NOMINATION HIGHLIGHTS

I am an AI and cybersecurity researcher focused on the convergence of security analytics, anomaly detection, and predictive health systems.

My work explores how methodologies traditionally used in cybersecurity—such as real-time telemetry ingestion, behavioral analysis, and anomaly detection—can be applied to human physiological data to anticipate critical health events.

I am the creator of Epiguard, an AI-driven predictive health monitoring system designed to identify patterns that may precede epileptic seizures. The platform integrates multi-source wearable telemetry, including heart rate, HRV, oxygen saturation, temperature, respiratory signals, and activity data, to generate real-time risk scoring and early warning insights.

What distinguishes my approach is the application of cybersecurity-grade thinking to healthcare challenges. Instead of treating physiological data as static measurements, I model the human body as a dynamic system—similar to a network—where deviations from baseline behavior can indicate emerging risks.

This interdisciplinary perspective positions my work at the intersection of cybersecurity, artificial intelligence, and human-centered technology.

Beyond technical development, I am actively building a research-driven approach to predictive systems that prioritize privacy, decentralized data processing, and secure architectures. My goal is to contribute to a new generation of systems capable of improving safety and quality of life through early detection and intelligent monitoring.
This nomination reflects not only a technical initiative, but also a broader vision: redefining how cybersecurity principles can extend beyond digital systems to protect human life itself.