Name: Fei Cheng

Title: Associate Professor

Phone:(86)020-85290205

Email: chengfei@gig.ac.cn

Resume: Born in 1990, Ph.D., currently an associate professor at the Guangzhou Institute of Geochemistry, Chinese Academy of Sciences. Studied at Sichuan University from 2010 to 2014 and obtained a Bachelor's degree in Engineering. Pursued doctoral studies at the Guangzhou Institute of Geochemistry, Chinese Academy of Sciences from 2014 to 2019 and received a Doctorate degree in Science. Visited Helmholtz Centre for Environmental Research in Germany in 2019. Since 2024, working at the Guangzhou Institute of Geochemistry, Chinese Academy of Sciences. Published over 20 papers in journals such as Environ. Sci. Technol., Environ. Sci. Technol. Lett., and Water Res.

Artificial intelligence for science and environmental toxicology, utilizing a research model that combines data-driven approaches (natural language processing and deep learning) with knowledge-driven methods (high-resolution mass spectrometry and effect-directed analysis), to intelligently identify the causes of mixture risks. Special focus is given to the intelligent identification of toxic pollutants, mining environmental pollution data, and assessing mixture risks.

1. Cheng, F. et al. Deep learning bridged bioactivity, structure and HRMS-readable evidence to decipher non-target toxicants in complex mixtures. Environ. Sci. Technol. 58, 15415–15427 (2024). https://doi.org/10.1021/acs.est.3c10814

2. Huang J. H., Cheng, F. et al. Effect driven prioritization of contaminants in wastewater treatment plants across China: A data mining-based toxicity screening approach. Water Res. 264, 122223 (2024). https://doi.org/10.1016/j.watres.2024.122223

3. Cheng, F. et al. Text mining-based suspect screening for aquatic risk assessment in the big data era: Event driven taxonomy links chemical exposures and hazards. Environ. Sci. Technol. Lett. 10, 1004–1010 (2023). https://doi.org/10.1021/acs.estlett.3c00250

4. Cheng, F. et al. Data-driven endpoint selection in data-poor scenarios: Bioassay design for shale gas flowback and produced waters. Environ. Sci. Technol. Lett. 9, 1074–1080 (2022). https://doi.org/10.1021/acs.estlett.2c00648

5. Cheng, F., Li, H., Brooks, B. W. & You, J. Signposts for aquatic toxicity evaluation in China: Text mining using event-driven taxonomy within and among regions. Environ. Sci. Technol. 55, 8977–8986 (2021). https://doi.org/10.1021/acs.est.1c00152

6. Cheng, F., Li, H., Brooks, B. W. & You, J. Retrospective risk assessment of chemical mixtures in the big data era: An alternative classification strategy to integrate chemical and toxicological data. Environ. Sci. Technol. 54, 5925–5927 (2020). https://doi.org/10.1021/acs.est.0c01062