photo of Michael C. Burkhart

Michael C. Burkhart

About Me

I earned my Ph.D. in 2019 from Brown University's Division of Applied Mathematics. For my dissertation  (presentation ), I derived a novel approach to Bayesian filtering, the Discriminative Kalman Filter, motivated by and developed with my advisor M. Harrison and collaborators D. Brandman and L. Hochberg. We validated and successfully implemented this filter as part of the BrainGate Clinical Trial that enables participants with quadriplegia to communicate and interact with their environments in real time using mental imagery alone (paper ).
I then spent three years working as a machine learning scientist at Adobe in California. My main projects involved customer segmentation and causal inference. I also supervised intern projects in representation learning for semi-supervised classification (paper , patent ) and causal inference (paper , patent pending , presentation ).
In 2021, I joined Cambridge University as a research associate to develop machine learning-based approaches for the early diagnosis of neurodegenerative disease (abstract , presentation , paper ). I also investigated how sequential inference can be applied to optimization (paper ) and conditions for non-coprime actions in abstract groups to have fixed points (paper ).

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