Chris Romano

I am the Research Coordinator of the Health Aware Bits Lab (a.k.a. "HABits Lab"), an interdisciplinary group that uses wearable sensors and machine learning analytics to learn about health-risk habits and behaviors. I am also a Behavior Science Associate at Behavioralize LLC, where I consult for clients in the digital health sector.

I am applying for psychology/cognitive science doctoral programs matriculating Fall 2023. I am interested in cognitive theory and applying cognitive models and technologies to real-world problems. My theoretical work includes studying how interpersonal closeness and construal change perceptions and/or perceptual judgements, studying how groups arrange themselves and their thinking in pursuit of shared goals, and studying how conceptual specificity affects the value of a referent as a blame target in control restoration. My applied work includes designing a stethoscope that modifies its audio input to produce sound that is easier for individuals with auditory processing disorder to learn from, prototyping a wearable keyword spotter that indexes dichotomous thinking tendencies from speech by counting absolutist words, and using wearable patches to understand psychological stress through electrophysiological recordings.

Broadly, I am interested in the formality of systems that connect minds to matter and to each other. This includes rigid systems like mathematics—it is fascinating to study how people come to grasp the transformation rules that map physical and conceptual phenomena to mathematical expressions. This also includes systems like language, in which my interests are in the inter-subject variability of word-meaning associations and the resistance of natural language to formal characterization, i.e., the elusive 'context-free grammar'. I like to think about these things in terms of information theory, such as by studying how rulesets learned through examples become context-independent, or by using serial reproduction tasks to encode individual features as differences between repeatedly-generated text passages.

Working in machine intelligence, I have come to believe there is a sort of methodology renaissance happening, and so I am interested in trying new measurement and analysis methods in cognitive subject areas. Pervasive computing and the near-ubiquity of mobile technology offer new ways of capturing phenomena; and machine learning and related analytic methods offer new ways of exploring data. I am very excited to see what these advances have to offer in terms of cognitive modeling.