Fall 2026
Developer and instructor for a foundational Ph.D. course in computational linear algebra, optimization, numerical computing, and scalable scientific computation. Emphasizes efficient, stable, and trustworthy implementations.
Spring 2026 · Fall 2025 · Spring 2025
Undergraduate and graduate course on graph representations, ranking algorithms, graph neural networks, and spatiotemporal learning.
Fall 2024
Supervised and unsupervised learning, model selection, and evaluation, with attention to the assumptions, strengths, and limitations of different methods.
Spring 2024 · Fall 2023 · Spring 2023
Analytical, statistical, computational, and data visualization methods for extracting knowledge from complex datasets.
Fall 2023
Algorithm design and analysis, asymptotic complexity, sorting and graph algorithms, and supporting data structures.
Fall 2022 · Fall 2021
Formal languages, automata, and computability, including finite automata, context-free grammars, pushdown automata, Turing machines, and undecidability.
Spring 2022
Developed and taught a graduate course in graph representation learning, including graph neural networks and spatiotemporal models.