Education:
Ph.D.  2016 Purdue University, Dept. of Earth & Atmospheric Science
Supervisor: Qianlai Zhuang
B.S.  2011 Peking University, Dept. of Ecology

Professional Experience:
07/2018 – 01/2019   Lead Crop Scientist    Atlas AI P.B.C., Palo Alto, CA
07/2016 – 06/2018   Postdoc Scholar Dept. of Earth System Science, Stanford University  Supervisor: David Lobell
06/2015 – 08/2015   Science Team Intern Farmlogs, Ann Arbor, MI

Jessica has an extensive broad background in Earth Science and an interest in combining remote sensing imagery, data analysis, and ML for agricultural applications. Her planned research involves studying nutrient management strategies that promote water quality and sustainable agriculture practices. She received a B.A. from Vassar College in 2005 and a PhD from the University of Minnesota in 2011.

Nutrient budgets
Soil processes
Project management

Nanshan is interested in the crop production response to climate change and agricultural management, the greenhouse gas emission estimation, and the climate feedback of agricultural management. He has experience in crop type classification, early-season crop identification, and GPP modeling. His postdoctoral research will use Knowledge-guided machine learning (KGML) to predict water quality in the agricultural landscape. He received the B.S. degree from Wuhan University of technology in 2015, the M.S. degree from Peking University in 2018, and the Ph.D degree from the Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS).

Knowledge-guided machine learning
Water Quality Prediciton
Crop type and management mapping

Yufeng is interested in interdisciplinary fields that combine process-based modeling with hydrological, biological, and ecological observations. His proposed research involves modeling the responses of crop yield to nitrogen fertilization and nitrogen losses to the environment, which emphasizes on two global concerns, food security and environmental sustainability. He received the B.E. degree from Tsinghua University in 2016, and the M.S. degree from Beijing Normal University in 2019.

Agroecosystem modeling
N2O emissions
Carbon credits

Junxiong has experience in remote sensing image processing, including image fusion and classification. He is currently working on analyzing the impacts of climate changes on crop yields. He received the B.E. degree from China University of Geosciences in 2018, and the M.S. degree from Beijing Normal University in 2021. Google Scholar.

Knowledge-guied machine learning
Remote sensing products
Crop yield

Licheng has rich experience on process-based model development and simulation. Now he is working on using physical guided machine learning technique to address scientific questions on agriculture field. He received the B.S. degree from Peking University in 2013, and the Ph.D. degree from Purdue University in 2020.

Knowledge-guided machine learning
Earth Ecosystem modeling
Greenhouse Gas

The broad scope of Qi research interests is in understanding interactions between crop and environment, decoding crop-related spectral and visual features from remote sensing data, improving the regional crop growth modelling and mapping. He has experience on crop growth models, sequential data assimilation algorithms, remote sensing data analysis, and deep learning algorithms. His publications are available on Google Scholar. Qi Yang received the B.E. and Ph.D degrees from Wuhan University in 2016 and 2021.

Knowledge-guided machine learning
Agroecosystem carbon cycle
Data assimilation

Jie has experience in developing machine/deep learning models and interpreting model responses in the face of uncertainties inherent to agricultural systems. His research involves the development of advanced modeling approaches to investigate subjects concerning sustainable agricultural production under conditions of climate change. He graduated from Huazhong Agricultural University with a B.E. degree in 2018 and obtained his Ph.D. degree from Zhejiang University in 2023.

Agroecosystem modeling
Cropland mapping
Explainable machine learning

Leikun is interested in sustainable agriculture management utilizing remote sensing and ML/DL. Topics that interest him include not limited to staple/cash crop mapping, the role of cash crops in poverty alleviation, yield prediction, and crop phenology. He received his B.E. degree from Shandong Agricultural University in 2020 with research related to agricultural remote sensing. Leikun’s Github

Staple/cash crop mapping
Land-cover and land-use change
Machine learning/deep learning

Lab Alumni

  • Chenxi Lin (PhD student, 2019-2023)
  • Miao Tong (Visiting PhD student from Chinese Academy of Sciences, 2022)
  • Aidanne Forcier (Undergraduate student, 2022)
  • Taegon Kim (Research Associate, 2019-2021, now Assistant Professor at Jeonbuk National University, South Korea)
  • Zhiyong Lv (Visiting Professor from Xi’An University of Technology)
  • Ke Liu (Visiting Student from Peking University; now PhD student at California Institute of Technology)