Title: Precise quantification of natural behavior with computer vision Abstract: To understand the neural control of movement, cognition, and social interaction, we need to precisely quantify motor behaviors. Deep learning tools now enable to extract meaningful behavioral signals from raw videos, in high spatiotemporal resolution. These technologies are gaining increasing adoption in system neuroscience and…

Title: Multimodal Learning from Pixels to People Presenter: Carl Vondrick Abstract: People experience the world through modalities of sight, sound, words, touch, and more. By leveraging their natural relationships and developing multimodal learning methods, my research creates artificial perception systems with diverse skills, including spatial, physical, logical, and cognitive abilities, for flexibly analyzing visual data. This multimodal…

Title: Mapping the landscape of social of social behavior using high-resolution 3D tracking of freely interacting animals Presenter: Ugne Klibaite, PhD Harvard University, Department of Organismic & Evolutionary Biology (PI, Bence P. Ölveczky) Abstract: Social interaction is a fundamental component of animal behavior. However, we lack tools to describe it with quantitative rigor, limiting our understanding of…

ARNI coPI Kathy McKeown and ARNI faculty Carl Vondrick participate in the Panel 1: Empirical and Technological Questions: Current Landscape, Challenges, and Opportunities Link: https://www.engineering.columbia.edu/symposium-generative-ai-free-speech-public-discourse Article: https://www.engineering.columbia.edu/news/navigating-generative-ai-and-its-impact-future-public-discourse?utm_source=newsletter&utm_medium=email&utm_campaign=highlights030124

Weekly Meeting Group Discussion: Paper Topic: https://arxiv.org/abs/2302.00487 Zoom: https://columbiauniversity.zoom.us/j/3658091817?pwd=WHFJVzAwbDdQcFMzc2FreVplKzVMUT09