AI scientist with 6+ years developing and translating AI tools for clinical applications across pathology, radiology, retinal imaging, dermatology, and cardiac cine imaging. Trained WHO-aligned diagnostic and prognostic models on 2,500+ cases from 25+ clinical sites, with a tool now in prospective validation.
I am a Research Data Analyst in the Division of Computational Pathology at the Indiana University School of Medicine, where I develop, validate, and translate AI tools for clinical use across pathology, radiology, and cardiac imaging.
My work centers on developing deep learning and foundation models for biomedical imaging, from WHO 2021 glioma classification to prognostic risk stratification and echocardiography foundation models, bridging the gap between computational research and real-world clinical workflows, now in prospective evaluation.
Previously at UPenn, I built multimodal GBM prognostic models achieving AUC around 0.75 across 188 patients from multi-institutional cohorts. My research has been published in Neuro-Oncology, Modern Pathology, Cancers, Scientific Reports, and presented at MICCAI, SNO, ECDP, and AACR.
Develop and validate AI models on large multi-institutional datasets (2,500+ cases, 25+ sites) and maintain reproducible HPC workflows; delivered a tool now in prospective validation. Contribute aims, methodology, and preliminary results to NIH and other funded proposals.
Developed multimodal GBM prognostic models (histopathology + clinical data), AUC ~ 0.75 across 188 patients. Led data curation, methodology, implementation, and validation; co-authored manuscripts in Modern Pathology and Frontiers in Neuroscience.
QA for cloud migration of transaction modules (legacy → AWS / Java/Spring Boot) for a leading U.S. financial client. Led a 6-person testing component, improving deployment reliability and reducing post-migration incidents.
Built computer vision and edge-AI inference pipelines (6–8 fps on-device) for ANPR and mask detection.
AI-driven classification of glioma subtypes directly from H&E-stained slides, validated on 2,500+ cases across 25+ sites. Published in Neuro-Oncology (2026). Won Best Paper at CCBB 2025.
Interpretable AI for glioma IDH mutation status prediction directly from WSI, removing dependency on expensive molecular testing. Published in Neuro-Oncology Advances.
View PublicationMultimodal explainable AI integrating imaging + clinical features for glioblastoma patient stratification. AUC ~ 0.75 on multi-institutional test set. Published in Modern Pathology.
View PublicationEnd-to-end clinician-facing pipeline with GPU-backed inference (A100/H100 SLURM) enabling slide analysis in ~10 minutes vs. multi-week molecular workflows. Now in prospective validation.
View ProjectsOpen to research collaborations, speaking invitations, and discussions about AI in biomedical imaging.