Based in Switzerland
Unsupervised contrastive learning for cancer subtype discovery and survival stratification
Integrates gene expression, DNA methylation, and miRNA profiles using contrastive learning and a survival-aware loss function for effective clustering and patient stratification.
Adaptive fairness and explainability for real-time AI models
A modular framework that integrates fairness and explainability into AI systems, ensuring ethical, interpretable, and adaptable machine learning models.
Swift-based application for real-time scatter prediction in medical imaging
Leverages LiDAR, CoreML, and 3D spatial computation to assist radiological safety.
AI-powered photo organization
Uses machine learning to automatically sort and categorize images based on visual content.
Self-Organizing Survival Manifolds for unsupervised discovery of prognostic structures in biological systems
A theory where survival dynamics emerge from geodesic flows on latent biological manifolds, connecting survival, thermodynamic efficiency, and optimal transport for label-free prognosis.
Improved logarithmic estimate for the sum of primes
An analytical approach to approximate ∑p ≤ n p using enhanced integral bounds.
"Build with intent. Research with clarity. Ship with confidence."