CV

See below a web-version of my CV.

Contact Information

Name Morris Greenberg
Email morris.greenberg@mail.utoronto.ca
Phone +1-617-967-0609

Experience

  • 2025 - 2025

    Toronto, ON

    Course Instructor
    University of Toronto Statistical Sciences Department
    • Course co-instructor for An Introduction to Statistical Reasoning and Data Science (STA 130)
  • 2021 - present

    Toronto, ON

    Teaching Assistant
    University of Toronto Statistical Sciences Department
    • Course list includes: Advanced Computational Methods for Statistics I (STA 2311, Fall 2024), Bayesian Statistics (STA 365, Winters 2022-2025, Fall 2025), Statistical Consultation, Communication, and Collaboration (STA 490, 2022-2025), Introduction to Machine Learning (STA 314, Fall 2022, Winter 2026), Data Science in Practice (STA 2546, Winter 2022)
  • 2020 - 2021

    Durham, NC

    Research Assistant
    Li Ma Statistics Lab / Duke University Hospital
    • Developed novel hierarchical models using Gaussian processes to analyze the dynamics of microbiome composition of patients in bone marrow transplant studies
    • Designed a microbiome logistic tree package in R that includes microbiome methods for mixed effects modeling, Gaussian process modeling, and topic modeling
  • 2016 - 2019

    Boston, MA

    Analyst / Senior Analyst
    Analysis Group
    • Developed models to predict cardiovascular risk, chronic kidney disease, and chronic thromboembolic pulmonary hypertension (CTEPH) in Type II Diabetes Mellitus patients, which resulted in multiple academic journal articles
    • Created an elasticity model to describe the price sensitivity of patients, physicians, and providers towards cholesterol lowering drugs in expert reports for intellectual property litigation
    • Built machine learning tools (e.g. neural networks, random forests, Gaussian mixture models) to identify user types of opioids for the purposes of targeted treatment care
    • Analyzed the source code for millions of files across programming languages for tax litigation; measured the similarity between pairs of files to understand how source code was modified by developers over time
  • 2015 - 2016

    Washington, DC

    Baseball Operations Intern / Consultant
    Washington Nationals
    • Derived, coded, and maintained a Markov Chain simulator for the team
    • Worked on analytical problems using statistical learning methods (e.g. random forests, gradient boosted algorithms, and generalized linear models) in R

Education

  • 2021 - present

    Toronto, ON, Canada

    PhD
    Department of Statistical Sciences, University of Toronto
    Statistical Sciences
    • Advisors: Prof. Radu Craiu, Prof. Kieran Campbell
    • Research interests: Bayesian modeling and programming, scalable methods for complex health-care data (especially ‘omics data), graph inference, hierarchical models, approximate Bayesian computation
  • 2019 - 2021

    Durham, NC, USA

    MS
    Department of Statistical Science, Duke University
    Statistical Science
  • 2012 - 2016

    Medford, MA, USA

    BS
    Tufts University
    Quantitative Economics and Mathematics

Awards

  • 2023
    NSERC Postgraduate Scholarship - Doctoral
    National Sciences and Engineering Research Council

    $101,000 CAD

  • 2022
    Queen Elizabeth II Graduate Scholarship in Science and Technology
    Ontario Student Assistance Program

    $15,000 CAD

  • 2021
    Doctoral Recruitment Award
    University of Toronto

    $5,000 CAD

  • 2021
    BEST Award for Master's Research in Statistical Science
    Duke University

    $500 USD

Conference Presentations

  • Greenberg, M., Campbell, K., Craiu, R. (2026, June). Estimating Hierarchical Tissue Organization with Structure Learning. 2026 International Society for Bayesian Analysis World Meeting, Nagoya, Japan. (Poster)
  • Greenberg, M., Campbell, K., Craiu, R. (2025, September). Restricted Search Space Graph MCMC via Birth-Death Processes. The Fast and Curious 2: MCMC in Action, Toronto, ON, Canada.
  • Greenberg, M., Campbell, K., Craiu, R. (2025, May). Restricted Search Space Graph MCMC via Birth-Death Processes. Statistical Society of Canada Annual Meeting 2025, Saskatoon, SK, Canada. Advances in Statistical Inference and Computing.
  • Greenberg, M., Campbell, K., Craiu, R. (2025, January). Restricted Search Space Graph MCMC via Birth-Death Processes. University of Florida Department of Statistics’ Annual Winter Workshop: 2025 Computational Methods in Bayesian Statistics, Gainesville, FL, USA. (Poster)
  • Greenberg, M., Craiu, R., Campbell, K. (2024, June). Restricted Search Space Graph MCMC via Birth-Death Processes. International Chinese Statistics Association Canada Chapter Symposium, Niagara Falls, ON, Canada. Advances in Monte Carlo Methods.
  • Greenberg, M., Craiu, R., Campbell, K. (2023, May). Restricted Search Space MCMC With Adaptive Weighting And Sparsity Parameterization For Graph Inference. Statistics Society of Canada Meeting 2023, Ottawa, ON, Canada. Advances In Bayesian Modelling And Computation.
  • Greenberg, M., Craiu, R., Campbell, K. (2023, May). Restricted Search Space MCMC Methods for Graph Inference. The Fast and Curious: Modern Markov Chain Monte Carlo, Minneapolis, MN, USA.
  • Greenberg, M., Ma, L., Wang, Z., Ma, P., Sung, A. (2021, August). Logistic Tree Gaussian Processes (LoTGaP) for the Microbiome. Joint Statistical Meetings 2021, Next-Generation Sequencing and High-Dimensional Data Section.

Publications

Technical Skills

Programming: R, Python, C++, STAN, Matlab, LaTeX, Stata, SAS, SQL (MySQL, Oracle SQL Developer)
Languages: English, Spanish, Hebrew
Miscellaneous: Top 100 Scrabble Player in North America; came in 10th at the 2017 North American Championships