From power circuits to intelligent agents, I'm on a journey to make agents communicate more robustly.
My academic journey began in the world of Power Electronics at Isfahan University of Technology, where I developed a fascination with circuit design and energy systems. I spent countless hours in labs designing DC-DC converters and optimizing power systems.
But as they say, life is all about evolution. In 2024, I found myself increasingly drawn to the exciting field of Artificial Intelligence. What started as curiosity quickly turned into passion when I discovered Multi-Agent Reinforcement Learning (MARL).
Now at the University of New Haven, I'm researching how to make cooperative agents communicate more robustly, even when faced with adversarial scenarios. It's a thrilling transition that combines my engineering mindset with cutting-edge AI research.
This shift wasn't just about following trends—it was about finding an area where I could make meaningful contributions while constantly learning and growing. The more I work with MARL systems, the more I'm convinced this is where I belong!
BS in Electrical Engineering (Power Electronics) at Isfahan University of Technology
Research Assistant at Industrial Electronics Lab, working on high step-up DC-DC converters
MS in Electrical Engineering at University of New Haven, focusing on robust communication in MARL
Exploring how multiple AI agents can learn to coordinate, cooperate, or collaborate and communicate effectively in complex environments.
Developing techniques to make agent communication resilient against adversarial attacks and misalignment.
Developing firmware for microcontrollers to create efficient, reliable embedded systems for interactive devices.
Designing efficient DC-DC converters with reduced switching and conduction losses for renewable energy applications or electric vehicles.
At the SAIL Lab (University of New Haven), I work with Dr. Vahid Behzadan on developing mechanisms for robust communication in Multi-Agent Reinforcement Learning (MARL). Our proposed method, Communicative Power Regularization (CPR), enables cooperative agents to maintain effective coordination even under adversarial or misaligned communication.
This work has been accepted for publication at ECAI 2025 and selected for poster presentation at CoCoMARL 2025. I also presented it at Hartford AI Day 2025, where it received insightful feedback from the AI research community.
A customized multi-agent grid-world environment for testing communication protocols between agents under various conditions.
A real-time malware detection system using deep learning, deployed with AWS SageMaker for cloud-based inference on raw executable files.
View ProjectDesigned and implemented the microcontroller firmware for an industrial power meter at Tavan Pajouh Behrad Company, enabling precise measurement and monitoring of electrical parameters in power systems.
Designed and implemented a complete interactive game on an ARM microcontroller, featuring multiple game levels, game difficulty, real-time graphics, sound effects, and joystick controls in a resource-constrained environment.
View ProjectDesigned and prototyped a novel converter with reduced switching losses and improved efficiency for renewable energy applications.
View PaperI'm always open to discussing research, collaboration opportunities, or just chatting about AI and reinforcement learning.