Title: You Can Teach an Old Dog New Tricks – Deep Learning for Data-Starved Applications

Abstract: Deep learning has infiltrated nearly every major field of study. The unprecedented success of these models has, in many cases, been fueled by an explosion of data. Millions of labeled images, thousands of annotated ICU admissions, and hundreds of hours of transcribed speech are common standards in the literature. Clinical neuroscience is a notable holdout to this trend. It is a field of unavoidably small datasets, massive patient variability, and an arguable lack of ground truth information. My lab tackles the challenges of this domain by blending the interpretability of generative models with the representational power of deep learning. This talk will highlight three ongoing projects that span a range of “old school” methodologies and applications. First, I will discuss a joint optimization framework that combines dictionary learning with recurrent neural networks to predict behavioral deficits from multimodal brain connectivity. Second, I will describe a probabilistic graphical model for epileptic seizure detection using multichannel EEG. The latent variables in this model capture the spatiotemporal spread of a seizure; they are complemented by a nonparametric likelihood based on convolutional neural networks. Finally, I will touch on a recent initiative to inject emotional cues into human speech. Our approach combines diffeomorphic registration with generative adversarial networks.

Bio: Archana Venkataraman is a John C. Malone Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University. She directs the Neural Systems Analysis Laboratory and is a core faculty member of the Malone Center for Engineering in Healthcare. Dr. Venkataraman’s research lies at the intersection of artificial intelligence, network modeling and clinical neuroscience. Her work has yielded novel insights in to debilitating neurological disorders, such as autism, schizophrenia and epilepsy, with the long-term goal of improving patient care. Dr. Venkataraman completed her B.S., M.Eng. and Ph.D. in Electrical Engineering at MIT in 2006, 2007 and 2012, respectively. She is a recipient of the MIT Provost Presidential Fellowship, the Siebel Scholarship, the National Defense Science and Engineering Graduate Fellowship, the NIH Advanced Multimodal Neuroimaging Training Grant, the CHDI Grant on network models for Huntington's Disease, and the National Science Foundation CAREER award.Dr. Venkataraman was also named by MIT Technology Review as one of 35 Innovators Under 35 in 2019.