Shubham Innani

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.

Featured in IU School of Medicine News: AI glioma identification research Read story
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10+
Journal Publications
7
International Presentations
3
Podium Finishes · MICCAI 2026 EchoRisk

Who I Am

Shubham Innani
SI

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.

Biomedical Image Analysis Foundation Models Multimodal & Vision-Language Models Clinical AI Translation HPC / SLURM Pipelines Explainable AI (XAI)

Experience & Education

Experience

Dec 2023 - Present
Research Data Analyst
Indiana University, Indianapolis, USA

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.

Aug 2022 - Nov 2023
Research Specialist B / CIS Visiting Associate
University of Pennsylvania, Philadelphia, USA

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.

Jan 2021 - Sep 2022
Assistant System Engineer
Tata Consultancy Services, Pune, India

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.

Mar 2020 - Jun 2020
Data Science Intern
Nymo.ai, Bangalore

Built computer vision and edge-AI inference pipelines (6–8 fps on-device) for ANPR and mask detection.

Education

B.Tech · Electronics & Telecommunication
Shri Guru Gobind Singhji Institute of Engineering & Technology, Nanded, India
2016 - 2020

Recent Presentations

2026
Oral & Poster - SNO 2026, Philadelphia, PA
2025
Oral & Poster - SNO 2025, Honolulu, HI
2025
Oral — ECDP 2025, Barcelona, Spain
2025
Poster — AACR 2025, Chicago, IL
2024
Oral — SNO 2024, Houston, TX
2024
2× Oral — ECDP 2024, Vilnius, Lithuania

Featured Work

WHO 2021 Glioma Classification

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.

IDH Mutation Detection from Histology

Interpretable AI for glioma IDH mutation status prediction directly from WSI, removing dependency on expensive molecular testing. Published in Neuro-Oncology Advances.

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Multimodal GBM Prognostic Model

Multimodal explainable AI integrating imaging + clinical features for glioblastoma patient stratification. AUC ~ 0.75 on multi-institutional test set. Published in Modern Pathology.

View Publication

WSI Diagnostic Pipeline

End-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.

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Get In Touch

Open to research collaborations, speaking invitations, and discussions about AI in biomedical imaging.

Location
Indianapolis, IN, USA
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