About
I'm an incoming assistant professor at MIT EECS' Faculty of AI and Decision Making and CSAIL, and a Visiting Researcher at Google working on Auto Arborist. I’ve always loved the natural world, and I've seen a growing need for technology-based approaches to conservation and sustainability challenges. My research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including strong spatiotemporal correlations that lead to domain shift, imperfect data quality, fine-grained categories, and long-tailed distributions. I received my PhD in Computing and Mathematical Sciences (CMS) at Caltech, advised by Pietro Perona, where I recieved the Amori Doctoral Prize for my dissertation. I was honored to be awarded both the PIMCO Data Science Fellowship and the Amazon AI4Science Fellowship, which recognize senior graduate students that have had a remarkable impact in machine learning and data science, and in their application to fields beyond computer science. My work has been funded in part by an NSF Graduate Research Fellowship and the Caltech Resnick Sustainability Institute.
I seek to break down knowledge barriers between fields: I founded the successful AI for Conservation slack community (with over 1000 members), I am the Biodiversity Community Lead for Climate Change AI, and I am the founding director of the Caltech Summer School on Computer Vision Methods for Ecology. I work closely with Microsoft AI for Earth, Google Research, and Wildlife Insights where I help turn my research into usable tools for the ecological community.
I believe STEM should be accessible to all, regardless of gender, race, age, nationality, sexuality, or religion. My experiences as a professional ballerina, a nontraditional student, and a queer woman have taught me the value of unique and diverse perspectives, both inside and outside of the research community. I am passionate about increasing diversity and inclusion in STEM through mentorship, teaching, and outreach, and was honored to have my DEI efforts recognized with the inaugural Caltech Computing and Mathematical Sciences Department Gradient for Change Award and the inaugural Caltech Engineering and Applied Science Division New Horizons Award.
Research Group
I'm looking for postdoctoral researchers and PhD students to start with me at MIT in Fall 2023!
Interested in a PhD? I'll be considering students primarily via the MIT-WHOI joint program and MIT EECS, but I would be open to advising students with the right fit for my lab through MIT EAPS or MIT CEE if that is better aligned with your background. If you are interested in working with me on impact-driven computer vision research for the environment, biodiversity, conservation, and sustainability, apply to any of these programs in Fall 2022 and list me in your application.
Interested in a postdoc? Please email me directly at beery@mit.edu with [Postdoc starting 2023] in the title, and include a CV and short research statement, I'm particularly interested in hiring postdoctoral researchers with a strong background in quantitative ecology who would like to expand their research to include computer vision and machine learning approaches.
Selected Invited Talks
Computer Vision for Global-Scale Biodiversity Monitoring - Scaling Geospatial and Taxonomic Coverage Using Contextual Clues
- EPFL School of Computer & Communication Sciences Seminar, 2022
- ETH Zurich Sustainability Seminar, 2022
- MIT EECS Seminar, 2022
- Caltech Environmental Science and Engineering Seminar, 2022
- UC Berkeley EECS Seminar, 2022
- University of Washington Computer Science & Engineering Seminar, 2022
- Cornell University Computer Science Seminar, 2022
- UCSB PSTATS Seminar, 2022
- UCSB Computer Science & Computer Engineering Joint Seminar, 2022
- The Ohio State University Computer Science & Engineering Seminar, 2022
- University of Melbourne Computer Science Seminar, 2022
- Stevens Institute of Technology Data Science Seminar, 2022
- UCLA Electrical and Computer Engineering Seminar, 2022
- MIT Operations Research Center Seminar on Sustainability and Climate Change, 2022
- University of Sydney Computer Science Seminar, 2022
- Climate Change AI Seminar (with Dave Thau, Global Data and Technology Lead Scientist at WWF), 2022
- UCL Center for Biodiversity and Environment Research Seminar, 2022
- IST Austria Research Seminar, 2022
- Max Planck Institute for Intelligent Systems and Cyber Valley Scientific Symposium, 2022
- Georgia Tech Computational Science and Engineering Seminar, 2021
- University of New South Wales Cognitive Robotics Seminar, 2021
- Reed College Computer Science Seminar, 2021
- Berkeley AI + Climate Seminar, 2021
- University of Guelph CARE-AI and Biodiversity Institute Joint Seminar, 2021
- Seminar at Microsoft Research Cambridge, 2020
- Computational Sustainability (CompSust) Doctoral Consortium, 2020
Edge AI for Wildlife Conservation
- AJCAI Edge AI Workshop, 2022
Joint Human-AI Elephant Population Monitoring - A Case Study in the Greater Mara Ecosystem
- Keynote - The Second CV4Animals Workshop at CVPR, 2022
Beyond Benchmarks - Going from Competition-Winning Methods to Real-World Solutions
- LifeCLEF, 2021
- Queer in AI at ICML, 2021
AI-Assisted Biodiversity Monitoring
- Data Science Frontiers Seminar at the African Institute for Mathematical Sciences, 2021
- Leveraging AI to Extend Specimen Networks at iDigBio, 2021
- Princeton AI4All, 2021
Computer Vision for Biodiversity Monitoring and Conservation
- EPFL Joint Mathis Lab Seminar, 2021
- AI for Mankind, 2021
- Yale Center for Biodiversity and Global Change Seminar, 2020
Deep Learning + Camera Traps
- Plenary at Imaginecology Workshop (Deep Learning pour le traitement et l’analyse d’imageset de sons en écologie) at Le GDR EcoStat, 2020
Improving Computer Vision for Camera Traps: Leveraging Practitioner Insight to Build Solutions for Real-World Challenges
- Ecological Society of America Annual Meeting, 2020
- CompSust Open Graduate Seminar, 2020
- Camera Trap Technology Symposium, 2019
AI for Camera Traps - Challenges, Best Practices, Benchmarks, and De-Siloing Data
- World Agroforestry Centre (ICRAF) Seminar, 2020
- WILDLABS Virtual Meetup on Camera Trapping, 2019
- Computer Vision for Wildlife Conservation Workshop at ICCV, 2019
Computer Vision for Camera Traps
- Caltech AI4Science Workshop, 2019
- USC Center for AI in Society Symposium on AI for Conservation, 2019
- Research Seminar at Google Venice, 2019
Selected Publications
For a full list of publications please see my CV or Google Scholar. * denotes equal contribution
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Perspectives in Machine Learning for Wildlife Conservation
Devis Tuia*, Benjamin Kellenberger*, Sara Beery*, Blair R. Costelloe*, Silvia Zuffi, Benjamin Risse, Alexander Mathis, Mackenzie W. Mathis, Frank van Langevelde, Tilo Burghardt, Roland Kays, Holger Klinck, Martin Wikelski, Iain D. Couzin, Grant van Horn, Margaret C. Crofoot, Charles V. Stewart, Tanya Berger-Wolf
Nature Communications 2022
[paper]
Extending the WILDS Benchmark for Unsupervised Adaptation
Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang
ICLR 2022 (Oral)
[paper][code/data]
Scaling Biodiversity Monitoring for the Data Age
Sara Beery
ACM XRDS 2021
[paper]
Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton Earnshaw, Imran Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang
ICLR 2021 (Oral)
[paper][code/data]
Benchmarking Representation Learning for Natural World Image Collections
Grant Van Horn, Elijah Cole, Sara Beery, Kimberly Wilber, Serge Belongie, and Oisin Mac Aodha
CVPR 2021 (Oral)
[paper][code/data][video]
Species Distribution Modeling for Machine Learning Practitioners: A Review
Sara Beery*, Elijah Cole*, Joseph Parker, Pietro Perona, Kevin Winner
ACM COMPASS 2021
[paper]
ElephantBook: A Semi-Automated Human-in-the-Loop System for Elephant Re-Identification
Peter Kulits, Jake Wall, Anka Bedetti, Michelle Henley, Sara Beery
ACM COMPASS 2021
[paper]
A Deep Active Learning System for Species Identification and Counting in Camera Trap Images
Mohammad Sadegh Norouzzadeh, Dan Morris, Sara Beery, Neel Joshi, Nebojsa Jojic, Jeff Clune
Methods in Ecology and Evolution 2021
[paper]
Synthetic Examples Improve Generalization for Rare Classes
Sara Beery, Yang Liu, Dan Morris, Jim Piavis, Ashish Kapoor, Markus Meister, Neel Joshi, Pietro Perona
WACV 2020
[paper]
Finding Areas of Motion in Camera Trap Images
Agnieszka Miguel, Sara Beery, Erica Flores, Loren Klemesrud
IEEE ICIP 2016
[paper]
Datasets, Models, Competitions
WILDS: Benchmark of In-The-Wild Distribution Shifts
iNaturalist 2021 Dataset
NeWT 2021 Dataset
iWildCam Datasets
- iWildCam 2021 Competition
- iWildCam 2020 Competition
- iWildCam 2019 Competition
- iWildCam 2018 Competition
Caltech Camera Traps Dataset
Microsoft AI for Earth MegaDetector Codebase and API
Context R-CNN Codebase (in the Tensorflow Object Detection API)
Teaching
Summer School in Computer Vision Methods for Ecology
In Summer 2022 I will be directing the first annual Resnick Sustainability Institute Summer School on Computer Vision Methods for Ecology (https://cv4ecology.caltech.edu/). This intensive, three-week program will teach applied computer vision methods to senior ecology graduate students and postdocs.
Students will develop hands-on computer vision systems to help answer their own ecological research questions, using their own data. They will receive daily mentorship from a passionate team of computer vision experts with a track record of impact in conservation and sustainability. Each student will be provided with $2500 in cloud credits to facilitate their project development sponsored by Microsoft AI for Earth and Amazon AWS.
Our team of instructors will work with applicants leading up to the intensive to curate computer-vision-ready labels for their data that will be used to prototype systems for their research questions during the class. Students will leave the course empowered to build their own computer vision models for ecological applications, and gain skills in problem formulation, dataset curation, model training, model evaluation, and hosting models for inference.
Co-Instructor, Caltech EE/CS/CNS 148b - Advanced Topics in Computer Vision, Spring 2021
I helped adapt this advanced projects-based computer vision course to focus on conservation and sustainability applications. To set up project groups for success, I curated a set of ecological challenges with publicly available image and video datasets and matched projects to NGOs and research groups that would directly benefit to provide domain expertise and context. I mentored 5 teams of computer vision students in structuring and defining these real-world challenges as computer vision and machine learning problems, and assisted them in holistically probing and evaluating their solutions and effectively communicating them to both computer vision and machine learning experts and the ecological community.
Guest Lectures and Tutorials
- Lecture at Caltech Ge/Bi/BE/CNS/ESE147: Quantitative Ecology, 2022
- Lecture at Caltech Bi 1: Principles of Biology, 2022
- Tutorial at 2D3DAI, 2021
- Lecture at Georgia Tech VIP-4601 VVS: HumaniTech, 2020
- Lecture at Georgia Tech VIP-4601 VWE: GaTech4Wildlife, 2020
- Tutorial at CompSust Doctoral Consortium, 2020
- Tutorial at WILDLABS Tech Tutors, 2020
- Lecture at Caltech EE/CNS/CS 148: Advanced Topics in Computer Vision, 2020
CV & Bio
Download my CV here. Last updated September 2022.
Bio for invited talks
Sara Beery will join MIT as an assistant professor in the Faculty of Artificial Intelligence and Decision-Making in EECS in September 2023. Beery received her PhD in computing and mathematical sciences at Caltech in 2022, where she was advised by Pietro Perona. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including strong spatiotemporal correlations, imperfect data quality, fine-grained categories, and long-tailed distributions. She partners with nongovernmental organizations and government agencies to deploy her methods in the wild worldwide and works toward increasing the diversity and accessibility of academic research in artificial intelligence through interdisciplinary capacity building and education.
AI for Conservation Slack
In Fall of 2019 I started a Slack channel on AI for Conservation, to provide a shared, interdisciplinary space for researchers who work across the fields of computer vision, machine learning, and AI for conservation and sustainability applications to share opportunities, discuss best practices, and find collaborators. Now our community is over 1000 strong, with researchers from all over the globe. If you'd like to join us, just email (aiforconservation@gmail.com)
