**Main research interests: **

All aspects of random combinatorial objects and combinatorial stochastic processes and their applications in network science, population genetics, physics, and machine learning.

Five specific research lines are:

1. Foundations of statistical and inductive inference

2. Exchangeability and probabilistic invariance principles

3. Foundations of statistical network modeling

4. Combinatorial Markov processes

5. Inference for large combinatorial structures

** Selected publications**: (Complete publication list with links to pdf files)

** Graph- and partition-valued processes and exchangeability theory**

H. Crane. (2016). The ubiquitous Ewens sampling formula (with discussion and a rejoinder by the author). *Statistical Science*, **31**(1):1-39.

H. Crane. (2017). Exchangeable graph-valued Feller processes. *Probability Theory and Related Fields*, in press.

H. Crane. (2016). Dynamic random networks and their graph limits. *Annals of Applied Probability*, **26**(2):691-721.

H. Crane. (2014). The cut-and-paste process.
*Annals of Probability*, **42**(5):1952-1979.

** Network modeling**

H. Crane. (2015). Time-varying network models.
*Bernoulli* **21**(3):1670-1696.

H. Crane and W. Dempsey. (2016). Edge exchangeable models for network data.

H. Crane and W. Dempsey. (2016). A framework for statistical network modeling.

**Statistical methodology**

H. Crane. (2015). Clustering from categorical data sequences.
*Journal of the American Statistical Association* **110**(510):810-823. data sets

H. Crane. (2015). Generalized Ewens-Pitman model for Bayesian clustering.
*Biometrika*, **102**(1), 231-238.

H. Crane. (2016). A hidden Markov model for latent temporal clustering.

Please send questions and comments to hcrane@stat.rutgers.edu.