AI scientist with 6+ years of experience developing and translating AI tools for clinical applications, with expertise in deep learning, foundation models, multimodal data, vision-language models, and reproducible HPC-based workflows. I've built models across pathology, radiology, retinal imaging, dermatology, and cardiac cine imaging, taking them from development and multi-institutional validation to prospective evaluation in hospital workflows.
Research Data Analyst at Indiana University's Division of Computational Pathology (Dec 2023 – Present), working on histopathology AI for prognosis and diagnosis applications:
- Trained WHO-aligned diagnostic and prognostic models on 2,500+ cases from >25 clinical sites.
- Built reproducible HPC workflows for clinical translation; delivered a tool now in prospective validation.
- Contribute to NIH and other externally funded research proposals.
Previously: Research Specialist B at the University of Pennsylvania (2022–2023), Assistant System Engineer at Tata Consultancy Services (2021–2022), and Data Science Intern at Nymo.ai (2020).
- Computer vision and deep learning across domains 🔭
- Foundation models and vision-language models.
- Explainable/Interpretable AI.
- Translating AI from research to clinical practice: multi-institutional validation, prospective evaluation, and real-world adoption.
- 🥈 2nd Place, Early Prediction of Cardiotoxicity, EchoRisk Challenge, MICCAI 2026
- 🥉 3rd Place, Left Ventricular Dysfunction Classification, EchoRisk Challenge, MICCAI 2026
- 🏆 Best Paper Award, CCBB Retreat, Indiana University (2025)
- 5th Place, LLM-based report generation in pathology (REG), MICCAI 2025
- Innani et al., AI-based prognostic risk stratification of adult-type diffuse glioma patients from H&E-stained slides, Cancers, 2026.
- Innani et al., AI-driven WHO 2021 classification of gliomas based only on H&E-stained slides, Neuro-Oncology, 28(1):282–296, 2026.
- Innani et al., Interpretable AI based determination of glioma IDH mutation status directly from histology slides, Neuro-Oncology Advances, 2025.
- Baheti et al., Multimodal Explainable AI for Prognostic Stratification of Patients With Glioblastoma, Modern Pathology, 2025.
- Innani et al., Generative adversarial networks based skin lesion segmentation, Scientific Reports, 2023.
Full list on Google Scholar.
Reviewer for IEEE TMI, Scientific Reports, IEEE TNNLS, MICCAI, ISBI, npj Precision Oncology, and others. Program Committee, MOVI @ MICCAI 2024.