I am a Computer Science Ph.D. Candidate with experience implementing neural architectures called neuro-fuzzy networks (NFNs). I will defend my dissertation and graduate in the Spring of 2025, and am looking for a new opportunity to continue building eXplainable AI!
I am researching a new prototype NFN that can automatically reconfigure its knowledge base in response to its performance with respect to an objective function. These architectures natively handle missing data in the inputs and can easily transfer their knowledge to other models. The results of this novel work will be reported in my dissertation.
- Invented the first method to achieve fuzzy reinforcement learning in computer vision tasks (pending review).
- First publication to create NFNs for tasks with high-dimensions (to the best of my knowledge).
- Published the first work solely dedicated to offline model-free fuzzy reinforcement learning.
- First work to use fuzzy logic control to yield a pedagogical policy.
My expertise is in developing novel solutions to previously unsolvable problems and building scalable code libraries to put them into action. I am looking for a new role where I can push not only my boundaries, but the boundaries of entire fields by continuing to solve the unsolvable.
I implement a test-driven agile workflow that emphasizes modular and reusable code with high cohesion and low coupling. My code is clear, self-documenting, and minimal, yet I strongly advocate for comprehensive documentation, utilizing tools like Sphinx. I adhere to the relevant conventions of the programming language and maintain a consistent style, such as using Black for Python code formatting.
Email: [email protected] or [email protected]
My "coding buddy" is an adopted three year old Siberian Husky named Zoey!
