Fig. 01 — One ink: Vermillion
Research
| Shape | ID | Title | Blurb | Year | PC1 | PC2 |
|---|---|---|---|---|---|---|
| R-01 | AI Safety & Agent Harness Research Fellow Theorem.dev · Apart Research | LLM-driven specification synthesis that turns C and Rust code into machine-checked proofs in Frama-C and Lean 4. | 2026 | 0.47 | −0.86 | |
| R-02 | Quantitative Finance Researcher MIT CSAIL | Representation learning for markets: a 4.9M-parameter time-series transformer that learns temporal and cross-asset structure across 499 stocks. | 2026 | 0.40 | 0.63 | |
| R-03 | Scientific Machine Learning Researcher Johns Hopkins · Centrum IntelliPhysics | Neural operators that predict touch from vision: DeepONet and Fourier DeepONet models for visual-to-tactile acceleration. | 2026 | 0.45 | 0.62 | |
| R-04 | Student Researcher (First-Author) Harvard OpenBio | First-author study linking copy-number alteration burden to transcriptomic complexity across 3,527 tumors in 12 cancer types. | 2025 | −0.12 | 0.27 |
The common thread is representation learning and verification: what a model has actually learned, and whether we can check it. The subjects range from formal proofs of code to markets, touch and cancer genomics. Grey marks are the other five layers, kept in place so positions stay honest.