Abstract: Deep learning has disrupted nearly every major field of study from computer vision to genomics. The unparalleled 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 complex (largely unknown) phenomena. My lab tackles these challenges across a spectrum of projects, from answering foundational neuroscientific questions to translational applications of neuroimaging data to exploratory directions for probing neural circuitry. One of our key strategies in this data-starved regime has been to blend the structure and interpretability of classical methods with the representational power of deep learning.
This talk will highlight three projects that span a range of “old meets new” methodologies. On the foundational front, I will discuss our work to predict complex behavioral deficits from brain connectivity data. This framework combines dictionary learning for structure-function integration with recurrent neural networks to parse the evolving brain states. Next, I will describe our translational work on epileptic seizure detection from multichannel EEG. We develop a probabilistic graphical model, where the latent variables capture the spatiotemporal spread of a seizure; they are complemented by a deep data likelihood model. Finally, I will touch on an exploratory initiative to inject emotional cues into human speech. Our approach combines diffeomorphic curve 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, multimodal integration, 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, numerous best paper awards, 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.
Hosted by: Peter Ramadge and Kaushik Sengupta
- Center for Statistics and Machine Learning
- Electrical and Computer Engineering