I am a research-driven data scientist and machine learning engineer with a strong foundation in computer science and time-domain astronomy. With experience as a data scientist, software engineer, and data analyst, I specialize in machine learning, ensemble learning, and deep learning techniques, having worked extensively with CNNs, RNNs, and transformer models.

My research spans astrophysics, particularly time-domain astronomy, and self-supervised learning, where I explore representation learning using transformers. Recently, my work in instrumentation has deepened my expertise in signal processing and Fourier analysis. I am also delving into Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Prompt Engineering, with a keen interest in transitioning into Generative AI.

I'm passionate about continuous learning and innovation, and I constantly expand my technical stack and seek new challenges in data science and AI. While my research interests remain rooted in astronomy, my professional focus is leveraging cutting-edge machine-learning techniques to drive impactful research and technological advancements.