Sherrie Wang
- PhD Student, Stanford University
- sherwang (at) stanford (dot) edu
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 map land cover/land use,
especially related to agriculture and in settings 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, where I folded 3D shapes from DNA. I then worked in New York City post-graduation. Outside of research, I enjoy hiking, cycling, sketching, and reading books.
Interests. Remote sensing, machine learning, land cover/land use, sustainable development
Prior to my PhD, I studied Biomedical Engineering at Harvard University, where I folded 3D shapes from DNA. I then worked in New York City post-graduation. Outside of research, I enjoy hiking, cycling, sketching, and reading books.
Interests. Remote sensing, machine learning, land cover/land use, sustainable development
Publications
(* denotes equal contribution)
2020
- Mapping crop types in southeast India with smartphone crowdsourcing and deep learning, Remote Sensing. 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, Scientific Data. [code] [dataset] Sherrie Wang, Stefania Di Tommaso, Jillian Deines, David Lobell
- Meta-learning for few-shot land cover classification, CVPR EarthVision. Best Paper Award Marc Russwurm*, Sherrie Wang*, Marco Korner, David Lobell
- Weakly supervised deep learning for segmentation of remote sensing imagery, Remote Sensing. [code] Sherrie Wang, William Chen, Sang Michael Xie, George Azzari, David Lobell
2019
- 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, AAAI. [code] [blog] 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