Fang receives distinguished recognition as ‘Rising Stars (Engineering)’ from the Academy of Science, Engineering, and Medicine of Florida

Dr. Ruogu Fang has achieved the distinguished recognition of Rising Stars (Engineering) from the Academy of Science, Engineering, and Medicine of Florida (ASEMFL). She received this recognition for pioneering contributions in medical artificial intelligence for brain health, and for tireless education of diverse transdisciplinary researchers.

The ASEMFL is an esteemed gathering of Florida’s preeminent scholars, encompassing individuals who both reside and work in the state. Situated at the University of Central Florida in Orlando, ASEMFL is a non-profit organization that unites top-tier scholars and researchers hailing from various universities, public agencies, and industries throughout Florida. Their collective mission is to delve into critical issues at the intersection of science, engineering, and medicine that have a direct impact on the people of Florida. Furthermore, they provide impartial and expert advice concerning these matters.

Dr. Fang’s research revolves around the integration of artificial intelligence (AI) and deep learning with the intricacies of the human brain. Her research encompasses two principal themes: AI-empowered precision brain health and brain/bio-inspired AI. Her work involves addressing compelling questions, such as using machine learning techniques to quantify brain dynamics, facilitating early Alzheimer’s disease diagnosis through novel imagery, predicting personalized treatment outcomes, designing precision interventions, and leveraging principles from neuroscience to develop the next-generation of AI.

At the heart of her work is the Smart Medical Informatics Learning and Evaluation (SMILE) lab, where she is tirelessly dedicated to the creation of groundbreaking brain and neuroscience-inspired medical AI and deep learning models, as well as educating diverse transdisciplinary researchers. The primary objective of these models is to comprehend, diagnose, and treat brain disorders, all while navigating the complexities of extensive and intricate datasets.