Engineering Psychiatry Research Program Fellowship

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OVERVIEW:  The Department of Psychiatry and its Mental Health Tech Center formed an initiative in Fall 2017 to increase research collaborations between psychiatry and engineering. Psychiatry Faculty have created projects that attempt to problem solve an unmet challenge in the clinic or in research. The goal is to address the topic through an engineering solution. Students who are interested in this program can review the list of available projects and their descriptions below. The Engineering Psychiatry Research Program (EPRP) Fellowship is a quarterly unpaid research opportunity for ECE undergraduate and graduate students. Students can receive research credit for their participation via ECE 199/299. Students will have access to the tutors at the ECE Makerspace as well as Ph.D. students in Machine Learning and Data Science field.

TIME COMMITMENT: 10 hours weekly for 4 credits of ECE 199/299. 5 hours weekly for 2 credits of ECE 199/299.

BACKGROUND:  UCSD's Jacobs School of Engineering has world renowned faculty and resources in engineering, with several thousand students participating in several different courses at the undergraduate and graduate level.  As part of their coursework, these students could lend their talents to creating solutions to problems psychiatry faculty and trainees face in clinical care and in research.  Potential solutions might involve novel devices, diagnostic tools, software, new ways of analyzing data such as through machine learning, robotics, and many others.  It is our hope that, by linking students with identified problems and potential mentors in psychiatry, this effort will generate novel collaborations that could form the basis of new research projects, grant proposals, intellectual property and/or start up companies.  

TIMELINE:

Mid October 2018:  Program announcement. Projects solicited from faculty.

November 12, 2018:  Project proposals DUE from faculty

November 19, 2018: Projects posted on website. Intern application opens.

December 2, 2018:  Intern application DUE at 11:59 PM.*

December 10, 2018:   Intern application/information sent to faculty

Mid December to Early January 2019:  Faculty interviews applicants.

January 14, 2019:  Faculty decision deadline.

Early February 2019 (February 1st - 8th):  All applicants notified of application status.

Spring 2019:  Students enroll in research credit courses and start working on projects.

 

*Students can submit only one application per year and select a maximum of one project.  

 

***APPLICATION FOR SUMMER 2019 IS CLOSED.***

 

 

 

SPRING 2019 PROJECTS

 

FACULTY MENTOR

                                                                  PROJECT TITLE

Bloss, Cinnamon

 1.   Impact of Privacy Environments for Personal Health Data on Patients

Depp, Colin

 1.  Integrating Signals from On-Body and External Sensors to Predict Cognitive Aging

Ettenhofer, Mark

 1.   Eye Tracking and Virtual Reality for Traumatic Brain Injury

2.  Eye Tracking and Virtual Reality for Assessment of Traumatic Brain Injury

Granholm, Eric

 1.  Automated psychotherapy fidelity rating

Kaufman, Chris

 1.  A smartphone app to monitor sleep in older adults

Mausbach, Brent

 1.  Caregiver Project

Mishra, Jyoti

 1.   Designing Haptic Feedback Control for Brain States

Moore, Alison

 1.  Online screening and brief intervention for seniors with alcohol problems

2.  Identifying alcohol medication risks

Moore, Raeanne

 1.  Google Assistant for Cognitive Rehabilitation

Ramanathan, Dhakshin 

1.  Development/optimization of automatic rodent behavioral box

Van Patten, Ryan

1.  Leveraging Amazon’s Mechanical Turk for Big Data Investigations of Cognition in At-Risk Populations

 

SPRING 2018 PROJECTS

 

FACULTY MENTOR

                                                                  PROJECT TITLE

Bagot, Kara

 1.   A Mobile Health Intervention for Adolescent Cannabis Use

Granholm, Eric

 1.  Automated voice recognition therapy and symptom ratings in schizophrenia

 2.  Smartphone pupil dilation measurement to detect Alzheimer's disease

Jenkins, Janis

 1.   Mobile Health Technology for Middle School Students

Marcotte, Thomas

 1.  Tracking eye movements during acute cannabis use

 2.  Detecting THC and other cannabinoids using non-invasive biosensors

McEwen, Sarah

 1.  Movement based cognitive training

 2.  Simultaneous exercise and memory training for MCI patients

Mishra, Jyoti

 1.  Performance evaluations of real-time processing system for electrophysiological and behavioral signals

Moore, Raeanne

 1.  Mobile intervention to optimize social engagements

 2.  Alexia for Cognitive Rehabilitation

Owens, Robert

 1.  Actigraphy in the Intensive Care Unit

 2.  Freshmen Sleep and Health (FRoSH) Study

Twamley, Elizabeth

 1.  CogSMART app