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LIU Yun-Chung

Data Science MS Student

As an enthusiast of data, I always seek to utilize data to inform people of information hidden and to improve people’s well-being. I am a hungry learner, always looking for new algorithms (e.g. machine learning (ML), deep learning), applying them to solve real-world problems, and trying to enhance the performance of given tasks. For some of my recent projects, please have a look at the posts here or go to my GitHub Page.

I am an experienced data analyst with hand-on experience training ML Models. With my solid understanding in ML and its math foundations, I look to enhance current solutions with data and deploy real-world solutions. Currently, I am a master’s student in data science at Duke university. Before coming to Duke, I worked at National Taiwan University Hospital, developing proofs-of-concepts of ML-based medical decision support systems on various application scenarios (e.g. prognosis prediction, risk evaluation, diagnosis, and intervention recommendation). I have also worked as a sales data analyst at E-Sun bank and a human resources data analyst at Taiwan Semiconductor Manufacturing Company (TSMC) before .

Programming Skills

Python (PyTorch, TensorFlow, Pandas, NumPy, scikit-learn), SQL, R, Java, HTML, CSS, JavaScript, MATLAB

Certificates

Data Science Micromaster (Python for Data Science, Probability and Statistics, Machine Learning, and Big Data Analytics Using Spark, offered by UCSD, edX), Deep Learning, Natural Language Processing (offered by Deeplearning.ai, Coursera)

Selected Publications

Peer Reviewed Journals

  1. Chang T-H, Liu Y-C, Chiu P-H, et al. Clinical characteristics of hospitalized children with respiratory illness: using machine learning approaches to support pathogen prediction at admission. Journal of Microbiology, Immunology and Infection. 2023;56(4):772-781. doi: 10.1016/j.jmii.2023.04.011
  2. Chen S-H, Wu J-L, Liu Y-C, et al. Differential clinical characteristics and performance of home antigen tests between parents and children following household transmission of SARS-CoV-2 during the Omicron variant pandemic. International Journal of Infectious Diseases. 2023;128:301-306. doi: 10.1016/j.ijid.2023.01.014
  3. Liu Y-C, Cheng H-Y, Chang T-H, et al. Evaluation of the need for intensive care in children with pneumonia: machine learning approach. JMIR Medical Informatics. 2022;10(1). doi:10.2196/28934.

Conferennce Presentations

  1. Wu J-H, Lin S-R Lin, Liu Y-C, et al. Machine learning based mortality risk prediction in pediatric intensive care unit. 2022 Annual Meeting of Taiwan Pediatric Association. (Outstanding Young Scholar Award).
  2. Liu Y-C, Liu C-M, Tseng W-Y, et al. Semantic processing as neuro-cognitive endophenotype of Schizophrenia. 2014 Annual Convention of Association for Psychological Association.