Kristina P. Sinaga
"It's not about being seen working—it's about delivering something worth seeing."
Where Value-Driven Work Meets Innovation
Guest Editor - MDPI Future Internet Special Issue
I am honored to serve as Guest Editor for the prestigious MDPI Future Internet journal's special issue on "Clustered Federated Learning for Networks".
📢 We Invite Your Contributions!
Submit your groundbreaking research in:
📚 Journal Details
MDPI Future Internet (Impact Factor: 3.4)
Open Access • Fast Review • Global Reach
📅 Important Dates
Submission Deadline: August 20, 2026
Review Process: Fast-track available
Why Publish With Us?
Modern Work Philosophy
Creating Impact Through Purposeful Innovation
Explore My ApproachWork Philosophy
In today's evolving world of work, I believe performance is defined by clarity of thought, autonomy in execution, and the ability to create lasting value.
Outcome-Focused
Success measured by tangible impact and real-world applications, not just theoretical achievements.
Deep Focus
Quality work through uninterrupted concentration and deliberate problem-solving approaches.
Systems Approach
Building scalable frameworks that create lasting value in AI and mathematical applications.
Continuous Innovation
Staying at the forefront of AI research through constant learning and adaptation.
About Me
I’m a strategic thinker, problem-solver, and someone who doesn’t entertain clown logic. I hold a Ph.D. in Applied Mathematics from Chung Yuan Christian University (CYCU), Taiwan — but I don’t wear that title like a costume. I use it.
My passion? Building AI solutions that don’t just float in theory — they land, perform, and endure. I specialize in designing scalable, interpretable, and trustworthy models that work in decentralized, real-world environments — especially where resources are limited but expectations are high.
I work across clustering, multi-view learning, federated learning, and Edge AI — bridging theory and application like it’s second nature.
My approach is simple: No noise. No fluff. Just precision that performs.
Research Focus
Federated Learning & Edge AI
Developing privacy-preserving distributed learning systems for edge devices.
Multi-view (MV) Learning
Innovative approaches to combining multiple data perspectives for enhanced model performance.
Privacy-Preserving AI
Building secure and trustworthy AI systems that protect user privacy.
Industrial AI
Anomaly detection and monitoring systems for industrial applications.
Global Reach
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Contact
Email: kristinapestaria.sinaga@isti.cnr.it
Email: neuralglowai@gmail.com
Note: The email address kristinasinaga41@gmail.com is no longer under my authority. Please do not use it to contact me.