Research
Advancing the frontiers of artificial intelligence through innovative research that addresses real-world challenges and societal needs
Research Areas
Core focus areas driving innovation and discovery
Deep Learning & Generative AI
4 publications
Developing advanced generative AI models, tiny machine learning for edge devices, and novel neural network architectures for various applications.
Key Topics:
Cloud Computing & Optimization
4 publications
Research in cloud load balancing, process scheduling optimization, and distributed computing systems for enhanced performance.
Key Topics:
Computer Vision & Detection Systems
4 publications
Advanced computer vision applications including fake image detection, video tracking, and biometric authentication systems.
Key Topics:
Educational Technology & Applications
1 publications
AI-driven educational platforms, adaptive learning systems, and intelligent automation applications for enhanced learning experiences.
Key Topics:
Current Projects
Ongoing research initiatives and their potential impact
AI-Powered Healthcare Diagnostics
ActiveDeveloping machine learning models for early detection of diseases using medical imaging and patient data, with focus on accessibility and fairness across diverse populations.
Collaborators:
Expected Impact:
Potential to improve diagnostic accuracy while reducing costs
Explainable AI for Educational Technology
ActiveCreating interpretable AI systems that can provide personalized learning experiences while maintaining transparency in decision-making processes.
Collaborators:
Expected Impact:
Supporting 10,000+ students with personalized learning paths
Federated Learning for Privacy-Preserving Analytics
ActiveDeveloping secure machine learning algorithms that can train on distributed data without compromising user privacy or sensitive information.
Collaborators:
Expected Impact:
Enabling secure AI collaboration across organizations
Neural Architecture Search for Edge Computing
Recently CompletedAutomated design of efficient neural network architectures optimized for deployment on resource-constrained edge devices.
Collaborators:
Expected Impact:
50% reduction in model size with maintained accuracy
Research Philosophy
"My research is driven by the belief that artificial intelligence should serve humanity's greatest challenges. I focus on developing AI systems that are not only technically excellent but also ethical, fair, and accessible to all members of society."
Human-Centered
Focusing on real-world problems that impact people's lives
Ethical AI
Ensuring fairness, transparency, and accountability in AI systems
Open Science
Promoting collaboration and knowledge sharing in the research community
Interested in Collaboration?
I'm always looking for opportunities to collaborate with fellow researchers, industry partners, and talented students.