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Biography
Jacob Chalk is a Senior Research Associate at the University of Bristol and a member of the MaVi group. His research focuses on Multimodal Learning, aiming to develop methods that integrate complementary sources of information to better understand dynamic real-world environments. Previously, his thesis focused on leveraging multimodal data for egocentric video understanding, including audio-visual learning, anticipating upcoming object interactions using eye-gaze and 3D information, and long-term 3D dynamic object tracking.
Publications
Recent publications
25/09/2026Whareformer: European Conference on Computer Vision - Lecture Notes in Computer Science
European Conference on Computer Vision
HD-EPIC: IEEE/CVF Computer Vision and Pattern Recognition - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Spatial Cognition from Egocentric Video: International Conference on 3D Vision - International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)
2025 International Conference on 3D Vision (3DV)
EPIC-SOUNDS: A Large-Scale Dataset of Actions that Sound
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
TIM: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Thesis
Leveraging Multimodal Data for Egocentric Video Understanding
Supervisors
Award date
20/01/2026
Teaching
For Software Product Engineering (Year 2) and Games Project (Year 3), Jacob supported group-based project work. He mentored assigned student teams and provided guidance on technical issues, software design decisions, development practices, and project organisation. His role involved responding to team queries during scheduled sessions and supporting progress towards the project goal(s).
For Computer Graphics (Year 3) and Image Processing and Computer Vision (Year 3), Jacob acted as a laboratory assistant. During lab sessions, he answered technical and coursework-related questions, clarified concepts introduced in lectures, and provided guidance on implementation and debugging tasks. His involvement was focused on supporting students during hands-on practical work.
In Applied Deep Learning (Year 4), Jacob provided laboratory assistance and supported students with model implementation, experimentation, and interpretation of results. He also contributed to the development of coursework and participated in marking and feedback.
