Download A study note for students

Study Information for PhD students
Yun-Heh Chen-Burger
26 September 2010
This is a coarse grained PhD study framework and orientation document to help one carry out PhD studies. This
framework is aimed to provide a structure to help one understands where he/her is in his/her PhD research path as well
as what to do next and expect in the future. Naturally, everyone is different in his/her own study agenda. This is only
one example that may be considered.
In addition, you will already have been offered official guidance from Informatics as below:
http://wcms.inf.ed.ac.uk/pgrguide/handbook/manual/first-year.
If there is any contradiction between the above document and this document, the above official Informatics document
takes precedence.
There are fundamental qualifications that will be required for a PhD study:
 Adequate English ability, including reading, writing, listening and speaking skills;
 Adequate technical ability, this includes a good foundation of AI or CS knowledge, and programming
abilities;
 Self-motivation and commitment: it is important to be self-motivated; as PhD studies, unlike other studies,
require a lot of own initiatives.
Other important qualities that will help become a good PhD student are:
 Organisational skills – as a part of studies you will be working independently (under supervision) and with
materials of different sources.
 Time management
 Research skills: this is about the ways to find out about relevant information, organise them in useful fashions
that is supportive to your interested areas;
 Creativity: PhD research requires an individual to be creative, i.e. the ability to identify the strength and
weakness of existing methods and propose and implement novel techniques that offer new advantages over
existing methods, again, this is very different from undergraduate or MSc studies where creativity is less
critical,
 Confidence, and
 Communication skills, e.g. to explain your research to other people of different level of understanding of your
research areas and being able to understand what others are conveying to you, give pubic talks, write technical
reports and research papers.
You also need to have technical skills that are necessary for pursuing your study. All of them need to be at a proficient
level, e.g.
 Logic;
 Knowledge representation;
 (Logic) proof theory;
 Programming skills, e.g. Java, Prolog;
 Logic reasoning skills;
 Modelling abilities.
Other relevant info for studying for a PhD degree:
 Univ. requirements for English proficiency: http://www.scieng.ed.ac.uk/International/pgbefapp.html);
 Postgraduate study regulations: http://www.cpa.ed.ac.uk/calendar/Postgraduate.pdf and
http://www.drps.ed.ac.uk/.
Study periods for PhD students:
The standard studying periods for a PhD program are shown below:
Full time:
Prescribed is 36 months
Maximum is 48 months
(The thesis must be submitted at the end of the 4th year)
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Part Time
Prescribed is 48 months
Maximum is 60 months
(The thesis must be submitted at the end of the 5th year)
Basic Readings:
 Artificial Intelligence: A Modern Approach, by Stuart Russell, Peter Norvig, Prentice Hall.
 Simply Logical: Intelligent Reasoning by Example, by Peter Flach, John Wiley and Sons Ltd., 1994.
 PROLOG Programming for Artificial Intelligence, by Ivan Bratko, ISBN: 0-201-40375-7,
http://cwx.prenhall.com/bookbind/pubbooks/bratko3_ema/chapter0/deluxe.html
Phase I: Preparing for study
If you are brand new to the field of AI studies, it is recommended that you start with more introductory material that
have detailed explanations and examples, such as undergraduate and MSc level study materials. This is because
advanced readings often omit the fundamental and important information – it assumes that you knew it already.
1.
Subscribe yourself to relevant seminar mailing lists, both internal and external ones, e.g. the CISA seminar
list, the [email protected] (open to external subscribers, but maintained by AIAI/CISA), etc.
2. Familiar with logic theories, e.g. propositional, first order logic, (set theory, categories theory)
3. Familiar with logical computational languages, e.g. Prolog
4. Familiar with fundamental AI techniques, e.g. knowledge representations, knowledge based systems. For
example, using the AI3/Msc module lecture notes/slides as a study framework aided with additional book
reading recommended by the module.
5. Familiar with the local computing environment, e.g. Linux, PC
6. Familiar with the local computing tools, e.g. emailing system, emacs, power points, (Latex, Microsoft Words),
see computing FAQ here: http://www.inf.ed.ac.uk/systems/support/FAQ/#AC1.
7. Familiar with other relevant computational languages, e.g. JAVA
8. Familiar with fundamental research techniques, e.g. see Alan Bundy’s: know-how pages at:
http://homepages.inf.ed.ac.uk/bundy/how-tos/how-tos.html and “You and Your Research” by Hamming:
http://www.aiai.ed.ac.uk/~jessicac/project/student-study-note/hamming%20research%20method.pdf.
9. Consider Sitting-in some MSc courses, e.g. Fundamentals of AI (FAI), Knowledge Representation (KR),
Logical Programming, Knowledge Engineering (KE), and/or AI Programming in Java (AIPJ).
10. Consider attending other courses held by the University, e.g. the transferable skills workshops for (PhD) thesis
workshop, Managing your PhD (new induction course, first run in Nov 2007), research methodologies,
effective writing, teamwork and leadership, as well as Informatics internally run how to be a tutor workshop.
Phase II: Focus of study
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Identify, understand and familiar with a problem domain for research, this may be a theoretical problem or
more of an applied research nature
Identify key characteristics in the problem domain
Identify relevant existing solutions/methods to the problem
Compare the different solutions and identify the gap that has not been bridged
Working towards to bridge the gap in research
Milestone: Problem domain analysis – what is the problem that you are trying to tackle?
Milestone: State of the Art document – what are the existing techniques that offer possible solutions to your
problem? What is the research gap? Can you propose possible solutions?
Phase III: Embarking research: orientation
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Write a short paper to illustrate the problem domain in more details that
Illustrates the relevant solutions/methodologies/techniques that may solve this problem;
Categories those methods and compare and evaluate them against a set of chosen criteria;
Identify the gap between existing solutions, and propose ways to bridge the gap.
write-up a thesis proposal, this is a requirement of Informatics.
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Milestone: by the 6th month of the study, a PhD Thesis Proposal is required to be submitted to the
school.
Milestone: 1st year PhD Thesis Proposal talk – a public talk to Informatics. This talk will be evaluated
by a reviewing panel which will make recommendations to the progression of the student.
Milestone: a workshop paper to be published in an European/international conference.
Milestone: read Alan Bundy’s “The Researcher’s Bible” at: http://homepages.inf.ed.ac.uk/bundy/howtos/resbible.html.
Milestone: registered for the PhD thesis workshop.
Phase IV: Embarking research: developing solution
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Identify and provide a more detailed proposal for the different possible ways to bridge the gap, also provide
reasoning for the advantages and disadvantages for each approach.
Choose a likely successful route and investigate ways to accomplish the goal using this route.
Construct a small test case and test the proposal using it. If necessary, refine the proposal accordingly.
Develop the PhD thesis proposal, e.g. giving a conceptual framework, modelling methods, proposal of formal
languages, conceptual system architecture.
Ask yourself this question: is your research unique and novel? Does your research have significant
contribution to the field?
Milestone: system architecture and design document.
Milestone: an official annual progress report by the end of 2nd year.
Milestone: an internal public CISA talk, required by CISA.
Milestone: a conference paper to be published in an European/international conference.
Phase V: Embarking research: developing a Proof of Concept (POC)
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Based on the conceptual framework, build a computing system that realises those ideas to solve the intended
problems.
Evaluate the system: construct an evaluation framework and carry out (formal) evaluation, either on
theoretically or empirically ground or both, as appropriate.
Thesis write-up.
Ask yourself this question: is your research unique and novel? Does your research contribute significant new
knowledge to the field?
Milestone: an official annual progress report at the end of 3rd year
Milestone: a conference paper to be published in an European or international conference.
Milestone: an outline (and draft chapters) of your thesis.
Phase VI: After three years
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Finishing writing up thesis.
Complete and sent an “Intent to Submit” form to the Graduate School well in advance of a proposed Viva date
(e.g. 3 months in advance).
Attend and pass a PhD Viva.
Relax and have a post-viva celebration!!!
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Milestone: By the end of the 3rd year, submit a PhD thesis to the School.
Milestone: Attend and pass a PhD viva.
Goal: to develop into an independent researcher and can direct a team of researchers after graduation.
MANY CONGRATULATIONS!! YOU HAVE DONE IT.
Administration Information:
1. Thesis binding services: http://www.lib.ed.ac.uk/services/bindser.shtml.
2. Informatics and Graduate School: http://www.inf.ed.ac.uk/admin/IGS/
Specialised References:
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Other information: http://www.aiai.ed.ac.uk/~jessicac/project/student-study-note/
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