معرفی
Dena Asta is an Associate Professor of Statistics at Ohio State University's College of Arts and Sciences, Department of Statistics. She holds a PhD from Carnegie Mellon University and is affiliated with the Translational Data Analytics Institute. Her NSF-funded research focuses on applying geometric methods to non-parametric inference, network analysis, and manifold learning.
Dr. Asta investigates how network structures emerge as finite approximations of latent spaces, studying the interplay between geometric properties (like curvature) and statistical inference challenges. Her work has applications in diverse areas including medical imaging and social network analysis.
Her recent publications demonstrate consistent focus on developing statistical methods for non-Euclidean data, particularly in network modeling and spatial statistics. Research often involves collaborations across disciplines and addresses fundamental challenges in inference on structured spaces.
