Siri Yellu is a Graduate Research Assistant and Computer Science professional specializing in Data Science and Machine Learning. Her work focuses on building reliable and interpretable machine learning systems, with experience spanning feature engineering, evaluation-driven modeling, and applied AI system design.

She is the first author of peer-reviewed publications in IEEE and Springer conferences, where her research explores handcrafted image features, Bayesian optimization, ensemble learning, and generative models for medical image analysis, computational pathology, and multimodal AI applications. Her work emphasizes bridging rigorous evaluation with practical system design to ensure models are not only accurate, but trustworthy and maintainable.

Siri enjoys working at the intersection of theory and practice, translating research insights into real-world AI systems. She is particularly interested in applied machine learning architectures, system reliability, and building AI solutions that can be confidently deployed and maintained by engineering teams.