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StevenYuan666/README.md

Hi there！ 嘿大家好！ Salut！

👋 This is Ye Yuan.

🔭 I’m a Fourth-Year Ph.D. candidate in Computer Science at McGill University and Quebec AI Institute-Mila.

🧑‍🏫 It's fortunate to be supervised by Professor Xue (Steve) Liu.

📅 It's fortunate to have Professor Adriana Romero Soriano and Professor Gintare Karolina Dziugaite as my supervision committee member.

🔭 I obtained my Bachelor of Science Degree in Honorous Computer Science at McGill University.

🧐 My research centers on developing vision language models / large language models and score-based generative models to advance intelligent systems, with applications in (i) addressing challenges in offline black-box optimization, (ii) enabling knowledge-centric NLP tasks such as automatic knowledge base construction and retrieval augmented generation, and (iii) improving the efficiency and efficacy of agentic systems for real-world applications. My work is supported by multiple fellowships and has been published at top venues including NeurIPS, ICLR, ICML, ACL, EMNLP, and TMLR. Representative contributions include authoring a comprehensive survey on offline black-box optimization with Turing Award Winner Prof. Yoshua Bengio, which is accepted by TMLR with the Survey Certification, and presenting an EMNLP 2024 oral paper (top 3%), and contributing to an ICML 2026 spotlight paper (top 2%). My journey is also complemented by industrial research experiences or close collaborations at Microsoft Research, RBC Borealis, Samsung Research America, and Amazon.

💪 My greatest assets are my eagerness to learn new things, my curiosity to delve into uncharted territories, my drive to stay abreast of the latest developments, and my persistence to work hard.

📑 See my publications at Google Scholar.

📧 If you have any questions about my projects or have cooperation intentions with me, feel free to contact me via email: ye.yuan3 AT mail DOT mcgill DOT ca

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  1. mila-iqia/Awesome-Offline-Model-Based-Optimization mila-iqia/Awesome-Offline-Model-Based-Optimization Public

    📰 [TMLR 2026 Survey Certification] Must-Read Papers on Offline Model-Based Optimization 🔥

    30

  2. mila-iqia/ParetoFlow mila-iqia/ParetoFlow Public

    [ICLR 2025] Official Implementation of ParetoFlow: Guided Flows in Multi-Objective Optimization🧬🧬🧬

    Python 29 3

  3. microsoft/Structured-Entity-Extraction microsoft/Structured-Entity-Extraction Public

    Code for the EMNLP'24 paper "Learning to Extract Structured Entities Using Language Models"

    Python 53 8

  4. New-Ground-AI/QUACK New-Ground-AI/QUACK Public

    🦆 [EMNLP 2026] Official Implementation of QUACK: Questioning, Understanding, and Auditing CollaborativeKnowledge in Multimodal Social Deduction Agents

    Python 9 1

  5. BorealisAI/EBCAR BorealisAI/EBCAR Public

    [ICLR 2026] Official Implementation of Embedding-Based Context-Aware Reranker📄

    Python 13 1

  6. New-Ground-AI/Discrete-Diffusion New-Ground-AI/Discrete-Diffusion Public

    🔬 [Discrete Diffusion Survey] This is the curated paper list collected for our survey paper: "Discrete Diffusion Models: A Unified Framework from Tokenization to Generation"

    12 2