Building ML systems and writing highly efficient software.
MalwareNet – a PE malware classifier trained on EMBER2024.
- Architecture & Metrics: 273K parameters | AUC 0.9911 | Adversarially robust under PGD | 48% sparse winning ticket via Lottery Ticket Hypothesis.
- Performance: 0.041ms CPU inference.
- Deployment: Deployed as a native Rust desktop app with no Python runtime.
- High School Graduate (3.89 GPA, HomeLife Academy, TN)
- 💻 Completed professional JS Web Developer program at SoftUni at age 11
- 🏆 Holding 39 certifications across Python, Deep Learning, and Cybersecurity
🎯 Right now, I work on problems that matter. That means Security and AI.