Research Pillars
Five core areas where we're advancing the state of the art in AI engineering.
Edge AI Systems
Deploying intelligent models at the edge for real-time decision making and reduced latency.
- • Model quantization and pruning for resource-constrained environments
- • Real-time inference optimization with sub-10ms latency
- • Federated learning architectures for privacy-preserving AI
Academic & Research Partnerships
UPRM
MIT
NSF
Stanford
CMU
Our Research Methodology
From theoretical breakthrough to production deployment in four systematic phases.
🔍
Discover
Literature review and problem formulation
⚡
Prototype
Rapid experimentation and proof-of-concept
✅
Validate
Rigorous testing and peer review
🚀
Deploy
Production integration and scaling
Publications & Open Source
Contributing to the global AI research community through publications and open-source projects.
Recent Publications
Federated Learning for Edge AI: A Comprehensive Survey
IEEE Transactions on AI • 2024
Optimizing Geospatial ML Models for Real-time Applications
NeurIPS Workshop • 2024
Compliance-Aware Neural Architecture Search
ICML • 2023
Open Source Projects
edge-ai-toolkit
Lightweight ML inference for edge devices
⭐ 2.3k
geospatial-ml
Spatial-temporal modeling library
⭐ 1.8k
compliance-checker
Automated regulatory compliance validation
⭐ 945
Collaborate with Our Research Team
Interested in partnering on cutting-edge AI research? Get our latest white papers and research updates.