I am a Research Assistant with a strong focus on computer vision and reinforcement learning. My work revolves around building intelligent systems that can perceive, learn, and make decisions in complex environments. I am particularly interested in how visual understanding and adaptive learning can be combined to solve real-world problems, from scene interpretation to autonomous decision-making.

I have experience working with deep learning models, including convolutional neural networks and modern vision architectures, and I enjoy experimenting with reinforcement learning algorithms to develop agents that learn through interaction. My approach is driven by both theoretical understanding and practical implementation, allowing me to translate research ideas into working solutions.

I am deeply curious about the future of artificial intelligence and continuously explore new methods, tools, and frameworks to improve my skills. I value clean, reproducible research and actively build projects that reflect my learning and contribute to the broader AI community.