Jintai Chen

Jintai Chen

Postdoc at CS, UIUC; PhD from CS, ZJU. Passionate about AI for Healthcare.

Hey, I am Jintai Chen, a tenure-track assistant professor at HKUST, guangzhou campus.

Prior to that, I am a postdoctoral researcher at the University of Illinois at Urbana-Champaign, where I collaborated closely with Prof. Jimeng Sun. I obtained my Ph.D. from the College of Computer Science and Technology at Zhejiang University, under the supervision of Prof. Jian Wu. My research interests lie at the intersection of AI and healthcare, with a particular focus on developing generalizable and reliable foundation models to address real-world medical challenges, including clinical trial optimization, clinical predictive modeling, treatment recommendation, health monitoring, and biomedical discovery.

I am actively seeking highly motivated Ph.D. students, research assistants, and postdoctoral researchers with strong backgrounds in computer science, statistics, or other related subjects. Proficiency in coding is required.

For Ph.D. applications, please fill out the form.

For research assistant applications, please fill out the form.

For postdoc applications, please email me directly at jtchen147[AT]gmail[DOT]com.

For MPhil students, please contact me after passing the interview with the school’s Red Bird MPhil committee.

🔥 News

📄 Selected Publications

(*: Equal contribution; $\dagger$: Corresponding author(s))

Nature Communications
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TL;DR: Congenital heart disease is the most common category among congenital abnormalities, with an incidence rate approaching 1$\%$. Previously, ECGs were considered to have limited effectiveness in diagnosing congenital heart disease. AI demonstrated the value of ECGs in diagnosing congenital heart disease, which surpasses our previous cognition. While techniques like echocardiography and cardiac MRI are currently utilized for precise diagnosis, the cost-effectiveness and non-invasiveness of ECGs continue to harbor substantial potential for precise large-scale population screening and benefiting low-resourced regions.

Congenital Heart Disease Detection by Pediatric Electrocardiogram Based Deep Learning Integrated with Human Concepts [AI4H, AI4ECG, AI4Table] [Code], Jintai Chen$^*$, Shuai Huang$^*$, Ying Zhang$^*$, Qing Chang$^*$, Yixiao Zhang, Dantong Li, Jia Qiu, Lianting Hu, Xiaoting Peng, Yunmei Du, Yunfei Gao, Danny Chen, Abdelouahab Bellou$^\dagger$, Jian Wu$^\dagger$, Huiying Liang$^\dagger$, Nature Communications, 2024

IJCAI 2021
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  • TL;DR: Traditional ECG devices can only offer electrocardiograms from a limited number of angles, constrained by electrode positioning. Our Electrocardio Panorama System breaks this barrier, allowing users to effortlessly observe ECG signals from any angle in real-time, based on their queries.
  • Academic Impact: The benefits of our work are manifold: (i) panoramic observations of ECG signals; (ii) a unified representation of ECG signals captured by different ECG devices; (iii) Waveform-aligned Mixup for synthesizing new ECG cases (e.g., for data augmentation); (iv) reconstruction of corrupted ECG views; and (v) exploration of ECG theory.
  • New Data Annotations: We provided ECG wave segmentation annotations for Tianchi ECG dataset and PTB dataset.

Electrocardio panorama: Synthesizing new ECG views with self-supervision [AI4H, AI4ECG] [Code and Data], Jintai Chen$^*$, Xiangshang Zheng$^*$, Hongyun Yu$^*$, Danny Z. Chen, Jian Wu$^\dagger$, International Joint Conference on Artificial Intelligence (IJCAI), 2021

ICML 2021
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TL;DR: Neurons in the nervous system transmit signals by releasing different neurotransmitters that match different receptors. Motivated by the concepts of competitive neural networks, prototype learning, hierarchical clustering algorithms, and capsule neural networks, we introduce a novel neural network architecture. This neural network is constructed by neurons capable of generating “transmitters” to send semantic information to other neurons and possessing receptors to receive specific types of “transmitters” from other neurons. By generating “transmitters” to convey semantic information and binding them to specific receptors in the subsequent layer, our approach achieves transparent semantic feature parsing, part-to-whole semantic integration, unsupervised semantics understanding, and object relationship digging.

A receptor skeleton for capsule neural networks, Jintai Chen, Hongyun Yu, Chengde Qian, Danny Z. Chen, Jian Wu$^\dagger$, International Conference on Machine Learning (ICML), 2021

ICLR 2024 SpotLight
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TL;DR: Tabular data exhibits diversity in both feature and target definitions. How can we achieve transferability across such heterogeneity? We propose an approach to empower language models as a robust deep tabular prediction model. By training the language model to comprehend precise numeric values, our approach gains the capability to leverage tabular data from other domains to enhance predictions on EHR tables, where data availability is often limited.

Making Pre-trained Language Models Great on Tabular Prediction [AI4Table] [Code and Data], Jiahuan Yan, Bo Zheng, Hongxia Xu, Yiheng Zhu, Danny Chen, Jimeng Sun, Jian Wu$^\dagger$, Jintai Chen$^\dagger$, ICLR (SpotLight), 2024

MICCAI 2020 Oral
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TL;DR: This study transforms unstructured hand radiography images into a structured semantics represented as a table /graph, utilizing clinical prior information (the TW3 approach used in clinical practice). We then use a GNN to process such structured data, leading to impressive and interpretable bone age assessments. It’s noteworthy that many medical images are semi-structured data, and this paper introduces a potentially interpretable and efficient approach for processing such semi-structure.

Doctor imitator: Hand-radiography-based bone age assessment by imitating scoring methods [AI4H, AI4MIA], Jintai Chen, Bohan Yu, Biwen Lei, Ruiwei Feng, Danny Z. Chen, and Jian Wu$^\dagger$, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI, Oral), 2020

🎖 Awards

  • 2023.04, Excellent Doctoral Graduates of Zhejiang Province, China (Top 1%)
  • 2023.04, Excellent Doctoral Graduates of Zhejiang University (Top 1%)
  • 2023.04, 产学研合作创新成果奖，浙江省产学研合作创新与促进奖
  • 2023.02, 医学影像智能处理关键技术创新与应用, 产学研合作创新成果二等奖, 中国产学研合作促进会
  • 2023.02, 科技进步二等奖，中国电子学会
  • 2022.10, Huawei Fundamental Research Scholarship (Top 3%)
  • 2021.10, Tencent Doctoral Scholarship (Top 1%)
  • 2021.10, National Scholarship of China (Top 1%)
  • 2020.10, Outstanding Doctoral Student Scholarship (Top 3%)
  • 2019.10, Doctoral Freshman Scholarship (Top 3%)
  • 2016.10, Chinese Bank Scholarship (Undergraduate) (Top 1%)
  • 2015.10, National Scholarship (Undergraduate) (Top 1%)

💬 Talks

  • 2022.11, How to Excel in AI-for-healthcare Researches, @ Shanghai University
  • 2022.10, ECG Signal Processing and Synthesis for Computer-Aided Heart Disease Diagnosis, @ Carnegie Mellon University
  • 2022.10, ECG Synthesis for New View and New Data, @ Shanghai AI Lab
  • 2022.09, Part-Hierarchy Learning, @ ByteDance
  • 2022.06, Supervised Tabular Learning, @ UberAI
  • 2021.10, Domain Mixup for Distant Transfer Learning, @ Shanghai Jiaotong University

🔎 Professional Services

  • Reviewer @ ML Conferences: NeurIPS, ICLR, ICML;
  • Reviewer @ AI Conferences: AAAI, IJCAI, AISTATS;
  • Reviewer @ CV Conferences: CVPR, ICCV, ECCV;
  • Reviewer @ DM Conferences: KDD;
  • Reviewer @ NLP Conferences: ACL, EMNLP;
  • Reviewer @ AI4H Conferences: MICCAI, ISBI;
  • Review for Journals: TPAMI, TNNLS, TCBB, JBHI, Frontiers in Public Health, JBSM, TCDS, Frontiers in Genetics, Scienstific Report
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