Alifadhilah Aditya

Engineered MedicalResNet3D for early detection of Parkinson’s by combining 3D scans and tabular clinical data. This enabled more explainable and accurate predictions in longitudinal medical studies.

Deployed privacy-preserving ML models on various datasets using federated learning frameworks. Integrated encrypted vertical pipelines to simulate cross-institution collaboration while maintaining regulatory compliance.

Designed and prototyped digital workflow automation tools to streamline preventive health coaching programs. This reduced administrative overhead by 30% and boosted patient follow-up engagement by an estimated 15%.

Developed a decentralized fraud detection pipeline utilizing NVIDIA FLARE for secure data sharing and shipped functional code to PhD researchers. Evaluated various collaborative filtering models and Transformer-based architectures to identify effective approaches for relevance optimization, delivering comparative performance results to stakeholders.

Studied core computer science principles including data structures, algorithms, and software engineering. Gained expertise in advanced topics such as machine learning, deep learning, natural language processing, and computer vision. Covered foundational mathematics including probability, statistics, linear algebra, and calculus.