interventions.