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Sabbir M. Rashid

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

Email: rashis3@rpi.edu
View publications

Sabbir M. Rashid is a Postdoctoral Research Associate at the RPI Tetherless World Constellation. Dr. 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. His dissertation research involved the application of deductive and abductive inference techniques over linked health data, such as in the context of chronic diseases like diabetes. Before 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.

Thesis defense: 
https://tw.rpi.edu/media/thesis-defense-employing-ensemble-reasoning-support-clinical-decision-making-sabbir-rashid

Thesis document: 
https://www.proquest.com/openview/da085fc6138578a8929d275089fade2b/

Google Scholar: 
https://scholar.google.com/citations?user=F3pzzo4AAAAJ&hl=en 

Talks: 
https://tw.rpi.edu/media/twed-talk-sabbir-rashid-employing-ensemble-reasoning-support-clinical-decision-making-21-mar 

https://tw.rpi.edu/media/twed-talk-sabbir-rashid-creating-trading-card-game-using-knowledge-graphs-24-apr-2024 

https://www.youtube.com/watch?v=dUum3R56xnY 

https://www.youtube.com/watch?v=KYvMXu60FjY 

https://www.youtube.com/watch?v=x54GC9XRHt0 


Projects as Research Staff

Whyis

Whyis is a provenance-aware 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.

Semantic Data Dictionaries (SDD)

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.

Semantic Extract, Transform, and Load-er (SETLr)

Semantic Extract, Transform, and Load-er (SETLr) is a flexible, scalable tool for providing semantic interpretations to tabular, XML, and JSON-based data from local or web files.

Automated clusteRing Curriculum LearnIng Guided by Human Training (ARCLIGHT)

Automated clusteRing Curriculum LearnIng Guided by Human Training (ARCLIGHT), is a classification engine capable of (1) automated discovery and characterization of objects and activities in multimedia data and (2) solicitation of input from human analysts to refine, correct, or update its interna

Projects as Research Assistant

Health Empowerment by Analytics, Learning, and Semantics (HEALS)

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.

NanoMine: Ontology-Enabled Polymer Nanocomposite Open Community Data Resource

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

CHEAR (Child Health Exposure Analysis Repository)

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 aimed at adding or expanding the inclusi

The Human-Aware Data Acquisition Infrastructure (HADatAc)

HADatAc (Human-Aware Data Acquisition framework) is an open-source infrastructure that enables combined acquisitions of data and metadata in a way that metadata is properly and logically connected to data.

MaterialsMine: An open-source, user-friendly materials data resource guided by FAIR principles

The MaterialsMine Team brings together expertise across five research institutions in the fields of mechanics, materials, design, manufacturing, data science, and computer science to build and develop an open-source, user-friendly materials data resource guided by FAIR principles, with current mo