Title: 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.