Research Interests

Dan Lin 林丹

dl862@cornell.edu
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I'm a bioinformatician, working on AI-driven Digital Animal Health and Health Informatics. My work seeks to improve animal's health and well-being through unlocking the potential of ubiquitous data (i.e., macro-level physiological records and wearable time-series, and micro-level genome and metagenome), and cutting-edge AI techniques (i.e., predictive and interpretable machine and deep learning models). My research includes projects on human, mammals, and insects from the Americas and Eurasia.

Growing up in several cross-disciplinary projects spanning life science and engineering, I became fascinated by the idea of translating human-oriented technologies to animals, which uses diverse data streams to understand, monitor and predict animal health. In the meanwhile, the profound impact of COVID-19 further reinforced my awareness of the deep interconnection between human and animal health. It motivated me to embrace the One Health perspective in my work, recognizing that preventing disease in animals is an essential step toward protecting global public health.

Short bio: My current affiliation is a Postdoctoral Scholar in McArt Dairy Cow Lab at Cornell University. I completed my Ph.D. in Bioinformatics at City University of Hong Kong in Spring 2024, under the CityU–Cornell collaborative Interdisciplinary PhD program (advisors: Drs. Jun Li and Jessica McArt). Before that, I received my master's degree in Biomedical Engineering from The Chinese University of Hong Kong in 2019, and my bachelor's degree in Biomedical Engineering from Shenzhen University in 2018.

About me

Research

AI-Driven Health Monitoring in Animals

One of my primary research programs focuses on using AI models (machine and deep learning) to improve health prediction and monitoring in dairy cows. I have developed predictive models that leverage wearable sensor time-series (e.g., activity, rumination) and milk FTIR spectra to detect early signs of key health events and postpartum diseases in Holstein cows, including pregnancy loss, metritis, and mastitis. These efforts aim to support non-invasive, data-driven decision-making in dairy herd management and enhance early intervention strategies. Check our key publications:

Dan Lin, Jessica McArt. (2025). Prediction and classification of metritis and mastitis in Holstein cows using transition milk spectra under different modeling strategies – prospective assessment following parturition
Journal of Dairy Science [In revision] [project website] [code] [shiny app] [poster]
Dan Lin, Jun Li, Jackson Seminara, Jessica McArt. (2025). Predicting postpartum diseases in Holstein cows using milk spectra and machine learning – retrospective assessment from diagnosis date
Journal of Dairy Science [In revision] [project website] [code] [poster]
Dan Lin, Ákos Kenéz, Jessica McArt, Jun Li. (2023). Transformer Neural Network to Predict and Interpret Pregnancy Loss from Activity Data in Holstein Dairy Cows
Computers and Electronics in Agriculture [paper][code]

Genomic and Microbiome Insights into Animal's Adaptation and Health

With a background in bioinformatics, I have contributed to several projects that explore the genomic and microbiome underpinnings of host traits and disease susceptibility, including uncovering recent adaptive evolution in the high-altitude Himalayan honeybee (Apis laboriosa) through comparative genomics, as well as characterizing gut microbiome responses in mice to probiotics and dietary compounds in controlled experimental models. Check our key publications:

Dan Lin, Lan Lan, Tingting Zheng, Peng Shi, Jinshan Xu, Jun Li. (2021). Comparative Genomics Reveals Recent Adaptive Evolution in Himalayan Giant Honeybee Apis laboriosa
Genome Biology and Evolution [paper][supplemental files][code]
Qiuwen He, Jiating Huang, Tingting Zheng, Dan Lin, Heping Zhang, Jun Li, Zhihong Sun. (2021). Treatment with mixed probiotics induced enhanced and diversified modulation of the gut microbiome of healthy rats
FEMS Microbiology Ecology [paper]
Kwun Kwan Lo, Junsheng He, Dan Lin, Felicianna, Hoi Kit Matthew Leung, Fangfei Zhang, Marsena Jasiel Ismaiah, Hani El-Nezami, Jun Li. (2025). Theabrownin-induced Bifidobacterium pseudolongum enrichment mitigates experimental colitis in mice
npj Science of Food [Under review]

Generative Modeling of Sensor and Imaging Data for Clinical Decision Support

My recent work focuses on generative AI—especially diffusion and transformer models—for synthesizing biomedical and wearable sensor data. I developed SynLS, a diffusion-transformer framework that generates realistic livestock activity time series to support disease prediction. In parallel, I co-led the development of a generative foundation model for chest radiography, enabling zero-shot clinical tasks from large-scale medical imaging. Together, these projects reflect my broader interest in building cross-domain generative systems for physiological and clinical data. Check our key publications:

(# equal contribution)

Yuanfeng Ji#, Dan Lin#, Xiyue Wang#, Lu Zhang, Wenhui Zhou, Congjian Ge, Ruihang Chu, Xiaoli Yang, Junhan Zhao, Junsong Chen, Xiangde Luo, Sen Yang, Jin Fang, Ping Luo, Ruijiang Li. (2025). A Generative Foundation Model for Chest Radiography
Nature Health [Under review]
Dan Lin#, Yuanfeng Ji#, Jessica McArt, Jun Li. (2025). SynLS: A novel diffusion-transformer framework for generating high-quality wearable sensor time series data to enhance health monitoring
biorxiv [In preparation] [preprint][project website]

My undergraduate work

Dan Lin, Lei Tian, Shu Zhang, Like Wang, Ying Jie, Yongjin Zhou. (2019). Keratoconus Diagnosis: Validation of a Novel Parameter Set Derived from IOP-Matched Scenario
Journal of Ophthalmology [paper]
Yuanfeng Ji, Hao Chen, Dan Lin, Xiaohua Wu, Di Lin. (2019). PRSNet : Part Relation and Selection Network for Bone Age Assessment
MICCAI 2019 [paper]
Shengyun Liang, Tianyue Chu, Dan Lin, Yunkun Ning, Huiqi Li, Guoru Zhao. (2018). Pre-impact Alarm System for Fall Detection Using MEMS Sensors and HMM-based SVM Classifier
In 2018 40th Annual International Conference of the IEEE EMBC [paper]

Teaching

  • CityUHK PH8001 (2024) - Guest Lecturer, Classroom, 'Experimental designs', 1h lecture for graduate students
  • CityUHK PH8001 (2024) - Guest Lecturer, Classroom, 'Regression models and machine learning models', 1h lecture for graduate students
  • CityUHK VM2100 (2024) - Guest Lecturer, Classroom, 'Confidence interval and sample size', 2h lecture for undergraduate students
  • CityUHK VM2100 (2024) - Guest Lecturer, Classroom, 'Study design and Literature review', 2h lecture for undergraduate students
  • CityUHK VM2100 (2022-2023) - Teaching Assistant, On-Line, 'How comparative genomics and statistical analysis reveal the evolution of Apis laboriosa', 20-min case study for undergraduate students

Experiences

  • Visiting PhD student, Cornell University, Feb 2023 – Jun 2023
  • Research Assistant, City University of Hong Kong, Jun 2019 – Aug 2019
  • Visiting undergraduate student, Chinese Academy of Sciences (ShenZhen), Jan 2017 – Nov 2017

Selected Honors

  • Institutional Research Tuition Scholarship, City University of Hong Kong, 2022
  • Dean's List Award, The Chinese University of Hong Kong, 2019
  • Distinguished Graduate, Shenzhen University, 2018

Professional Memberships

  • American Dairy Science Association, 2024 – Present
  • American Society for Microbiology, 2025 – Present
  • Chinese Association of Animal Science and Veterinary Medicine, 2024 – Present
  • Chinese Association for Artificial Intelligence, 2024 – Present

Review Services

Preventive Veterinary Medicine (1), BMC Bioinformatics (1), Computers and Electronics in Agriculture (1)




© Dan Lin | Last updated: May. 2025