“At the adversarial frontier of AI, robotics, and cybersecurity — I teach machines how to see, think, and deceive,
then I architect the systems to defend against them.”
My work is a meditation on perception — coding machines to interpret our world while interrogating the authenticity of the digital reflection.
I build autonomous robotic systems that navigate physical environments and AI models that discern fabricated realities —
from fake voice detection to adversarially robust image classifiers.
I’m endlessly fascinated by the thin boundary between simulation and understanding —
the moment an algorithm stops imitating intelligence and begins to reason.
I operate where AI, cybersecurity, and robotics converge — designing intelligent systems that both learn and defend.
My recent work includes:
- 🛰 AI-driven Network Intrusion Detection Systems (NIDS) for anomaly detection
- 🤖 Pose estimation and vision systems using MediaPipe and OpenCV
- 🚀 Autonomous rover prototypes with AI-guided navigation and sensor fusion
- 🧪 Malware analysis labs and penetration testing environments for adversarial research
🎓 B.Tech in Computer Science (Cybersecurity specialization) — DIT University
My studies and research explore:
- Neural and probabilistic Machine Learning & Deep Learning architectures (TensorFlow, PyTorch)
- Cryptography, secure algorithm design, and adversarial resilience
- Network Security, IPSec, and firewall architecture
- AI ethics and adversarial search theory
🔬 My academic and professional pursuits merge perception, deception, and defense —
a continuous dialogue between creation and containment.
