I’m a PhD student at Stanford studying applied math (ICME),
advised by Professor David Lobell
at the Center on Food Security and the Environment.
My research uses modern data science techniques to understand agricultural management and productivity,
especially in developing countries where ground truth labels are scarce.
Toward this end, I work with large-scale satellite imagery,
develop machine learning methods for low-data regimes,
and explore the use of non-traditional data sources for ground truth.
For more details, please see my CV.
Prior to my PhD, I studied Biomedical Engineering at
Harvard University (2014) and worked in New York City post-graduation.
Outside of research, I enjoy hiking, cycling, sketching, and reading books.
Interests. Remote sensing, machine learning, sustainable development
(* denotes equal contribution)
Mapping crop types in southeast India with smartphone crowdsourcing and deep learning,
Sherrie Wang, Stefania Di Tommaso, Joey Faulkner, Thomas Friedel, Alexander Kennepohl, Robert Strey, David Lobell
Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive,
Sherrie Wang, Stefania Di Tommaso, Jillian Deines, David Lobell
Meta-learning for few-shot land cover classification,
Best Paper Award
Marc Russwurm*, Sherrie Wang*, Marco Korner, David Lobell
Weakly supervised deep learning for segmentation of remote sensing imagery,
Sherrie Wang, William Chen, Sang Michael Xie, George Azzari, David Lobell
Satellites reveal a small positive yield effect from conservation tillage across the US Corn Belt,
Environmental Research Letters.
Jillian Deines, Sherrie Wang, David Lobell
Tile2Vec: Unsupervised Representation Learning for Spatially Distributed Data,
Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David Lobell, Stefano Ermon
Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques,
Remote Sensing of Environment.
Sherrie Wang, George Azzari, David Lobell