Figures for Papers
[](https://oosmetrics.com/repo/ChenLiu-1996/figures4papers)
[](https://www.linkedin.com/in/chenliu1996/)
[](https://x.com/ChenLiu_1996)
[](https://scholar.google.com/citations?user=3rDjnykAAAAJ&sortby=pubdate)
I am [Chen Liu](https://chenliu-1996.github.io/), a Computer Science PhD Candidate at Yale University.
This is a centralized repository of my own **Python scripts for high-quality figures**.
These figures appear in top venues including but not limited to *Nature Machine Intelligence*, *ICML*, and *NeurIPS*.
Can you do me a favor and **star this repository** too: [https://github.com/ChenLiu-1996/LM-Dispersion](https://github.com/ChenLiu-1996/LM-Dispersion)? Thank you!
### Bar plots for quantitative comparison
### Bar plots for composition breakdown
Radar plots Line plots
### Trend plots
### Heat maps
### 3D spheres
### Miscellaneous: figures not made end-to-end in Python
These figures were made partially in Python. I included them to acknowledge the time and efforts I spent on them.
## LLM skill integration
I want to show appreciation to my friend [Shan Chen](https://shanchen.dev/) who suggested doing this.
The **scientific figure making** skill lives in `scientific-figure-making/`. Demo figures live in `assets/`. Project-specific scripts and outputs live in `figure_*/`.
### Skill folder hierarchy
```
scientific-figure-making/
├── SKILL.md # Quick reference: metadata, when to use, patterns, links
└── references/
├── api.md # API/conventions to implement (palette, helpers, export)
├── common-patterns.md # Reusable figure patterns
├── demos.md # Real-world figure_* projects (with URLs)
├── design-theory.md # Style rationale and design principles
└── tutorials.md # Step-by-step guides
```
### Using this skill in an AI coding agent
No installation (path-based)
You can use this skill **without installing anything**: open this repo in your AI coding agent (e.g. [Cursor](https://cursor.com), Claude Code, etc.) and reference the skill by path in your prompts. The agent reads `scientific-figure-making/SKILL.md` and the `references/` files from the repo—no symlinks or plugins required.
**Simple AI workflow**
1. Open this repository in your AI coding agent (e.g. Cursor).
2. Ask the AI to create or update a plotting script in your target folder (for example `figure_PROJECT_NAME/`).
3. In your prompt, explicitly ask it to follow `scientific-figure-making/SKILL.md` and `scientific-figure-making/references/design-theory.md`.
4. Run the generated script and check the exported figure.
**Prompt template (copy/paste)**
```text
Create a publication-quality figure script at .
Use the Scientific Figure Making skill conventions from:
- scientific-figure-making/SKILL.md
- scientific-figure-making/references/design-theory.md
- scientific-figure-making/references/api.md (palette, helpers, export)
Implement or adapt the patterns (apply_publication_style, make_* helpers, finalize_figure). See figure_* folders for reference scripts.
Input data: .
Output files: .png and .pdf.
Keep the style consistent with this repository.
```
Install as a skill (symlink)
From the repository root, run:
| Agent | Commands |
|------------|----------|
| **Cursor** | `mkdir -p ~/.cursor/skills` then `ln -s "$(pwd)/scientific-figure-making" ~/.cursor/skills/scientific-figure-making` |
| **Claude Code** | `mkdir -p ~/.claude/skills` then `ln -s "$(pwd)/scientific-figure-making" ~/.claude/skills/scientific-figure-making` |
| **Codex** | `mkdir -p ~/.codex/skills` then `ln -s "$(pwd)/scientific-figure-making" ~/.codex/skills/scientific-figure-making` |
Restart the agent (or refresh its skill list) after linking. You can then invoke or cite the skill by name in addition to using path-based references when the repo is open.
## Related Papers
ImmunoStruct
[](https://www.nature.com/articles/s42256-025-01163-y)
[](https://www.nature.com/articles/s42256-025-01163-y.pdf)
[](https://huggingface.co/datasets/ChenLiu1996/ImmunoStruct)
[](https://huggingface.co/ChenLiu1996/ImmunoStruct)
[](https://github.com/KrishnaswamyLab/ImmunoStruct)
```bibtex
@article{givechian2026immunostruct,
title={ImmunoStruct enables multimodal deep learning for immunogenicity prediction},
author={Givechian, Kevin Bijan and Rocha, Jo{\~a}o Felipe and Liu, Chen and Yang, Edward and Tyagi, Sidharth and Greene, Kerrie and Ying, Rex and Caron, Etienne and Iwasaki, Akiko and Krishnaswamy, Smita},
journal={Nature Machine Intelligence},
volume={8},
pages={70--83},
year={2026},
publisher={Nature Publishing Group UK London}
}
```
Dispersion
[](https://openreview.net/forum?id=pd6A7jB5D6)
[](https://icml.cc/virtual/2026/poster/61492)
[](https://arxiv.org/abs/2602.00217)
[](https://arxiv.org/pdf/2602.00217)
[](https://github.com/ChenLiu-1996/LM-Dispersion)
```bibtex
@inproceedings{liu2026dispersion,
title={Dispersion loss counteracts embedding condensation and improves generalization in small language models},
author={Liu, Chen and Sun, Xingzhi and Xiao, Xi and Van Tassel, Alexandre and Xu, Ke and Reimann, Kristof and Liao, Danqi and Gerstein, Mark and Wang, Tianyang and Wang, Xiao and Krishnaswamy, Smita},
booktitle={International conference on machine learning},
year={2026},
organization={PMLR}
}
```
RNAGenScape
[](https://arxiv.org/abs/2510.24736)
[](https://arxiv.org/pdf/2510.24736)
```bibtex
@article{liao2025rnagenscape,
title={RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamics},
author={Liao, Danqi and Liu, Chen and Sun, Xingzhi and Tang, Di{\'e} and Wang, Haochen and Youlten, Scott and Gopinath, Srikar Krishna and Lee, Haejeong and Strayer, Ethan C and Giraldez, Antonio J and Krishnaswamy, Smita},
journal={arXiv preprint arXiv:2510.24736},
year={2025}
}
```
brainteaser
[](https://openreview.net/forum?id=3oQDkmW72a)
[](https://neurips.cc/virtual/2025/loc/san-diego/poster/120001)
[](https://huggingface.co/datasets/ChenLiu1996/Brainteaser)
[](https://arxiv.org/abs/2505.10844)
[](https://arxiv.org/pdf/2505.10844)
[](https://github.com/stephenxia1/brainteasers)
```bibtex
@article{han2025creativity,
title={Creativity or Brute Force? Using Brainteasers as a Window into the Problem-Solving Abilities of Large Language Models},
author={Han, Simeng and Dai, Howard and Xia, Stephen and Zhang, Grant and Liu, Chen and Chen, Lichang and Nguyen, Hoang Huy and Mei, Hongyuan and Mao, Jiayuan and McCoy, R. Thomas},
journal={Advances in neural information processing systems},
year={2025}
}
```