Talk Title: Towards Global-Scale Biodiversity Monitoring – Scaling Geospatial and Taxonomic Coverage Using Contextual Clues
Talk Abstract: Biodiversity is declining globally at unprecedented rates. 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. Biodiversity data is correlated in time and space, resulting in overfitting and poor generalization to new sensor deployments. Environmental monitoring sensors have limited intelligence, resulting in objects of interest that are often too close/far, blurry, or in clutter. Further, the distribution of species is long-tailed, which results in highly-imbalanced datasets. These challenges are not unique to the natural world, advances in any one of these areas will have far-reaching impact across domains. To address these challenges, we take inspiration from the value of additional contextual information for human experts, and seek to incorporate it within the structure of machine learning systems. Incorporating species distributions and access across data collected within a sensor at inference time can improve generalization to new sensors without additional human data labeling. Going beyond single sensor deployment, there is a large degree of contextual information shared across multiple data streams. Our long-term goal is to develop learning methods that efficiently and adaptively benefit from many different data streams on a global scale.
Bio: Sara Beery has always been passionate about the natural world, and she saw a need for technology-based approaches to conservation and sustainability challenges. This led her to pursue a PhD at Caltech, where she is advised by Pietro Perona and funded by an NSF Graduate Research Fellowship, a PIMCO Fellowship in Data Science, and an Amazon/Caltech AI4Science Fellowship. Her research focuses on computer vision for global-scale biodiversity monitoring. She works closely with Microsoft AI for Earth and Google Research to translate her work into usable tools, including widely-used models and benchmarks for detection and recognition of animal species in challenging camera trap data at a global scale. She has worked to bridge the interdisciplinary gap between ecology and computer science by hosting the iWild-Cam challenge at the FGVC Workshop at CVPR from 2018-2021, and through founding and managing a highly successful AI for Conservation slack channel which provides a meeting point for experts from each community to discuss new methods and best practices for conservation technology. Sara’s prior experience as a professional ballerina and a nontraditional student has taught her the value of unique and diverse perspectives in the research community. She’s passionate about increasing diversity and inclusion in STEM through mentorship and outreach.