I am a 2014 graduate of the Psychology department at Carnegie Mellon University, and a 2018 graduate of the Computer Science department at the University of Pittsburgh. I specialize in perceptual computation, having assisted with and conducted research in human perceptual cognition and computer vision over several years as a post-baccalaureate research associate, and aim to contribute to the advancement and permeation of modern machine learning techniques as well as the understanding of human perception by developing software systems inspired by the structure and function of the brain.
I’m interested in the cognitive mechanisms and neural architecture underlying perception. More specifically, I’m interested in the processing, internal representation and integration of visual and auditory sensory information, the relationship between perceptual and conceptual representations, and how this relationship is exploited in the hierarchies of perceptual cognition (e.g. object, scene, and event recognition) and language processing. The degree to which existing feature representations derived from other modalities are recruited during mid-to-high-level cognitive processing of, nominally, one modality (e.g. using intermediate compositional auditory representations during visual scene processing) is an open research question which I hope to make an attempt to answer.
I am especially interested in synesthesia - the cortical architecture that gives rise to its many known (and as yet unknown) forms, and the similarities and differences between synesthetes and non-synesthetes in how their perceptual processes interact with semantic representations. I view synesthesia as a potential window into the relationship between perception and conception, and aim to understand how the experiences of those with synesthesia may point to an underlying framework of multisensory integration. My research combines approaches from both cognitive neuroscience and machine learning - an enduring goal is not only to develop better process models of higher-level multimodal cognition in humans, but to use these models to construct novel artificial neural network models which are capable of such advanced integrated sensory processing in applied settings and which would prove useful in a variety of contexts.
In the course of my research I employ both neuroimaging and computational modeling techniques to further understand the phenomena I’m interested in. I perform EEG and fMRI imaging, in addition to dMRI connectivity mapping, and plan to explore deep network architectures that may approximate both synesthetic and non-synesthetic multimodal integration, both to better understand the corresponding cortical mechanisms and to apply these mechanisms to real-world computational and perceptual problems.
Master’s of Science in Computer Science, University of Pittsburgh, Pittsburgh, PA
Bachelor’s of Science in Computer Science, University of Pittsburgh, Pittsburgh, PA
Bachelor’s of Science in Psychology, Carnegie Mellon University, Pittsburgh, PA
Research Associate/Lab Manager for TarrLab, Carnegie Mellon University Department of Psychology, Pittsburgh, PA
PI: Dr. Michael Tarr; Focus: Vision science
Research Assistant for Perfetti Lab, University of Pittsburgh Learning Research & Development Center, Pittsburgh, PA
PI: Dr. Charles Perfetti; Focus: Cognition of language
Large-scale neuroimaging dataset comprising fMRI scans of brain activity in response to over 5000 images drawn from prominent computer vision datasets. Assisted with various stages, including experiment implementation, and software solutions for data management.
Jayanth Koushik, Austin Marcus, Aarti Singh, and Michael J. Tarr (2018, May). Real-Time Optimization for Visual Feature
Identification. Poster session presented at the annual meeting of the Vision Sciences Society, St. Pete Beach, FL.
John A. Pyles, Emily D. Grossman, Austin I. Marcus, and Michael J. Tarr (2018, May). Combined Structural and Functional Mapping of the Superior Temporal Sulcus. Poster session presented at the annual meeting of the Vision Sciences Society, St. Pete. Beach, FL.