Teaching

I enjoy designing new courses and have developed both an undergraduate and PhD courses on network analysis at CMU. I have taught Introduction to Statistical Inference, Statistical Analysis of Networks, Undergraduate Research, Statistical Practice, Networks and Graphs in undergraduate, MSc, PhD, and Executive programs. As part of my teaching, I have provided statistical consulting for both for-profit organizations and not-for-profit partners with my students.

Given the technical nature of the Statistics curriculum, Diversity, Equity and Inclusion (DEI) is not a natural focus or topic of class discussion in Statistics. Yet, given the large and diverse student population interested in Statistics, attention to inclusive teaching is important if we want all students to succeed. In 2022, I was awarded a CMU Provost’s Inclusive Teaching Fellowship to develop course material to enhance inclusive teaching.

Carnegie Mellon University – Department of Statistics & Data Science

36-690 Statistical Practice (Spring 2023, 2024)
This course in the Master’s of Applied Data Science program teaches students how to structure a consulting session, elicit and diagnose a problem, manage a project, and conduct and report an analysis. The students engage in statistical consulting for industrial or academic clients.

36-717 Statistical Network Models I (Fall 2021, mini 1)
This PhD-level mini course is a rapid introduction to the statistical modeling of networks. Emphasis is on the statistical methodology and subject-matter-agnostic models and concepts, but we also discuss their applicability.

36-718 Statistical Network Models II (Fall 2021, mini 2)
This PhD-level mini course covers advanced statistical network models, such as for network dynamics, dynamic processes on networks, causal inference under interference, and network topological data analysis. We also discuss open problems in methodological network research, to bring students with a working knowledge of network modeling close to the research frontier.

36-311 Statistical Analysis of Networks (Fall 2018, 2019, 2020)
This course gives undergraduate students an introduction to network science, mainly focusing on social network analysis. Illustrated by empirical examples, it covers an overview of concepts used when describing networks, and network visualization. We discuss random graph models and basic statistical network models.

36-226 Introduction to Statistical Inference (Spring 2019–2023)
This undergraduate course addresses the formalisms behind frequently used statistical methods, and develops a link between statistical theory and practice. Topics include methods and properties of estimation, hypothesis testing, linear models, and analysis of variance.

36-490 Undergraduate Research (Spring 2018)
This course is designed to give undergraduate students experience using statistics in real research problems. Small groups of students are matched with clients and do supervised research for a semester. Students gain skills in approaching a research problem, critical thinking, statistical analysis, scientific writing, and conveying and defending their findings.

University of Groningen – Department of Sociology

SOBA221 Social Networks (Semester II b 2017)
Students participating in this undergraduate course acquire knowledge of the main theories, methods, and applications of social network research, as well as basic skills to collect, analyse and visualise network data. After the course, they should be able to design and conduct a small-scale network study on their own.