2023-08 I used Github years ago to store my earlier MATLAB GUI software. I hope to use Github as a vehicle to communicate my research efforts. Depending on the demands, I'm sharing Python or other open source code for past or current research work. Python codes for Metaheuristics (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO)) and Word2vec modeling are shared.
2021 A special website was designed and shared for interactive browsing data published in the feature article "ES&T in the 21st century: A data-driven analysis of research topics, interconnections, and trends in the past 20 years".
2019 ISMLR package (and application) was shared. The open accessed software was developed to conduct full dataset development and modeling using iterated stepwise multiple linear regression, which provided simutaneously missing data management and feature selection.
Associate research scholar (i.e., research assistant professor) in the Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment at the Princeton University.
Data4Environ: Data science and modeling research and applications to environmental science and engineering
Common Tags: Environmental Engineering, Wastewater, Mechanstic Modeling, Data Science, Machine Learning, Deep Learning, Hybrid Modeling, Natural Language Processing, Soft Sensor, Text Mining, Metaheuristic
(Citation numbers are based on semanticscholar API, check Google Scholar for more accurate numbers)
Zhu, J.-J., Yang, M., & Ren, Z. J. (2023). Machine Learning in Environmental Research: Common Pitfalls and Best Practices. Environmental Science & Technology, In print. https://doi.org/10.1021/acs.est.3c00026.
Zhu, J.-J., Jiang, J., Yang, M., & Ren, Z. J. (2023). ChatGPT and Environmental Research. Environmental Science & Technology, In print. https://doi.org/10.1021/acs.est.3c01818.
Zhu, J.-J., Dressel, W., Pacion, K., & Ren, Z. J. (2021). ES&T in the 21st Century: A data-driven analysis of research topics, interconnections, and trends in the past 20 Years. Environmental Science & Technology, 55(6), 3453-3464. https://doi.org/10.1021/acs.est.0c07551
. (Front Cover, Feature, Editors’ Choice, Most Read Articles, Open Access). Interactive website
Zhu, J.-J., Kang, L., & Anderson, P. R. (2018). Predicting influent biochemical oxygen demand: Balancing energy demand and risk management. Water Research, 128, 304-313. https://doi.org/10.1016/j.watres.2017.10.053.
Zhu, J.-J., & Anderson, P. R. (2017). Effect of long-term successive storm flows on water reclamation plant resilience. Water Research, 111, 1-9. https://doi.org/10.1016/j.watres.2016.12.026.
“Common Pitfalls and Best Practices in ML for Environmental Research”. June, 2023. 2023 AEESP conference. Northeastern University, Boston, MA. Presentation Slides
“Utilizing Soft Sensor System for Process Control and Optimization”. October, 2021. 2021 WRF/WEF Leaders Innovation Forum for Technology (LIFT) Intelligent Water Systems (IWS) Challenge. 2021 WEFTEC, Chicago, IL. (First place prize). News Webpage
“Hyperparameter optimized deep learning for soft sensing hard-to-measure wastewater information”. November, 2020. Panel presentation. Hybrid Models in Wastewater: Merging Mechanistic with Data-Drive. International Water Association (IWA) webinar. Webinar Webpage
“Using data science for prediction and decision-making in operation and literature: Two research examples”. August, 2020. Panel presentation. NSF Mid-scale Research Infrastructure Workshop for Intelligent Water Systems. Princeton, NJ. Workshop Webpage
“A soft-sensor software application for environmental data: Examples from water resources”. February, 2018. Invited presentation. 2018 AEESP Distinguished Lecturer Conference. Purdue University. West Lafayette, IN. Conference Brochure
