Hi, I'm Yeyang Liu
AI Researcher & ML Engineer & Kaggle Competition Expert
Master's student at Universität Zürich, specializing in Artificial Intelligence and Computational Linguistics. Passionate about NLP, LLMs, and building intelligent systems.
// About Me
Background and technical expertise
🎓 Education
M.Sc. Informatics — Universität Zürich (5.5/6)
Major: AI | Minor: Computational Linguistics
Related courses: Large Language Models · Machine Learning for NLP · Model Analysis and Interpretability in NLP · Deep Learning · Deep Generative Models · Prompt Engineering
B.Sc. Computer Science (AI) — Universiti Malaya (3.95/4)
Related courses: Machine Learning · Natural Language Processing
🏆 Achievements
Kaggle Competition Expert Silver Medal — AI Mathematical Olympiad: Progress Prize 3 Bronze Medal — Learning Equality Curriculum Recommendations Top 5% (community) — LLM Agentic (Swiss) Legal Information Retrieval CLEF Shared Task 1st Place — GutBrainIE 2025 & 2026 (NER · Entity Linking · Relation Extraction) 2nd Place — TalentCLEF 2026 Task B (Job-Skill Retrieval · Ranking)
// Skills
Click a skill to filter the projects below.
// Projects
Selected works and research
Swiss Legal RAG
Retrieves the Swiss federal law citations a Bundesgericht decision would cite for an English legal question. Bilingual retrieval + LLM reranking + LangGraph agentic verification; ranked top 5% on Kaggle.
View Details → 2026 🥇 1st PlaceGutBrainIE 2026 System
Information-extraction system for gut-brain axis PubMed abstracts, covering named entity recognition, entity linking, and relation extraction. Ranked 1st in all four GutBrainIE 2026 subtasks.
View Details → 2026 🥈 2nd PlaceTalentCLEF Job-Skill Retrieval
Retrieval system matching free-text job titles to the most relevant skills in the ESCO taxonomy. Reached 0.7913 graded nDCG for 2nd place, pairing a fine-tuned bi-encoder with LLM reranking.
View Details → 2026 DatathonRobinReal Challenge
Conversational multi-modal property search across four languages, treating listing photos as a first-class retrieval signal. Built the hybrid retrieval stack fusing dense, sparse, and cross-modal image rankers.
View Details → 2025 Master ProjectMR Anomaly Detection
Deep learning-based anomaly detection for MR-only radiotherapy QA. Benchmarked 10 models using Anomalib, achieved 0.90 patient-level Dice with PatchCore and validated multi-center generalizability.
View Details → 2025 🥇 1st PlaceBiomedical NER System
Developed a MedicalBERT-CRF system for extracting entities from gut-brain PubMed abstracts. Improved micro-f1 by 3% using ensemble and data augmentation strategies.
View Details → 2024 Knowledge GraphGraph-Based Movie Chatbot
QA chatbot answering natural language questions over a movie knowledge graph. Integrated BERT, fuzzy matching, and LLM-based NER with hybrid SPARQL and embedding-based retrieval.
View Details → 2024 LLM + RAGVirtual Patient System
Medical education chatbot using Llama2 and RAG. Features a Virtual Patient module simulating patient interactions and an MCQ generator using MilvusDB and Dragon+ model.
View Details → 2023 🥉 Bronze MedalCurriculum Recommender
Kaggle competition solution matching curriculum contents with topics. Used SimCSE for retrieval with iterative hard example training, achieving 76th place.
View Details →// Small Tools
Selected utility projects
// Experience
Professional background
Algorithm Developer Intern
- Developed domain-specific QA chatbot using ChatGLM2, comparing LoRA fine-tuning with RAG (Milvus vector DB)
- Improved item-based recommendation from word2vec to widedeep, increasing ROC by ~3%
Algorithm Developer Intern
- Applied ML for user segmentation and profiling aligned with business logic
- Refactored recommendation project, switching from deepwalk to lightfm
- Developed price prediction model using AutoGluon with CAD-extracted features