Ye Yuan 袁野

Our paper "Prompting Wireless Networks: Reinforced In-Context Learning for Power Control" was accepted by ICML 2025 ML4Wireless Workshop!

My first-authored paper "Understanding 6G through Language Models: A Case Study on LLM-aided Structured Entity Extraction in Telecom Domain" was released!

I started my new summer research internship at RBC Borealis and worked with Dr. Amin Shabani, Dr. Siqi Liu, and Dr. Jiawei (Eric) He.

My (co)first-authored paper "Design Editing for Offline Model-based Optimization" was accepted by TMLR!

Our survey paper collaborated with Turing Award Recipient Professor Yoshua Bengio "Offline Model-Based Optimization: Comprehensive Review" was released!

I attended ICLR 2025 in Singapore.🇸🇬

My (co)first-authored paper "Design Editing for Offline Model-based Optimization" was accepted by ICLR 2025 DeLTa Workshop!

Our paper "Large Language Models for Wireless Networks: An Overview from the Prompt Engineering Perspective" was accepted by IEEE WCM!

I started the collaboration with Samsung Research America.

I successfully passed the Ph.D. oral comprehensive exam and formally became a Ph.D. candidate!

My (co)first-authored paper "ParetoFlow: Guided Flows in Multi-Objective Optimization" was accepted by ICLR 2025!

Our paper "Generative AI as a service in 6g edge-cloud: Generation task offloading by in-context learning" was accepted by IEEE WCL!

I attended EMNLP 2024 in Miami.🇺🇸

My (co)first-authored paper "Learning to Extract Structured Entities Using Language Models" was accepted by EMNLP 2024 and selected as an oral presentation paper (top 7%🔥🔥🔥)!

Our survey paper "Large language model (llm) for telecommunications: A comprehensive survey on principles, key techniques, and opportunities" was accepted by IEEE COMST!

Our paper "Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control" was released!

Our survey paper "Retrieval-Augmented Generation for Natural Language Processing: A Survey" was released!

I started my informal research visiting to several professors in Nanjing, Hangzhou, Shanghai, Shenzhen, and Hong Kong China.🇨🇳

I attended my first in-person conference at NeurIPS 2023 in New Orleans.🇺🇸

I received the BMO (Bank of Montreal) Responsible AI fellowship, and was awarded the title of (BMO) Responsible AI Senior Scholar.

My first (co)first-authored paper "Importance-aware co-teaching for offline model-based optimization" was accepted by NeurIPS 2023! I was so excited to publish this paper on the one of the best venues as a first-year Ph.D. student!

I started my internship as an Associate Researcher at Noah's Ark Lab Canada, where I worked on the accelerating LLM inference and next-generation language model architectures.

I started the close collaboration at Microsoft Research's Alexandria team with Dr. Bhaskar Mitra, Dr. James Hensman, Liana Mikaelyan, Pavel Myshkov, Dr. Alexander Meulemans (during his internship at MSR), Dr. Jan Tönshoff (during his internship at MSR), Dr. Taketomo Isazawa, and Dr. Tom Minka. We work on knowledge foundation model and automatic knowledge base construction.

I started my first research project on Offline Black Box Optimization under the mentorship of Can (Sam) Chen.

I received my Bachelor's Degree of Science in Honours Computer Science at McGill University.

I received the official offer from McGill University and decided to do my direct-entry Ph.D. program at McGill University with Professor Xue (Steve) Liu as my supervisor.

I joined in Professor Xue (Steve) Liu's CPS Lab as a research assistant and attended the group meetings regularly.

I started two research projects about Multi-agent Reinforcement Learning and Domain Adaptation for Human Activity Recognition with Dr. Jikun (Jaxon) Kang and Professor Xi (Alex) Chen. Both of them were Professor Xue (Steve) Liu's students before.

I started the course COMP 597 (Applications of Machine Learning in Real World Systems) at McGill University, which is a research-oriented course taught by Professor Xue (Steve) Liu. I was impressed by the research projects of the students in the course, and I decided to apply for the Ph.D. program at McGill University.

I found I was not interested in the software engineering field, and I decided to do more research on Machine Learning and Deep Learning. Then I sent the first email to Professor Xue (Steve) Liu, who was my Ph.D. supervisor later.

I started my first industrial internship as a software engineer at CAMLUNI Education, a startup company in Beijing. Since Beijing is a large city, it took me 2 hours to commute to the office every day. During then, I studied the basics of Machine Learning and Deep Learning through self-study by watching Hung-yi Lee's lectures on YouTube.

I met my first research mentor, Professor Jie Fu, in Montreal, and I started to slowly learn the research career track.

I started to learn French.

I started my undergraduate study at McGill University.