I am Zhengxiao Yan (could be pronounced like John-Shawn Yan๐), received Ph.D. at Florida State Universityโs FAMU-FSU College of Engineering, specializing in Civil Engineering, working with Dr. Nasrin Alamdari. My academic journey began with a B.S. in Environmental Science from Hohai University, an M.E. in Environmental Engineering from Northwest University under the supervision of Dr. Jun Xia and Dr. Jinxi Song, and an exchange program in Civil Engineering at the University of New Mexico, where I spent part of my time working with Dr. Ricardo Gonzalez-Pinzon. My passion for environmental science and engineering has driven me to explore various aspects of geosciences.
My research interests lie at the intersection of machine learning, climate change, ecological modeling, carbon flux, storage, and sequestration, environmental health, hydrology and geographical modeling, remote sensing, and water & food resources and security. I am currently focused on modeling terrestrial ecosystem carbon fluxes between land and the atmosphere and carbon storage in the agricultural landscape in the USA, working with Dr. Conghe Song at University of North Carolina at Chapel Hill. Generally, my work often involves using advanced algorithms/modeling and multi-source data to develop process-based or ML-based models for environmental decision-making and policy development. I have published several papers in the top journals in my research fields
๐ Educations
- 2021.08 - 2024.12, Ph.D. in Civil Engineering, FAMU-FSU College of Engineering, Florida State University.
- 2021.08 - 2023.12, M.E. in Civil Engineering, FAMU-FSU College of Engineering, Florida State University.
- 2019.08 - 2020.06, Graduate Exchange Student in Civil Engineering, School of Engineering, University of New Mexico.
- 2018.08 - 2019.06, Joint-Supervision Program, Guangzhou Institute of Geography.
- 2017.08 - 2020.06, M.E. in Environmental Engineering, College of Urban & Environmental Sciences, Northwest University.
- 2013.08 - 2017.06, B.S. in Environmental Science, College of Environment, Hohai University.
๐ฌ Research Experiences and Services
- 2024.12 - NOW, Postdoctoral Research Associate, Department of Geography and Environment, College of Arts and Sciences, University of North Carolina at Chapel Hill.
- 2021.08 - 2024.12, Research & Teaching Assistant, Department of Civil and Environmental Engineering, FAMU-FSU College of Engineering, Florida State University.
- 2020.10 - 2021.07, Research Assistant, School of Earth System Science, Tianjin University.
- 2019.09 - 2020.01, Laboratory Assistant, School of Engineering, University of New Mexico.
- 2017.08 - 2020.06, Research Assistant, College of Urban & Environmental Sciences, Northwest University.
๐ฅ News
- 2024.07: ๐๐ "Integrating temporal decomposition and data-driven approaches for predicting coastal harmful algal blooms" is accepted by the Journal of Environmental Management.
- 2024.02: ๐๐ "Predicting coastal harmful algal blooms using integrated data-driven analysis of environmental factors" is accepted by the Science of The Total Environment.
๐ Publications
Yan, Z., & Alamdari, N. (2024). Integrating temporal decomposition and data-driven approaches for predicting coastal harmful algal blooms. Journal of Environmental Management, 364, 121463.
Yan, Z., Kamanmalek, S., Alamdari, N., & Nikoo, M. R. (2024). Comprehensive Insights into Harmful Algal Blooms: A Review of Chemical, Physical, Biological, and Climatological Influencers with Predictive Modeling Approaches. Journal of Environmental Engineering, 150(4), 03124002.
- A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction, Zhou, W., Yan, Z., & Zhang, L. (2024). A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction. Scientific Reports, 14(1), 5905.
Yan, Z., Kamanmalek, S., & Alamdari, N. (2024). Predicting coastal harmful algal blooms using integrated data-driven analysis of environmental factors. Science of The Total Environment, 912, 169253.
Yan, Z., Zhou, W., Wang, Y., & Chen, X. (2022). Comprehensive Analysis of Grain Production Based on Three-Stage Super-SBM DEA and Machine Learning in Hexi Corridor, China. Sustainability, 14(14), 8881.
Analysis for spatial-temporal matching pattern between water and land resources in Central Asia
Zhang, Y., Yan, Z., Song, J., Wei, A., Sun, H., & Cheng, D. (2020). Analysis for spatial-temporal matching pattern between water and land resources in Central Asia. Hydrology Research, 51(5), 994-1008.
- Research progress on design hyetographs in small and medium-scale basins (ไธญๅฐๆตๅ่ฎพ่ฎกๆด้จ้จๅ็ ็ฉถ่ฟๅฑ), Yan, Z., Xia, J., Song, J., Zhao, L., & Pang, G. (2020). Research progress on design hyetographs in small and medium-scale basins. Prog Geogr, 39(7), 1224-1235.
๐ฌ Presentations
- 2024, EWRI24 - Yan Z, Alamdari N. Evaluating the Effects of Climate Change on Harmful Algal Blooms in Coastal Estuaries Integrating Data-Driven and Downscaling Approaches, Milwaukee.
- 2024, 21st CPASW - Yan Z, Alamdari N. Climate Change Impacts on Harmful Algal Blooms: An Integration of Data-Driven and Downscaling Approaches, Tallahassee.
- 2023, AGU23 - Yan Z, Alamdari N. Tackling Coastal HAB Prediction in Coastal Estuaries: A Hybrid Decomposition and Machine Learning Framework, San Francisco.
- 2023, ISES23 - Azimi P, Keshavarz Z, Caballero C, Pakdehi M, Yan Z, Allen JG, Ahmadisharaf E. Understanding the interrelationship among human behaviors, building and flood characteristics, mold growth, and respiratory health after Hurricanes Ian and Ida, Chicago.
- 2023, EWRI23 - Yan Z, Alamdari N. Machine Learning-Based Approach to Predict Time-series Harmful Algal Blooms in Biscayne Bay, Southeast Florida, Henderson.
- 2022, AGU22 - Yan Z, Azimi P, Keshavarz Z, Caballero C, Ahmadisharaf E, Allen JG. Key drivers of mold growth and human respiratory illness after a hurricane: Lessons from Hurricane Ida, Chicago.
- 2022, AGU22 - Yan Z, Alamdari N. Harmful algal blooms prediction in coastal areas with environmental stressors using machine learning Methods: Case study of Biscayne Bay, South Florida, Chicago.
๐ Honors and Awards
- 2021.08 Research Assistantship, Florida State University.
- 2024.08 Teaching Assistantship, Florida State University.