Research Areas

Computational Biology

Computational Biology aims to solve some of the most pressing problems in biology by developing new techniques in computer science. Research in computational biology and bioinformatics at UEA spans the biological hierarchy from the genome through to the ecosystem.

In addition to carrying out high quality research with national and international partners, members of the UEA laboratory collaborate closely with scientists across Norwich Research Park, including experts in bioinformatics and systems biology based at The Genome Analysis Centre and The John Innes Centre.

 

Graphics, Vision and Speech

Developing machines with the same (or enhanced) capabilities as humans has been an exciting and challenging research theme in computer science for decades.  To accomplish this task, computers must process and generate audio and visual signals.

The Graphics, Vision and Speech Laboratory is concerned with the analysis, processing, recognition and generation of these signals in applications such as colour vision, machine vision, computer graphics, avatars and speech, music and language processing.  These technologies have many common theoretical foundations that include signal-processing, machine learning, statistical pattern recognition, time-series estimation, automata theory etc.  Each area within the laboratory is an internationally-leading research group in its own right, and there is collaboration and synergy between them in research in several application areas.

Machine Learning, Statistics and Data Mining

Making sense of increasing amounts of data is one of the most pressing needs in today's society.  Statistics which has its roots in mathematics and economics, and data mining with its roots in computing science, are devoted to the collection, analysis, interpretation or explanation, and presentation of data. Machine learning is concerned with the design of computer programmes that can improve their performance by learning from experience and is connected to both Statistics and Data Mining.

The Machine learning and Statistics group has experts in those 3 related disciplines.  They work in many theoretical aspects of data analysis as well as applying their expertise in a variety of interdisciplinary areas, often in collaboration with the schools of Medicine, Biology, Environmental Sciences and with industrial partners.