About Me

About Me

I am a Ph.D. graduate in Computational Materials Science with a strong background in machine learning, deep learning, and data-driven research. Currently, I work as a Research Engineer at Predeeption, a startup focused on developing deep learning solutions for battery life-cycle prediction. My expertise spans computational modeling, predictive analytics, and algorithm development, leveraging Python, C++, and SQL to extract meaningful insights from complex data.

I am passionate about applying cutting-edge AI-driven solutions to real-world problems, particularly in materials science and energy storage. With a solid foundation in both scientific research and software development, I thrive in interdisciplinary environments where I can bridge the gap between theory and application.

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I explore and discuss the latest advancements in machine learning, including new architectures, innovative AI techniques, and ML-based solutions to real-world challenges. If you’re interested in staying up-to-date with the evolving landscape of AI, follow along for fresh insights and discoveries!