All Publications

  • Elephants and algorithms: a review of the current and future role of AI in elephant monitoring JOURNAL OF THE ROYAL SOCIETY INTERFACE Brickson, L., Zhang, L., Vollrath, F., Douglas-Hamilton, I., Titus, A. J. 2023; 20 (208): 20230367


    Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behaviour and conservation strategies. Using elephants, a crucial species in Africa and Asia's protected areas, as our focal point, we delve into the role of AI and ML in their conservation. Given the increasing amounts of data gathered from a variety of sensors like cameras, microphones, geophones, drones and satellites, the challenge lies in managing and interpreting this vast data. New AI and ML techniques offer solutions to streamline this process, helping us extract vital information that might otherwise be overlooked. This paper focuses on the different AI-driven monitoring methods and their potential for improving elephant conservation. Collaborative efforts between AI experts and ecological researchers are essential in leveraging these innovative technologies for enhanced wildlife conservation, setting a precedent for numerous other species.

    View details for DOI 10.1098/rsif.2023.0367

    View details for Web of Science ID 001142505000003

    View details for PubMedID 37963556

    View details for PubMedCentralID PMC10645515

  • Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics. bioRxiv : the preprint server for biology Weinreb, C., Osman, M. A., Zhang, L., Lin, S., Pearl, J., Annapragada, S., Conlin, E., Gillis, W. F., Jay, M., Shaokai, Y., Mathis, A., Mathis, M. W., Pereira, T., Linderman, S. W., Datta, S. R. 2023


    Keypoint tracking algorithms have revolutionized the analysis of animal behavior, enabling investigators to flexibly quantify behavioral dynamics from conventional video recordings obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into the modules out of which behavior is organized. This challenge is particularly acute because keypoint data is susceptible to high frequency jitter that clustering algorithms can mistake for transitions between behavioral modules. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ("syllables") from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to effectively identify syllables whose boundaries correspond to natural sub-second discontinuities inherent to mouse behavior. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior, and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq therefore renders behavioral syllables and grammar accessible to the many researchers who use standard video to capture animal behavior.

    View details for DOI 10.1101/2023.03.16.532307

    View details for PubMedID 36993589

    View details for PubMedCentralID PMC10055085

  • Animal pose estimation from video data with a hierarchical von Mises-Fisher-Gaussian model Zhang, L., Dunn, T., Marshall, J., Olveczky, B., Linderman, S., Banerjee, A., Fukumizu, K. MICROTOME PUBLISHING. 2021