teaching

materials for courses i've taught

University of California, Berkeley

Postdoctoral Lecturer
Computational Social Science 1

  • Reproducibility and transparency — examen the principles of open and reproducible research, version control, and best practices for documenting and sharing data and analysis
  • Ethics and critical evaluation — examining bias, fairness, and limitations of computational methods in social research
  • Machine learning foundations — supervised learning (regression, classification, tree-based methods, ensemble methods, BART, deep learning), unsupervised learning (clustering, PCA/dimensionality reduction), and ensemble methods
  • Applied social science projects — 4 hands-on analyses of real-world datasets, including predictive modeling and policy-relevant applications using key Python libraries(e.g., sklearn, numpy and matplotlib), the Anaconda framework, and GitHub.
  • GitHub repository
  • Syllabus

Computational Social Science 2

  • Natural Language Processing (NLP) — text as data, fundamentals of text analysis, unsupervised and supervised methods, classification, vector models, neural networks, and transformer models for natural language processing
  • Causal inference — randomized experiments, DAGs, matching methods, natural experiments, instrumental variables, difference-in-differences, synthetic controls, and regression discontinuity designs
  • Sensitivity and robustness analysis — evaluating assumptions, performing sensitivity checks, and interpreting causal estimates in observational and experimental data
  • Machine learning for causal inference — applying supervised and ensemble methods to longitudinal and policy-relevant datasets

  • Applied social science projects — 5 hands-on projects analyzing real-world data that integrates NLP, machine learning, and causal inference methods using key libraries in Python (e.g., spacy, tensorflow, keras) and R (e.g., tidyverse, SuperLearner, tmle), as well as using cloud computing in Google Colab

  • GitHub repository
  • Syllabus

City University of New York, The Graduate Center

Graduate Student Instructor
Quantitative Methods for Social Scientists 1

  • Foundations of statistical analysis — units of analysis, levels of measurement, measures of central tendency, dispersion, inequality, diversity, and variable transformations.
  • Sampling and inference — population vs. sample, random and complex sampling, panel and cross-sectional data, use of sampling weights, confidence intervals, p-values, Type I/II errors, and power analysis.
  • Comparing groups and associations — t-tests, chi-square tests, ANOVA, cross-tabulations, and correlation measures.
  • Introduction to causal analysis and regression — basic causal logic, OLS regression, interpretation of coefficients and interactions, robust/clustered/bootstrapped SEs, handling missing data, logistic regression, fractional and negative binomial regression, and using margins for interpretation.
  • Hands-on data analysis in Stata — learning to manage, reshape, and analyze data with practical code examples, exercises, and applied labs in Stata and JMP using real-world social science datasets.
  • Syllabus

Quantitative Methods for Social Scientists 2

  • Advanced methods — exposure to contemporary techniques including regression extensions, causal inference, and introductory machine learning/data mining approaches.
  • Advanced regression and modeling techniques — Tobit, Heckman, hurdle models, Poisson/negative binomial, multinomial/ordinal logistic, quantile regression, and hierarchical/multi-level models.
  • Data reduction and dimensionality methods — stepwise regression, PCA, factor analysis, LASSO/ElasticNet, scale construction, and sheaf coefficients.
  • Machine learning and predictive methods — CART, random forests, clustering (hierarchical & k-means), neural networks, KRLS, and latent class analysis.
  • Causal inference and program evaluation — randomized and natural experiments, fixed effects, instrumental variables, propensity scores, regression discontinuity, and difference-in-differences.
  • Capstone project — independent data analysis applying multiple methods to answer a research question of the student’s choice.
  • Syllabus

Hunter College, City University of New York

Adjunct Professor
Sociological Statistics

  • Undergraduate introductory statistics course for social scientists
  • Fundamentals of statistical analysis — descriptive statistics, probability, and the logic of inference.
  • Applying statistical procedures in sociology — using data to analyze social issues such as inequality, housing, and public opinion.
  • Hands-on data analysis with Stata — computing and interpreting statistics through weekly labs, assignments, and exams.
  • Syllabus