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Dream research is ancient and, at the same time, genuinely frontier science — we still cannot fully say what the brain is doing when we dream. I treat that honestly: the work below is a set of open questions I'm pursuing through NeuroDream, not a body of settled findings. Each theme connects to the six-layer infrastructure that makes systematic study possible in the first place.

Dream Intelligence

Understanding dreams as a form of cognitive computation rather than messages to decode. The central methodological commitment is the individual baseline: meaning is read as deviation from a person's own established pattern, never against a universal symbol dictionary. Open questions: how many dreams are needed for a stable baseline, and which signals are robust enough to be worth surfacing at all.

Computational Dreaming

Can dreams be modeled, analyzed, and — eventually — generated through AI? This spans the practical (NLP coding of dream reports into Hall–Van de Castle dimensions; longitudinal pattern modeling) and the speculative (what it would mean for a generative system to produce, not just analyze, dream-like cognition). The honest boundary: AI's understanding of dreams has real limits, and overclaiming here is the fastest way to lose credibility.

Consciousness

What can dreaming teach us about subjective experience? Dreams are a nightly, universal instance of consciousness decoupled from external input — arguably the most accessible natural laboratory for studying the felt interior. The questions here are the hardest and least resolved, and I approach them as a builder gathering data, not as someone claiming to have solved the hard problem.

Sleep & Brain Science

Exploring the relationship between sleep, memory, and imagination — memory consolidation, emotional regulation, REM dynamics, and the capture conditions (the 2–5 minute golden window after waking) that determine whether any of it is recoverable at all. This is where I lean most on dialogue with neuroscientists and sleep researchers, and where the science is moving fastest.

Dream Data Infrastructure

Building the tools and standards for large-scale dream research: structured representation, privacy-first and individually-owned data, confidence labeling, and shared formats. This is the layer almost no one is building, and the one I think matters most — because every other question above is bottlenecked on whether the data can be captured, structured, and trusted.

If you research dreams, sleep, consciousness, or the infrastructure around them — academically or in industry — I'd like to talk. Reach out.

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