Aarnav Bhat
From aspiring AI researcher to IEEE conference acceptance for lung cancer biomarker discovery with AUC 0.983
Where Aarnav Started
His Background
- • High school student passionate about AI and medicine
- • Interested in applying machine learning to healthcare
- • Wanted to contribute to early cancer detection
- • Learning data science and bioinformatics
His Goals
- • Apply AI to improve cancer diagnosis
- • Publish in a prestigious IEEE conference
- • Develop expertise in biomedical AI
- • Build foundation for medical research career
His Vision
"I wanted to apply AI to early cancer detection. Lung cancer is the leading cause of cancer deaths, and I believed machine learning could help identify biomarkers for earlier diagnosis."
— Aarnav, before joining YRI
The Research
Working with his YRI mentor, Aarnav developed a machine learning approach for discovering biomarkers in lung cancer. His model achieved an exceptional AUC of 0.983, demonstrating the potential for early detection and prognosis.
Machine Learning Approaches for Lung Cancer Biomarker Discovery
Lung cancer is often diagnosed too late for effective treatment
Machine learning analysis of genomic data for biomarker discovery
AUC of 0.983 for lung cancer biomarker identification
Potential for earlier diagnosis and improved patient outcomes
Biomarker Discovery
Aarnav's machine learning model identifies key biomarkers associated with lung cancer, achieving near-perfect discrimination. This approach could enable earlier detection when treatment is most effective.
Enabling earlier diagnosis for improved survival rates
The Outcome
Conference Paper Accepted
AI & Biomedical Research
IEEE International Conference on IT, Security, and Innovation Future 2026
The YRI Fellowship gave me PhD mentorship, publication opportunities, ISEF coaching, and lasting support beyond the program.
Wanted to apply AI to cancer detection but didn't know how to start
IEEE conference accepted with a lung cancer biomarker model achieving AUC 0.983
Why This Research Matters
AUC score demonstrating exceptional model performance
Detection enables treatment when it's most effective
Potential to improve survival rates through earlier diagnosis
Ready to Start Your Research Journey?
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