What do we do?
You can join the group of any of the 6 academics below. Each academic has offered a number of topics they are recruiting into.
Dima Damen: Professor of Computer Vision with research interests in egocentric vision, video understanding and fine-grained action understanding.
- Video Object Segmentations – semantic or instance segmentations
- Audio-visual fine-grained action recognition, localisation or retrieval
- Action Generalisation/Adaptation
- Problems using EPIC-KITCHENS or Ego4D datasets
Peter Flach: Professor of Artificial intelligence with research interests in evaluation and improvement of machine learning models, mining highly structured data, and human-centred AI.
- Classifier calibration
- Uncertainty representation and propagation
- Knowledge-intensive AI
- Explainability and interpretability
Majid Mirmehdi: Professor of Computer Vision with research interests in human and animal behaviour understanding and medical image/volume analysis
- Human action understanding and assessment, e.g. in healthcare for action quality scores
- Animal action analysis and understanding using camera-trap, drone for other footage
- Segmentation, Classification, and Prediction in medical images and volumes
Raul Santos-Rodriguez: Associate Professor in Data Science and AI with research interests in the foundations of (human-centric) machine learning and its applications to healthcare and climate science.
- Explainability, transparency and fairness
- Weakly supervised learning
- Human visual perception in machine learning
- Human/agent interaction and collaboration
Michael Wray: Assistant Professor of Computer Vision with research interests in video understanding and Natural Language Processing.
- Video Corpus Moment Retrieval
- Episodic Memory for querying long form videos
Zahraa S. Abdallah: Assistant Professor in Data science and Machine Learning with research interest in time series and its healthcare applications and learning from multiple modalities.
- Time series analysis (classification, clustering, explainability)
- Adaptive and active learning
- Genomic data analysis e.g., protein analysis for early detection of cancer
- Data fusion and multi-modalities for combining various types of data sources