Latest news Latest news

CMP and Earlham Institute researchers develop new phylogenetics software package

Together with researchers in the Earlham Institute, members of Phylogenetics group in CMP have developed SPECTRE, a new open-source software package, that simplifies the complex business of...

CMP New Funding Success

Prof Elena Kulinskaya (PI) with Prof Julia Koricheva (RHUL) and Dr Gillian Petrokofsky (Oxford) have been successful in the NERC 2017 Evidence Synthesis Training Pilot Scheme call. The...

Medical Research Council funding for pioneering NNUH and CMP collaboration

The Medical Research Council (MRC) has awarded £847,000 to an innovative collaboration between Mr John Phillips , an Ear, Nose and Throat Consultant at Norfolk and Norwich University...

Dr Nikoloulopoulos was invited speaker at second event of Data Science Norfolk

The  second event  of the Meetup Group Data Science Norfolk  took place on Thursday 11 May 2017 at the historic AVIVA Marble Hall at Norwich’s Surrey Street. ...

UEA CMP and MED scientists makes breakthrough in Prostate Cancer Research

New test which distinguishes prostate cancer 'tigers' from 'pussycats' developed by Prof Vincent Moulton (CMP) and Dr Bogdan Luca (CMP) collaborated with Prof Colin Cooper (MED) and Dr. Daniel...

Latest Events and Seminars Latest Events and Seminars


Practical approaches to teaching the CS theory course: nondecision problems and real computer programs

Date and Time: 4th October, 13:00-14:00

Location: SCI 0.31

Speaker: Dr. John MacCormick's seminar

Institution: Dickinson College, PA, USA

Organiser: Dr. Michal Mackiewicz



Computational and complexity theory are core components of the computer science curriculum, and in the vast majority of cases they are taught using decision problems as the main paradigm.  We present evidence that nondecision problems (such as optimization problems and search problems) are preferable, when the material is targeted for undergraduates encountering the theory of computation for the first time. We discuss some interesting reformulations of standard material (e.g., P and NP) that are required for this approach. In addition, we show how to maintain rigor while adopting real computer programs (written, for example, in Python) as the dominant computational model instead of Turing machines. Finally, since I will be visiting UEA for the next two years, I will mention some other projects and interests in computer vision, distributed systems, and the public understanding of computer science.