Alliance MBS Big Data Forum

The Big Data Forum is hosted by the Alliance Manchester Business School, bringing together a number of experts in Big Data from within and outside the school. The Big Data Forum primary aim is to bring together the multitude of existing activities within the school related to theories, techniques and applications of Big Data.

Alliance Manchester Business School is uniquely positioned in the field of business applications of Big Data models and techniques, indeed for historical reasons and due to its size it brings together expertise covering not only different application areas such as finance, marketing, innovation and operations, but also methodological expertise including data management, and algorithm design.

The Big Data Forum has the following four objectives:

  1. To foster communication of ideas and project results within and outside Alliance MBS
  2. To facilitate collaboration in this area within the school and with outside stakeholders
  3. To support the conception and development of research grant and contract proposals
  4. To disseminate work in Big Data done in Alliance MBS to outside stakeholders

In pursuit of these objectives, they organise a range of activities including dissemination events, technical workshops and collaboration meetings with external organisations. At the level of the University of Manchester, the Big Data Forum collaborates with the Data Science Institute and Manchester Informatics initiatives.

Main Contacts:

Professor Nikolay Mehandjiev

Nikolay is a Professor of Enterprise Information Systems at the Alliance Manchester Business School. He specialises in researching approaches and models which enable non-technical audiences to design dynamic service systems using intelligent software systems and formalised domain knowledge. Key techniques he uses are intelligent software systems and formalised domain knowledge, and an example of a key result is a recursive model linking goals and processes involved in setting up and coordinating the work of dynamic collaborative organisations. His research is grounded in the needs of industrial partners, collaborating extensively within the telecommunications, IT, e-commerce, automotive and energy sectors such as BT Research Labs, BG Technology, SAP and IBM.

Dr Julia Handl

Julia's research interests relate to the development and application of advanced analytical techniques (concretely, optimization methods, machine learning and simulation) for complex real-world problems. Many of the methods she works with have applications across disciplines, and current cross-faculty collaborations include work with the School of Computer Science and the Faculty of Life Sciences. Her publications span both theoretical and empirical work related to the multiobjective formulation of a variety of different problems including unsupervised clustering, semi-supervised classification, feature selection and protein structure prediction.