Shruthi Chari

I am a Computer Science PhD candidate at Rensselaer Polytechnic Institute advised by Professor Deborah L. McGuinness and Professor Oshani Seneviratne.
My research interests lie in the area of Clinical Natural Language Processing (NLP) and Explainable Artificial Intelligence (XAI). Specifically, I apply semantics, machine learning, and natural language processing techniques to further the fields of human-centered explainable AI and healthcare informatics..

Previously, I received my B.S. in Computer Science at PES Institute of Technology and M.S. in Computer Science at Rensselaer Polytechnic Institute.

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News

8/2023: Started to contribute to the explainability focus of the IARPA HIATUS project.
4/2023: Passed my Candidacy Presentation as part of my PhD program requirements. Committee: Prof. Deborah L. McGuinness, Co-Advisor: Prof. Oshani Seneviratne, Members: Prof. James A. Hendler, Dr. Prithwish Chakraborty and Dr. Pablo Meyer.
7/2023: Our work on Explanation Ontology V 2.0 appeared on Semantic Web J.
2/2023: Our work on Contextualizing Model Explanations using Medical Guidelines appeared on Artificial Intelligence in Medicine J.
8/2021: Completed my externship at the Center for Computational Health, IBM Research, and was hosted by Dr. Prithwish Chakraborty.
8/2021: We were one of the top best paper award winners at the Joint KDD 2021 Health Day and 2021 KDD Workshop on Applied Data Science for Healthcare for our work on Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case
11/2020: Awarded the best resource track paper for, Explanation Ontology: A Model of Explanations for User-Centered AI, at ISWC 2020.
3/2020: Our book chapters on Foundations of and Directions for Explainable Knowledge-Enabled Systems are available as published by IOS Press as a part of the Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges book.
9/2019: Started my PhD in Computer Science at RPI.
8/2019: Completed my internship at IBM Research, Yorktown Heights and hosted by Dr. Ching-Hua Chen.
3/2019: Defended my Masters thesis on Semantic Modeling of Cohort Descriptions, advised by Prof. Deborah L. McGuinness and Committee Members: Prof. Kristin Bennett, Prof. James A. Hendler and Dr. Amar K. Das.

Research - Selected Publications

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Full list of publications: Google Scholar

	Explanation Ontology: A General-Purpose, Semantic Representation for Supporting User-Centered Explanations Shruthi Chari, Oshani Seneviratne, Mohamed Ghalwash, Sola Shirai, Daniel M. Gruen, Pablo Meyer, Prithwish Chakraborty, Deborah L. McGuinness, Semantic Web Journal 2023 Paper / Code We introduce version 2.0 of the Explanation Ontology with better support for state of the art explainer methods and more explanation types, we now support the modeling of 15 explanation types.
	Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes Shruthi Chari, Prasant Acharya, Daniel M. Gruen, Olivia Zhang, Elif K. Eyigoz, Mohamed Ghalwash, Oshani Seneviratne, Fernando Suarez Saiz, Pablo Meyer, Prithwish Chakraborty, Deborah L. McGuinness, Artificial Intelligence in Medicine Journal 2023 Paper We introduce an end-end method to contextualize model predictions and explanations in a comorbodity risk prediction setting by implementing a question-answering pipeline to extract relevant sentences from medical guidelines.
	Explanation Ontology: A Model of Explanations for User-Centered AI Shruthi Chari, Oshani Seneviratne, Daniel M. Gruen, Morgan A Foreman, Amar K Das, Deborah L. McGuinness, International Semantic Web Conference, Resource Track, 2020 Arxiv We introduce our Explanation Ontology (EO), a resource to represent system-, user- and interface- dependencies of explanations. We are able to represent nine different explanation types within the EO.
	Directions for Explainable Knowledge-Enabled Systems Shruthi Chari, Oshani Seneviratne, Daniel M. Gruen, Deborah L. McGuinness, Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges, 2020 Arxiv We suggest directions in terms of research areas that will be important for AI explainability. We also introduce a definition for explanations that accounts for imporant dimensions such as context, user intent and knowledge.
	Knowledge Extraction of Cohort Characteristics in Research Publications Jade Franklin, Shruthi Chari, Morgan A Foreman, Oshani Seneviratne, Daniel M. Gruen, Jamie P. McCusker, Amar K Das, Deborah L. McGuinness, American Medical Informatics Association (AMIA) Annual Symposium, 2020 Paper Code We introduce an extraction and compositon pipeline for cohort description tables from clinical trial PDFs. The tables are converted into Study Cohort Ontology (SCO) structured knowledge graphs using our multi-step extraction pipeline.

Source code credit to Dr. Jon Barron