Artificial Selves

Integrating Selves and Research on Selves in the AI Community

Distinguishing progress in data and algorithms

Dataset curation, synthetic data generation, and LLM training, fine-tuning, and evals to distinguish and quantify the effects of data improvements, separately f

Adversarially robust workload classification using hardware sensors

I've previously developed a classifier that distinguishes training and inference based on Nvidia software telemetry. This project will achieve that using physic

CalCompute Framework Report Research Funding

Researching Public Compute for California

Developing a Latin American analytical framework for AI

Support to complete a peer-reviewed paper applying a Latin American decolonial framework to artificial intelligence

Fund development of p(Doom)1

A nightmarishly hard AI safety strategy game about holding p(Doom) down. You can't win; you can only buy time.

Mechanistically estimating the outputs of wide random convolutional neural nets

Safeguarding DNA Synthesis from Biological Risks Posed by Advanced AI

We will test whether circuits in protein language models can detect function-preserving redesigns of known toxins that evade homology-based DNA-synthesis screen

Separatrix

Creating conditions for cooperative strategies to dominate adversarial ones among near-future AIs while we still can

Travel Grant for Undergrad to present at MICCAI Mechanistic AI Safety Workshop

Seeking travel and registration fee support to present my sole-authored mechanistic audit of medical AI at MICCAI 2026 MI4MedFM workshop at Strasbourg, France

Supplementing Summer Program for Applied Rationality and Cognition 2026 (SPARC)

Giving AI DNA Style evolution and brain structure

Do Language-Model Process Reports Track Their Underlying Computation?

Testing an architectural explanation for tension–flow reports during recursive self-focus

Editing models inside their own geometry

One training-free geometry fitted to a model's residual-stream activations that reads a state, moves it, and tests whether the behaviour follows

AI Safety Nepal

Five-week AI safety fellowship + 3-month project mentorship

Originality, Quantification, and Decomposition of Generalization.

Perform research to rigorously elucidate and quantify generalization versus memorization, and examine evidence of originality in LLMS.

Empirical econ - applied AI transition for quantitative econometrician

Fund demonstrated/rigorous quantitative researcher (already run reproduction/audit pipelines on published economics) for 6-month AI safety transition, shipping

What Factors Influence Chain-of-Thought Faithfulness?

Measuring whether CoT monitoring fails when an influence reaches an agent through a tool return rather than the user message.

TARP - The Answer to Roadside Pens

An Intervention Towards Animals Under Extreme Heat Stress

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