Uncovering Rules For Robustness In Biological Neural Networks
Tutorial, UW Psychology Ph.D. Research Festival, Seattle, WA
I gave a 15 minute talk geared towards a general audience about my research.
Talks and presentations
Encrypted Machine Learning
Tutorial, AAAI 2024 Vancouver, Vancouver, Canada
I led the hands-on discussion and code walkthrough where attendees were given a hands-on introduction to FHE-backed Machine Learning. I started with a naive first-pass implementation, then walked them through how they might optimize their own code, leading us to an optimized logistic regression implementation
An Informal Introduction to Monoids, Functors and Monads in the Context of Machine Learning
Talk, THAT Conference 2023, Round Rock, TX, USA
I gave an experimental talk where I chatted about how concepts in category theory, primarily monoids functors and monads lead to better, cleaner code.
Homomorphic Encryption for Encrypted Machine Learning
Talk, eScience Institute Data Science Seminar, Seattle WA, USA
I led the hands-on discussion and code walkthrough where attendees were given a hands-on introduction to FHE-backed Machine Learning. I started with a naive first-pass implementation, then walked them through how they might optimize their own code, leading us to an optimized logistic regression implementation
Thoughts on the Brain and Machine Learning: Biological Plausibility, SNNs, and beyond
Talk, Pie & AI Amsterdam, Remote
I covered a bit of Neuroscience, discussed the plausibility of backpropagation in the brain, highlighted spiking neural networks and analyzed the Neural Gradient Representation by Activity Differences (NGRAD) algorithm.