Research Methods
Organised by Michael Zakharyaschev
Department of Computer Science and Information Systems
Birkbeck University of London
1 Aims
This is a short course on research methods for research in
Computer Science
and Information Systems. The course does not have any
coursework but it is part of our training programme for research
students and
research staff.
Attendance is compulsory for all first-year research students, both
full-time
and part-time, as well as MRes students. Other research students and
research staff are most welcome to
attend.
The course will start on 18 October 2011 and will be held in Room 250 in the Department of Computer Science and Information Systems on Tuesdays, or in
SHA07, Shakespeare College, 34 Bloomsbury Street, WC1B 3QJ on Wednesdays. The first lecture is followed by
refreshments, giving an opportunity for students and staff to meet
each other.
2 Outline of Lectures
- Lecture 1: 18 Oct. 2011, 18:00-19:00, Room 250. Introduction (followed by drinks and snacks) - Mark Levene.
- Lecture 2: 26 Oct. 2011, 18:00-19:00, SHA07, Shakespeare College. Information Systems - Dave Wilson.
- Lecture 3: 26 Oct. 2011, 19:15-20:15, SHA07, Shakespeare College. Data Research Methods in Computer Vision - Steve Maybank.
- Lecture 4: 1 Nov. 2011, 18:00-19:00, Room 250. Logic and Language Theory Part I - Peter Wood.
- Lecture 5: 1 Nov. 2011, 19:15-20:15, Room 250. Bioinformatics - Nigel Martin.
- Lecture 6: 8 Nov. 2011, 18:00-19:30, Room 250. Logic and Language Theory Part II - Alex Poulovassilis.
- Lecture 7: 8 Nov. 2011, 19:45-20:45, Room 250. Machine Learning- Dell Zhang.
- Lecture 8: 15 Nov. 2011, 18:00-19:00, Room 250. Simulation - George Magoulas.
- Lecture 9: 15 Nov. 2011, 19:15-20:45, Room 250. Logic and Language Theory Part III - Roman Kontchakov.
- Lecture 10: 23 Nov. 2011, 18:00-21:00, SHA07, Shakespeare College. Theoretical Computer Science - Trevor Fenner.
- Lecture 11: 29 Nov. 2011, 18:00-20:00, Room 250. Ubiquitous and Pervasive Computing- George Roussos.
- Lecture 12: 7 Dec. 2011, 18:00-19:00, SHA07, Shakespeare College. What Makes Good Research in Software Engineering? - Keith Mannock.
- Lecture 13: 7 Dec. 2011, 19:15-20:15, SHA07, Shakespeare College. Empirical Research Methods in Software Engineering. - Keith Mannock.
- Lecture 14: 17 Jan. 2012, 17:00-18:00, Ground Floor, London Knowledge Lab, 23-29 Emerald Street, London, WC1N 3QS. Reviewing the Research Literature - Alex Poulovassilis.
- Lecture 15: 24 Jan. 2012, 17:00-18:00, Ground Floor, London Knowledge Lab, 23-29 Emerald Street, London, WC1N 3QS. Writing Papers and the Review Process - Alex Poulovassilis.
- Lecture 16: 31 Jan. 2012, 17:00-18:00, Ground Floor, London Knowledge Lab, 23-29 Emerald Street, London, WC1N 3QS. Writing the PhD Thesis and the PhD Examination Process - Alex Poulovassilis.
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Topics covered: How to get a PhD. The CSIS Birkbeck PhD programme. Developing a research proposal. Planning your research. The wider community. Resources and Tools. Having a grounding in Computer Science. Career development.
Topics covered: Ontology and epistemology of IS research viz-a-viz Research in Computer Science. Common Methods in IS Research: Case study, Action research, Ethnography. Mixing methods in a research project.
Topics covered: image compression, the discrete cosine transform and its application to image compression, linear classification, the Fisher linear discriminant, Markov random fields and the application of MRFs to the updating of measurements using prior information.
Topics covered: Automata and formal languages, with applications to database research.
Topics covered: What is bioinformatics. Genomics and proteomics. Bioinformatics techniques and resources. Challenges for the computer scientist: biological data management, biological data analysis. Current research themes and approaches.
Topics covered: Lambda calculus, domain theory, denotational semantics, functional programming, analysis and optimisation.
Topics covered: What is machine learning and how does it relate to other disciplines; Basic concepts and techniques of machine learning illustrated with examples; Various applications of machine learning.
Topics covered: The computer simulation approach: Importance of models; What is simulation?; Time and randomness in simulation; Applications of simulation; How a simulation model works? : The process of simulation; The elements of a simulation model; Performing simulation studies; Examples.
Topics covered: Logical systems and complexity of reasoning.
Topics covered: Design and analysis of algorithms. Computational complexity and computability. Formal models of computation. Mathematical models: discrete mathematics, graph theory, probability.
Topics covered: The ubiquitous computing paradigm, elements of ubiquitous computing, auto-identification, sensing, actuation, networking, trust, applications, conducting experimental research in ubiquitous computing.
Topics covered: Problem selection, research paradigm, and validation of results.
Topics covered: Basics of Doing Research, Experiments, Case Studies, and Analysis Methods.
Topics covered: Finding out about your research area. Literature search strategy. Writing critical reviews. Identifying venues for publishing your research. Preparing and submitting research papers.
Topics covered: The conference review process. Making use of the referees' reports. Preparing and presenting your paper. The journal review process. Group exercise in reviewing research papers.
Topics covered: Planning the thesis. Writing the thesis. Thesis structure. Writing up schedule. The Oral examination.


