• Food informatics and artificial intelligence
• Predictive life cycle assessment for sustainable food systems
• Synthetic biology for future food and processing

Qualifications

• Assistant Professor, Department of Food Science & Technology, National University of Singapore, 2025-present
• Postdoctoral Researcher in Artificial Intelligence & Sustainability, ETH Zurich, 2022-2025
• PhD in Computational Biology, CAS-German Max Planck Society Partner Institute for Computational Biology, Chinese Academy of Sciences, 2017-2022
• BE in Food Science & Technology, Shandong Normal University, 2013-2017

Honours

• Young Scientist Award, awarded by the International Union of Food Science and Technology (IUFoST), 2024
• Irving Sigal Global Mobility Award, awarded by the American Chemical Society (ACS), 2024
• President’s Award, awarded by the Chinese Academy of Sciences (Top 1%), 2021

Member, Stakeholder Community of the European Food Safety Authority, 2025-present
Executive Guest Editor, Future Foods (Elsevier, JCR Q1), 2025-present
Associate Editor, Food Safety and Health (Wiley), 2024-present

Editorial Board Member
Journal of Food Science (Wiley, JCR Q2, IFT’s flagship journal), 2025-present
Discovery Food (Springer Nature, JCR Q2), 2025-present
Animal Research and One Health (Wiley), 2023-present

Early-Career Editorial Board Member
Journal of Future Foods (Elsevier, JCR Q1), 2023-present
Resources, Environment and Sustainability (Elsevier, JCR Q1), 2023-present
Journal of Integrative Agriculture (Elsevier, JCR Q1), 2023-2025

Selected publications (as the first or corresponding author)

• Co-assembly of hybrid microscale biomatter for robust, water-processable, and sustainable bioplastics. Science Advances, 2025, 11, eadr1596.
• Artificial intelligence in smart seafood safety across the supply chains: Recent advances and future prospects, Trends in Food Science & Technology, 2025, 163, 105161
• Unveiling the chemical complexity of food-risk components: A comprehensive data resource guide in 2024. Trends in Food Science & Technology, 2024, 148, 104513.
• Discovery of toxin-degrading enzymes with positive-unlabeled deep learning. ACS Catalysis, 2024, 14, 3336–3348.
• Enhanced deep-learning model for carbon footprints of chemicals. ACS Sustainable Chemistry & Engineering, 2024, 12, 2700–2708.
• High-throughput prediction of enzyme promiscuity based on substrate–product pairs. Briefings in Bioinformatics, 2024, 25, bbae089.
• Data-driven elucidation of flavor chemistry. Journal of Agricultural and Food Chemistry (ACS), 2023, 71, 6789-6802.
• Data-driven prediction of molecular biotransformations in food fermentation. Journal of Agricultural and Food Chemistry (ACS), 2023, 71, 8488-8496.
• MycotoxinDB: a data-driven platform for investigating masked forms of mycotoxins. Journal of Agricultural and Food Chemistry (ACS), 2023, 71, 9501-9507.
• Analysis of public opinion on food safety in Greater China with big data and machine learning. Current Research in Food Science, 2023, 6, 100468.
• Elimination of Fusarium mycotoxin deoxynivalenol (DON) via microbial and enzymatic strategies: Current status and future perspectives. Trends in Food Science & Technology, 2022, 142, 96-107.
• A data-driven integrative platform for computational prediction of toxin biotransformation with a case study. Journal of Hazardous Materials, 2021, 15, 124810.
• Development of 3D-QSAR models for predicting the activities of chemicals to stimulate muscle growth via beta2-adrenoceptor. Toxicology In Vitro, 2021, 77, 105251.
• ChemHub: a knowledgebase of functional chemicals for synthetic biology studies. Bioinformatics, 2021, 37, 4275–4276.
• An integrated platform for identification of novel coronavirus by a consensus sequence-function model. Bioinformatics, 2020, 37, 1182-1183.
• FRCD: A comprehensive food risk component database with molecular scaffold, chemical diversity, toxicity, and biodegradability analysis. Food Chemistry, 2020, 318, 126470.
• FADB-China: A molecular-level food adulteration database in China based on molecular fingerprints and similarity algorithms prediction expansion. Food Chemistry, 2020, 327, 127010.
• AdditiveChem: A comprehensive bioinformatics knowledge-base for food additive chemicals. Food Chemistry, 2019, 308, 125519.
  The full list is available at https://orcid.org/0000-0003-2467-6286

Selected presentations (as the presenting author)

• SETAC Europe 35th Annual Meeting, Vienna, Austria, 2025
  Poster spotlight, Estimating the carbon footprint of 130,000 organic chemicals with FineChem2
• Committee on Data of the International Science Council (CODATA), Digital Data Standards Sustainability in the Chemical Sciences, Delitzsch, Germany, 2025 Representative of IUFoST
• IUFoST 5th Future of Food Summit, Beijing, China (Invited), 2024
  Platform presentation, Decoding the antioxidative power of polyphenols with multi-task deep learning
• IUFoST 22nd World Congress of Food Science and Technology, Rimini, Italy, 2024
  Platform presentation, AI-driven enzyme discovery: Transforming the fight against food contaminants
• 5th International Electronic Conference on Foods, Online (Invited), 2024
  Platform presentation, Deep learning enables rapid identification of food contaminant-degrading enzymes
• American Chemical Society Fall 2024, Denver, USA, 2024
  Platform presentation, Decoding nature’s defense mechanisms: Identifying natural mycotoxin-degrading enzymes with bioinformatics analysis
• ICFSN Symposium on Food Nutrition and Health, Chengdu, China (Invited), 2024
  Section Chair, Platform presentation, Unveiling the chemical complexity of food-risk components with text mining and large language models
• SETAC Europe 34th Annual Meeting, Seville, Spain, 2024
  Platform presentation, FineChem2: An enhanced deep-learning model for estimating carbon footprints of chemicals
• The University Alliance for Food Science and Education Conference, Wuxi, China (Invited), 2024
  Keynote presentation, Food safety in the age of complexity: An overview of emerging food-risk component databases
• Italian LCA Network Association Workshop, Milano, Italy (Invited), 2024
  Keynote presentation, When LCA meets machine learning: An enhanced model for carbon footprints of chemicals