graph distances have proven quite useful in machine learning/statistics, particularly in the estimation of euclidean or geodesic distances. the talk will include a partial review of the literature, and then present more recent developments on the estimation of curvature-constrained distances on a surface, and well as on the estimation of euclidean distances based on an unweighted and noisy neighborhood graph.