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 computational principles we can learn from biology and the brain and how we can exploit them for next-generation AI/ML technologies. I plan on graduating in spring 2025 and am looking for a research scientist role in the neuroAI field.

Download my CV (last updated 2023).

Interests

Bridging theoretical neuroscience and machine learning
Spiking neural networks
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.

PhD dissertation proposal

‘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

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Publications

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Kion Fallah, Alec Helbling, Kyle A. Johnsen, Christopher J. Rozell (2023). Manifold Contrastive Learning with Variational Lie Group Operators. arXiv.

Cite DOI arXiv