Profile

I am a Ph.D Candidate at National University of Singapore (NUS), working with Prof. Feng Mengling. 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

• [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]

• 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

• 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)

• 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

• Medical Intervention Duration Estimation Using Language-enhanced Transformer Encoder with Medical Prompts

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

• 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

• 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)

• 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

🎖 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, WWW, AAAI, KDD, TNNLS, TIST, Health Data Science.