معرفی
Emily E. Haroz is an Associate Professor at the Johns Hopkins Bloomberg School of Public Health, Department of International Health, with a focus on Social and Behavioral Interventions and Mental Health. Her work bridges psychiatric epidemiology, implementation science, and artificial intelligence to improve mental health services for underserved populations, particularly Indigenous communities.
- PhD, Johns Hopkins Bloomberg School of Public Health (2015)
- MHS, Johns Hopkins Bloomberg School of Public Health (2011)
- MA, Columbia University (2009)
- BA, University of Puget Sound (2004)
Her research centers on suicide prevention, leveraging AI and implementation science to enhance care delivery. She investigates culturally appropriate interventions, risk modeling in American Indian populations, and digital tools for real-time suicide risk assessment. Her work emphasizes equity, community engagement, and scalable solutions.
Recent publications highlight trends in machine learning for suicide risk prediction, home-visiting programs for mental health and chronic disease, and pandemic-related educational and mental health challenges. Her studies span global contexts, including Native American communities and urban slums in New Delhi.
Notable research projects include:
- NATIVE-RISE: Risk Identification for Suicide and Enhanced care for Native Americans (NIMH)
- Family Spirit Strengths: Home visiting for caregivers with mental distress (NIDA)
- Reclaiming Indigenous Children’s Futures (Lego Foundation)
- Adolescent suicide care systems (Johns Hopkins-Kaiser Permanente)
She collaborates widely across institutions and disciplines, with publications in high-impact journals like JAMA Psychiatry and npj Mental Health Research. Her work has been picked up by news outlets and discussed on social media, indicating broad relevance. Though no formal advisees are listed, she is likely mentoring students through her active research programs. She is not known to have received individual scientific awards, but her grant funding reflects strong peer recognition.
