Exploring ideas that could unlock
efficiency and capability.
I’m Thomas J. Richner, a research scientist at Mayo Clinic. I study neural networks and stochastic dynamical systems. I bring mathematics and engineering to foundational questions.
I investigate how adaptation shapes effective connectivity and stability in recurrent networks. My current research asks how networks can learn through local rules while maintaining stable dynamics.
Read the paper ↗ Explore the code ↗
Using Gaussian process models and Bayesian optimization to guide the search for effective stimulation parameters in a closed-loop seizure-control system.
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Developing transparent micro-electrocorticography arrays and integrating electrical recordings with optogenetics and advanced photonics.
Read the paper ↗How can models keep learning from new observations? Sequential Bayesian updating offers a mathematical perspective on the problem—and raises questions about what can scale and the accuracy and computational cost of approximations.
How can separately learned components be combined into a useful model? I’m interested in the structure that makes composition possible, including connections to linear and abstract algebra.
How can we identify what limits a model and make targeted parameter changes when optimization stalls? I’m interested in connecting an understanding of model structure with methods for improving its behavior.
How can we infer interactions and forecast behavior in systems shaped by noise and nonlinear coupling? I’m interested in stochastic vector autoregressive models and their nonlinear extensions, connecting physical-system modeling with questions in quantitative research.
My background spans mathematics, engineering, and computational modeling. My research in systems, optimization, and neural interfaces has involved moving between questions, theories, numerical methods, and experiments. I enjoy going from whiteboard to Python.
At Mayo Clinic, I investigate network stability and use electrical brain stimulation for system identification.