About Me

I’m a Computer Science Ph.D. student at Purdue University, where I am exploring research in artificial intelligence and machine learning. My current interests center on interpretable and reliable machine learning, with a focus on continual and sample-efficient learning in multimodal and vision systems.

Before Purdue, I earned my B.S. in Computer Science from the University of Texas at San Antonio, with concentrations in Data Science and Software Engineering and a minor in Mathematics. As an undergraduate researcher, I worked across several areas of AI and machine learning, including data-efficient learning and scientific image segmentation, distributed deep learning for resource-constrained edge devices, AI education, and machine learning for malware detection. My research has resulted in peer-reviewed publications, conference presentations, and undergraduate research awards.

Broadly, I’m interested in understanding how learning systems can adapt efficiently to new data and tasks while remaining reliable and interpretable. During my Ph.D., I hope to investigate these questions across multimodal and vision settings, with applications to real-world domains.