About Me

My name is Tyler King and I am an undergraduate engineering student at Cornell University working towards my Bachelors of Science in Computer Science. I am currently associated with McMahon Lab, where I work on simulating dynamics of the coherent Ising machine for vehicle routing problems (in collaboration with ExxonMobil) and simulated dynamics of oscillator neural networks. Last summer I interned at AWS AI leveraging foundational models for image annotation/tagging as a part of Ground Truth's human annotation pipeline.

While my research interests tend to fluctuate, my overarching focus is on efficient machine learning. To support this I draw from various fields including physical systems, human cognition, and classical machine learning approaches. My current and past work operates at this intersection, including:

• Physical inductive biases for performant low-parameter model architectures.
• Model reduction with explicitly encoded cognition states for human-based nonlinear optimization tasks.
• Model reduction for edge ML with space-to-space imaging applications.
• Accelerating foundational model invocations with multicore implementations.

I've additionally contributed to several open-source initiatives, including development of a repository for simulating dynamics of various coherent Ising machines, which can be viewed at cim-optimizer (for which I currently maintain).

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