AI researcher focused on LLM systems and efficient inference.
Research and engineering experience across SUTD, A*STAR collaboration, LLM reliability, model inference, and deployment-oriented systems.
LLM RESEARCH · INFERENCE SYSTEMS
Building capable models.
Making inference dependable.
Currently, I serve as a Research Assistant under Prof. Li's supervision at the University of Macau (UMacau), Macau🇲🇴🇨🇳 (Sep. 2026 – Present). I will commence my Master's degree program in Sep. 2027, with an expected completion date of Aug. 2029.
My prior appointments include an Artificial Intelligence Engineer position at LexinFintech Ltd., Shenzhen, China (June 2025 – August 2026), a Visiting Researcher position at ISTD, Singapore University of Technology and Design (SUTD), Singapore🇸🇬 (May 2024 – April 2025), and a remote project collaboration with scientists from ASTAR Institute of Advanced Intelligence and Computing (ASTAR IAIC), Singapore (2023-2025). I earned my B.Eng. from Southwest Jiaotong University (SWJTU), Chengdu, China🇨🇳 (June 2023).
我目前在澳门🇲🇴🇨🇳的澳门大学(UMacau)担任研究助理,由Li 教授指导(2026 年 9 月至今)。我将于 2027 年 9 月开始硕士学位课程,预计于 2029 年 8 月完成学业。
此前,我曾在中国深圳的乐信集团(LexinFintech Ltd.)担任人工智能工程师(2025 年 6 月至 2026 年 8 月),在新加坡🇸🇬的新加坡科技设计大学(SUTD) ISTD 担任访问研究员(2024 年 5 月至 2025 年 4 月),并于 2023 至 2025 年与新加坡ASTAR Institute of Advanced Intelligence and Computing(ASTAR IAIC)的科学家开展远程项目合作。我于 2023 年 6 月获得中国成都🇨🇳的西南交通大学(SWJTU)工学学士学位。
Research and engineering experience across SUTD, A*STAR collaboration, LLM reliability, model inference, and deployment-oriented systems.
Three research tracks: efficient LLM inference, reliable model workflows, and memory-aware language models.
Exploring inference efficiency, serving behavior, and deployment-aware optimization for practical large language model systems.
Open-source exploration around full-loop LLM workflows, model behavior analysis, and methods for reducing hallucination in practical systems.
Research interest in how large language models store, retrieve, revise, and use memory across long interactions and knowledge-intensive workflows.
Peer-reviewed medical AI paper on multimodal skin lesion classification using clinical images, dermoscopy images, and metadata.
The work proposes an uncertainty-based hybrid fusion strategy for multimodal skin cancer diagnosis, combining image-level and metadata-level information for more adaptive classification.
@article{zuo2025multistage,
title = {A multi-stage multi-modal learning algorithm with adaptive multimodal fusion for improving multi-label skin lesion classification},
author = {Zuo, Lihan and Wang, Zizhou and Wang, Yan},
journal = {Artificial Intelligence in Medicine},
volume = {162},
pages = {103091},
year = {2025},
doi = {10.1016/j.artmed.2025.103091}
}
Experiment code for the AIIM 2025 paper on multi-stage multimodal skin lesion classification.
Working on the Multi-Modality Dementia Detection Model with members from SUTD and Icekredit. 🇸🇬
A structured academic timeline covering education, research experience, professional work, projects, and publication record.
Professional role focused on algorithm engineering, model development, and applied AI systems.
First-author paper: A multi-stage multi-modal learning algorithm with adaptive multimodal fusion for improving multi-label skin lesion classification.
Worked on large language models at SUTD and collaborated with scientists from A*STAR.
Applied multimodal AI project focused on dementia detection with cross-institution collaboration.
Worked on medical image analysis, leading into the multimodal skin lesion classification publication.
Undergraduate training in artificial intelligence, with foundations in machine learning, programming, and intelligent systems.