Events Calendar

NIH Funding Opportunities for Methodological Research in Mental Health

This seminar of the Center for Quantitative Methods and Data Science (QM&DS), in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center at Tufts CTSI and the Data-Intensive Studies Center (DISC) at Tufts University, is Wednesday, December 1, 2:00-3:30PM via Zoom. The topic is NIH Funding Opportunities for Methodological Research in Mental Health, presented by Christine M. Ulbricht, PhD, MPH.

This session will focus on opportunities for methodological research at the National Institute of Mental Health (NIMH). Christine Ulbricht, PhD, MPH, the Chief of the Methodological Research Program in the Services Research and Clinical Epidemiology Research Branch at NIMH, will provide an overview of the NIH and discuss NIMH’s funding priorities, programs, and mechanisms. She will discuss recent advances in statistical methods for mental health services research and funding opportunities for such research, such as NIMH-funded studies of machine learning applications to prevent suicide.

Faculty

Christine M. Ulbricht, PhD, MPH is a psychiatric epidemiologist who oversees the extramural Methodological Research Program within the Services Research and Clinical Epidemiology Research branch of the Division of Services and Intervention Research at the National Institute of Mental Health (NIMH). Prior to joining NIMH, she was an assistant professor at the University of Massachusetts Medical School, where her primary research interests were in applying novel statistical methods to understand heterogeneity of treatment effects, improve mental health services, and improve suicide prevention. She has served as the principal investigator of several NIH-funded studies leveraging big data to examine major depressive disorder, serious mental illness, and suicide among younger and older long-term care residents. Additionally, Dr. Ulbricht has been a co-investigator of studies on improving suicide risk identification in healthcare systems and on examining pain among older adults. She also served as associate faculty of the UMass Medical School’s Department of Psychiatry’s Implementation Science and Practice Advances Research Center and Eunice Kennedy Shriver Center and as the faculty co-director of the student chapter of the International Society of Pharmacoepidemiology

Details

Wednesday, December 1, 2:00-3:30PM, via Zoom

Registration

To attend, please register here via Tufts CTSI I LEARN.

Overview

Join us for a half-day mentoring virtual event geared towards fellows and early career faculty who are interested in exploring mentorship and career development in clinical and translational research.

The event provides networking, panel discussions, and one-to-one mentoring with senior faculty from
local clinical and translational science institutions. It is an excellent opportunity to obtain career advice from mentors and presenters.

Agenda

• 8:30AM: Networking and introductions
• 9:00AM: One-to-One Mentoring Sessions
• 10:00AM: Panel discussion, Getting to K and Beyond
• 11:00AM: Exploring Career Opportunities in Translational Science
• 12:00PM: Closing remarks/questions

Registration

To attend, please register here.

Note: Mentees will be asked to provide an NIH formatted biosketch ahead of the mentoring session.

Details

Friday, December 10, 8:30AM-12:30PM

Online

Download a flyer (PDF).

How can data science and machine learning be used to illuminate and reduce inequality in health care and public health?

The December seminar of the Center for Quantitative Methods and Data Science (QM&DS), in partnership with the Biostatistics, Epidemiology and Research Design (BERD) Center at Tufts CTSI and the Data-Intensive Studies Center (DISC) at Tufts University, is Wednesday, December 15, 2:00-3:30PM via Zoom. The topic is Using Machine Learning to Increase Equality in Health Care and Public Health, presented by Emma Pierson, PhD, MS.

Our society remains profoundly unequal. Worse, there is abundant evidence that algorithms can, improperly applied, exacerbate inequality in health care and other domains. This talk pursues a more optimistic counterpoint – that data science and machine learning can also be used to illuminate and reduce inequality in health care and public health – by presenting vignettes about women’s health, COVID-19, and pain.

Faculty

Emma Pierson, PhD, MS is an Assistant Professor of computer science at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion, and a computer science field member at Cornell University. She develops data science and machine learning methods to study inequality and healthcare. Her work has been recognized by a Rhodes Scholarship, Hertz Fellowship, Rising Star in EECS, MIT Technology Review 35 Innovators Under 35, and Forbes 30 Under 30 in Science. She has written for The New York Times, FiveThirtyEight, The Atlantic, The Washington Post, Wired, and various other publications.

Details

Wednesday, December 15, 2:00-3:30PM, via Zoom

Registration

To attend, please register here via Tufts CTSI I LEARN.