Teaching

Irise: Research Institute for StoryArc Exploration Permalink

PhD Mentor and Collaborator, University of Texas at Austin, 2026

This program re-imagines both the undergraduate research experience and the PhD student teaching and mentoring experience. Rather than working on a small piece of a preexisting project, undergraduate and PhD students collaborate in teams to co-design and conduct their research from beginning to end, gaining hands-on experience across each phase of the research process (research problem, literature review, developing research questions, data collection, data analysis, and dissemination).

Teaching Assistant: INF 385T - Datafication and Its Consequences Permalink

Graduate course, University of Texas at Austin, School of Information, 2026

I served as TA for this Course under Dr. Elliott Hauser. Course Description: Processes, techniques, and technologies that generate inscriptions (ready-to-take data), especially from or about people(s) or culture(s). Contexts, consequences, and history of datafication practices. Purposive intervention with datafication processes, practices, and artifacts.

Python Data Science: An Introduction to Regression

Workshop, Toronto Public Library: Creation Loft Digital Hub, 2024

Course Overview

This workshop introduced participants to simple, multiple, polynomial and logistic regression. Particpants learned the math behind these algorithms, including solutions through ordinary least squares, evaluation metrics such as mean squared error (MSE) and variants, and how to implement and evaluate regression in Python via the coefficient of determination. Participants learned to train a basic regression classifier.

An Introduction To Network Analysis and Visualization with Gephi

Workshop, Toronto Public Library: Creation Loft Digital Hub, 2024

Course Overview

This workshop introduced participants to graph theory and social network theory, including a basic mathematical understanding of graph properties such as degree, paths, walks, trails, direction, connectivity, cycles and graph structure including directed, undirected, and hypergraphs. Participants were introduced to graph adjacency lists and matrices and centrality and eccentricity measures including degree, distance, diameter, closness centrality, betweeness centrality, eccentricity, eigenvector centrality, clustering coefficient, modularity, Pagerank algorithm, assortativity coefficient, and diachronic graphs. Participants learned to analyze and visualize custom datasets on Gephi.

Python Data Science: An Introduction to Deep Learning with Tensorflow

Workshop, Toronto Public Library: Creation Loft Digital Hub, 2024

Course Overview

This workshop introduced students to the basic math behind multi-layer perceptrons, including activation and loss functions, the universal approximation theorem, and how to implement an image and text classifier through the Python Tensorflow library.

Python Data Science: An Introduction to Classification Algorithms

Workshop, Toronto Public Library: Creation Loft Digital Hub, 2024

Course Overview

This workshop introduced participants to classical classification algorithms such as Naive Bayes and Decision Trees, classification metrics such as precision, recall, accuracy, F-Measure, MCC, ROC, cross-entropy, etc and how to implement and compare the performance of these algorithms via Python on sample datasets. Participants learned the math behind Bayes’ Theorem and decision tree construction with Shannon information Gain.