About
I'm Ren Yi (任一), a master's student in Digital Humanities at EPFL. My research applies LLM and AI systems where they're least available — women doctors hidden in French medical directories and maternal healthcare in Zanzibar, Tanzania.
I believe every field should be able to benefit from the rapid development of AI. Not only the fields that draw the most money, but also the fields that matter to us as humans, to our lives, for example, history, education, literature, health, gender equality. Being able to apply AI systems to these fields makes me feel excited, responsible and proud.
I enjoy having my work cover the entire lifecycle of a project: system design, implementation, evaluation, and further improvement. Everything is open-sourced on my GitHub.
Research & Projects
On-device Medical Chatbot for Nurse-Midwives in Zanzibar
Feb 2026 – Jun 2026Researcher & Engineer · D-tree International, Zanzibar, Tanzania
Built an on-device medical chatbot aiming to provide real-time, evidence-based, and locally relevant information to nurse-midwives — running fully offline on edge devices in low-connectivity settings. Deployed Google Gemma 4 E4B (int4) with RAG over official clinical guidelines, and rigorously evaluated the on-device system — including against a frontier model (Qwen3.5-397B-A17B-FP8) as a ceiling — to quantify where it helps and where the gaps remain.
In Zanzibar, the Internet is very unreliable, that's why we need to run the model completely offline. We are pushing the limits on how well an on-device model can perform to provide healthcare guidance, with the help of RAG.
I know there is still a long way before it could be deployed to the field, but I am happy to be able to make the small steps towards there.
Quantifying the Invisible: Women Doctors in Rosenwald Guides
Aug 2025 – Feb 2026Researcher · EPFL Laboratory for History of Science and Technology
Collaborated with historians to design a double-triangular annotation framework combining LLMs and human labeling, achieving >50% reduction in annotation effort at >99% accuracy. Built a benchmark of 2,600+ entries and extracted 577,000+ records from the Rosenwald Guides, discovering 3,700+ female doctors. Preprint available on arXiv.
I feel very proud to be able to cooperate with Jérôme Baudry from EPFL, Mikhaël Moreau and Amélie Puche from Institut des humanités en médecine (Lausanne). They are certainly among the most professional and respectable researchers I've ever worked with. They have clear goals and offered me the resources to achieve them, while giving me the freedom to conduct the research with my own taste and judgment.
I like this project a lot. For one thing, discovering the female doctors in the Rosenwald Guides is very meaningful work. I'm happy to be able to extract the data so that historians could make use of them. For another, the annotation framework I proposed is simple, elegant and effective. I like this kind of work.
Sentiment Contagion on Social Media
Mar 2025 – Jun 2025Course Project · EPFL
Analyzed 6.4 million comments and 1 million posts from Reddit's r/unpopularopinion using RoBERTa-based sentiment classification with manual validation. Confirmed statistically significant emotional contagion: post sentiment significantly influences comment sentiment.
I've always felt sensitive to emotions on social media. Now I have the proof of the emotion contagion. I will try my best to take care of myself there :)
Design Prototype for True Cost of Food
Sept 2024 – Dec 2024Course Project · EPFL
Developed a Figma design prototype offering recipe-based food recommendations weighted by true environmental cost. Promoted sustainable food culture through UX design. Received the highest grade among all designs in the track.
This project gave me a lot. I'm very lucky to stay good friends with my teammates — they listened and supported me. And I gained my first real understanding of environmental thinking. At first I didn't really understand why people are so serious about it. But over time, also boosted by my Swiss roommate from the Greens, I started to understand and try to contribute when I can. My diet is also getting healthier and more sustainable :)
Papers
Double Triangle Annotation: A Scalable Human-in-the-Loop Framework for High-Precision Historical Document Annotation
2026Yi Ren
- Designed an annotation framework around a consensus principle, applied recursively at two layers — between two independent MLLMs (Claude, Qwen, Llama, Grok), then between two human-in-the-loop systems built from them — escalating only disagreements to a human jury.
- Achieved a Word Error Rate of 0.003 on the Rosenwald Guides, with >85% of fields auto-accepted by model consensus — cutting total human review workload by more than 50% compared to a full manual annotation by one annotator.
- Released the first structured-extraction benchmark for the Rosenwald Guides (13,595 annotated fields across 60 columns) to support future work on historical document processing.
MAM-AI: An On-Device Medical Retrieval-Augmented Generation System for Nurses and Midwives in Zanzibar
2026Yi Ren
- A redesigned system prompt is a major lever on answer quality — cutting deflective answers that only said “refer to a senior doctor” from 32.7% of questions to 3.2%, while roughly doubling key-fact recall from 0.139 to 0.279 and raising potentially-harmful answers only from 12.8% to 15.7%.
- On-device retrieval is strong, but ranking is not the lever: on my retrieval benchmark the 300M on-device embedder ranks among the cloud retrievers, yet a better embedder or a fine-tuned reranker leaves end-to-end answers unchanged — the larger levers on answer quality are the generator and corpus coverage and quality.
- The generator matters most, and faithfulness decides deployment: answer quality rises with model size before plateauing, while quantization and retrieval barely move it; among two same-size on-device candidates, the more faithful but less helpful Gemma 4 E4B is deployed over Gemma 3n E4B — a safety-versus-usefulness choice.
mamabench and mamaretrieval: Benchmarks for Evaluating Medical Retrieval-Augmented Generation in Maternal, Neonatal, and Reproductive Health
2026Yi Ren
- Assembled both benchmarks — mamabench (25,949 QA items) and mamaretrieval (3,185 retrieval queries) — by scoping and filtering expert-authored sources (licensing exams, physician panels, guidelines) rather than authoring new clinical questions.
- Decomposed clinical relevance into a graded, multi-dimensional rubric (0–6) — a topical gate over three graded dimensions: meaningful content, actionable guidance, and density — rather than a binary relevant/not-relevant label, separating strong retrieval from merely adequate retrieval.
- Measured and disclosed the limits of the LLM-assigned labels — via scope-classifier agreement, a frontier-judge check, and a pooling-completeness audit — rather than presenting the benchmarks as an oracle.
Internship
Securing LLM-enhanced Human-Machine Interfaces
Aug 2026 – Jan 2027Intern · Digiinov SA, Yverdon-les-Bains, Switzerland
Internship at Digiinov, with academic guidance from the EPFL Center for Digital Trust (C4DT). Building conversational agents for Digiinov HMI, a human-machine interface platform for machines and instruments, enabling more efficient machine operation; and enabling natural-language interaction with Digiinov Inspect, HMI software for test and measurement equipment, simplifying testing procedures.
Education
Master in Digital Humanities
Aug 2024 – Jun 2027EPFL
PhD Program in Information Systems (withdrew to pursue MSc at EPFL)
Aug 2022 – Jul 2024Tsinghua University
GPA: 4.00 / 4.00
Bachelor's Degree, Computer Science and Technology
Aug 2018 – Jul 2022Tsinghua University
GPA: 3.89 / 4.00 · National Scholarship (Top 2%) · Outstanding Graduate (Dept.)
Skills
Contact
I'm always happy to talk about AI for social good, research ideas, or potential collaboration. Feel free to reach out — I read every message.
Social Work
Student Representative & Member of Teaching Committee
Sept 2024 – Aug 2025Digital Humanities, EPFL
Olympic Family Assistant
Jan 2022 – Apr 2022Beijing 2022 Winter Olympics & Paralympics