Hi, my name is Ewan. I’m a senior Computer Science undergrad at the University Of Vermont, working towards a PhD in Theoretical/Computational Neuroscience. I am interested in how In Silico models can make the brain interpretable, and how far that can go with sparse, everyday signals of cognition.
Modern deep networks are exceptionally good at predicting brain responses to stimuli, partly due to advancements in Representations for text, images, and video. I am curious about probing what these models have learned, and the potential for learning more about the brain in turn. Even models trained to predict classical single neuron recordings (Lad et al., 2026) (highly recommend reading this paper) offer a whole new medium for interpreting circuits en masse.
These models can also be run in reverse, probing the brain with exploratory, almost adversarial stimuli, a new kind of experiment design with neuroimaging feedback. Well-funded academic research (Henderson et al., 2026) has piqued my interest in pushing this further, guiding deep learning research toward real neural representations. Conversely, this line of work also allows for novel investigation into the content Representation models themselves (Prince et al., 2026): if two models predict natural data equally well, what stimuli would make them separable? On a much smaller scale, I’ve been doing experiments with more open tools like TRIBE v2, and impractical ones like Maximizing Activation through Differentiable Content Consumption.
Ultimately I am only an undergrad with limited funding (my entire disposable income), so I have taken this line of work to sparse, everyday signals. It is fun to think about how much of our internal brain state is already partially observable, e.g. what you are saying out loud or what video you are watching serve as decent cognition heuristics for your thoughts (Wu et al., n.d.). As fun as it is to apply evolutionary algorithms to TRIBE v2 inputs, it is a population-level model, and an fMRI setup is one of the few things my lab of a bedroom doesn’t have. Instead I start with two devices: a mediocre 4-channel BrainAccess HALO headset, and a Tobii ET 5, a gaming peripheral which will fight you on using it as a research eye tracker. There’s also what we already have. With the amount of content we consume, I find interest in obsessively recording personal data as behavioral sources: computer activity (ActivityWatch), transcription/diarization of speech, and the writing timesink that is this Digital Garden vault (+git timeseries data). The same goes for Health and Longevity data; a large part of my work relies on wearable data for tracking physiology and the effects of interventions. On their own they seldom have value, but they are partial views of the same underlying state, aligned by timestamp. I want to know how much of a shared representation can be learned from several poor sensors together, and what each one adds.
Further out, I’m interested in using this model to test causal interventions against benchmarks (Anki, Chess): shaping content diet, and even extensions into other methods of Self Configuration (for me that is Neuropharmacology and nootropics, for most just stick to what you already consume).
In my lab at UVM I work with speech data and transcription/diarization, post-training standard speaker representation models to better capture acoustic features that predict cognitive impairment. My room is always a lab and every kind of lab: homelab as the compute for Quantified Self data work and collection en masse, 3d Printing and ESP32s for everything, and a drug lab for pursuing the ever diminishing returns of health optimization.
Banner inspired by Simone Conradi’s Eigenfish.
Don’t navigate this garden through the folders unless you just care about my class notes. Use the tags, described in website.
Thoughts Notes How to use Inspirations Some Fun Facts About Me
Bibliography
- Henderson, M. M., Luo, A. F., Park, S., Tarr, M. J., & Wehbe, L. (2026). Diffusion-Based Stimulus Optimization Reveals Functional Organization across Higher Visual Cortex (p. 2026.05.12.724119). bioRxiv. https://doi.org/10.64898/2026.05.12.724119
- Lad, V., Franke, K., Shaham, T. R., Ganguli, S., Tolias, A. S., Sanborn, S., & Karantzas, N. (2026). Letting the Neural Code Speak: Automated Characterization of Monkey Visual Neurons through Human Language. arXiv. https://doi.org/10.48550/arXiv.2605.12485
- Prince, J. S., Wang, B., Fel, T., Jagadeesh, A. V., Vaziri, P. A., Alvarez, G. A., Livingstone, M. S., & Konkle, T. (2026). Parametric Neural Control Differentiates Top Neural Network Models of Primate Visual Cortex (p. 2026.08.16.745063). bioRxiv. https://doi.org/10.64898/2026.08.16.745063
- Wu, Z., Schrater, P., & Pitkow, X. (n.d.). Inverse POMDP: Inferring What You Think from What You Do.