AI Nutrition Genomics Laboratory
Research Field
Dr. Sing-Chung Li is a mentor and researcher at the School of Nutrition and Health Sciences, Taipei Medical University. He received his Ph.D. in Agricultural Chemistry from National Taiwan University and has an interdisciplinary background in nutrition, food science, biotechnology, and nutrigenomics. Dr. Li leads the AI Nutrition Genomics Laboratory, which integrates nutrition genomics, functional food science, artificial intelligence, and digital health to explore innovative strategies for disease prevention and precision nutrition.
His research focuses on bioactive compounds from Taiwanese local food resources, including sweet potatoes, mushrooms, and green bananas, using cell, animal, and human study models to investigate their potential effects on metabolic health, insulin resistance, diabetes-related complications, and healthy aging. In parallel, his laboratory is developing AI-assisted image-based dietary assessment tools to improve dietary self-management and nutrition education among university students. By combining basic science, food technology, and AI-driven nutrition applications, Dr. Li’s research aims to bridge laboratory findings with practical health solutions.
As an IIPP mentor, Dr. Li provides a supportive and interdisciplinary research environment for international students to gain hands-on experience in nutrition science, functional food research, AI-assisted dietary assessment, and digital health innovation in Taiwan.
- The AI Nutrition Genomics Laboratory (AING Lab), led by Dr. Sing-Chung Li at Taipei Medical University, conducts interdisciplinary research integrating nutrition, nutrigenomics, functional foods, food biotechnology, artificial intelligence, and digital health technologies. The laboratory aims to translate basic nutrition science and food research into practical strategies for chronic disease prevention, precision nutrition, and dietary self-management.
- The laboratory’s research areas include nutrition-related metabolic diseases, diet-related diabetes, vitamin and mineral nutrition, disease-specific formula foods, health food function evaluation, dietary assessment, molecular nutrition, and AI-assisted image-based dietary assessment. Particular emphasis is placed on functional ingredients from Taiwanese local food resources, including sweet potatoes, edible mushrooms such as Lingzhi, Nameko, and Pleurotus species, and green bananas, as well as their potential roles in diabetes, insulin resistance, sarcopenic obesity, and maternal–infant nutrition.
- The AING Lab conducts experimental studies using cell, animal, and human research models, and also develops AI-assisted dietary recording and nutrition education tools for improving dietary self-management among university students. Students and interns receive training in laboratory techniques, dietary data analysis, scientific writing, and interdisciplinary research. With active collaborations, including Academia Sinica and international partners, the laboratory provides an international-friendly and hands-on research environment for IIPP interns interested in nutrition science, functional food research, AI nutrition, and digital health innovation.
- Our laboratory focuses on AI nutrition genomics, functional foods, and the prevention of metabolic diseases, with particular emphasis on diabetes, insulin resistance, and sarcopenic obesity. One major research direction investigates the effects of pulsed light-treated mushrooms on metabolic sarcopenia, using cell-based, animal, and human research models to evaluate their potential roles in muscle metabolism, insulin sensitivity, inflammation, oxidative stress, and healthy aging. This research aims to explore how mushroom-derived bioactive compounds and vitamin D-enhanced functional foods may contribute to the prevention or management of metabolic decline and sarcopenia.
- Another key research topic is the development and feasibility evaluation of an AI-assisted image-based dietary recording tool for dietary self-management among university students. This project integrates nutrition science, artificial intelligence, image-based dietary assessment, and digital health technologies to improve dietary recording accuracy, nutrition education, and self-management behaviors. Through these two research directions, the laboratory bridges functional food science, molecular nutrition, AI-assisted dietary assessment, and practical health promotion applications.
- Teaching Material Innovation Award, Academic Year 2007
- Creative Teaching Award, Academic Year 2008
- Teaching Material Innovation Award, Academic Year 2009
- First Place Poster Award, Taiwan Society for Parenteral and Enteral Nutrition Autumn Academic Conference, Mackay Memorial Hospital, Tamsui, New Taipei City, Taiwan, 2025.
Lin, S. P., Chen, C. M., Chiu, S. H., Hsiao, P. J., and Li, S. C. Association between protein and specific amino acids in diabetic kidney disease. - Best Poster Award, 7th International Nutrition Research Conference, Changi, Singapore, 2025.
Nguyen, D. H. N., Chen, C. M., Lam, H. N., Su, C. T., Liu, K. E., and Li, S. C. Food supply and nutritional imbalance in Taiwan: Implications for dietary policy and sustainability. - Honorable Mention, Research Award for the Development of Functional Ingredients in Health Foods, International Symposium on Health Food Science and Technology in the Post-Pandemic Era, Fu Jen Catholic University, Taiwan, 2021.
Wang, C. J., Yao, R. W., Ko, H. Y., Huang, H. Y., and Li, S. C.
Education:
- Ph.D. in Agricultural Chemistry, National Taiwan University, 2001
- M.S. in Agricultural Chemistry, National Taiwan University, 1994
- B.S. in Health and Nutrition, Taipei Medical College, 1992
Experience:
- Associate Professor, Department of Health and Nutrition, Taipei Medical University, 2011.02-present
- Assistant Professor, Department of Health and Nutrition, Taipei Medical University, 2003.09-2011.01
- Visiting Scholar, Duke University Medical Center, 2003.06-2003.08
- Ph.D. Research, Institute of Biomedical Sciences, Academia Sinica, 2002.07-2003.08
- Ph.D. Research, Institute of Plant and Microbial Biology, Academia Sinica, 2001.07-2002.06
Job Description
Specific tasks may include assistance with cell culture experiments, preparation of bioactive compound extracts from edible mushrooms, basic biochemical assays, data organization and analysis, and support for literature review and scientific writing. The intern will also engage in regular research meetings and academic discussions.
Preferred Intern Educational Level
Applicants should be senior undergraduate students, master’s students, or early-stage PhD students majoring in nutrition, food science, biomedical sciences, biotechnology, or related disciplines.
Skill sets or Qualities
Basic knowledge of nutrition or life sciences, strong motivation for research, willingness to learn laboratory techniques, good communication skills in English, and the ability to work independently as well as collaboratively in a research team. Prior laboratory experience is preferred but not mandatory.
Job Description
Key responsibilities include assisting with literature review, experimental preparation, cell-based or nutrition-related data collection, dietary image organization and annotation, basic statistical analysis, and scientific writing. The intern may also contribute to studies involving pulsed light-treated mushrooms, functional food evaluation, metabolic sarcopenia, AI-assisted dietary assessment, and university student dietary self-management. The intern will be encouraged to participate in research meetings, interdisciplinary discussions, and preparation of research reports or presentation materials.
Preferred Intern Educational Level
Applicants should be senior undergraduate students, master’s students, or early-stage PhD students in nutrition, public health, food science, biotechnology, biomedical sciences, data science, computer science, artificial intelligence, digital health, or related fields. Applicants with interests in functional foods, metabolic diseases, sarcopenia, dietary assessment, AI applications, or health promotion are especially encouraged to apply.
Skill sets or Qualities
Applicants should have a strong interest in nutrition science, functional foods, metabolic health, AI-assisted dietary assessment, or digital health. Basic knowledge of nutrition, food science, biology, data analysis, or image-based dietary assessment is preferred. Familiarity with spreadsheets, statistical software, literature review, or AI/image-analysis tools would be an advantage. Good English communication skills, careful data handling, willingness to learn, and the ability to work both independently and collaboratively are highly desirable.