I solve problems in everything from computer vision to deep reinforcement learning(RL) applied to algorithmic trading. Finishing a paper on my revolutionary sports prediction architecture employing novel deep learning models in sync with classical ML techniques. Strong background in linear alg, probability, set theory, and more. Utilizing lie groups in the context of theoretical physics in my free time(gauge theory) while dissecting some of the titular problems of our time.
My knowledge of ML algorithms spans from classic methods(random forests, SVMs), to cutting-edge approaches(capsule networks, ANNs learning to knowledge graphs, 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 created many effective recommendation systems, developed novel CV and NLP models, including tabular problems, correctly predicted stress on unlabeled 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.
As you can hopefully tell, I consider myself an eloquent writer as well. I bring a new vision uninhibited by prior bureaucratic thinking. Hope to speak about the project at your earliest convenience.
Regards,
Austin