Da Chen

Dr Da Chen

Dr. Da Chen is currently a Lecturer in the Department of Computer Science at the University of Bath. Prior to this, he was a Postdoc researcher in a joint project between Alibaba Group and The Institute of Automation, Chinese Academy of Sciences.

His research focuses on computer vision, machine learning, multimodal learning, and related areas. He is particularly interested in solving complex practical tasks under “limited” data conditions, such as learning with limited labelled data, out-of-distribution data, cross-domain learning, incremental learning, etc. To this end, he has proposed multiple solutions for few-shot learning image classification, out-of-distribution detection, and other tasks. He is also interested in video-related computer vision and multimodal learning tasks such as video summarization, video object detection, dense video captioning, etc.

Research interests:
  • Computer vision, deep learning, multimodal learning: general CV/ML tasks in practical scenarios.
  • Learning with limited data: Learning with imbalanced data, long-tailed data, open-set data, out-of-distribution data, etc; incremental learning, few-shot learning, etc.
  • Video processing: multimodal learning with video with visual, textual, and audio information.


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Professor Özgür Şimşek

Özgür’s research is on artificial intelligence and machine learning, with emphasis on reinforcement learning. She is particularly interested in open-ended learning in complex, dynamic, uncertain environments. Areas of interest include: 1) Statistical properties of natural environments that enable fast, effective learning, 2) Autonomous construction of hierarchies of reusable skills, 3) Intrinsic motivation and curiosity.


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Dr Julian Padget

Julian’s main research focus is the use of formal approaches to validation and verification with computational logic-based models. Application domains include legal reasoning, checking and monitoring security policies, investigating interactions between policies and their automatic revision (inductive logic programming), gaming, virtual and mixed environments and agent-based modelling.


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Professor Eamonn O’Neill

Eamonn’s research has the overarching goal of developing an applied science of human-computer interaction (HCI). This involves developing a sound theoretical footing for HCI and deriving design principles for the development of human-computer systems that are theoretically well-founded, empirically tested and operationalised for people’s use.


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Dr Tom Fincham Haines

Tom applies machine learning (ML) to a wide selection of problems, particularly those involving computer visions and graphics. He has strong interests in graphical models, Bayesian non-parametric models, directional statistics and active learning, and working on projects involving tools to help artists, ML for education, online/realtime ML, causality, and scaling non-parametric methods to big data.


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Professor James Davenport

James’s main research interest is computer algebra, especially symbolic integration, simplification and equation solving. One specific application has been using computer algebra to generate numerical code. He has side-interests in efficient parallelism, electronic mathematical publishing and “mathematics on the (semantic) Web”, robot motion planning and cryptography, especially cracking US public-key cryptosystems.


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