See my CV for a list of publications and talks.

Advances in data collection have led to the increased presence and availability of network-indexed data, that is, data that has an inherent structure. Networks are composed of nodes (actors, vertices, egos, units) and edges (relationships, ties, links) and are found in a number of diverse fields including the social sciences, technology, and biology. My research centers on developing novel modeling methods and computational advancements that improve our understanding of this progressively more prevalent data structure.

My methodological research has been motivated by the following two goals: (1) to understand or explain a vertex attribute of interest in a manner that utilizes information from both the network’s structure and pertinent covariates and (2) to offer a logical and flexible framework to model graph edges or edge responses that can capture basic network properties while retaining computational feasibility. My research in these areas extends to the following topics: Bayesian modeling, hierarchical modeling, Bayesian variable selection techniques, exponential random graph models, and latent space network models.

Have you ever heard the sayings “Bad association spoils useful habits” or “Birds of a feather flock together”? Are these true? Can we prove or disprove the idea that the people we hang out with, our social network, has an impact on the decisions we make in life? Do we change our own behavior to match that of our friends, or do we self-select friends based on similiarities in behavior? Questions like these can be analyzed with social network analysis. We can quantitatively assess the social structures in a network and the corresponding behaviors of individuals that compose said network. We can also draw inferences on the relationships between the social structures and behaviors.

My recent research in this area attempts to understand the independent and combined influence of individual and network (egocentric and sociometric) factors on risk behaviors among marginalized populations. I am also analyzing the data collection methods used to gather social network data on these populations in order to improve the accuracy of corresponding analyses.

I truly enjoy sharing my passion for statistics with others. Thus, my future research plans also include the development of pedagogical tools to better serve the needs of our students studying statistics and data science in the undergraduate classroom. I would love to tackle this endeavor through a social network analysis lense. Project idea anyone?

Interested in a summer research position or an honors thesis? I have included some possible topics below but there are endless possibilities. Feel free to set up a meeting if you are interested in discussing.

Possible topics:

-Regression models for network data: how can we incorporate network structure (and dependence) in our regression framework when modeling a vertex-indexed response?

-Identify effects shaping network structure. For example, in social networks, we often see transitivity (we have a mutual friend, thus, we are more likely to be friends). How can we capture or test this effect, and others, in a regression framework when modeling edge-indexed responses?

-Extending models for multilayer networks. Current methodologies combine edges from multiple networks in some sort of weighted averaging scheme. Could a penalized multivariate approach yield a more informative model?

-Developing algorithms to make inference on large networks more efficient.

-Any topic in linear or generalized linear modeling (including mixed-effects regression models, zero-inflated regressions, etc.).

-Applied statistics research. In collaboration with a scientist or social scientist, use appropriate statistical methodology to answer an interesting scientific question.

Possible colloquium topics:

-Any applied statistics research project/paper

-Topics in linear or generalized linear modeling

-Network visualizations and statistics

-Network sampling methods

-Networks as applied to…anything!