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Teaching Experience
​

Invited Guest Instructor

  1. “Microbial Fuel Cells” (50 min x 3) in BE 4400/4401 - Sustainable Energy Engineering, Clemson University, US (2021 Spring; Prof. Rui Xiao)

  2. “Marine Pollutants: Source, Transport, and Fate” (3 hr x 1) in CMSS 650-01 - Topics in Environmental Fluids, Coastal Carolina University, US (2018 Spring; Prof. Shaowu Bao)


Teaching Assistant​

  1. CE 481 Environmental Engineering II, University of Tennessee, Knoxville, US (2014 Spring, Prof. Joseph KO Amoah)

  2. CE 381 Environmental Engineering I, University of Tennessee, Knoxville, US (2014 Fall, Prof. Qiang He; 2015 Spring, Prof. Joseph KO Amoah; 2015 Fall, Prof. Kimberly E. Carter)

  3. Environmental Statistics, Peking University, China (2010 Spring, Prof. Shu Geng)

Training Experience
 

Online Courses

         www.udemy.com

  1. Data Science, Deep Learning and Machine Learning with Python (12 hr; by Frank Kane)

  2. Learning Python for Data Analysis and Visualization (21 hr; by Jose Portilla)

 

Workshops

  1. Workshop of “Geospatial Data Science & Machine Learning in GIS” using ArcGIS Pro and R-ArcGIS bridge, Clemson Computing and Information Technology, SC, 20 Mar 2019 (8 hours)

  2. "SWAT for Beginners", Auburn University Water Resources Center, AL, US, 5-6 Feb 2019
  3. Workshops by Clemson Computing and Information Technology, SC, Spring 2019
    • Introduction to Linux (2/25, 4 hours); Introduction to Research Computing on the Palmetto Cluster (2/27, 2.5 hours); Introduction to Data Science using R – Part 1 (4/2, 3 hours) and Part 2 (4/4, 3 hours); Introduction to Machine Learning using R – Part 1 (4/9, 3 hours), Part 2 (4/11, 3 hours), and Part 3 (4/11, 3 hours).​
  4. “GIS Fundamentals Workshop Series”, Clemson Center for Geospatial Technologies, SC, Spring 2019 (10 workshops x 3 hours)
    • Introduction to GIS (1/17); GIS Data Creation and Management (1/24); Working with Tabular Data in GIS (1/31); Basic Spatial Analysis (2/7); Intermediate Spatial Analysis (2/14); Introduction to Cloud Mapping and Story Maps (2/21); Efficient Field Data Collection with GIS (2/28); Using and Visualizing LiDAR in GIS (3/8); Basic Spatial Statistics (3/24); Data Visualization & Analytics with Tableau (3/29); Unmanned Aerial Vehicles (UAVs) and LiDAR for Mapping (4/5).​
  5. "Modeling Microbial Dynamic and Processes from Cells to Ecosystems" on KBase, FREDA, PFLOTRAN, 2018 AGU Fall Meeting, Washington, D.C., 9 Dec 2018
  6. Using 13C NMR, 1H NMR, and FT-ICR MS to characterize the natural organic matter (black and white ash from prescribed fire and wildfire and litter samples from the field decomposition), Environmental Molecular Sciences Laboratory at Pacific Northeast National Laboratory, hosted by Dr. Nancy Washton, WA, US, 2-18 May 2018
  7. Using X-ray fluorescence to analysis the halogens in the decomposed litters, Molecular Environmental Geochemistry Group at Princeton University, hosted by Prof. Satish Myneni, NJ, US, 23-28 Feb 2018
  8. "An in-depth introduction on using R for high-performance computing", National Institute for Mathematical and Biological Synthesis at the University of Tennessee, Knoxville, TN, US, 27 Feb 2015

 

Ph.D. in Civil Engineering & M.S. in Statistics (2013-2016)

  1. Environmental Chemistry (ENVE 511; 2013 Fall)

  2. Environ Transport/Kinetics (ENVE 512; 2013 Fall)

  3. Environmental Microbiology (ENVE 513; 2014 Spring)

  4. Adv Appl/Water Waste Treatment (ENVE 550; 2014 Spring)

  5. Statistics for Research II (STAT 538; 2014 Spring)

  6. Solid/Hazardous Waste Mgt (ENVE 558; 2014 Summer)

  7. Applied Multivariate Methods (STAT 579; 2014 Summer)

  8. Air Pollution Engr/Control (ENVE 574; 2014 Fall)

  9. Statistics for Research I (STAT 537; 2014 Fall)

  10. Probability/Mathemtl Statistics (STAT 563; 2014 Fall)

  11. Environmental Systems Biology (ENVE 655; 2015 Spring)

  12. Applied Time Series (STAT 575; 2015 Spring)

  13. Pollut Fate Model/Risk Assess (ENVE 653; 2015 Fall)

  14. Adv Conc: Air Pollution Engr (ENVE 671; 2015 Fall)

  15. Multivriate & Data Mining Tech (STAT 576; 2015 Fall)

  16. Data Mining Methds & Applicatn (STAT 577; 2016 Spring)

 
Field Sampling
 

  1. Water, wood, and ash samples before and after prescribed fire (pile burning), University of California, Berkeley - Sagehen Creek Field Station, 1st (Apr 30 – May 1, 2018), 2nd (Oct 31 – Nov 2, 2018)

  2. Vegetation and fuel loading surveys before a prescribed fire, Santee Experimental Forest, Cordesville, SC, 20 Mar 2018

  3. Monthly water sample collection from unmanaged and managed (by regular prescribed fire) watersheds, Santee Experimental Forest, Cordesville, SC, Aug 2017 – Jan 2018

  4.  Monthly greenhouse gas sample collection from two sites at the wetlands along a salinity gradient, Winyah Bay (a coastal estuary), SC, Aug 2017 – Jan 2018

 

Instrumental Experience
 

  1. GC/FID for H2S gas

  2. Solid phase extraction or liquid-liquid extraction following with UHPLC-UV/MS  or GC/MS for water samples

  3. Pyrolysis GC/MS for soil/litter samples or water sample after freezing dry

  4. GC/TCD-FID-ECD for greenhouse gas (CH4 and CO2)

  5. X-ray fluorescence or Analytikjena Multi X for halogens

  6. Fluorescence Excitation-Emission Matrix for water samples

  7. Total Organic Carbon for solid and water samples

 

2

Programming/Software/Modeling Experience
 

  1. RStudio, Matlab, Python, Linux

  2. ArcGIS

  3. AP42, ArcSWAT

 

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