Sabbir M. Rashid

Sabbir M. Rashid
Ph.D. 2023
Employing Ensemble Reasoning to Support Clinical Decision-Making
Advisor: Deborah L. McGuinness


Sabbir M. Rashid received his Ph.D. from Rensselaer Polytechnic Institute in 2023, working with Professor Deborah McGuinness on research related to data annotation and harmonization, ontology engineering, knowledge representation, and various forms of reasoning. Prior to attending RPI, Sabbir completed a double major at Worcester Polytechnic Institute, where he received B.S. degrees in both Physics and Electrical & Computer Engineering. Much of his graduate studies at RPI involved research related to the semantic annotation and transformation of data using Semantic Data Dictionaries. His research interests include the application of deductive and abductive inference techniques over linked health data, such as in the context of chronic diseases like diabetes.

Thesis defense:

Thesis document:

Projects as Research Assistant

The Center for Health Empowerment by Analytics, Learning, and Semantics (HEALS) is a five-year collaboration between Rensselaer and IBM aimed at researching how the application of advanced cognitive computing capabilities can help people to understand and improve their own health conditions.

Whyis is a nano-scale knowledge graph publishing, management, and analysis framework. Whyis aims to support domain-aware management and curation of knowledge from many different sources. Its primary goal is to enable creation of useful domain- and data-driven knowledge graphs.

Our evolving semantics=driven data resource, named NanoMine, is an open access, user friendly, living, growing, data resource for the polymer nanocomposites community that is scalable and enables improved understanding of processing – structure - property relationships and thus facilitates faster

The aim of the Semantic Data Dictionary (SDD) approach is to annotate datasets such that it is machine readable, uses best practice ontologies, and follows FAIR Guiding Principles.
The United States’ National Institute of Environmental Health Sciences has established an infrastructure, the Children’s Health Exposure Analysis Resource (CHEAR), to provide the extramural research community access to laboratory and statistical analyses aim