Deep Latent Variable Modeling of Physiological Signals

Name: Khuong An Vo

Chair: Prof. Nikil Dutt

Date: May 24, 2024

Time:  9:30 AM

Location: Zoom

Committee: Prof. Erik Sudderth, Prof. Hung Cao, and Prof. Ramesh Srinivasan

Title: Deep Latent Variable Modeling of Physiological Signals

Abstract: A deep latent variable model is a powerful tool for modeling complex distributions. These models assume that underlying structures, but unobserved, are present within the data. In this dissertation, we explore high-dimensional problems related to physiological monitoring using latent variable models. First, we present a novel deep state-space model to generate electrical waveforms of the heart using optically obtained signals as inputs. This can bring about clinical diagnoses of heart disease via simple assessment through wearable devices. Second, we present a brain signal modeling scheme that combines the strengths of probabilistic graphical models and deep adversarial learning. The structured representations can provide interpretability and encode inductive biases to reduce the data complexity of neural oscillations. The efficacy of the learned representations is further studied in epilepsy seizure detection formulated as an unsupervised learning problem. Third, we propose a framework for the joint modeling of physiological measures and behavior. Existing methods to combine multiple sources of brain data provided are limited. Direct analysis of the relationship between different types of physiological measures usually does not involve behavioral data. Our method can identify the unique and shared contributions of brain regions to behavior and can be used to discover new functions of brain regions. The success of these innovative computational methods would allow the translation of biomarker findings across species and provide insight into neurocognitive analysis in numerous biological studies and clinical diagnoses, as well as emerging consumer applications.

Short Bio: Khuong An Vo is a Ph.D. candidate in Computer Science at UC Irvine, advised by Prof. Ramesh Srinivasan, Prof. Nikil Dutt, and Prof. Hung Cao. Prior, he received a B.Eng. (Honors) degree in CS from HCMC Vietnam National University with research revolved around statistical natural language processing and has been applied in large-scale commercial systems. Now, Vo’s research focuses on deep latent variable models in biomedical signal processing and neurocognitive modeling. He is also interested in the application of efficient deep learning in healthcare IoT. Vos’ work is supported by National Science Foundation (NSF) and National Institutes of Health (NIH) grants.