Large Language Models as Data Interfaces for Health Applications [Video and Slides]

Expeditions in Experiential AI Seminar with Silvio Amir


Silvio Amir is a core faculty member at EAI and assistant professor in the Khoury College of Computer Sciences. His research develops Natural Language Processing and Machine Learning methods for personal and user generated text, such as social media and clinical notes from Electronic Health Records. Amir is primarily interested in methods for tasks involving subjective, personalized or user-level inferences (e.g., opinion mining and digital phenotyping). In particular, his work aims to improve the reliability, interpretability and fairness of predictive models and analytics derived from personal and user generated data. His research is part of ongoing efforts to develop Human-centered AI (i.e., to empower rather than replace humans) and AI for Social Good (i.e., to tackle meaningful social, societal, and humanitarian challenges). To achieve these goals, he often collaborates with domain experts in multidisciplinary projects to address real-world problems in the social sciences, medicine, and epidemiology.

Amir earned his doctorate from the University of Lisbon, conducting part of his doctoral research as a visiting researcher at the University of Texas at Austin and at Northeastern University in Boston. He then moved to John Hopkins University, where he completed his postdoctoral research in the Center for Language and Speech Processing and served as a lecturer at the Whiting School of Engineering.