My research lies at the intersection of information theory, statistical inference, and machine learning. I am particularly interested in developing reliable and trustworthy AI systems through tools such as conformal prediction, hypothesis testing, and information-theoretic risk control. My recent work explores applications ranging from hyperparameter optimization and anomaly detection to graph-based learning and wireless systems. I am also broadly interested in complexity analysis and efficient representations of graph-structured data, with applications in network science and GNNs.