NCI Data Jamboree (Project Abstract Submission): Submission #51
Submission information
Submission Number: 51
Submission ID: 189161
Submission UUID: c934abcc-e454-42c8-b149-2406c885e74e
Submission URI: /nci/datajamboree/abstractsubmission
Submission Update: /nci/datajamboree/abstractsubmission?token=doaLigiaOIdBhWgR1_XvCWAdrQz1EmbruzD3k7LiK8o
Created: Mon, 07/27/2026 - 15:35
Completed: Mon, 07/27/2026 - 15:43
Changed: Mon, 07/27/2026 - 15:43
Remote IP address: 10.208.24.192
Submitted by: Anonymous
Language: English
Is draft: No
Webform: NCI Data Jamboree (Abstracts)
Submitted to: NCI Data Jamboree (Project Abstract Submission)
Presenter Information
Juhi
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Anand
Master of Science Business Analytics (University of Illinois Chicagio - USA), Master of Science Computer Science (University College Dublin - Ireland)
Research Specialist
University Of Illinois Cancer Center
Aurora
Additional Authors
Abstract Information
Evaluating data quality for reproducibility and AI-readiness
AI-readiness; data quality; Cancer Research Data Commons; multimodal oncology data; data leakage
Project Seeker: joining "AI-Readiness Scorecard: A Task-Relative Profiler for Multimodal Cancer Research Data Commons Datasets".
I am a confirmed member of the ready-to-go team for "AI-Readiness Scorecard: A Task-Relative Profiler for Multimodal Cancer Research Data Commons Datasets" (Lead: Nikita, University of Illinois Cancer Center) and am not seeking assignment to another project.
My primary contribution will be the development and validation of the statistical integrity engine, including batch and site-effect analysis, informative missingness detection, class imbalance assessment, data leakage detection, and robust cross-validation strategies to ensure reproducible AI-readiness evaluation. In addition, I will collaborate with other team members on integrating repository adapters with the common intermediate representation, validating oncology-specific quality metrics, refining the scoring framework, and testing the end-to-end pipeline to ensure all components work cohesively.
My background includes a Master of Science in Business Analytics from the University of Illinois Chicago and a Master of Science in Computer Science from University College Dublin. I currently work as a Research Specialist at the University of Illinois Cancer Center, where I develop data-driven solutions for oncology research using Python, machine learning, statistical analysis, SQL, and healthcare data. Through this project, I hope to contribute to an open, reproducible framework for evaluating AI-readiness of multimodal cancer datasets while expanding my expertise in biomedical AI, multimodal data integration, and research software development. I also look forward to working closely with the team across repository integration, clinical data validation, and the final demonstration to help deliver a cohesive and impactful solution.
My primary contribution will be the development and validation of the statistical integrity engine, including batch and site-effect analysis, informative missingness detection, class imbalance assessment, data leakage detection, and robust cross-validation strategies to ensure reproducible AI-readiness evaluation. In addition, I will collaborate with other team members on integrating repository adapters with the common intermediate representation, validating oncology-specific quality metrics, refining the scoring framework, and testing the end-to-end pipeline to ensure all components work cohesively.
My background includes a Master of Science in Business Analytics from the University of Illinois Chicago and a Master of Science in Computer Science from University College Dublin. I currently work as a Research Specialist at the University of Illinois Cancer Center, where I develop data-driven solutions for oncology research using Python, machine learning, statistical analysis, SQL, and healthcare data. Through this project, I hope to contribute to an open, reproducible framework for evaluating AI-readiness of multimodal cancer datasets while expanding my expertise in biomedical AI, multimodal data integration, and research software development. I also look forward to working closely with the team across repository integration, clinical data validation, and the final demonstration to help deliver a cohesive and impactful solution.