This is really abridged – please contact me for full and latest CV.
I have over 15 years of building and validating data-driven tools for health, science, and society — from predictive biomarkers to enterprise-grade AI systems.
Core Expertise
- Health data science, clinical ML, medical imaging, and informatics
- Predictive modeling, statistical learning, and confound-aware pipelines
- Generative AI (LLMs) for automation, insight, and product strategy
- End-to-end research software engineering — open-source, reproducible, battle-tested
Translational Impact
- Improved forecasting pipelines for Fortune 500 clients — enabling real-world business decisions through robust model design and validation
- Adapted Generative AI / LLMs to enhance insights in both consumer and healthcare domains
- Led cross-functional innovation at the intersection of clinical data, ML, and decision support
Software Contributions
Designed and led development of many open source tools, now used across academic labs and industry worldwide!
- neuropredict: Benchmarking predictive biomarker performance
- confounds: Handling nuisance effects in predictive models
- visualqc: Systematic quality control for neuroimaging
- graynet: Individualized morphometric network biomarkers
- pyradigm: Scalable, traceable data management for ML workflows
- mrQA: Automated protocol compliance for DICOM scans
Open Science
- Created and led the neuroimaging Quality Control (niQC) Special Interest Group at INCF
- Contributed to core Python ecosystem: numpy, scipy, scikit-learn, matplotlib
- Advocated for reproducibility and benchmarking through open-source practice
- Delivered 45+ invited talks, 20+ peer-reviewed papers (h-index 14, including 5 software papers)
Education
- PhD in Biomedical Engineering, Simon Fraser University, Canada
- M.Sc in Physics, IIT Madras, Tamil Nadu, India
- B.Sc in Mathematics, Physics, and Computer Science, India.
cross invalidation 