My main goal for curriculum and to teach students is understanding creative uses for artificial intelligence. To approach this, I breakdown the concepts from the ground up, but make the math behind it approachable so they don't need a background in calculus, linear algebra, or computer science as a whole. Also bring in real world applications that they run into from day to day, making the material less theoretical but useful in practice.
Synth Design in Max with Machine Learning
CalArts - Winter Session 2026
In this course, we will take a look at select machine learning algorithms and how to apply them to synthesis. The class will take advantage of the FluComa library and use Max as our main software for implementation. A review on simple synth design and basics in Max will occur on the first day, making it open to people that have not used Max or beginners.
Machine learning algorithms we will cover include Multi Layer Perceptron's (MLPs), K Nearest Neighbors (KNN), K-Dimensional Tree (KD-Tree), Principle Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), Hidden Markov Models (HMMs), and other supervised and unsupervised machine learning algorithms.
After getting a basic understanding of these tools, we will begin using them to mess around with parameters of effects, manipulate your sound files, create generative music, but that is only scratching the surface as the possibilities are endless.
Feel free to contact for lesson plans, slideshow, and selected class recording. The syllabus and final projects from the class can be viewed on here.
Experience also includes being a teacher assistant for the class seen below:
California Institute of the Arts: 2025- Present
Coding Fundamentals, Introduction to Max, Machine Orchestra, Intermediate Max
Peabody Institute of Johns Hopkins University: 2022- 2024
Laptop Ensemble