Enabling startup, clinicians and researchers to translate biomedical data into actionable insight through interpretable ML and generative AI.
I help clinicians researchers & digital health teams to:
- 🔬 Enable clinicians and researchers to apply state-of-the-art AI to biomedical signals through intuitive, no-code interfaces
- 🧠 Design interpretable & publishable ML pipelines for translational research
- 🚀 Deploy scalable end-to-end solutions in clinical or startup environments
- Prodromal_Parkinson_XAI – Predictive modeling and SHAP explainability for Parkinson prodrome detection from IMU data
- EMG_SpasticGait_XAI – Deep learning + SHAP on EMG signals to identify muscle drivers in spastic gait (HSP disorder)
- FallRiskPredictor – Explainable Streamlit webapp to estimate fall risk in neurological patients
- Fall risk prediction with XAI for Parkinson’s patients
- Prodromal signature discovery using SHAPSetPlot on wearable data
- Muscle importance analysis for rare disorders via BiLSTM + CNN on EMG
- Synthetic data generation (ctGAN) for class balancing in clinical datasets
- Machine Learning Approach to Support the Detection of Parkinson's Disease via IMU Gait Analysis
Published in Sensors (2022) – 150+ citations - Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI
Published in Sensors (2024) – 60+ citations
And many others..
I'm open to research, clinical or AI product collaborations in:
- Prodromal Parkinson detection
- Generative AI for rare disease datasets
- Clinical explainability (SHAP / SHAPSetPlot)
- Fall prediction systems
📩 Let’s connect if you'd like to collaborate!
