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Centre for Machine Intelligence

Who We Are | Centre for Machine Intelligence

We are a world-leading group of researchers, thinkers, and entrepreneurs.

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The Centre for Machine Intelligence was initiated by the department of Electronics and Computer Science and is a centre of expertise for Artificial Intelligence, Machine Learning, and Autonomous Systems. We work with industry, policy makers, and the general public to address both national and global challenges.

Centre for Machine Intelligence Board

Enrico Gerding

Dr Enrico Gerding is an Associate Professor. He specialises in Game Theory and Mechanism design and consults for major e-commerce and smart transportation companies.

Corina Cirstea

Corina Cirstea is a lecturer and holds a DPhil in Computation from the University of Oxford (2000). She was the holder of a Junior Research Fellowship in Computer Science at St. John's College Oxford prior to joining Southampton.

Jonathon Hare

Jonathon's research interests lie in the area of multimedia data mining, analysis and retrieval, with a particular focus on large-scale multimodal approaches. This research area is at the convergence of machine learning and computer vision.

Shiyan Hu

Shiyan is a Professor in Cyber-Physical System Security and the director of Cyber Security Academy. His main research interests include cyber-physical system security, and machine learning for cyber-physical systems.

Kate Farrahi

Kate is an assistant professor focused on machine learning, particularly deep learning, often applied to health-related applications. She is interested in developing machine learning techniques to address novel sensor-driven healthcare problems.

Luis-Daniel Ibáñez

Luis-Daniel is a Lecturer. He has interests in Data Science support systems including Dataset Search and Data Mining and analysis for non-experts

Steering Committee

The members of the committee include respected academics and representatives from Industry. Under their guidance, the CMI aims to broaden its impact and reach out to communities employing machine intelligence in their research and practice. Our current members are:

  • Themis Prodromakis 
  • Stephen Prior
  • Steve Beeby
  • Tim Norman
  • Michalis Zervas
  • Koushik Maharatna

 

Related Staff Member

Steffen Staab 

Professor for Web and Computer Science

My Research: How can computers know something about the world? In my research I am interested in how computers can have explicit or implicit knowledge. Explicit knowledge can be organized in huge knowledge graphs. They may capture data bases, but typically go beyond in expressivity and range. I research how to manage knowledge graphs and how to exploit them by querying or machine learning. Implicit knowledge may be derived from data and in particular from text. There, I am interested how to uncover implicit knowledge using text and data mining, information extraction and text clustering.

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Related Staff Member

Dr. Danesh Tarapore 

Lecturer

My research: Dr. Tarapore’s research is interdisciplinary in nature, focused on understanding biological systems, and developing bio-inspired approaches to the design of fault-tolerant and resilient robots. Through the development and application of theoretical models, agent-based models, multi-scale stochastic simulations, and the physical instantiation of biological models in robots, he has addressed a diverse range of important problems in task allocation and division of labour in large-scale multiagent systems, immunological maintenance of self-tolerance in the adaptive immune system, and fault-detection and fault-tolerance for both single-robot systems, and large-scale robot swarms. Through addressing such diverse problems, Danesh has gained expertise in a wide range of fields besides robotics, including: bio-inspired learning algorithms, swarm intelligence, robot behaviour representation, and evolutionary robotics.

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Related Staff Member

Dr. Jie Zhang 

Lecturer

Jie Zhang works on fundamental aspects of Algorithmic Game Theory. It involves the design of algorithms for effectively and efficiently allocating limited resources, as well as determining payments, in order to meet desirable objectives such as fairness, profit maximisation and to incentivise good behaviour. It employs mathematical rigor tools to evaluate the designed mechanisms from the worst- and average-case perspectives, and conduct analyses that continuously interpolates between these two.

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Related Staff Member

Prof. Mike Wald 

Professor of Computer Science

Professor Mike Wald leads research into accessible technologies in the Web and Internet Science Group, ECS, University of Southampton. He has had many years of experience working with disabled users in research, development, support and provision of knowledge in the areas of disability, assistive technology and digital accessibility. He was a founder member of the International Liberated Learning Consortium that included leading universities and organisations investigating how speech recognition captioning and transcription could be enhanced. Over 20,000 people in more than 150 countries have enrolled on his online inclusion and digital accessibility courses.

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Related Staff Member

Dr. Sebastian Stein 

Associate Professor

Dr Sebastian Stein’s research focusses on resource allocation problems in multi-agent systems, where he is applying techniques from mechanism design, incentive engineering and decision-making under uncertainty. He is passionate about applying his work to addressing important societal challenges and he has worked on electric vehicle (EV) charging, intelligent transportation systems, crowdsourcing , participatory sensing for pollution monitoring and cloud systems.

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Related Staff Member

Dr. Stuart Middleton 

Senior Research Engineer

My research interest lies in the area between natural language processing and information extraction, developing novel algorithms to discover and exploit patterns in free text and metadata to extract actionable human intelligence and machine-readable knowledge. In a juxtaposition to deep learning and big data approaches, my research has focussed on developing novel solutions to problems where training sets are small, sparse or fragmented in nature. I am interested in investigating emergent patterns in language use by communities, how domain knowledge and expert feedback can bootstrap algorithms and approaches that can preserve domain semantics, provenance and promote explainable AI.

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Related Staff Member

Prof. Kirk Martinez 

Professor of Computer Science

My research creates solutions to environmental sensing problems. Previous research includes novel sensor networks in glaciers using new networks and decision making algorithms. Current research is into semi-autonomous UAVs aiding in the deployment and recovery of sensor network nodes on glaciers in Iceland.

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