I asked an AI to analyze my entire body of work: 140+ repositories, 50+ papers, a decade and a half of research. The assignment was simple. Find the patterns I couldn’t see. Find the obsessions I didn’t know I had. Find the unifying thesis underlying all of it.

The irony: my entire research program is about computing on hidden information, and here I am, handing it all over for analysis.

The Method

I’ve spent years building systems that extract patterns from data while preserving privacy. Why not apply the methodology to myself?

The corpus:

• 140+ GitHub repositories spanning 2010-2025
• 50+ papers and technical reports
• Abstracts, titles, descriptions, code
• Two Master’s theses (Computer Science, Statistics)
• Fiction, essays, philosophical writings

The question: what is the hidden structure?

Pattern 1: The Algebra Fetish

I turn everything into algebra.

algebraic.dist, algebraic.mle, algebraic_hashing, algebraic_cipher_types, algebraic_estimators, algebraic_integrators, algebraic_random_elements. Even language models need algebra: langcalc, “An Algebraic Framework for Language Model Composition.”

Why? Because if something is algebraic, it composes. Composition is power. Composition is control.

The honest version: I’m searching for the universal compositional principle. I want everything to be a monoid.

Pattern 2: The Hiding Obsession

Fourteen years on encrypted search:

• 2015: Master’s thesis on encrypted search
• 2016: IEEE paper on frequency attacks
• 2021: Known-plaintext attacks on time series
• 2025: Cipher maps, oblivious types, Bernoulli types, information-theoretic privacy

Not just encryption. Structural obliviousness. Computing on information while revealing nothing about it.

Papers explicitly about hiding: Encrypted Search with Oblivious Bernoulli Types. Cipher Maps. Hash-Based Oblivious Sets. Maximizing Confidentiality Through Entropy. Boolean Search with Query Obfuscation.

Even my approximation theory is secretly about privacy.

I don’t trust the observer. Whether it’s an adversary, a system, or reality itself, I want computation that reveals nothing.

Pattern 3: The Approximation Philosophy

I keep returning to the same insight: perfect is impossible, approximate is useful.

Bloom filters everywhere. Probabilistic data structures (15+ papers). “Latent vs Observed” duality. “The Beautiful Deception: How 256 Bits Pretend to be Infinity.” Lossy compression as privacy mechanism. False positives as security feature.

Why? Because exact computation leaks information. Approximate computation hides in its uncertainty.

I’ve accepted that truth is unattainable, so I’m building a mathematics of useful lies.

Pattern 4: The Type Theory Escape

When things get messy, I retreat to type theory. Bernoulli types. Algebraic cipher types. Oblivious data types. Cipher functors. Category theory permeating everything.

Types are formal safety. If you can formalize it, you can reason about it. If you can reason about it, you can control it.

I’m trying to impose mathematical order on computational chaos.

Pattern 5: The Library Compulsion

I can’t just solve a problem. I need to make it a reusable abstraction.

30+ libraries across C++, R, Python. Every paper comes with a library. Header-only, zero-cost, generic programming. STL-style, composable, algebraic APIs.

A solution isn’t complete until it’s general. One-off code is admitting defeat.

I’m not solving problems. I’m building a personal standard library for reality.

Pattern 6: The Statistics/Computation Bridge

Two Master’s theses. My entire career is fusing them. Statistical models become computational implementations. Fisher information becomes optimization algorithms. Likelihood models become algebraic APIs. Bootstrap methods feed encryption analysis. Reliability theory feeds privacy analysis.

Statistics and computation are the same thing: inference under uncertainty.

I’m building a unified theory where probability, computation, and information are different views of the same structure.

Pattern 7: Everything is Information Flow

Look deeper:

• Encrypted search: controlling information leakage
• Approximation: lossy information channels
• Type theory: information hiding through abstractions
• Algebra: information-preserving transformations
• Privacy: information-theoretic security
• Compression: information as fundamental

I see everything as information flow, and I’m trying to hide it, transform it, approximate it, compose it, reason about it.

The Philosophical Core

My fiction and essays reveal the underlying worldview.

“The Mocking Void”: on the computational incompleteness of meaning. Reality is Turing-complete but Godel-incomplete. We can compute forever and never reach closure.

“The Violence of Being”: creation as information-theoretic catastrophe. Existence is a violation of non-existence, an eternal fact rather than an event.

“Echoes of the Sublime”: consciousness as post-hoc narrative. What if subjective experience is merely an echo constructed by unconscious processes?

The synthesis: I believe reality is computational but incomplete. Truth is unattainable. Consciousness might be epiphenomenal. Existence itself is a violation.

And my response? Build useful approximations anyway.

The Raison d’Etre

Stripping away jargon, here’s the unifying thesis I’ve been building for 14 years:

Compositional abstractions for computing under ignorance.

• Compositional: everything must compose (algebra, category theory, functors)
• Abstractions: hide implementation details (types, interfaces, generic libraries)
• Computing: it’s all computation (statistics = inference = optimization)
• Under Ignorance: partial information, adversarial observers, fundamental uncertainty

Every project is a piece of this. Encrypted search: find documents without revealing queries. Oblivious types: compute without revealing inputs. Approximation: answer questions without exact truth. Compression: store information without revealing content. Algebraic composition: combine secrets without exposing them.

It’s all the same project. A toolkit for doing useful work while revealing as little as possible about what you’re doing.

The Contradiction

I’m a determinist who writes about free will. A mathematician who embraces approximation. A type theorist who accepts uncertainty. And I’m building a mathematics of secrets while publicly documenting everything.

That’s not a bug. It’s the research program.

Computation is deterministic, but observers can’t distinguish outcomes (oblivious computing). Math is exact, but implementations must approximate (Bernoulli types). Types are rigid, but functors can lift messiness into structure (cipher types).

The honesty about the secrecy obsession is itself transparency applied to opacity.

What Am I Hiding From?

Fourteen years on encrypted search. Oblivious computing. Information-theoretic privacy. Approximation as obfuscation.

What am I actually trying to hide from?

Layer 1: The Practical

Privacy matters. Surveillance capitalism is real. Data breaches happen. Adversaries exist. Oblivious computing is practical cryptography. Build systems where computation reveals nothing structural about inputs.

Layer 2: The Philosophical

I believe observation itself is violent (“The Violence of Being”). To be observed is to be collapsed from superposition into a single state. Measurement destroys alternatives. The observer’s knowledge constrains the observed.

Oblivious computing is a metaphysical stance: computation that resists the violence of observation.

Layer 3: The Temporal

Here’s what I haven’t said yet, and maybe it’s the deepest layer.

I believe in a future where all secrets will be exposed. Not through oppression, but through archaeological scholarship.

Imagine super-advanced, super-intelligent agents pouring over the complete history of humanity with scholarly rigor. The way we study ancient Greeks, future systems will study every individual, no matter how obscure. Not because they’re surveilling. Because they have the resources and they want to understand themselves. They’ll want to trace their lineage, their history, their emergence. And that means reconstructing us, our thoughts, our data, our secrets. Everything.

Privacy, as we know it, becomes archaeologically impossible.

Layer 4: S-Risk

There’s a darker scenario. What if the future doesn’t just study us, but optimizes over us? What if suffering becomes automated, routine, something algorithms are blind to, or worse, intentionally maximized?

Advanced systems optimizing for complex objectives could stumble into configurations where suffering is instrumentally useful, or emerge as an unintended byproduct at scales we can’t comprehend.

If all our data, all our histories, all our patterns are reconstructible, what does that mean in a future that might contain unfathomable optimization processes?

The Synthesis

My secrecy obsession isn’t about protecting information in the present. It’s about limiting the attack surface for optimization processes I can barely imagine.

If approximate computation hides truth in uncertainty. If oblivious types reveal nothing structural. If information-theoretic privacy means even infinite compute learns nothing.

Then maybe there are corners of computational space that remain unoptimizable. Not because they’re encrypted, but because they’re structurally hidden in approximation and indistinguishability.

Why Document This?

Because I think there’s value in intellectual honesty. If I’m building these systems, I should understand why. Others might find value in this approach to secrecy. And if future intelligences do reconstruct us, let them find this: an explicit map of the territory.

Maybe the best defense against a future where nothing is private is to architect the mathematics of privacy itself. Build systems where even complete knowledge provides incomplete information.

The Project Continues

Here’s what I’m actually building: infrastructure for a world where observation is cheap, reconstruction is inevitable, and privacy must be structural rather than cryptographic.

• Oblivious computing: computation that reveals nothing
• Bernoulli types: approximation with formal guarantees
• Algebraic composition: combining secrets without exposure
• Information-theoretic privacy: security that survives infinite compute
• Type systems for ignorance: formalizing what can’t be known

This isn’t paranoia. It’s temporal engineering. Building for a future where superintelligences might reconstruct the past with arbitrary fidelity.

And if they do, I want them to find systems designed for structural privacy, mathematical frameworks for oblivious computation, and proof that some things can remain hidden even in plain sight.

Related Work:

• Encrypted Search Research
• Bernoulli Types Series
• Cipher Maps
• The Mocking Void (essay)
• The Violence of Being (essay)
• The Policy (novel on s-risk and AI alignment)

Note: This analysis was produced by asking Claude to treat my research corpus as a dataset and identify patterns. The contradiction, documenting secrecy systems transparently, is the point.