Researcher at the intersection of solar PV, machine learning, and applied data science.

I am a data-driven researcher working at the intersection of solar photovoltaic systems, machine learning, and integrated energy analysis.

My work focuses on unsupervised anomaly detection in real-world PV datasets, where the goal is to identify abnormal operational patterns without labeled data. A key aspect of my research is ensuring high-quality, well-processed datasets to enable reliable downstream analysis and robust model performance.

Beyond solar energy, I have experience in optical and photonic materials research, particularly chalcogenide-based systems, where I explore their structural and optical properties for infrared applications.

I enjoy bridging experimental science with computational methods, using data science and machine learning to extract meaningful insights from complex systems.