R&D Lab · FES Labs

Scientific Innovation in Autonomous Medical Computing

We aim to develop the engineering and mathematical foundations to enable medical and health systems to operate with high efficiency, absolute privacy, and complete isolation from centralized cloud storage servers.

Our Active Research Tracks

We are currently leading 6 fully complex engineering research tracks within FES Labs departments to ensure data sovereignty and operational independence.

📡

Edge Computing

Studying the optimization of query algorithms for large local medical databases locally, achieving 0ms response time without consuming processor power on mobile devices.

🔒

Data Security (Zero-Knowledge)

Developing local encryption protocols and secure communication channels ensuring patient records are completely isolated at the device level, preventing decryption by anyone other than the practitioner.

📷

Offline Image Processing (Stateless OCR)

Innovating algorithms to extract laboratory tables and digital data and interpret them within the device's temporary memory (RAM) with immediate and automatic deletion of uploaded files.

🧠

AI Model Localization (On-Device SLMs)

Research to develop small language models (under 1 billion parameters) operating entirely locally to summarize patient reports without sending a single character to external servers.

🔄

Isolated Network Sync (P2P Local Sync)

Studying secure instant synchronization protocols for medical records across multiple clinic devices (like iPad and PC) over a fully encrypted local Wi-Fi network without a cloud intermediary.

Ultra-Fast Local Search (Local Search Indexing)

Developing compressed data structures and advanced indexes to provide query speeds of under 5ms when searching through 600,000 food items offline on older mobile devices.

Research Papers and Technical Documents (Work in Progress)

Active research drafts and technical studies for 2026 fully led within FES Labs departments and laboratories.

Active Research (2026)

Optimization of Distributed Relational Databases for Edge Computing Healthcare Systems

FES Systems & Architecture Division · Status: Active / In-Progress
Active Research (2026)

Zero-Knowledge Cryptographic Architectures for Localized EMR and Patient Sourced Data

FES Cryptography & Security Labs · Status: Active / In-Progress
Active Research (2026)

Stateless Optical Character Recognition Algorithms for Mobile Medical Lab Reports Parsing

FES AI & Pattern Recognition Division · Status: Active / In-Progress
Active Research (2026)

On-Device Small Language Models (SLMs) for Clinical Summary Generation at the Edge

FES Deep Learning & NLP Group · Status: Active / In-Progress
Active Research (2026)

Decentralized Peer-to-Peer Synchronisation Protocols for Clinical Sovereignty Networks

FES Distributed Systems Lab · Status: Active / In-Progress
Active Research (2026)

Compressed Prefix Trees (Tries) for Memory-Constrained Offline Food Informatics Indexes

FES Computational Biology & Algorithms Division · Status: Active / In-Progress
🎓

Academic and Research Collaboration

We welcome collaborations with computer science and software engineering departments at universities and research institutions to participate in graduation theses and scientific studies on autonomous health computing and sovereign data encryption.

✉️ Contact us to start a research collaboration