Sara Beery – Computer Vision for Global-Scale Biodiversity Monitoring

Abstract: 
We require a real-time, modular earth observation system that unites efforts across research groups in order to provide the vital information necessary for global-scale impact in sustainability and conservation in the face of climate change. The development of such systems requires collaborative, interdisciplinary approaches that translate diverse sources of raw information into accessible scientific insight. For example, we need to monitor species in real time and in greater detail to quickly understand which conservation efforts are most effective and take corrective action. Current ecological monitoring systems generate data far faster than researchers can analyze it, making scaling up impossible without automated data processing. However, ecological data collected in the field presents a number of challenges that current methods, like deep learning, are not designed to tackle. These include strong spatiotemporal correlations, imperfect data quality, fine-grained categories, and long-tailed distributions. My work seeks to overcome these challenges, and includes methods which can learn from imperfect data, systematic frameworks for measuring and overcoming performance drops due to domain shift, and the deployment of efficient human-AI systems that have made significant real-world conservation impact. My future research agenda will expand upon the strong foundation built by my past and current research. It will seek to make effective use of all available modalities of data, incorporate expert knowledge systematically, and ensure these systems are equitable and ethical – all fundamental and unresolved challenges for CV&ML. 

Bio: 
Sara Beery is a final-year PhD Candidate in Computing and Mathematical Sciences at Caltech, advised by Pietro Perona. She has always loved the natural world and has seen a growing need for technology-based approaches to conservation and sustainability challenges. Her research focuses on building computer vision methods that enable efficient, accessible, and equitable global-scale biodiversity monitoring. She 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. Her work is funded in part by an NSF Graduate Research Fellowship and the Caltech Resnick Sustainability Institute. She seeks to break down knowledge barriers between fields: she founded the successful AI for Conservation slack community (with over 650 members), and she is the founding director of the Caltech Summer School on Computer Vision Methods for Ecology. She works closely with Microsoft AI for Earth, Google Research, and Wildlife Insights where she helps turn her research into usable tools for the ecological community. Sara’s experiences as a professional ballerina, a nontraditional student, and a queer woman have taught her the value of unique and diverse perspectives, both inside and outside of the research community. She is passionate about increasing diversity and inclusion in STEM through mentorship, teaching, and outreach.

Details

• Date: Thursday, March 24
• Time: 10:00 am - 11:00 am
• Category: Special Seminar
• Location: Grier A (34-401A)

Host

• Daniela Rus and Bill Freeman
• Email: fern@csail.mit.edu