6
Total Projects
4
Active Projects
$300K+
Total Funding
8
Collaborations
Active Projects
FedHealth: Privacy-Preserving Healthcare Analytics Platform
Healthcare AI • 2023-2024NSF Grant - $150,000
A comprehensive federated learning platform designed specifically for healthcare institutions to collaboratively train ML models while maintaining HIPAA compliance and patient privacy. The system incorporates differential privacy, secure aggregation, and advanced cryptographic techniques.
Key Achievements:
- Deployed in 5 major hospitals
- Reduced privacy leakage by 95%
- Improved model accuracy by 12%
FedFinance: Collaborative Financial Fraud Detection
Financial Technology • 2024Industry Partnership
A federated learning system that enables financial institutions to collaboratively detect fraud patterns while keeping transaction data private. Implements advanced anomaly detection algorithms and handles highly imbalanced datasets.
Key Achievements:
- 15% reduction in false positives
- Detected 200+ new fraud patterns
- Partnership with 3 major banks
EduFL: Personalized Learning with Federated AI
Educational Technology • 2024Department of Education Grant
An educational technology platform that uses federated learning to create personalized learning experiences while protecting student privacy. The system adapts to individual learning patterns and provides intelligent tutoring recommendations.
Key Achievements:
- Tested with 500+ students
- 20% improvement in learning outcomes
- Privacy-compliant design
MedFL: Federated Drug Discovery Platform
Pharmaceutical AI • 2024-2025NIH SBIR Grant
A collaborative platform for pharmaceutical companies to share insights and accelerate drug discovery while protecting proprietary research data. Uses federated learning for molecular property prediction and drug-target interaction modeling.
Key Achievements:
- Collaboration with 3 pharma companies
- Novel molecular representation learning
- Accelerated discovery pipeline
Completed Projects
SecureFL: Framework for Secure Federated Learning
Open Source • 2022-PresentGoogle Research Grant
An open-source framework that provides easy-to-use APIs for implementing secure federated learning with various privacy-preserving techniques. Supports multiple aggregation algorithms, differential privacy mechanisms, and secure multi-party computation protocols.
Key Achievements:
- 1,200+ GitHub stars
- Used by 50+ researchers
- Featured in ICML workshop
PrivateVision: Federated Computer Vision for Smart Cities
Computer Vision • 2023City of Excellence Partnership
A privacy-preserving computer vision system for smart city applications including traffic monitoring, crowd analysis, and emergency detection. The system uses federated learning to train models across multiple edge devices without centralizing sensitive visual data.
Key Achievements:
- Deployed in 20 city locations
- Real-time processing
- 99.9% uptime
Interested in Collaboration?
I'm always looking for new research collaborations and opportunities to apply federated learning and privacy-preserving AI to real-world problems. Whether you're from academia, industry, or government, let's discuss how we can work together.