office:317 Cockins Hall
I'm an assistant professor at The Ohio State University in the Department of Statistics. Previously, I obtained a dual Ph.D. in Engineering and Public Policy (EPP) and Statistics under the supervision of my advisor, Cosma Shalizi at Carnegie Mellon University. Before that, I obtained an MPA from Institut d'├ętudes politiques de Paris (Sciences-Po) and a B.S. in Mathematics with a specialization in Economics from the University of Chicago. I'm interested in bringing geometric methods to bear on network inference, non-parametric non-Euclidean methods, and subsequent applications.


I'm generally interested in statistics that either reveals or depends upon interesting geometry in the data.

Network Inference

Network inference is about inferring a distribution of random graphs (networks) from sample graphs (networks). This is useful, for example, in assessing whether changes in networks over time, environment, or other conditions are significant or mere fluctuations. It is often natural to assume that the networks of interest are finite approximations of some interesting space. I am interested in the interplay between the geometry of the latent space (e.g. curvature) and the problems of network inference (e.g. estimator consistency).

Estimation on Curved Spaces

Data and their generative models are usually parametrized as collections of numbers, but both are often more naturally regarded as points in a non-Euclidean space. Some examples of such data are directional headings (the space SO(3) of 3x3 special orthogonal matrices), distortions in the spacetime continuum (symmetric positive definite matrices), or latent positions of large-scale networks (hyperbolic spaces). In all such examples, Euclidean distances do not reflect the true distance between the data, regarded as points in a true latent space. Similarly, parametric families of generative models are often studied as geometric spaces where the distance between two such generative models is their Fisher distance. I am interested in extending standard constructions and results regarding Euclidean statistics, such as kernel density estimation and maximum likelihood estimation, for the non-Euclidean setting.


[1] C. Shalizi and D. Asta, "Consistency of Maximum Likelihood for Continuous Space Network Models," submitted, arXiv preprint 1711.02123, (2019).
[2] D. Asta, "Kernel Density Estimation on Symmetric Spaces," submitted, arXiv preprint 1411.4040, (2019). A short version of this paper appeared in Proceedings of Geometric Science Information (GSI), Springer LNCS 9398, pages 779-787, 2015.
[3] A. Smith, D. Asta, and C. Calder, "The Geometry of Continuous Latent Space Models for Network Data," Statistical Science, arXiv preprint 1712.08641, (2019).
[4] D. Asta and C. Shalizi, "Geometric Network Comparisons," Proceedings of the 31st Annual Conference on Uncertainty in AI (UAI) [pdf], arXiv preprint 1411.1350, (2015).
[5] D. Asta, "Nonparametric Density Estimation on Hyperbolic Space," Neural Information Processing Systems (NIPS) workshop: Modern Nonparametric Methods in Machine Learning, workshop paper, (2013).
[6] D. Asta and C. Shalizi, "Identifying Influenza Outbreaks via Twitter," Neural Information Processing Systems (NIPS) workshop: Social Network and Social Media Analysis - Methods, Models and Applications, (2012).
[7] D. Asta and C. Shalizi, "Separating Biological and Social Contagions in Social Media: The Case of Regional Flu Trends in Twitter," manuscript in preparation, (2013).

I'm currently teaching Statistical Theory I (OSU STAT 6801). My office hours are Monday and Wednesday 10:10am-11:10am in Cockins Hall 317. All course material and annoucements are on Carmen.

I've previously taught:

Introduction to Time Series (OSU STAT 5550) in Spring 2018, 2019

Probability for Statistical Inference (OSU STAT 6301) in Autumn 2016, 2017, 2018

Intermediate Data Analysis II (OSU STAT 5302) in Spring 2016, 2017, 2018

Intermediate Data Analysis I (OSU STAT 5301) (co-taught with James Odei) in Autumn 2015