PhD – Neural correlates of stress resilience
Standardized three-phase stress resilience assay in larval zebrafish, quantifying locomotor rebound after acute osmotic stress and relating individual outcomes to resilience labels.
Neuroscience · Machine Learning · Zebrafish · Odd Experiments
Stress, circuits & curious side projects.
I’m a neuroscientist and ML enthusiast studying how brains cope with stress, building analysis tools, and occasionally running unnecessary-but-fun experiments.
Research & Publications
From stress resilience behavior to brain-wide circuits.
My research focuses on how individuals differ in their ability to recover after stress. I combine larval zebrafish assays, whole-brain activity mapping, and graph-theoretic analysis to link behavior to underlying neural networks.
Academic projects
Brain-wide pERK resilience networks
Voxel- and region-level pERK/tERK analyses mapped onto the Z-Brain atlas, combined with functional connectivity and graph metrics to identify stress vs. resilience subnetworks.
EPFL – Unsupervised quantification of behavior
Developed unsupervised pipelines to embed and cluster Drosophila behavior from pose-tracking data, exploring structure in high-dimensional behavioral repertoires.
Selected publications
- Title of first main-author paper
- Title of second main-author paper
Code & Tools
Analysis pipelines, teaching material, and ML experiments.
I build Python tools for behavioral analysis, whole-brain imaging, and machine learning, with an emphasis on reproducible workflows and clear documentation.
ERK-Analysis
Modular framework for pERK/tERK datasets: normalization (including batch correction), regional statistics, functional connectivity, and graph-theoretic analysis with rich plotting.
Computational Neuroscience teaching materials
Workshop notebooks and exercises covering learning principles, neural network basics, and spike waveform classification for medical and neuroscience students.
PillProphet
Prototype ML pipeline that parses structured and unstructured clinical trial data (e.g., inclusion criteria, outcomes) to predict the probability that a drug reaches successful endpoints.
More code is available on my GitHub profile. PillProphet is under active development; feel free to reach out if you’re interested.
Miscellany
Side quests: data, drinks, and cinema.
Not everything needs to be a grant proposal. These are projects I run for fun, usually with too many spreadsheets.
Energy drink tasting study
Blind tasting, rating, and clustering of energy drinks based on flavor, perceived effectiveness, and questionable subjective measures. Includes tasting protocol, aggregated scores, and exploratory analyses.
View project →Cinema & Letterboxd
I keep a running log of what I watch, with ratings and short notes, and occasionally overthink film structures more than my own analysis pipelines.
Letterboxd profile →
Contact
Let’s talk.
I’m open to opportunities in computational neuroscience, ML for biology/health, and data-heavy R&D roles.
Email
Jhc2309@gmail.com
Elsewhere
GitHub ·
Google Scholar ·
LinkedIn