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.
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.
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.