Profile

I am a Ph.D candidate at the National University of Singapore (NUS), advised by Prof Feng Mengling, and working closely with Prof Hong Shenda at Peking University and Prof Bryan Hooi at NUS. Before Ph.D, I graduated from NUS with a master’s degree in electrical & computer engineering and did my research intern at IBM Research.

Research Interests: My research focuses on designing and applying AI models to address real-world healthcare challenges, with a long-term goal of building expert-level multimodal generalists to enhance clinical decision-making and patient care. My recent work concentrates on advancing multimodal large language models, leveraging their remarkable capacity for synergizing diverse modalities for reasoning and planning. Previously, I worked a lot on self-supervised learning models to improve label efficiency in the medical domain. I am also interested in time series modeling, and much of my work has involved time series data, as it is one of the most prevalent modalities in healthcare. I have published 10+ papers at the top international AI conferences and journals such as ICLR, ECCV, AAAI, Information Fusion, and TIST.

🔥 News

• [04. 2025] Our paper on multimodal EHR modeling is accepted in AIME 2025.
• [03. 2025] Our paper on empowering MLLM for grounded ECG understanding now available at arXiv.
• [06. 2024] Our paper on multi-stage contrastive learning is accepted in ECCV 2024.
• [06. 2024] I was awarded for the Graduate Student Research Award AY2023/2024!
• [01. 2024] Our paper on contrastive learning for time series applications is accepted in ICLR 2024.
• [10. 2023] Our survey on LLM for healthcare applications is accepted in Information Fusion.
• [05. 2022] Our paper about self-supervised learning for ECG is accepted in AAAI 2022.

📝 Publications

Preprint 2025

GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images

Xiang Lan, Feng Wu, Kai He, Qinghao Zhao, Shenda Hong, Mengling Feng

Project Contributions

• First Unified Multimodal ECG Model.
• First High-granularity ECG Grounding Dataset.
• Clinically Oriented Diagnostic System.

[Page][Code]

ICLR 2024

Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach

Xiang Lan, Hanshu Yan, Shenda Hong, Mengling Feng

Project Contributions

• First study to investigate the bad positive pair problem exists in time series contrastive learning.
• A simple yet effective algorithm designed as a lightweight plug-in.
• Enhancing the performance of existing state-of-the-art methods.

[Paper][Code]

ECCV 2024

Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning

Jihai Zhang*, Xiang Lan*, Xiaoye Qu, Yu Cheng, Mengling Feng, Bryan Hooi
(*co-first author)

Project Contributions

• First multistage contrastive learning framework.
• First work to discuss and address feature suppression in both unimodal and multimodal contrastive learning.
• Adaptable to various contrastive learning settings.

[Paper][Code]

AAAI 2022

Intra-Inter Subject Self-Supervised Learning for Multivariate Cardiac Signals

Xiang Lan, Dianwen Ng, Shenda Hong, Mengling Feng

Project Contributions

• First work that integrates medical knowledge into self-supervision to boost the performance of cardiac arrhythmias diagnosis.
• Novel intra and inter subject self-supervision mechanism.
• State-of-the-art performance.

[Paper][Code]

• P-Transformer: A Prompt-based Multimodal Transformer Architecture For Medical Tabular Data

  Yucheng Ruan*, Xiang Lan*, Daniel J Tan, Hairil Rizal Abdullah, Mengling Feng

  23rd International Conference on AI in Medicine (AIME 2025) (*co-first author)

• Language Modeling on Tabular Data: A Survey of Foundations, Techniques and Evolution

  Yucheng Ruan*, Xiang Lan*, Jingying Ma, Yizhi Dong, Kai He, Mengling Feng

  preprint (*co-first author)

• Federated Learning for Electronic Health Records

  Trung Kien Dang*, Xiang Lan*, Jianshu Weng, Mengling Feng

  ACM Transactions on Intelligent Systems and Technology (*co-first author)

• A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics

  Kai He, Rui Mao, Qika Lin, Yucheng Ruan, Xiang Lan, Mengling Feng, Erik Cambria

  Information Fusion

• The SingHealth Perioperative and Anesthesia Subject Area Registry (PASAR), a large-scale perioperative data mart and registry

  Hairil Rizal Abdullah, Daniel Yan Zheng Lim, Yuhe Ke, Nur Nasyitah Mohamed Salim, Xiang Lan, Yizhi Dong, Mengling Feng

  Korean Journal of Anesthesiology

• Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets

  Dianwen Ng, Xiang Lan, Melissa Min-Szu Yao, Wing P. Chan and Mengling Feng

  Quantitative Imaging in Medicine and Surgery

• Identification of 27 abnormalities from multi-lead ECG signals: An ensembled SE ResNet framework with Sign Loss function

  Zhaowei Zhu*, Xiang Lan*, Tingting Zhao, Yangming Guo, Pipin Kojodjojo, Zhuoyang Xu, Zhuo Liu, Siqi Liu, Han Wang, Xingzhi Sun, Mengling Feng

  Physiological Measurement (*co-first author)

• Adversarial Domain Adaptation with Correlation-Based Association Networks for Longitudinal Disk Fault Prediction

  Xiang Lan*, Dianwen Ng*, Yi Liu, Jiongzhou Liu, Fan Xu, Cheng He and Mengling Feng

  IJCNN 2021

• Classification of cardiac abnormalities from ECG signals using SE-ResNet

  Zhaowei Zhu, Han Wang, Tingting Zhao, Yangming Guo, Zhuoyang Xu, Zhuo Liu, Siqi Liu, Xiang Lan, Xingzhi Sun, Mengling Feng

  Computing in Cardiology 2020

🎖 Honors and Awards

• 2024 Graduate Student Research Award, NUS
• 2020 Championship, SG Healthcare AI Datathon 2020
• 2020 2nd Runner-up, PhysioNet/Computing in Cardiology Challenge 2020

📖 Educations

• 2021.07 - now, Doctor of Philosophy, National University of Singapore.
• 2018.07 - 2019.06, Master of Science, National University of Singapore.
• 2014.07 - 2018.06, Bachelor of Science, University of Electronic Science and Technology of China.

🌍 Academic Services

I serve as a reviewer for ICLR, NeurIPS, WWW, AAAI, KDD, TNNLS, TIST, Health Data Science, npj Digital Health.