About Me

I am a Software Engineer at Google based in the San Francisco Bay Area. I received my Ph.D. in Informatics from Pennsylvania State University, where I was advised by Dr. Aron Laszka.

My research focuses on the intersection of Deep Reinforcement Learning, Multi-Agent Systems, Game-Theoretic Security, and Cybersecurity for Cyber-Physical Systems (CPS) — spanning autonomous transportation networks, smart energy microgrids, and industrial control systems.

Previously, I completed my M.S. in Computer Science at the University of Houston and my B.S. in Computer Engineering at Shahid Beheshti University. In Summer 2024, I was an AI & Machine Learning Co-OP at Schneider Electric (Triconex), where I collaborated on U.S. patents for automated Layers of Protection Analysis (LOPA) and applied adversarial reinforcement learning to safety controllers.

Research Focus & Interests

• Multi-Agent & Deep Reinforcement Learning: Developing scalable MARL algorithms, hierarchical decision-making, and game-theoretic solvers (e.g., Double Oracle) for complex adversarial settings.
• Cyber-Physical Systems & Critical Infrastructure Security: Autonomous threat detection, proactive defense using Moving Target Defense (MTD), and false-data injection mitigation in vehicular routing and smart grids.
• Distributed Systems & Scalable Computing: High-performance distributed computing on HPC clusters (Slurm, MPI), containerized microservices, and secure system architectures.

Recent Highlights & News

• 📑 May 2026: Paper on Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing accepted at ICCPS 2026.
• 🎓 Feb 2026: Defended Ph.D. Dissertation on Adversarial Reinforcement Learning Applications in Cyber-Physical Systems Security at Pennsylvania State University.
• 🚀 Dec 2025: Joined Google (Sunnyvale, CA) as a Software Engineer.
• 💡 Jul 2024: Collaborated on U.S. patent applications for Automated Layers of Protection Analysis (LOPA) at Schneider Electric.
• 📑 May 2024: Published our paper on hierarchical multi-agent reinforcement learning for transportation network security at AAMAS 2024 in Auckland, New Zealand.
• 🏆 Jul 2023: Received the Distinguished Paper Award at the 32nd USENIX Security Symposium (USENIX Security ‘23) for our empirical research on the bug-bounty ecosystem.