Shruthi Chari

Shruthi is a fourth-year PhD student advised by Prof. Deborah L. McGuinness. Her research interests lie in applying semantics, machine learning, and natural language processing techniques to further the fields of human-centered explainable AI and health informatics. She is currently pursuing a PhD in Computer Science and also previously received a Masters from Rensselaer, with a thesis on Semantic Modeling of Cohort Descriptions. Her PhD research is funded on the IBM-RPI Health Empowerment by Analytics and Semantics project, and she is part of the AI explainability workstream in the project. Her PhD research in explainable AI aims to improve human-centered explainability with a focus on formalizing techniques to combine insights from different model explainers and knowledge sources and extract authoritative knowledge from clinical literature. Since her research is interdisciplinary, she is also training in conducting user studies and interaction sessions with domain experts to understand the value of explanations and help improve them based on user feedback. She has published research papers at top AI and medical informatics conferences and workshops, including at venues such as the International Semantic Web Conference (ISWC), American Medical Informatics Association (AMIA), and Knowledge Data and Discovery (KDD) conferences and has also co-authored book chapters on foundations and directions for explainability. She has been recognized with several awards for her research, including the best resource paper award at ISWC 2020, best workshop paper award at the 2021 KDD Workshop on Applied Data Science for Healthcare, and the best poster award at the MIT-IBM AI Week 2019.

In her spare time, she likes to travel, hike, try new cuisines and workout. Currently, she also serves as a social media chair on the Computer Science Graduate Council. In the larger scheme, she hopes that her research will contribute towards making AI more trustworthy and readily human usable.

View Shruthi's Google Scholar page.