Sherrie Wang
ContactInformation

Massachusetts Institute of Technology
77 Massachusetts Avenue
Cambridge, MA 02139

E-mail: sherwang@mit.edu
Website: https://sherriewang.github.io
Lab Website: https://earthintelligence.mit.edu

Appointments

d’Arbeloff Career Development Assistant Professor, 2023–present
Department of Mechanical Engineering, Institute for Data, Systems, and Society,and Laboratory for Information & Decision Systems (LIDS)
Massachusetts Institute of Technology, Cambridge, Massachusetts USA
Ciriacy-Wantrup Postdoctoral Fellow, 2021–2023
Host: Solomon Hsiang
Goldman School of Public Policy
University of California, Berkeley, Berkeley, California USA

Education

Stanford University, Stanford, California USA
Ph.D., Computational and Mathematical Engineering, 2021
Dissertation: “Machine learning for satellite imagery when labels are scarce”
Advisor: David Lobell
M.S., Computational and Mathematical Engineering
Harvard University, Cambridge, Massachusetts USA
A.B. Biomedical Engineering, Magna cum laude with high honors, 2014

Preprints(* denotes equalcontribution)

1. Stefania Di Tommaso, Sherrie Wang, Rob Strey, and David B. Lobell. Mapping sugarcane
globally at 10 m resolution using GEDI and Sentinel-2. Under review, 2024+
2. Mutlu Ozdogan, Sherrie Wang, Devaki Ghose, Eduardo Pinheiro Fraga, Ana M Fernandes,and Gonzalo Varela. Field-scale rice area and yield mapping in Sri Lanka with optical remotesensing and limited training data. Under review, 2024+
3. Philippe Rufin, Sherrie Wang, Sá Nogueira Lisboa, Jan Hemmerling, Mirela G. Tulbure,and Patrick Meyfroidt. Taking it further: leveraging pseudo labels for field delineation acrosslabel-scarce smallholder regions. Under review, 2024+

Publications

1. Chenhui Zhang and Sherrie Wang. Good at captioning, bad at counting: Benchmarking
GPT-4V on Earth observation data. 2024 IEEE/CVF Conference on Computer Vision andPattern Recognition Workshops (CVPRW), 2024
2. A. Patrick Behrer and Sherrie Wang. Current benefits of wildfire smoke for yields in the US
Midwest may dissipate by 2050. Environmental Research Letters, 2024
3. Jordi Laguarta, Thomas Friedel, and Sherrie Wang. Combining deep learning and street
view imagery to map smallholder crop types. Proceedings of the AAAI Conference on Artificial
Intelligence, 2024
4. Simon Greenhill, Hannah Druckenmiller*, Sherrie Wang*, David A. Keiser, Manuela Girotto,
Jason K. Moore, Nobuhiro Yamaguchi, Alberto Todeschini, and Joseph S. Shapiro. Machinelearning predicts which rivers, streams, and wetlands the Clean Water Act regulates. Science,2024
Last updated June 2024

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5. Marc Russwurm, Sherrie Wang, Benjamin Kellenberger, Ribana Roscher, and Devis Tuia.
Meta-learning to address diverse earth observation problems across resolutions. Communications Earth & Environment, 2024
6. Stefania Di Tommaso, Sherrie Wang, Vivek Vajipey, Noel Gorelick, Rob Strey, and David B.
Lobell. Annual field-scale maps of tall and short crops at the global scale using GEDI andSentinel-2. Remote Sensing, 2023
7. Sherrie Wang, Francois Waldner, and David B. Lobell. Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision. RemoteSensing, 2022
8. Marissa Childs, Jessica Li, Jeffrey Wen, Sam Heft-Neal, Anne Driscoll, Sherrie Wang, CarlosGould, Minghao Qiu, Jennifer Burney, and Marshall Burke. Daily local-level estimates ofambient wildfire smoke PM2.5 for the contiguous US. Environmental Science & Technology,
2022
9. Ju Young Lee, Sherrie Wang, Anjuli Jain Figueroa, Rob Strey, David B. Lobell, Rosamond L.
Naylor, and Steven M. Gorelick. Mapping sugarcane in central India with smartphone crowdsourcing. Remote Sensing, 2022
10. Christopher Yeh*, Chenlin Meng*, Sherrie Wang*, Anne Driscoll, Erik Rozi, Patrick Liu,Jihyeon Lee, Marshall Burke, David B. Lobell, and Stefano Ermon. SustainBench: Benchmarks
for monitoring the Sustainable Development Goals with machine learning. NeurIPS Datasets
and Benchmarks Track, 2021
11. Stefania Di Tommaso, Sherrie Wang, and David B. Lobell. Combining GEDI and Sentinel-2for wall-to-wall mapping of tall and short crops. Environmental Research Letters, 2021
12. Dan M. Kluger, Sherrie Wang, and David B. Lobell. Two shifts for crop mapping: Leveraging
aggregate crop statistics to improve satellite-based maps in new regions. Remote Sensing ofEnvironment, 262:112488, 2021
13. Sherrie Wang, Stefania Di Tommaso, Joey Faulkner, Thomas Friedel, Alexander Kennepohl,
Rob Strey, and David B. Lobell. Mapping crop types in southeast India with smartphone
crowdsourcing and deep learning. Remote Sensing, 12(18), 2020
14. Sherrie Wang, Stefania Di Tommaso, Jillian M. Deines, and David B. Lobell. Mapping
twenty years of corn and soybean across the US Midwest using the Landsat archive. ScientificData, 7(1):307, 2020
15. Marc Rußwurm*, Sherrie Wang*, Marco Körner, and David B. Lobell. Meta-learning for fewshot land cover classification. 2020 IEEE/CVF Conference on Computer Vision and Pattern
Recognition Workshops (CVPRW), pages 788–796, 2020
EarthVision 2020 Best Paper Award
16. Sherrie Wang, William Chen, Sang Michael Xie, George Azzari, and David B. Lobell. Weakly
supervised deep learning for segmentation of remote sensing imagery. Remote Sensing, 12(2),2020
17. Jillian M. Deines, Sherrie Wang, and David B. Lobell. Satellites reveal a small positive yield
effect from conservation tillage across the US Corn Belt. Environmental Research Letters,
14(12):124038, 201918. Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David Lobell, and Stefano Ermon.
Tile2Vec: Unsupervised representation learning for spatially distributed data. Proceedings ofthe AAAI Conference on Artificial Intelligence, 33:3967–3974, 201919. Sherrie Wang, George Azzari, and David B. Lobell. Crop type mapping without field-level
labels: Random forest transfer and unsupervised clustering techniques. Remote Sensing ofEnvironment, 222:303–317, 20192

20. Lo-Hua Yuan, Anthony Liu, Alec Yeh, Aaron Kaufman, Andrew Reece, Peter Bull, AlexFranks, Sherrie Wang, Dmitri Illushin, and Luke Bornn. A mixture-of-modelers approach
to forecasting NCAA tournament outcomes. Journal of Quantitative Analysis in Sports, 11,2015
Conference
Presentations

1. Chenhui Zhang, Sherrie Wang. “Good at captioning, bad at counting: Benchmarking GPT4V on Earth observation data.” CVPR EarthVision Workshop and ICLR ML4RS Workshop,2024 (Orals)
2. Jordi Laguarta, Thomas Friedel, Sherrie Wang. “Combining deep learning and street view
imagery to map smallholder crop types.” AAAI, 2024 (Oral)
3. Sherrie Wang, Jordi Laguarta, Thomas Friedel. “Combining deep learning and street view
imagery to map smallholder crop types.” American Geophysical Union Fall Meeting, 2023(Oral)
4. Kerri Lu, Sherrie Wang, Claire Monteleoni, Anastase Charantonis. “Hyperspectral RemoteSensing of Soil Properties Using Machine Learning.” American Geophysical Union Fall Meeting, 2023 (Poster)
5. Simon Greenhill, Trinetta Chong, Hannah Druckenmiller, Solomon Hsiang, Joel Ferguson,Cornelia Ilin, Andreas Madestam, Hikari Murayama, Eugenio Noda, Anna Tompsett, Nicklas
Nordfors, Sherrie Wang, Taylor Kee. “A window back in time: Combining aerial photography
and machine learning to map urban change over the 20th century.” American GeophysicalUnion Fall Meeting, 2023 (Oral)
6. Hikari Murayama, Trinetta Chong, Hannah Druckenmiller, Simon Greenhill, Solomon Hsiang,Joel Ferguson, Cornelia Ilin, Taylor Kee, Andreas Madestam, Eugenio Noda, Anna Tompsett,Nicklas Nordfors, Sherrie Wang. “Developing high resolution, historical land cover maps in
Africa using deep learning and an aerial photography archive.” American Geophysical Union
Fall Meeting, 2023 (Oral)
7. Joel Ferguson, Trinetta Chong, Hannah Druckenmiller, Simon Greenhill, Solomon Hsiang, Cornelia Ilin, Taylor Kee, Andreas Madestam, Hikari Murayama, Eugenio Noda, Anna Tompsett,Nicklas Nordfors, Sherrie Wang. “Combining modern high resolution satellite imagery withhistorical aerial photography to map changes in population distribution.” American Geophysical Union Fall Meeting, 2023 (Oral)
8. Chenhui Zhang, Sheng Wang, Kaiyu Guan, Haozhen Zheng, Sherrie Wang, Bruce Rhoads,Shengnan Zhou, Yawen Deng, Andrew Margenot. “Quantifying streambank erosion in two
watersheds of Illinois through high-resolution aerial imagery and Segment Anything Models.”American Geophysical Union Fall Meeting, 2023 (Poster)
9. Sherrie Wang, Stefania Di Tommaso, Vivek Vajipey, Noel Gorelick, Rob Strey, and David
B. Lobell. “Global mapping of tall crops in regions without field labels using GEDI andSentinel-2.” American Geophysical Union Fall Meeting, 2022 (Oral)
10. Sherrie Wang, Francois Waldner, and David B. Lobell. “Large-scale agricultural field delineation in India using deep learning and weak supervision.” American Geophysical Union FallMeeting, 2022 (Poster)
11. Hikari Murayama, Sherrie Wang, Ron Cohen, and Solomon Hsiang. “Measuring CO2 emissions
using deep learning and remote sensing.” American Geophysical Union Fall Meeting, 2022(Poster)
12. Sherrie Wang, Francois Waldner, and David B. Lobell. “Delineating smallholder fields usingtransfer learning and weak supervision.” American Geophysical Union Fall Meeting, 2021(Oral)313. Sherrie Wang, Stefania Di Tommaso, and David B. Lobell. “Combining GEDI and Sentinel-2for wall-to-wall mapping of tall and short crops.” American Geophysical Union Fall Meeting,2021 (Oral)
14. Marissa Childs, Jeff Wen, Sam Heft-Neal, Jessica Li, Anne Driscoll, Sherrie Wang, Jennifer
Burney, and Marshall Burke. “A tractable model of ambient particulate matter exposure fromwildfire smoke.” American Geophysical Union Fall Meeting, 2021 (Poster)
15. Jillian M. Deines, Anthony D. Kendall, Sherrie Wang, James J. Butler, Bruno Basso, DavidHyndman, and David B. Lobell. “Assessing causal relationships in agricultural soil and water
management from observational satellite data.” American Geophysical Union Fall Meeting,2021 (Invited Oral)
16. Christopher Yeh*, Chenlin Meng*, Sherrie Wang*, Anne Driscoll, Erik Rozi, Patrick Liu, Jihyeon Lee, Marshall Burke, David B. Lobell, and Stefano Ermon. “SustainBench: Benchmarks
for monitoring the Sustainable Development Goals with machine learning.” NeurIPS Datasets
and Benchmarks Track, 2021 (Oral)
17. Erfan Rostami, Sherrie Wang, Stefania Di Tommaso, and David B. Lobell. “Toward globalscale field boundary delineation using deep learning.” American Geophysical Union Fall Meeting, 2020 (Poster)
18. Sherrie Wang and David B. Lobell. “Deep learning for label-scarce remote sensing applications.” American Geophysical Union Fall Meeting, 2020 (Oral)
19. Sherrie Wang*, Marc Rußwurm*, Marco Körner, and David B. Lobell. “Meta-learning forfew-shot time series classification.” IGARSS, 2020 (Oral)
20. David B. Lobell, Walter T. Dado, Jillian M. Deines, Stefania Di Tommaso, and Sherrie Wang.
“Landsat-based reconstruction of corn and soybean yield histories in the United States since1999.” IGARSS, 2020 (Oral)
21. Marc Rußwurm*, Sherrie Wang*, Marco Körner, and David B. Lobell. “Meta-learning forfew-shot land cover classification.” CVPR EarthVision Workshop, 2020 (Oral)
22. Sherrie Wang, Stefania Di Tommaso, Joey Faulkner, Thomas Friedel, Alexander Kennepohl,
Rob Strey, and David B. Lobell. “Mapping crop types in India with crowdsourced data anddeep learning.” American Geophysical Union Fall Meeting, 2019 (Oral)
23. Jillian M. Deines, Sherrie Wang, and David B. Lobell. “Investigating the yield impacts ofconservation tillage in the US Corn Belt using Landsat.” American Geophysical Union FallMeeting, 2019 (Oral)
24. Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David Lobell, and Stefano Ermon.
“Tile2Vec: Unsupervised representation learning for spatially distributed data.” AAAI, 2019(Oral)
25. Sherrie Wang, William Chen, George Azzari, and David B. Lobell. “Weakly supervised deeplearning for segmentation of cropland in remote sensing imagery.” American GeophysicalUnion Fall Meeting, 2018 (Oral)
26. Zhenong Jin, George Azzari, Sherrie Wang, and David B. Lobell. “Mapping the yields of major
crops in Sub-Saharan African smallholder farming system.” American Geophysical Union FallMeeting, 2018 (Poster)
27. Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David Lobell, and Stefano Ermon.
“Tile2Vec: Unsupervised representation learning for spatially distributed data.” Women inMachine Learning Workshop, 2018 (Poster)
28. Sherrie Wang, Sebastian Le Bras, George Azzari, and David B. Lobell. “Toward global croptype mapping using a hybrid machine learning approach and multi-sensor imagery.” American
Geophysical Union Fall Meeting, 2017 (Poster)429. Evan Wu, Adam Marblestone, Sherrie Wang, Nick Perkons, Thomas Schaus, Sun Wei, andPeng Yin. “Controllable release of gold nanoparticles from a switchable DNA box.” 10th
Annual Conference, Foundations of Nanoscience, 2013 (Poster)
Selected Invited “Remote Sensing and Machine Learning for Sustainability”
Talks
Keynote, Machine Learning for Remote Sensing Workshop, ICLR. May 2024
Agriculture and Climate Change Workshop, Harvard University. May 2024
“Planetary-Scale Insights via Remote Sensing and Machine Learning”
Workshop on AI for Science, NeurIPS. December 2023
Microsoft Research New England. October 2023
Department of Civil & Environmental Engineering, Tufts University. October 2023
Department of Earth, Atmospheric and Planetary Sciences, MIT. October 2023
“Crop field boundary mapping using machine learning and very high-resolution data.” Food andAgriculture Organization of the United Nations (FAO) Webinar. May 2023
“Monitoring the SDGs via Remote Sensing and Machine Learning.” Global Engagement & Empowerment Forum on Sustainable Development (GEEF), Yonsei University. February 2023
“Planetary-Scale Digital Agriculture via Remote Sensing”
Department of Earth, Environmental & Planetary Sciences, Brown University. March 2023
ITU-FAO Focus Group for AI and Agriculture. October 2022
Sustainable Development Seminar, Columbia University. October 2022
“Deep learning for label-scarce remote sensing applications.” AGU Fall Meeting, Session on Solving
Training Data Bottlenecks for Artificial Intelligence in Earth Science. December 2020
“Meta-learning for remote sensing.” Workshop on Knowledge-Guided Machine Learning, Universityof Minnesota. August 2020
“Satellites, satellites all around, but not enough ground truth labels: Lessons from crop type mapping.” Expert Workshop on Advancing Application of Machine Learning Tools for NASA Earth
Observation Data, Washington, DC. January 2020
“Mapping crop types in southeast India with crowdsourced data and deep learning.” Bay Area
Scientific Computing Day, Lawrence Berkeley National Lab. December 2019
“Applying machine learning to solve global food security challenges.” Women in Data Science
Podcast. September 2019
“Weakly supervised learning for satellite imagery: Applications in crop mapping.” Computational
Sustainability Network Webinar. March 2019
Honors andAwards

Best Paper Award, ICLR ML4RS Workshop, 2024
Ciriacy-Wantrup Postdoctoral Fellowship, 2021–2023
Top 5% of papers, International Geoscience and Remote Sensing Symposium (IGARSS), 2020
Best Paper Award, CVPR EarthVision Workshop, 2020
ICME Department Short Course Instructor Award, Stanford University, 2019
Stanford Graduate Fellowship, 2015 – 2018
Phi Beta Kappa, 2013

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TeachingExperience

Massachusetts Institute of Technology, Cambridge, Massachusetts USA
Lab Instructor, 2.086: Numerical Computation for Mechanical Engineers

Fall 2023

Stanford University, Stanford, California USA
Instructor, Intermediate Topics in Deep Learning (New ICME Workshop)
August 2021
Instructor, Introduction to Deep Learning (ICME Workshop)
August 2020 & 2021
Instructor, CME 250: Introduction to Machine Learning
Winter 2019
Harvard University, Cambridge, Massachusetts USA
Teaching Assistant, ES 53: Quantitative Physiology

Professional
ServiceFall 2012

Organizer, Workshop on Computational Sustainability, NeurIPS, 2023
Speaker, MIT Mechanical Engineering Graduate Association of Women (MEGAWomen), 2023
Convener, Session on “Remote Sensing for Sustainable Agriculture,” AGU Fall Meeting, 2023
Speaker, UC Riverside Data Science Summer Fellowship Program, 2022
Speaker, Women in Data Science (WiDS) UC Riverside Conference, 2022
Vlogger, Women in Data Science (WiDS) High School Outreach, 2020
Mentor, Stanford Women in Math Mentoring, 2016 – 2019
Member, ICME Student Council, 2016 – 2019
Organizer, Women in Data Science (WiDS) Conference Datathon, 2018 – 2019
Mentor and judge, Stanford Big Earth Hackathon, 2018
Math and reading tutor, East Palo Alto Tennis and Tutoring (EPATT), 2016 – 2017
Reviewer: Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing, International
Journal of Applied Earth Observation and Geoinformation, NeurIPS Datasets and Benchmarks
Track, NeurIPS Spatiotemporal Decision-making Workshop, NeurIPS Machine Learning for Development Workshop, ICML Computer Vision for Global Challenges Workshop, KDD Workshop onData-driven Humanitarian Mapping
Member: American Geophysical Union, IEEE

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