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Zhuang-Fang Yi, PhD, has a diverse range of work experience in various industries. They are currently working as a Senior Machine Learning Engineer at Regrow Ag, where they are focused on utilizing science and technology to transform farming and contribute to reversing climate change. Additionally, Zhuang-Fang Yi is the owner of Geoyi.Art, an art business where profits are donated to conservation, humanitarian, and AI Ethics NGOs.

Prior to these roles, Zhuang-Fang Yi worked at Development Seed as the GeoAI Team Lead & Machine Learning Engineer, where they built AI and Machine Learning algorithms for international development projects. They were recognized as one of the Leading Women in ML for Earth Observation in 2021.

Zhuang-Fang Yi also participated in The Data Incubator's highly competitive data science fellowship program and worked as a Data Science Fellow. During this time, they gained experience in machine learning, MapReduce, Spark, TensorFlow, SQL, data cleaning, and web scraping.

Furthermore, Zhuang-Fang Yi worked as a Data Engineer Consultant at OmniEarth and as a Postdoctoral Fellow at The Morton Arboretum, where they presented at conferences and conducted location searches for potential arboretums.

Prior to these roles, they worked as the Lead Geospatial Analyst and GIS Web Apps Developer for the China-UK Collaboration on International Forest Investment & Trade Program (InFIT). They conducted field investigations, analyzed geospatial and demographic data, and created an interactive map web application.

Before joining InFIT, Zhuang-Fang Yi was an Assistant Professor at the Chinese Academy of Sciences, where they managed grants, authored numerous journal articles and supervised students on data mining, analysis, forestry, conservation, and ecotourism projects.

They also worked as a Research Scientist in Ecological Economics at the World Agroforestry Centre, East and Central Asia office, CGIAR, and as a Visiting student and Intern at the Conservation Science Group, Department of Zoology at the University of Cambridge. During this time, they focused on land-use scenarios, carbon sequestration, water balance modeling, and remote sensing image analysis.

Overall, Zhuang-Fang Yi's work experience highlights their expertise in machine learning, AI, data analysis, geospatial analysis, and ecological economics, with a focus on utilizing technology to address environmental and conservation challenges.

Zhuang-Fang Yi, PhD, has a diverse education history with a focus on geography, data science, and ecological economics.

In 2003, they began their undergraduate studies at Sun Yat-sen University and successfully completed their Bachelor of Applied Science (BASc) degree in Geography in 2007.

Following their bachelor's degree, they pursued a Doctor of Philosophy (Ph.D.) in Ecological Economics from 2007 to 2012 at the University of Chinese Academy of Sciences.

In 2017, Zhuang-Fang Yi pursued further education in data science, completing a Data Science Certificate Program from The Data Incubator and a Certificate in Data Science Intensive from Springboard.

Additionally, they have obtained several certifications to enhance their skills and knowledge. These include certifications in "Custom Models, Layers, and Loss Functions with TensorFlow" from Coursera, "Natural Language Processing with Attention Models" from Coursera, "Docker Technologies for DevOps and Developers" from Udemy, and "Deep Learning in Python Course" from DataCamp. Zhuang-Fang has also obtained a Program Graduate - Data Science certification from The Data Incubator, a Visualization Data with D3.js certification from Udemy, a Data Visualization certification from DataCamp, and an ArcGIS Online Certification on "Going Places with Spatial Analysis" from Esri. Additionally, they hold the certification of "Analyst of processing high-resolution satellite image" from the Institute of Computing Technology, Chinese Academy of Sciences.

Zhuang-Fang Yi has demonstrated a commitment to continuing education and the acquisition of knowledge to excel in the fields of geography, data science, and ecological economics.