With a variety of interests concerning human health, cognition, and behavior, my research activities are interdisciplinary in nature and span clinical psychology, neuroscience, and cognitive science fields. Specifically, I am interested in the following:
NEUROCOGNITIVE HEALTH & DISEASE
How to preserve high cognitive functioning in the face of age-related neuropathology, cognitive decline, and brain injury.
Why people develop and maintain mental illness or addiction, and engage in aberrant, harmful, or self-injurious behaviors.
HUMAN COGNITIVE PROCESSING
How to optimize human cognitive processing to increase daily function and productivity (e.g., creativity, problem-solving).
ETHICAL ARTIFICIAL INTELLIGENCE
How artificially intelligent systems (e.g., GPT-3, Tesla, and Chatbots) are ethically and morally designed, consumed, and integrated.
I am currently exploring these areas through multiple research projects, which are further described below, and have been funded by two industry sponsored grants as well as the Undergraduate Research Opportunties Program.
ETHICS & TECHNOLOGY
UNDERSTANDING PRODUCT DESIGN & CONSUMER BEHAVIOR
Harvard University | Harvard Business School, Ethical Intelligence Lab, Department of Psychology
Affiliated Researcher | Advisor: Julian De Freitas, PhD, MSc | Remote Work
Examining the intersection of ethics and technology in behavioral, social, and consumer science. Currently focused on (1) automation control preference for autonomous vehicles (i.e., preferring control over safety); (2) vehicle preference in the case of front blind zones (i.e., preferring style or personal safety over public safety); and (3) the mediating role of stigma and mental health in smart chatbots. Results will yield real-world implications for technology development, manufacturing, and marketing.
MACHINE LEARNING & MEASUREMENT
IMPROVING VALIDITY OF OPIOID ABUSE SCREENING TOOLS
Yale School of Medicine, Emerge Research Program, Department of Psychiatry
Collaborating Researcher | Advisor: Lynnette Averill, PhD | Remote Work
Using machine learning (ML) methods (i.e., random forests, support vector machines, gradient boosting machines, and deep neural networks) to further validate clinical instruments, develop precision-based short forms, and enhance real-world implementation and utility. Currently enhancing predictive power and feature selection of the Opioid Abuse Risk Screener (OARS) via ML techniques, as well as validating the measure across healthy, chronic pain, and substance abuse samples. Ultimately working to integrate the OARS into medical settings to improve opioid risk assessment, stratification, and prescribing practices.
Ad-hoc reviewer for Chronic Stress by SAGE publications.
INTERPERSONAL NEURAL SYNCHRONY
UNDERSTANDING CREATIVE INTERACTION & PROCESSING
Duke University, Mind at Large Lab at Duke University, Department of Psychology & Neuroscience
Evaluating interpersonal neural synchronization in brainstorming dyads (i.e., interacting minds) and how this changes across populations (assed via psychobehavioral tasks and electrophysiological activity). In other words, how people connect with each other, get on the same page, "rev" one another up, and come together to produce ideas. Specifically focused on group creative interaction and underlying neurophysiological processes. Also exploring mind-wandering, dreaming, and psychedelics.
KETAMINE ASSISTED PSYCHOTHERAPY
IMPROVING NEUROPSYCHIATRIC SYMPTOMS VIA KETAMINE
Riverwoods Behavioral Health, Ketamine Assisted Psychotherapy Educational & Research Program
Co-Principal Investigator & Program Director | Advisor: Patricia Henrie-Barrus, PhD | Onsite Work
Testing a novel integrated approach to ketamine assisted psychotherapy (KAP), which combines sub-anesthetic ketamine (a rapidly-acting glutamatergic-based drug) with psychotherapy, mind-body skills (mindfulness, diaphragmatic breathing, and body scan), ambient music, and textile art for adults with major depression (study 1) and older adults with co-morbid major depression and early cognitive decline (study 2). Following the NIH Stage Model for behavioral intervention development, beginning with pilot feasibility studies.