Present Institution:

Worcester Polytechnic Institute

Location:

Worcester MA US

Career Status:

Faculty Academia
Professor

Bio

Dr. Jean King is the WPI Peterson family Dean in the School of Arts and Sciences. She also serves as a Professor of Biology and Biotechnology
affiliate Professor in Biomedical Engineering Department
and Professor in the Neuroscience Program. Prior to joining WPI
she was Vice Provost for Biomedical Research at the University of Massachusetts Medical School; a tenured professor of psychiatry
radiology
and neurology; and Director of the university’s Center for Comparative Neuroimaging. Dr. King’s research is broadly focused on the adverse effects of stress on the brain
body
and behavior
including her current five-year
nine-million-dollar NIH grant to use AI to predict the response to mindfulness treatment for chronic pain. She is an active proponent for professional and diversity development through her service and leadership roles at the local
national
and international
including leadership roles with the National Institutes of Health
UMass Memorial Health Care
the Society for Neuroscience and World Women in Neuroscience organizations
and her diversity centered work has been featured in Nature. Dr. King is the recipient of several mentoring and community service awards
including the University of Massachusetts President’s Public Service Award for Exemplary Public service to the Commonwealth of Massachusetts.

Interested in presenting:

Behavioral
Drug studies
Imaging (fMRI)
Career advancement
Learning about networking
Managing a team
Negotiating a raise
Leading project teams
How to run a lab
Global Networking
Microaggressions
how to deal with them

Sample of a Science/Scholarship Abstract

What type of talks have you presented in the past?

Chronic pain is currently diagnosed using verbal self-reports
which present a challenge for patients with cognitive or physiological disorders. Prior work has explored machine learning prediction of pain from clinical data
which requires active user involvement and does not capture their behavior in natural settings. Passive objective assessment is desirable. Circadian Rhythms
including sleep-wake cycles
are biological processes that reoccur every 24 h and can be derived from physiological data such as heart rate
activity
and sleep
gathered using widely-owned smart wearables. This study investigated the feasibility of using machine learning and rest-activity circadian rhythm features to predict patients’ pain
including pain intensity
its interference with the patient’s life (dysregulation)
and their difficulty in performing physical functions using passively gathered actigraphy data. We found that chronic pain predictions were more accurate using rest-activity rhythm features than sleep or activity features. Of all the rhythmic features
Intradaily Variability (IV) was the most predictive feature
with elevated values in pain associated with disturbed sleep. PainRhythms provides preliminary evidence that rest-activity rhythms can effectively detect subjects with chronic pain. In future work
we aim to gather more data and confirm our preliminary findings on a large
class-balanced and diverse dataset.