Kyle Johnsen

PhD Candidate
Biomedical Engineering

Biography

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 manipulate population-level variables of neural activity.

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 plan on looking for an industry research scientist role post-graduation.

Download my CV (last updated 2023).

Interests

NeuroAI: Bridging theoretical neuroscience and machine learning
Brain-computer interfaces

Education

PhD in Biomedical Engineering, in progress

Georgia Institute of Technology/Emory University
BS in Bioinformatics, 2019

Brigham Young University

Projects

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 simulation of closed-loop real-time stimulation, electrode recording, 2P imaging, and optogenetics with spiking network models and provides a modular interface to facilitate future additions.

CLOCTools

A set of software to aid in real-time closed-loop optogenetic control. I have been involved in developing/maintaining ldsCtrlEst and supervising the development of lqmpc and nnmpc.

PhD dissertation proposal

Towards Optogenetic Feedback Control of Neural Population Dynamics

‘Step the Brain along a Path’ Lobby Installation

A foray into neuroscience-based music and art

tklfp: Teleńczuk Kernel LFP

A lightweight Python package approximating LFP from spikes alone as in Teleńczuk et al., 2020. Developed for use in Cleo to prototype real-time manipulation of LFP, but hopefully other people will find it useful too.

MMAPPR2: Mutation Mapping from RNA Sequences

The fruit of my undergraduate research in bioinformatics. MMAPPR2 maps mutations resulting from pooled RNA-seq data from the F2 cross of forward genetic screens.

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 process and what I learned; finding the right tools wasn’t easy since this is such a niche area.

Last updated on Feb 20, 2024 4 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 $\phi_{ij}(x_i,x_j)$ for all edges $(x_i , x_j)$ in the graph.

Jan 11, 2024 3 min read coursework

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 $s_j=1$ when the patient has the symptom and $s_j=0$ otherwise.

Jan 9, 2024 4 min read coursework

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 $x\sim F^{-1}(u)$, where $F^{-1}$ is the inverse of the CDF $F$.

Jan 9, 2024 4 min read coursework

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 (CRF) log-linear model with $k$ features, given a data set $\mathcal{D}$ with $M$ instances.

Jan 7, 2024 5 min read coursework

See all posts

Publications

Yenho Chen, Noga Mudrik, Kyle A. Johnsen, Sankaraleengam Alagapan, Adam S. Charles, Christopher J. Rozell (2024). Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics. arXiv.

Cite arXiv URL

Kyle A. Johnsen, Nathanael A. Cruzado, Zachary C. Menard, Adam A. Willats, Adam S. Charles, Jeffrey E. Markowitz, Christopher J. Rozell (2024). Bridging Model and Experiment in Systems Neuroscience with Cleo: The Closed-Loop, Electrophysiology, and Optophysiology Simulation Testbed. bioRxiv.

Cite DOI URL

Kion Fallah, Alec Helbling, Kyle A. Johnsen, Christopher John Rozell (2023). Manifold Contrastive Learning with Variational Lie Group Operators. Transactions on Machine Learning Research.

Cite URL