PhD dissertation proposal
Towards Optogenetic Feedback Control of Neural Population Dynamics
Kyle Johnsen
PhD Candidate
Biomedical Engineering
Structured Information for Precision neuroengineering Lab (SIPLab)
Hello there!
I’m a PhD student advised by Chris Rozell at Georgia Tech, developing advanced closed-loop optogenetic control techniques for neuroscience. Specifically, I am applying optimal, model-based control to precisely perturb population dynamics.
I’m fascinated by the biological computational principles and how we can exploit them for next-generation AI/ML technologies. And vice-versa: how we can apply modern ML to neurotech/neuroscience, especially for building life- and time-saving brain-computer interfaces. I am looking for an industry research scientist role post-graduation.
Education
PhD Biomedical Engineering
2019
present
Georgia Institute of Technology/Emory University
BS Bioinformatics
2015
2019
Brigham Young University
Interests
Brain-computer interfaces
Brain-inspired machine learning
High-throughput neuroscience
Recent Publications
(2026).
Bridging Model and Experiment in Systems Neuroscience with Cleo: The Closed-Loop, Electrophysiology, and Optophysiology Simulation Testbed.
J. Neurosci..
(2025).
Modeling Latent Dynamics of the Autonomic Nervous System in Response to Trauma Recall and Non-Invasive Vagus Nerve Stimulation.
IEEE Transactions on Biomedical Engineering.
(2024).
Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics.
Advances in Neural Information Processing Systems.
(2023).
Manifold Contrastive Learning with Variational Lie Group Operators.
Transactions on Machine Learning Research.
Featured Projects
'Step the Brain along a Path' Lobby Installation
A foray into neuroscience-based music and art
Cleo: Closed-Loop, Electrophysiology, and Optophysiology experiment simulation testbed
A Python package built around Brian 2 designed as a testbed for bridging computational models and experiments for mesoscale neuroscience. Specifically, it allows for convenient …
Recent & Upcoming Presentations
NAISys 2024 poster
A foundation for causal characterization of latent neural dynamics with limited observational and interventional capacity
Sep 28, 2024 — Oct 2, 2024
Optogenetics GRC 2024 poster
Bridging model and experiment with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology experiment simulation testbed
Jul 21, 2024 — Jul 26, 2024
SNUFA 2023 poster
Enhancing the Cleo experiment simulation testbed to support all-optical control, multi-channel optogenetics, and easier integration into data analysis pipelines
Nov 7, 2023 12:00 PM
Recent Posts
Sonification of neural data: my experience and outlook
As explained here, in 2022 I had the opportunity to participate in a project bringing together neuroscience and fine arts to engage the public. In this post I want to share my …
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3 min read
Mean field variational inference
In this problem, you will investigate mean field approximate inference algorithms (Koller & Friedman1 11.5). Consider the Markov network in the above figure. Define edge potentials …
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2 min read
Approximate inference via Gibbs sampling
Consider a setting in which there are $D$ diseases and a patient either has ($d_i=1$) or does not have ($d_i=0$) each disease. The hospital can measure $S$ symptoms, where $s_j=1$ …
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4 min read
Markov chain Monte Carlo sampling
Inverse CDF sampling A simple sampling method adopted by many of the standard math libraries is the inverse probability transform: draw $u \sim \text{Unif}(0, 1)$, then draw $x\sim …
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3 min read
Parameter learning in probabilistic graphical models
Parameter learning in Bayesian networks and Markov random fields Cost of learning CRF parameters Consider the process of gradient-ascent training for a conditional random field …
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4 min read
Learning maximum likelihood tree structure with the Chow-Liu algorithm
Write a function ChowLiu(X) -> A where X is a D by N data matrix containing a multivariate data point on each column that returns a Chow-Liu maximum likelihood tree for X. The tree …
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1 min read
Expectation-maximization for a Markov chain mixture model
Assume that a sequence $v_1,\ldots,v_T \in \{1,\dots,V\}$ is generated by a Markov chain. For a single chain of length $T$, we have $$ p(v_1,\dots,v_T) = p(v_1)\prod_{t=1}^{T-1} …
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8 min read
Learning edge direction in a Bayesian network model
Our interest here is to discuss a method to learn the direction of an edge in a belief network. Consider a distribution $$ P(x,y | \theta,M_{y\to x}) = …
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3 min read
Comparing the basic and extended Kalman filters
This notebook doesn't offer much in the way of explanation, but explores implementations of the basic and extended Kalman filters and compares them for different nonlinearities. …
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5 min read