Jonathan Stewart, Ph.D.

Jonathan Stewart, Ph.D.

CV

A PDF of my CV can be found [HERE].

Employment

Education

Published papers and papers in press

  1. Babkin, S., Stewart, J., Long, X., and Schweinberger, M. Large-scale estimation of random graph models with local dependence. Computational Statistics and Data Analysis, (2020), 107029. [PDF]
  2. Schweinberger, M., Krivitsky, P., Butts, C., T., and Stewart, J. Exponential-family models of random graphs: Inference in finite-, super-, and infinite-population scenarios. Statistical Science, 35 (4) (2020), 627-662. [PDF]
  3. Stewart, J. Consistent estimation of high-dimensional random graph models with dependent edge variables. Ph.D. Thesis, Rice University, (2020).
  4. Schweinberger, M. and Stewart, J. Concentration and consistency results for canonical and curved exponential-family models of random graphs. The Annals of Statistics, 48 (2020), 374-396. [PDF]
  5. Stewart, J., Schweinberger, M., Morris, M., and Bojanowski, M. Multilevel network data facilitate statistical inference for curved ergms with geometrically weighted terms. Social Networks, 59 (2019), 98-119. [PDF]
  6. Campbell, I. M., Stewart, J. R., James, R. A., Lupski, J. R., Stankiewicz, P., Olofsson, P., and Shaw, C. A. Parent of origin, mosaicism, and recurrence risk: Probabilistic modeling explains the broken symmetry of transmission genetics. The American Journal of Human Genetics, 95 (4) (2014), 345-359. [PDF]

Papers in preparation

  1. Stewart, J. R. and Schweinberger, M. Pseudo-likelihood-based M-estimation of random graphs with dependent edges and parameter vectors of increasing dimension, in preparation (2021+). [Preprint]

Invited talks

  1. 2020 International Conference on Computational and Methodological Statistics (CMStatistics), virtual
    Maximum pseudolikelihood estimation for models of social network data
  2. 2020 Department of Statistics, Florida State University, Tallahassee, FL
    Scalable and consistent estimation of random graph models using the pseudolikelihood
  3. 2020 Joint Statistical Meetings, delivered virtually
    A Probabilistic Framework for Models of Dependent Network Data, with applications to brokerage in social networks
  4. 2020 Department of Mathematics, Tulane University, New Orleans, LA
    A Probabilistic Framework for Models of Dependent Network Data, with Statistical Guarantees
  5. 2020 Department of Statistics and Data Science, Cornell University, Ithaca, NY
    A Probabilistic Framework for Models of Dependent Network Data, with Statistical Guarantees
  6. 2020 Department of Statistical Science, Southern Methodist University, Dallas, TX
    A Probabilistic Framework for Models of Dependent Network Data, with Statistical Guarantees
  7. 2020 Department of Statistics, Florida State University, Tallahassee, FL
    A Probabilistic Framework for Models of Dependent Network Data, with Statistical Guarantees
  8. 2019 International Conference on Computational and Methodological Statistics (CMStatistics), London, UK
    Generalized β-models with dependent edges and parameter vectors of increasing dimension

Contributed talks

  1. 2019 International Sunbelt Social Network Conference, Montreal, CA
    Multilevel ERGMs with overlapping subsets of nodes: models, methods, and statistical theory
  2. 2012 Joint Statistical Meeting (JSM), Section on Statistical Graphics, San Diego
    Graphical inference and the hanging rootogram

Honors

  1. Inaugural winner of the 2019 James R. Thompson Student Award, Rice University
    Awarded annually to up to two Ph.D. students in the Department of Statistics for excellence in research
  2. Travel Award, Department of Statistics, Rice University
    Funding to attend and present at the 2019 CMStatistics conference in London
  3. Travel Award, International Network for Social Network Analysis
    Funding to attend and present at the 2019 INSNA Sunbelt conference in Montreal
  4. Travel Award, Department of Statistics, Rice University
    Funding to attend and present at the 2019 INSNA Sunbelt conference in Montreal

Software