Sara Beery

About

I'm an assistant professor at MIT EECS' Faculty of AI and Decision Making and CSAIL, and I was previously 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 received 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

PhD Students
Julia Chae
Timm Haucke
Neha Hulkund
Justin Kay
Eddie Vendrow

Undergraduates
Kai Van Brunt
Ari Pero
Lauren Shrack
Avi Sundaresan (Caltech)

Interested in joining the group?

If you are interested in any position with the group (Postdoc, PhD, MEng, UROP, Visitor), instead of emailing me please fill out the interest survey below so I can better organize my responses. I am unlikely to respond to individual emails.

Interested in a PhD? I consider students primarily via MIT EECS and the MIT-WHOI joint program, 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 and list me in your application. Unfortunately I am unlikely to be able to meet with prospective applicants before they have gone through the formal review process.

Interested in a postdoc? I will be welcoming postdocs on an ongoing basis, depending on funding availability. If our research interests align, please fill out the interest survey below and include any ideas you have for projects you would be interested in exploring together. I am happy to develop fellowship proposal(s) together. I'm particularly interested in bringing on a postdoctoral researcher with a background in quantitative large-scale biodiversity monitoring to explore the intersection between machine learning outputs and ecological modeling inputs with me and my group.

Fill out the Interest Survey here.

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

This list is a selected subset and may not be up to date. For a full list of publications please see my CV or Google Scholar.
* denotes equal contribution

Datasets, Models, Competitions

MegaDetector Model Codebase

WILDS: Benchmark of In-The-Wild Distribution Shifts

iNaturalist 2021 Dataset

• iNaturalist 2021 Competition

NeWT 2021 Dataset

iWildCam Datasets

• iWildCam 2021 Competition
• iWildCam 2020 Competition
• iWildCam 2019 Competition
• iWildCam 2018 Competition

Caltech Camera Traps Dataset

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

Dr. Sara Beery is the Homer A. Burnell Career Development Professor in the MIT Faculty of Artificial Intelligence and Decision-Making. She was previously a visiting researcher at Google, working on large-scale urban forest monitoring as part of the Auto Arborist project. She received her PhD in Computing and Mathematical Sciences at Caltech in 2022, where she was advised by Pietro Perona and awarded the Amori Doctoral Prize for her thesis. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including geospatial and temporal domain shift, learning from imperfect data, fine-grained categories, and long-tailed distributions. She partners with industry, nongovernmental organizations, and government agencies to deploy her methods in the wild worldwide. She works toward increasing the diversity and accessibility of academic research in artificial intelligence through interdisciplinary capacity building and education, and has founded the AI for Conservation slack community, serves as the Biodiversity Community Lead for Climate Change AI, and founded and directs the Summer Workshop on Computer Vision Methods for Ecology.

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)