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I solve problems in everything from computer vision to deep reinforcement learning(RL) applied to algorithmic trading. Currently finishing a paper on my revolutionary sports prediction architecture employing novel deep learning models in sync with specialized ‘classic’ ML techniques. Strong background in linear algebra, probability, set theory, and other mathematics. Utilizing lie groups in the context of theoretical physics in my free time(gauge theory) along with dissecting some of the titular problems of our time.
My knowledge of ML algorithms spans from classic methods(linear CoF, random forests, SVMs), to cutting-edge approaches(capsule networks, ANNs learning to knowledge graphs for probabilistic reasoning, pointer networks). Ergo, I can reason about the optimality of a particular architecture in a given problem domain.
While I consider myself primarily focused on reinforcement learning and top-down AGI, I have implemented multiple effective recommendation systems, developed novel computer vision and NLP models, including tabular problems, correctly predicted stress on unlabelled structured data in an unsupervised context and even fit Harr Wavelets and Radon Transforms to produce a lightweight image similarity framework. As such, I’ve situated myself as a top-flight engineer and researcher at the cutting edge of my field.
I bring a new vision uninhibited by prior bureaucratic thinking. Hope to speak about the project at your earliest convenience.
Thanks,
Austin