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
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.
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
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.