I am a BS/MS student in materials science and engineering with a keen interest in materials data science. My research primarily focuses on developing algorithms and tools for accelerating the analysis of 2D XRD diffraction data, leveraging deep learning, ontologies and FAIR principles. Through the program, I aim to develop my skills to be able to generalize my work to wider applications in synchrotron research. My overarching goal is to approach contemporary materials characterization techniques with an algorithmic data-driven perspective to improve scalability and utility.

My research focus lies in the realm of 2D X-ray diffraction data analysis, where I leverage computer vision and deep learning techniques to extract complex features. Additionally, I am actively involved in the development of federated learning frameworks, ensuring collaborative model training across multiple clients while prioritizing privacy protection. My overarching goal is to delve into the theoretical foundations of deep learning algorithms, identifying and optimizing models tailored to specific tasks in diverse areas.