Zhonghua Zheng Ph.D. (December 2020) Department of Civil and Environmental Engineering University of Illinois at Urbana-Champaign (UIUC)

Email: zhonghua.zheng@outlook.com Web: https://go.illinois.edu/zhonghua Follow @zzuiuc

I am actively seeking opportunities for collaborative research in Environmental Data Science and Sustainable Urban Systems Modeling and Engineering (e.g., Urban Heat Stress and Air Quality).

SHORT BIO | (Academic Curriculum Vitae) | (One-Page Résumé)

I will receive my Ph.D. degree in Environmental Engineering in Civil Engineering with a concentration in Computational Science and Engineering at the University of Illinois in December 2020. I am supervised by Prof. Nicole Riemer and Prof. Lei Zhao. I also closely work with Prof. Matthew West and Prof. Christopher Tessum.

I worked as a Data Scientist Intern at Bayer (Innovation Center at University of Illinois) for three years (January 2018 to December 2020), mainly working for The Climate Corporation. I was a Graduate Assistant at the Department of Computer Science (CS) @ Illinois during academic year 2018–2019; an ORISE Ph.D. Intern/Researcher at Oak Ridge National Laboratory (ORNL) during Summer 2018, working with National Center for Computational Sciences - Advanced Data and Workflow Group; and a Data Scientist Intern during Spring 2018 at Monsanto Innovation Center where I mainly worked for The Climate Corporation.

I am passionate about learning and solving practical problems using Data Science (DS), Artificial Intelligence (AI), Cloud Computing, and High-Performance Computing (HPC). My work focuses on computer simulation, modeling, and spatiotemporal analysis of (1) urban climate and environment, (2) air quality and aerosol properties, and (3) complex agriculture-environment nexus system.

LATEST NEWS!

• [2020.11] I have deposited my Ph.D. dissertation titled "Coupling Data Science and Numerical Simulations to Empower Atmospheric and Environmental Research".


• [2020.09] I have successfully defended my Ph.D. thesis on September 9, 2020! Thanks a lot for the support and feedback from Dr. Riemer, Dr. Zhao, Dr. West, and Dr. Anantharaj!


• [2020.08] I will be working with Prof. Christopher Tessum and Prof. Nicole Riemer this Fall semester on the project "Machine-learned atmospheric chemical mechanisms" (funded by U.S. EPA Early Career Awards).


• [2020.07] I will continue my internship at Bayer AG // The Climate Corporation this Fall semester.


• [2020.06] Our project towards global urban heat waves projections via machine learning has been released on EarthArXiv.


• [2020.05] Our project towards integration of detailed aerosol process modeling and a large-scale earth system model via machine learning has been released on EarthArXiv.


• [2020.05] I have been awarded by Microsoft AI for Earth for my research!


• [2020.04] I have been awarded by AWS on Earth and Amazon Sustainability Data Initiative for my research!


• [2020.03] I will continue my internship at Bayer AG // The Climate Corporation this summer.


• [2020.01] I gave a talk about Future Climate-Driven Risks to Urban Area during Night of Ideas Chicago 2020 at The Field Museum.


• [2020.01] I am the co-chair (three sessions) of 19th Conference on Artificial Intelligence for Environmental Science, 100th American Meteorological Society Annual Meeting.


• [2019.12] I am invited to attend and give a talk about the intergration of machine learning and particle-resolved aerosol simulations in the International Aerosol Modeling Algorithms Conference (IAMA).

EDUCATION

• Ph.D., Environmental Engineering in Civil Engineering, University of Illinois at Urbana-Champaign, 2020 (December)
• Concentration: Computational Science and Engineering
• Advisors: Prof. Nicole Riemer and Prof. Lei Zhao
• Dissertation Committee Members: Dr. Valentine G. Anantharaj and Prof. Matthew West
• Ph.D. Dissertation: Coupling Data Science and Numerical Simulations to Empower Atmospheric and Environmental Research
• M.S., Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, 2016 (December)
• Advisor: Prof. Xinlei Wang
• Thesis Committee Members: Prof. Richard S. Gates and Prof. Liangcheng Yang
• M.S. Thesis: Impedance-based moisture content sensor assessment for gas-phase biofilters
• B.Eng., Biosystems Engineering (Top 2 "A+" Agricultural Engineering Program in China), Zhejiang University, 2015
• Advisor: Prof. Zhangying Ye
• Student, English Language Courses, The University of Manchester, 28 January – 21 February, 2013
• Introduction to University Life and British Cultural Studies, Univeristy Language Centre

TEACHING EXPERIENCE

• 08/2019 – 05/2020: Graduate Teaching Assistant for Engineering Risk & Uncertainty
• Affiliation Department of Civil and Environmental Engineering (CEE)
• Product: GitHub | GitHub Pages + Travis CI
• 08/2018 – 05/2019: Graduate Assistant for Numerical Methods
• Affiliation: Department of Computer Science (CS)
• 09/2015 – 12/2015: Graduate Grader for Environmental Control & HVAC Systems
• Affiliation: Department of Agricultural and Biological Engineering

PROFESSIONAL EXPERIENCE

• 09/2019 – 12/2020: Data Scientist Intern at Bayer AG (Crop Science Division)
• Affiliation (The Climate Corporation): Science - Enabling - Sustainability (05/2019 – 12/2020)
• Affiliation (The Climate Corporation): Science - Data Insights & Discovery (09/2019 – 05/2020)
• 09/2018 – 07/2019: Data Scientist Intern at Bayer AG (Crop Science Division)
• Affiliation (The Climate Corporation): Science - Data Insights & Discovery (01/2019 – 07/2019)
• Affiliation (The Climate Corporation): Science - Measurements Team (09/2018 – 12/2018)
• 05/2018 – 08/2018: Researcher at Oak Ridge National Labratory (ORNL)
• Affiliation: National Center for Computational Sciences - Advanced Data and Workflow Group
• Media: ORNL
• 01/2018 – 05/2018: Data Scientist-UIUC Innovation Center- at Monsanto Company
• Affiliation (The Climate Corporation): Science - Measurements Team
• Affiliation (Monsanto Company): GLB Breeding - Analytics & Pipeline Design
• Got summer intern (05/2018 – 08/2018) return offer

SELECTED AWARDS

• Student Registration Waiver Grant (previously known as travel grants), American Association for Aerosol Research, 2020
• AI for Earth, Microsoft, 2020
• Earth on AWS & Amazon Sustainability Data Initiative, Amazon, 2020
• Kuck Computational Science & Engineering Scholarship, Grainger College of Engineering, UIUC, 2019
• Loh Kwan Chen Fellowship, College of Engineering, UIUC, 2019
• Travel Grant, Deep Learning for Science School at Lawrence Berkeley National Laboratory (Berkeley Lab), 2019
• Outstanding Poster Award, School of Earth, Society, & Environment, UIUC, 2019
• Travel Grant, 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences (18 AI), American Meteorological Society (AMS) 99th Annual Meeting, 2018
• Finalist, SMOKY MOUNTAIN Computational Sciences and Engineering Conference (SMC) Data Challenge, 2018
• First Place, Student Paper Award, Association of Overseas Chinese Agricultural, Biological, and Food Engineers (AOCABFE), 2016
• Tau Beta Pi (The Engineering Honor Society), inducted 2016
• Alpha Epsilon (The Honor Society of Agricultural, Biological & Food Engineering), inducted 2015

SELECTED CERTIFICATES AND GRADUATE COURSEWORK

• Deep Learning, a 5-course specialization by deeplearning.ai on Coursera. Specialization Certificate earned on June 8, 2018 (Verifiable Link)
• Reinforcement Learning, a 4-course specialization by University of Alberta & Alberta Machine Intelligence Institute on Coursera. Specialization Certificate earned on November 26, 2020 (Verifiable Link)
• Machine Learning, issued by Stanford University on Coursera, 2018 (Verifiable Link)
• Fundamental Engineer (FE) in Environmental Engineering, issued by NCEES, 2017 (Verifiable Link)
• Community Earth System Model (CESM) Tutorial, 2019 (Certificate)
• Statistical Learning Learning and Data Analytics (UIUC)
• STAT 542 / CSE 542, Statistical Learning (A+)
• STAT 420, Methods of Applied Statistics (A+)
• GEOG 480, Principles of GIS (Geographic Information System) (A)
• Atmospheric Science, Air Quality, and Environmental Engineering (UIUC)
• ATMS 420, Atmospheric Chemistry (A+)
• CEE 545, Aerosol Sampling and Analysis (A+)
• CEE 442, Environmental Engineering Principles, Physical (A)

RESEARCH THEMES

Agriculture, Atmospheric Science, Computational Science and Engineering, Data Science, Environment

• Peer-reviewed Journals // Conference Papers // Technical Reports
1. Zheng, Z.*, Curtis, J. H., Yao, Y., Gasparik, J. T., Anantharaj, V. G., Zhao, L., West, M., & Riemer, N.* (2020). Estimating Submicron Aerosol Mixing State at the Global Scale with Machine Learning and Earth System Modeling. Earth and Space Science. doi:10.1029/2020EA001500
2. Zheng, Z.*, Ching, J., Curtis, J. H., Yao, Y., Xu, P., West, M., & Riemer, N. (2020). Unsupervised Regionalization of Particle-resolved Aerosol Mixing State Indices on the Global Scale. Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020), AI for Earth Sciences Workshop. arXiv: 2012.03365
3. Zheng, Z., Riemer, N., West, M., & Anantharaj, V. G. (2019). Evaluation of machine learning approaches to estimate aerosol mixing state metrics in atmospheric models. Oak Ridge National Laboratory (ORNL) Technical Memo. doi: 10.2172/1513380
4. Zheng, Z., Yang, L., Gates, R. S., Wu, J., & Wang, X. (2017). Impedance-based moisture content sensor assessment for gas-phase biofilter media. Transactions of the ASABE, 60(5), 2163-2173. doi: 10.13031/trans.12335
5. Zheng, Z., Lin, X., Zhu, S., He, J., Cao, Y., & Ye, Z. (2016). Investigation on the bactericidal efficacy of atomized slightly acidic electrolyzed water. Chinese Journal of Disinfection, 33(4), 312-317. doi: 10.11726/j.issn.1001-7658.2016.04.004 (Peer-viewed, In Chinese)
• Thesis
1. Zheng, Z. (2016). Impedance-based moisture content sensor assessment for gas-phase biofilters. Master thesis, Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign.
• Preprints
1. Zheng, Z., Oleson, K. W., Riemer, N., & Zhao, L. (2020). Large model parameter and structural uncertainties in global projections of urban heat waves. doi:10.31223/osf.io/f5pwa.
• Oral Presentations
1. Zheng, Z., West, M., Xu, P., & Riemer, N. (2021). Machine Learning to Estimate and Regionalize Global Aerosol Mixing State. Oral Presentation in 23rd Conference on Atmospheric Chemistry, AMS 101st Annual Meeting, American Meteorological Society, Virtual, January 13. (View)
2. Zheng, Z., & Zhao, L. (2020). Future Climate-Driven Risks to Urban Area. Oral Presentation in Night of Ideas: Alive! (part of La Nuit des Idées, a global series coordinated worldwide by the Institut Français; this event is organized by the Field Museum and the Consulate General of France in Chicago), Chicago, IL., January 31. (Media)
3. Zheng, Z., & Riemer, N. (2019). Coarse-Graining of Aerosol Mixing State Metrics Empowered by Machine Learning. Oral Presentation in International Aerosol Modeling Algorithms (IAMA) Conference 2019, Davis, CA., December 5. (Invited Speaker)(View)
4. Zheng, Z., Yang, L., & Wang, X. (2016). Monitoring moisture content of gas-phase biofilter based on impedance under different conditions. Oral Presentation in 2016 ASABE Annual International Meeting, American Society of Agricultural and Biological Engineers, Orlando, FL., July 20.
• Poster Presentations
1. Zheng, Z., Ching, J., Curtis, J. H., Yao, Y., Xu, P., West, M., & Riemer, N. (2020). Unsupervised Regionalization of Particle-resolved Aerosol Mixing State Indices on the Global Scale. (Virtual) Poster Presentation in Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020) – AI for Earth Sciences Workshop, December 12. (View)
2. Zheng, Z., Oleson, K. W., & Zhao, L. (2020). Physics-Informed Machine Learning for Urban Climate Modeling. (Virtual) Poster Presentation in 2020 AGU Fall Meeting, American Geophysical Union, December 8. (View)
3. Zheng, Z., West, M., & Riemer, N. (2020). Error in Aerosol Mixing State Induced by Aerosol Representation Assumptions. (Virtual) Poster Presentation in AAAR 38th Annual Conference, American Association for Aerosol Research, October 8. (View)
4. Zheng, Z., Oleson, K. W., Riemer, N., & Zhao, L. (2019). Projections of Global Urban Heat Waves Empowered by Machine Learning. Poster Presentation in 2019 AGU Fall Meeting, American Geophysical Union, San Francisco, CA., December 10. (View)
5. Zheng, Z., Anantharaj, V. G., Gasparik, J., Curtis, J. H., Yao, Y., Hughes, M. P., Schmidt, D., West, M., & Riemer, N. (2019). Machine Learning to Predict Multi-Aerosol Mixing State Metrics. Poster Presentation in AMS 99th Annual Meeting - 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences (18AI), American Meteorological Society, Phoenix, AZ., January 7. (View)
6. Zheng, Z., Dash, S., Schmidt, D., Yin, J., Riemer, N., West, M., & Anantharaj, V. G. (2018). A Machine Learning Approach to Estimate Multi-Aerosol Mixing State Metrics at a Global Scale in Earth System Models. Poster Presentation in 2018 AGU Fall Meeting, American Geophysical Union, Washington, D.C., December 10. (View)
7. Zheng, Z., Fu, K., Balasubramanian, S., Koloutsou-Vakakis, S., McFarland, D. M., & Rood, M. J. (2017). Evaluation of WRF Parameterizations for Air Quality Applications over the Midwest USA. Poster Presentation in 2017 AGU Fall Meeting, American Geophysical Union, New Orleans, LA., December 14. (View)

CONTACT INFORMATION

Email: zhonghua.zheng@outlook.com

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