The Rutgers Center for Biomedical Informatics & Health Artificial Intelligence (BMIHAI) has announced the selection of four research teams to receive funding through its Postdoctoral Health AI Research (PAIR) Fellowship program. The initiative aims to support postdoctoral researchers and foster interdisciplinary mentorship in biomedical informatics and health artificial intelligence.
The fellowship provides one year of funding for postdoctoral fellows who will work on new interdisciplinary projects under the guidance of mentors from various health AI-related fields.
Leslie Lenert, director of BMIHAI and professor at Robert Wood Johnson Medical School, said, “Investing in the next generation of biomedical informatics researchers is essential to advancing health innovation. These awards are intended to empower researchers to mentor emerging scholars who will shape the future of biomedical informatics and health AI.”
Antonina Mitrofanova, deputy director of BMIHAI and associate professor at Rutgers School of Health Professions, added, “Through the Postdoctoral Health AI Research (PAIR) Fellowships, we’re not only strengthening research capacity but also cultivating a collaborative environment where fresh perspectives and cutting-edge methodologies can thrive.”
The selected projects include:
– Project AiCCESS (Artificial Intelligence for Comprehensive Care, Equity, and Sustainability in Surgery), which seeks to use multimodal AI to predict, detect, and manage surgical site infections. The team stated, “Project AiCCESS is redefining surgical care by harnessing multimodal AI to predict, detect, and manage surgical site infections.” The project is led by Mayur Narayan with co-mentors Divya Kewalramani, Les Barta, Rachel Choron, and Dimitris Metaxas.
– Combined motor and brain function analysis using AI for screening cognitive impairment. According to the research team: “The proposed approach uses a novel dual-task test to accentuate subtle brain function alterations due to Alzheimer’s disease that are measured using fNIRS, providing the opportunity to reduce the cost and duration of cognitive screening.” This project is led by Nima Toosizadeh with co-mentors Michal Schnaider Beeri and Laleh Najafizadeh.
– Advancing ICU Care with Real-Time, AI/ML-Driven, Patient-Specific Digital Twins (AID-TWIN), which aims to create a modular digital twin framework for intensive care units. The team described their goal as creating “a modular, AI/ML ICU digital twin framework to accelerate proactive, individualized care and build workforce capacity through immersive, cross-disciplinary training.” Jag Sunderram leads this project with co-mentors Ioannis Androulakis, Julie Goswami, Sabiha Hussain, Aesha Jobanputra, Shantenu Jha, Kaushik Kumar, and Thomas Nahass.
– Transfer Learning for Brain-Based Prediction in Psychotic Illness. The team said: “We will develop transfer-learning models leveraging large datasets to improve brain-based predictions of symptoms and treatment response in bipolar depression and schizophrenia across clinical cohorts.” This effort is led by Avram Holmes with co-mentor Waheed Bajwa.
BMIHAI operates within the Institute for Health (IFH) at Rutgers University. It brings together educational programs and research initiatives involving data science across health disciplines under one center focused on advancing research through artificial intelligence applications. More information about BMIHAI can be found at https://ifh.rutgers.edu/center-for-biomedical-informatics-and-health-artificial-intelligence/
