Pencinta ilmu (Guna Chrome atau Firefox)

Isnin, 23 Julai 2012

I Won't Hire People Who Use Poor Grammar. Here's Why.

If you think an apostrophe was one of the 12 disciples of Jesus, you will never work for me. If you think a semicolon is a regular colon with an identity crisis, I will not hire you. If you scatter commas into a sentence with all the discrimination of a shotgun, you might make it to the foyer before we politely escort you from the building.

Some might call my approach to grammar extreme, but I prefer Lynne Truss's more cuddly phraseology: I am a grammar "stickler." And, like Truss — author of Eats, Shoots & Leaves — I have a "zero tolerance approach" to grammar mistakes that make people look stupid.

Now, Truss and I disagree on what it means to have "zero tolerance." She thinks that people who mix up their itses "deserve to be struck by lightning, hacked up on the spot and buried in an unmarked grave," while I just think they deserve to be passed over for a job — even if they are otherwise qualified for the position.

Everyone who applies for a position at either of my companies, iFixit or Dozuki, takes a mandatory grammar test. Extenuating circumstances aside (dyslexia, English language learners, etc.), if job hopefuls can't distinguish between "to" and "too," their applications go into the bin.

Of course, we write for a living. iFixit.com is the world's largest online repair manual, and Dozuki helps companies write their own technical documentation, like paperless work instructions and step-by-step user manuals. So, it makes sense that we've made a preemptive strike against groan-worthy grammar errors.

But grammar is relevant for all companies. Yes, language is constantly changing, but that doesn't make grammar unimportant. Good grammar is credibility, especially on the internet. In blog posts, on Facebook statuses, in e-mails, and on company websites, your words are all you have. They are a projection of you in your physical absence. And, for better or worse, people judge you if you can't tell the difference between their, there, and they're.

Good grammar makes good business sense — and not just when it comes to hiring writers. Writing isn't in the official job description of most people in our office. Still, we give our grammar test to everybody, including our salespeople, our operations staff, and our programmers.

On the face of it, my zero tolerance approach to grammar errors might seem a little unfair. After all, grammar has nothing to do with job performance, or creativity, or intelligence, right?

Wrong. If it takes someone more than 20 years to notice how to properly use "it's," then that's not a learning curve I'm comfortable with. So, even in this hyper-competitive market, I will pass on a great programmer who cannot write.

Grammar signifies more than just a person's ability to remember high school English. I've found that people who make fewer mistakes on a grammar test also make fewer mistakes when they are doing something completely unrelated to writing — like stocking shelves or labeling parts.

In the same vein, programmers who pay attention to how they construct written language also tend to pay a lot more attention to how they code. You see, at its core, code is prose. Great programmers are more than just code monkeys; according to Stanford programming legend Donald Knuth they are "essayists who work with traditional aesthetic and literary forms." The point: programming should be easily understood by real human beings — not just computers.

And just like good writing and good grammar, when it comes to programming, the devil's in the details. In fact, when it comes to my whole business, details are everything.

I hire people who care about those details. Applicants who don't think writing is important are likely to think lots of other (important) things also aren't important. And I guarantee that even if other companies aren't issuing grammar tests, they pay attention to sloppy mistakes on résumés. After all, sloppy is as sloppy does. That's why I grammar test people who walk in the door looking for a job. Grammar is my litmus test. All applicants say they're detail-oriented; I just make my employees prove it.

- Kyle Wiens is CEO of iFixit, the largest online repair community, as well as founder of Dozuki, a software company dedicated to helping manufacturers publish amazing documentation.

Khamis, 14 Jun 2012

Analisa NVivo - Panduan awal

Banyak maklumat boleh diperolehi berkenaan perisian NVivo dan cara-cara menggunakannya. Bagi saya, NVivo sangat mudah jika anda faham cara manual untuk menganalisa data kualitatif. Perisian akan memudahkan lagi kerja pengurusan data kualitatif yang begitu banyak berbanding data kuantitatif.


Buku yang saya mula rujuk pada awal PhD saya enam tahun lepas ialah tulisan Pat Bazeley. Di samping itu juga saya menghadiri kursus 2 hari pengenalan kajian kualitatif dan NVivo oleh pensyarah UUM di UIAM. Kursus itu nampaknya hanya sebagai ulangan dari apa yang telah dibaca kerana pelajar-pelajar yang hadir merupakan kosong atau permulaan pengetahuan mengenai kajian kualitatif dan NVivo.




Di bawah ini, saya sertakan tajuk-tajuk dalam buku Pat Bazeley:


1 Perspectives: Qualitative computing and NVivo
Qualitative research purposes and NVivo
Issues raised by using software for qualitative data analysis
What does an NVivo project look like?
About this book

2 Starting a project
Starting
Starting with software
Saving your project

3 Making data records
Data for your project
Data in cases
Data preparation
Data records in NVivo
Managing data sources in NVivo

4 Working with data
Goals for early work with data
Gaining perspective on the text
Building knowledge of the data through coding
Storing coding in nodes
Reflecting on the case

5 Connecting ideas
Development of the coding system
Making connections across trees
Coding in practice
Managing coding

6 Managing data
Managing data sources
Bringing demographic or other quantified data into your analysis
Scoping queries

7 The ‘pit stop’
Seeing data afresh in nodes
Searching text
Revisiting the literature
Pausing to ‘play’ with models
The periodic pause

8 Going further
The analytic journey
Queries in NVivo
Starting the journey…
Going further with cases
Going further with concepts
Going further with narrative and discourse
Using numerical counts
Going further into theory building
Moving on – further resources

Selamat menganalisa data

~yba~

Sabtu, 2 Jun 2012

Analisa SPSS - Permulaan

Di bawah ini saya sertakan nasihat, tip dan prosedur dari Julie Pallant yang perlu pelajar dan pengguna SPSS fikirkan sebelum memulakan analisa data.

Starting your data analysis

Most students get quite excited when they finish entering data and they have a data file to analyse. However, before diving in to address all your research questions there are a few things you need to do first. I have listed these below, along with the related chapter in the SPSS Survival Manual

Check the characteristics of the subjects that make up your sample. You will need this information for the method section of your report. Chapter 6
Check all the variables in your data file for errors (particularly out-of-range values). Chapter 5
Obtain descriptive statistics for each of the variables you will be using in your study. These should include means, standard deviations, kurtosis, skewness, and minimum and maximum values. Check that these values are appropriate. Chapter 6
Check the distribution of scores on each of your variables—depending on the variable, you will need to use histograms, boxplots, bar graphs or stem and leaf plots. Look out for very skewed distributions or any unusual pattern of scores. Also check for extreme outliers—these can affect some analyses and may need to be recoded or removed. Chapter 5, 6, 7
Perform the necessary data manipulation procedures (e.g., recode, compute) to create any new variables you need. This is important when creating total scores on a scale, or collapsing down a variable into a smaller number of categories. Afterwards, always run Frequencies on these new variables to check that the procedure has been done correctly. Chapter 8
Check the reliability of the scales you intend using in your analyses. What are the Cronbach alpha values for each scale? How do these results compare to those reported in the literature? Chapter 9
For your continuous variables, check the pattern of intercorrelations. How strongly and in which direction are your variables related? How does this compare with the results reported in the literature? You may also need to obtain scatterplots of the correlation between pairs of your major variables. These are useful for checking for linear relationships between variables. Chapter 11
When choosing which statistical technique to use for your analysis, always check that you have the right type of variables (categorical/continuous). Consider whether a parametric or a non-parametric technique is the most appropriate. Chapter 10
Check with your statistics books and the SPSS Survival Manual to ensure you are not violating any of the major assumptions for the analyses you intend to conduct. This might involve checking that you have enough subjects in your groups, that the variance for each group is similar, or that the distribution of scores on your variables is not too skewed. Parts Four and Five
Remember that SPSS will conduct the analyses that you ask it to do, whether or not these analyses are appropriate. The old saying 'Garbage in, garbage out' applies. It is up to you to ensure that you understand what you are doing and also what the output means. SPSS Survival Manual

A few additional tips

1. Save your output regularly so that if the computer crashes you have not lost too much work. All output files should be saved with a .spo extension onto your disk in the A:/ drive. Give your output file a suitable name so you will able to identify it later, for example 8aug96a.spo. Keep a list of your output files with details of what is included. SPSS produces a lot of output and it is very easy to get lost, so get organised—it will save you a lot of time. 

2. If you need to recode a variable, always create a new variable. Keep the original variable so that if there are any problems you have not lost the data. 

3. If you create any new variables, always check in your codebook that the name you intend to use has not already been used. Otherwise you will lose all the original information. Record the name and explanation of the new variable in your codebook. Keep detailed notes of everything you do. This should include details of cut-off points you use to recode variables, reasons for doing things, reminders to yourself about how to do the analyses, problems that might have occurred etc. 

4. Finally, make sure that when doing your analyses, you get up and stretch, walk around etc., at least every hour. SPSS for Windows can be addictive, a bit like eating peanuts—just one more, and then, just one more … Plan what analyses you intend to do, break your analyses into blocks, and give yourself time to digest the output.

Selamat mencuba dan menganalisa.

~yba~

Isnin, 28 Mei 2012

Industrial PhD


Since this type of PhD route is quite new in Malaysia, students often get confused with the traditional PhD. I compile here some basic information about industrial Phd for new and potential or existing students to understand first before embarking on a 3-year or more application PhD project. The sources are from two Malaysian universities (UUM and UTM) and The Danish Agency for Science, Technology and Innovation in Denmark

What is Industrial PhD Programme?

Malaysia requires a large pool of PhDs to give impetus for sustainable growth of our economy. There is a huge opportunity for researchers to resolve work related issues, problems, or find solution to problems. Recognising the importance and the positive impact that will accrue from this strategy, the Malaysian government has formulated policies with financial incentives, to encourage full-time Malaysian employees from  both public and private sector organisations, to join the Industrial PhD Programme which is more application oriented.  It is an alternative to the traditional PhD, which is more inclined to knowledge enhancement. The expected outcome is that, the research should add value to the organisation through tangible (economic gains) / intangible (social) benefits, or improve / enhance organisation’s overall efficiency, effectiveness, performance, profitability and that lead to enhancement of competitiveness of our nation in the long run.

In UUM, all students must undertake:

1. research at the workplace;
2. research with the topic and scope based on real problems at their workplaces, related to their jobs, and in their respective field of specializations;
3. research projects that have potentials in improving the operation, services, and management of  the organisation/industry.

Students shall also be subjected to the existing UUM regulations related to post-graduate studies for conferment of PhD degree.

INDUSTRIAL PhD AT UTM

Innovative knowledge for Industry and Academia

An Industrial PhD is a Doctoral Research Program which accommodates the range of activities that support original and innovative work that encompasses the academic, professional and technological fields and is not restricted or related solely to a traditional 'scientific method'. Qualitatively, Industrial PhD is equivalent to all other types of doctoral programs, including the conventional PhD.

Industrial PhD is a doctoral program that provides opportunities for students/researchers to bridge the gap between two worlds. The objective of the program is to establish cooperation between the university, a company and student/researcher, on a specific research project that is of high value to the company and the participating parties.

There is researcher demand, workforce trends, industry needs and/or national priorities which give rise to the need for the development of such an industrial doctoral program, in transforming the country into an innovation-led economy (New Economic Model).

The development of such an industrial doctoral program is a clear strategic priority for the institution of higher learning, and the Ministry of Higher Education Malaysia (MyBrain15 Initiative).

Industry-based PhD Programme or Industrial PhD at UTM an attractive alternative to the conventional PhD, being better suited to the needs of the industry, and providing a more vocationally oriented doctorate with industrial relevance. It is highly flexible and able to accommodate candidates from all levels of management. It is a full-time postgraduate programme where candidates have the opportunity to spend most of their time carrying out the research at their respective organisations or industries.

INDUSTRIAL PhD FEATURES

·       Research focus based on industrial issues or problems;
·       Joint supervision by experts from the University and industry;
·       Research conducted in the industry and candidates do not have to leave their workplace.
·       A PhD program distinct from the conventional PhD degree in term of mode of research, but preserves the mark of original or innovative research and scholarship expected of a doctoral study;
·       The company benefits from access to new/innovative and valuable knowledge acquired by the student/researcher during his or her studies. In return, the University gains access to new knowledge and innovation provided by the private company.
·       PhD student/researcher is employed by a private company during the entire research work, while being registered as a PhD student at the university.


DENMARK

In Denmark, an Industrial PhD project is defined as an industrially focused PhD education. The research project is conducted in cooperation between a private company, an Industrial PhD student and a university. Third parties from both the public and private sector can be attached. (See http://en.fi.dk/funding/funding-opportunities/industrial-phd-programme/what-is-an-industrial-phd)

In Denmark, a mandatory part of the Industrial PhD education is the special business course offered by the Danish Agency for Science, Technology and Innovation. The course concludes with a business report, which must be about the commercial aspects of the Industrial PhD project in a theoretical and company-relevant context. For Industrial PhD projects in the public sector, the business report must be about the institutional benefits of the project.

See the Guidelines for the Industrial PhD Programme in Denmark at http://en.fi.dk/funding/funding-opportunities/industrial-phd-programme/guidelines-for-the-industrial-phd-programme

Selamat berjaya.

~yba~

Jumaat, 18 Mei 2012

Useful Things to Know About Ph. D. Thesis Research

By H.T. Kung

(Prepared for "What is Research" Immigration Course, Computer Science Department, Carnegie Mellon University, 14 October 1987)
Presentation Outline
  1. Introduction
  2. Why Ph.D. thesis could be really difficult for a student
  3. Types of Ph.D. theses (from Allen Newell)--not a topic of this talk
  4. Growth of a star (the transformation process that some students go through to become a mature researcher)--which stage are you in?
  5. Stages of Ph.D. thesis research
  6. Methods to get into the depth of a topic (or how to come up with good ideas)
  7. Breaking myths
  8. Pitfalls to avoid (easy ones to avoid listed first)
  9. Some other general advice
  10. All the effort is worth it (believe it or not)


1. Introduction
  • Ph.D. thesis is treated very seriously at leading universities.
    • Expectation is high.
      • Ph.D. thesis represents a substantial work. Faculty often tell other people that "We have a student working on this area for his or her Ph.D. thesis." Amazingly enough, this is usually sufficient to convince people that the problem is somehow going to be solved.
    • Ph.D. thesis research is a task to ensure that the student can later take on independent, long-term research commitments. (If a Ph.D. student does not intend to be a researcher, the Ph.D. thesis work is not worth the effort in general at least at CMU.)
    • Through the Ph.D. thesis process the student is transformed into a professional researcher.
    • Faculty are judged by the theses of their Ph.D. students.
    • High standard Ph.D. thesis is probably one of the most important factors that contribute to the success of graduate education at leading American universities.
    • Ph.D. thesis is probably the only real challenge for getting a Ph.D. degree.
      • Ph.D. qualifier is seldom a problem for motivated students.
  • Ph.D. thesis research is probably more mechanical than a new graduate student would think. (Of course the process is still too complex to be automated.)
    • Knowing this mechanism can be more important than thesis results themselves.
    • Some information presented here may be relevant to your whole research career, i.e., it is not just for the Ph.D. thesis per se.
  • This talk consists of pragmatic advice.
    • The talk is based on my personal experience (i.e., not based on any serious research)
      • I happen to have research experience in both theory and system areas. We will compare thesis research in these two areas.
    • This is a common sense talk and will have down to earth discussions.
      • "I wish someone told me this before."
 
2. Why Ph.D. thesis could be really difficult for a student
  • Most likely this is your first, major research experience.
    • A big challenge for most students
  • No simple recipe
    • Different talents
    • Different kinds of theses
    • Different approaches
  • The work is judged by thesis committee (mostly advisor). This produces anxiety.
    • Unlike other research you will do, the evaluation mechanism for thesis research is very unique.
    • No clear contract
    • No clear standard (we only know it is high)
    • Recall the Stanford murder case (the former student said, after he had finished--he did finish something-- his jail term, that he might do it again under a similar circumstance).


3. Types of Ph.D. theses (from Allen Newell)--not a topic of this talk
  • Opens up new area
  • Provides unifying framework
  • Resolves long-standing question
  • Thoroughly explores an area
  • Contradicts existing knowledge
  • Experimentally validates theory
  • Produces an ambitious system
  • Provides empirical data
  • Derives superior algorithms
  • Develops new methodology
  • Develops a new tool
  • Produces a negative result


4. Growth of a star (the transformation process that some students go through to become a mature researcher)--which stage are you in?
  • Knowing everything stage
    • Student: "I have designed a supercomputer even before graduate school."
    • Faculty: speechless
  • Totally beaten up stage
    • Student: speechless
    • Faculty: smiling at the student's progress so communication is possible now.
  • Confidence buildup stage
    • Student: "I am not stupid after all." (student thinks)
    • Faculty: "Uh oh, she is ready to argue." (faculty think)
  • Calling the shot stage
    • Faculty: "I am going to design an n-processor supercomputer."
    • Student: "You are crazy, because ..."


5. Stages of Ph.D. thesis research
  • Selection of area--not a topic of this talk
  • Selection of advisor--not a topic of this talk
  • Becoming a researcher in the area
    • Building up general knowledge, experience, and confidence
    • Knowing issues and important questions in the area
    • Capturing research opportunities
      • Don't let any idea or question go by without first giving it careful thought.
        • Be alert and diligent.
      • Pay attention to new technologies
        • Examples
          • VLSI, networking, and new chips such as the Weitek floating-point chips three years ago which in some sense gave the initial motivation for the Warp project
    • Some useful things to do (from Dave Gifford, MIT)
      • Read recent proceedings of the best conferences, and ask more senior people what were the best papers. Try to figure out what makes a great paper (and thus what makes great research).
      • Keep a notebook that contains your research notes. Put all of your empirical data and initial ideas in the notebook. Make notes on a paper as you read it and think about the assumptions of the author and the importance of the results.
      • Follow references from one paper to another until you know an area extremely well. Don't count on your advisor to hand you all of the relevant papers out of his file drawer. He doesn't have them all!
  • Thesis proposal
    • It is the most crucial stage in the sense that the basic concept is worked out here.
      • To get important results you need to ask important questions
      • This is the time you need your advisor most.
      • Problems in later stages are usually rooted from a weak thesis proposal.
    • Purpose
      • A research plan
        • A serious attempt to get an overview of the whole research course
        • Not really a contract
          • Need some flexibility because research always has uncertainty.
      • Forming the committee
        • Varies a lot
        • Choose people for your thesis committee that can help with needed expertise. For example, it is useful to have a relevant theory faculty member on a systems committee and vice-versa.
        • However, there is usually no need to optimize too much on the selection of the committee members--advisor still plays the most important role.
        • However it can be very important, when
          • you have a "questionable" advisor, or
          • you have an interdisciplinary topic.
      • A review
        • If there is any serious doubt, it had better show up now.
        • Proposal could sometimes be viewed as just a forcing function for taking care of certain things.
    • Some of the difficult questions always asked in a thesis proposal:
      • What is your approach and what is new?
      • What is your secret weapon? (Herbert Simon)
      • How do you measure your own progress?
      • What are the success or completion criteria?
      • How will the expected results change the-state-of- the-art?
    • The grand challenge for a thesis proposal is to come up with an approach or an experiment.
      • It is easy to identify a general problem area, but setting up an approach and designing an experiment can be difficult.
        • Need ideas
          • Just need one good idea, really
          • Unfortunately, there is no magic here (however see some hints below). This is the hard part of any research project for everyone (not just for students).
      • Need independent thinking
        • You should be good enough to start arguing with your advisor on technical issues and research tastes.
      • Need to elaborate on focus, approach, experiment, and potential impact
        • For theory research you may propose some new models of computation.
          • Examples: area-time complexity (new VLSI model in theory), parallel algorithms (new cost models)
        • For system research you may design experiments and argue their relevance.
          • Examples: multiprocessor architecture, compiler for a parallel machine
    • Useful things to know when preparing a thesis proposal
      • Be honest. There is no need to exaggerate your claims! If you point out the weaknesses in your approach you will disarm your critics.
      • Pick a project that is manageable so you can do an excellent job - things are always harder than they seem. It is far better to do an outstanding job on a moderate size project than a moderate job on a large project.
      • Include a tentative thesis outline and a month by month schedule in your thesis proposal.
        • This may be difficult to do but it is better than no plan at all.
        • This will also help gauge the total size of the work you are committing yourself to do.
  • Producing results
    • Lots of work--what else do you expect?
      • System--be inside an active project without losing sight of thesis
        • Need to be a worker as well as a conceptual person.
        • Your work depends on other people's work and vice versa
          • Opportunity to see real problems
          • Getting good support, including encouragement and demand, from the group
            • It seems that this arrangement really works in all cases.
        • Be quick, because you don't want to be overtaken by the environment (this is one of the pitfalls to avoid, as described below)
      • Theory--be lucky!
        • Be flexible
          • It is hard to insist that you will prove a theorem before you go to sleep.
        • Be quick, because theoretical results are totally portable and so competition can be keen.
    • Keep the committee informed (at least those "trouble makers")
      • You can get real help sometimes.
      • Committee members are obliged to talk to you.
        • Sometimes finding a qualified person beyond your advisor to discuss your work can be difficult.
      • Don't want surprises in the later stage of the thesis
    • Ways to finish a thesis
      • Incremental and adaptive approach
        • A sequence of incremental results
      • Big-bang approach (this is not recommended in general)
        • One big theorem
        • A big piece of software or hardware
  • Writing
    • Why some students find that Ph.D. thesis writing is very difficult
      • First major document
      • Writing is time-consuming--part of the .9999 perspiration (Satya)
        • Think how many good sentences you can write in an hour.
        • Fighting with fonts, figures, references, etc.?
          • Please don't be too picky.
      • When results are not totally solid, writing can be really difficult even for an experienced writer (now you know another reason why proposal writing is not easy)
        • Can't say too much and don't want to say any less
        • Writing about flaky results can be a real challenge.
          • In this case you should improve your results first.
      • Writing has to do with presentation rather than finding new results. So writing may not be as exciting..
    • However, thesis writing is useful in the sense that it helps reveal possible problem areas and provides new insights.
      • Help get a large picture on what you really have.
      • Help organize the concepts
      • Completeness is forced.
        • You must take care of things that you have been ignoring.
          • For example, you need to do comparison with other results
      • Correctness of the results is checked.
        • You had better have the proof now for any plausible "theorem" that you have been believing.
      • New insights on how things really work
        • New ways of looking at your results
    • Recommendations
      • Get some practice--write some papers before thesis
        • Write some joint papers with people who have substantial writing experience
      • Need to know the theme of the thesis very well
        • Outline first
        • Write the conclusion first (try it at least)
        • Start writing chapters which are more settled.
        • Write the introduction last
        • Iterative process
      • Make the writing as precise as possible, so that you know exactly what you are talking about. This will save lots of rewriting.
        • Precise writing usually also yields good English.
  • Getting final comments from the committee
    • Not too early or too late
      • Getting some committee members to read can be a challenge.
        • They are busy people. You want to give them an "optimal" version to make comments.
    • How much to ask for comments varies a lot
    • Should not have any surprises now.
      • You had better know what you have been doing by now.
      • However, if there is any problem, it had better show up now.
  • Defense
    • Mostly a formality and a happy occasion (should be like that)
      • You know that your results are good and you will present them well.
        • You should know the answer to the question - "What are the three main ideas in your thesis?". You should be able to rattle them off and relate them to previous work.
      • Getting a date set can be more difficult than you think.
        • Committee members do not necessarily stay at CMU as long as you do!
        • Weekend defense is not really desirable.
          • May be difficult to get audience.
    • However defense is still very important:
      • Opportunity for final improvements for the thesis
      • Formal presentation to the community
        • Many people form their opinion of your n-years' work from this presentation
      • Presentation material can be used for future presentations
        • Used in recruiting presentations if you have not settled on a job yet
      • Psychologically important
        • Once in a life time occasion--you will remember it always.
      • Don't want to blow it.
        • Absolutely no surprises
  • After defense
    • Usually there is still some minor work to be done for the thesis (too bad)
      • Defense was moved early for various reasons
      • New comments from defense
      • Did not have time or did not want to polish the thesis before defense
    • Publication
      • Articles, books (or give the thesis to your parents)
      • Very important to publish the results in journals
        • This is the only reliable way to archive your results. (You don't want to lose them after all these efforts, do you?)
        • Publication is important for academic career.
        • May break the thesis up in several articles. When appropriate, some articles may have joint authors such as your advisor.
        • Do it right away before you get on to the next thing.
      • Books can be good too.
    • Follow-on work
      • Keep mining the thesis--why not?
    • Finally you are free!


6. "Methods" to get into the depth of a topic (or how to come up with good ideas)
  • No magic, but we will still try ....
  • How to develop initial ideas
    • Study other work and do comparison
      • What are similar issues and solutions?
    • Look at examples
      • Generalization and abstraction
    • Make hypothesis and validate it formally or informally-- keep trying
      • You will discover issues at least.
    • Do modeling and abstracting
      • Get the essence
    • Just do something--be active
      • Implementation--details reveal issues
        • Join a project to do some real work!
        • Handle a smaller case
        • Implement a throw-away simulator, language, design, etc.
      • Start proving "theorems", even if they are known to be difficult.
        • Quick way to understand issues
    • Work with good, experienced researchers (don't forget to use your advisor!)
      • They might have deep insights on similar problems.
      • They can help calibrate the difficulty of the problem.
      • You learn the subject matter from them more quickly and directly.
      • You learn their techniques
        • Every successful researcher has his or her own bag of "tools":
          • Calculation, synthesis, analysis, persistence
      • If they also get stuck once in a while, you know that you are not that bad after all.
  • How to develop existing ideas further
    • Exploring problem and solution spaces
      • Enumerate parameters individually (and do quick pruning)
        • To see where your current ideas sit in the space
      • Correlate results
      • Generalize ideas and results to other points in the space
      • Produce phenomena and explain them (Herb Simon)
    • Brainstorming your ideas with others
    • Presenting your ideas in papers or/and seminars
      • Ideas will be checked out carefully and systematically (see above on thesis writing)
    • Example steps that can be used to get some depth from a simple result such as a speed-up curve
      • Explain the curve
      • Look at the problem and solutions spaces
      • Do some comparisons
      • Change the assumptions
        • How stable is the result?
        • How will results vary or correlate under different assumptions?
      • Derive some general principle
        • Similar curves for other situations?
  • General comments
    • Thinking is the key
      • Thinking is more important than reading
        • Books are not always right.
          • Note that in the system area with few exceptions people who build systems do not have time nor need to write up their experience--it is too bad but it is a reality.
      • Be alert on all sorts of opportunities
      • Do the thinking right away while you have it.
        • Ideas and interest may be lost more quickly than you like to believe
    • Talking to people
      • Don't over do it (you still need to do the work yourself)


7. Breaking myths
  • "Advisor is a stronger researcher than you."
    • It is true that advisor is experienced, wise, smart (maybe), and knowledgeable in general. Advisor also sees a bigger picture, and has contacts in the area.
    • However, advisor is not always right.
      • Advisor is not as focussed as you.
      • Advisor does not have more time or energy than you do.
      • Advisor is not as innovative in general.
        • They know too much.
        • They are more conservative.
          • They know too many horror stories.
        • Aging does not help.
      • Advisor's knowledge may be obsolete (don't say this in front of him or her!).
    • You must believe that you can do better than advisor for some research areas.
  • "System theses take longer than theory theses."
    • The most difficult part of a thesis is to come up with some good, new ideas. The difficulty in getting new ideas is the same for theory or system research.
      • Theory thesis is in general not about solving open problems.
        • Actually good theoreticians always work on new problems, models and methods so that they can solve the problems that are "solvable" in the first place.
          • Greatest contributions are ground breaking ones, such as new models.
          • New approaches give new insights to old problems. This is the way open problems usually get solved (e.g., the four-color problem).
      • For systems theses it is important that the major ideas in the thesis are independent of the implementation--the goal is to have the ideas live on in other systems as well. A good systems thesis usually has a new algorithm or new method at its core.
      • Few theory students who finish really early are likely those who have prior research experience. (Recall that theory results are highly portable!)
      • Incompetent theory students are more noticeable than weak system students. So we don't often see theory students who drag on for a long time.
    • There are some differences in systems and theory research however, but they should not have too much impact on the thesis research time.
      • System needs implementation, whereas theory needs more background study.
      • Theory research is self-sufficient and system implementation may depend on other people's work (you should not get into a situation where you don't have control).
  • "Ph.D. thesis research follows some standard guidelines."
    • Yes, a Ph.D. this must represent a substantial result in a very high standard.
    • But there are many ways to leave a mark in a research area. As long as you have come up with some good ideas and pushed the frontier of knowledge, you will be surprised sometimes how flexible your committee could be in terms of the research approach, acceptable results, and thesis presentation.
    • There is a small percentage of Ph.D. theses completed in unusual manner. Don't give up too early if you belong to this class. Try it or you will never know.


8. Pitfalls to avoid (easy ones to avoid listed first)
  • The goal is too big to reach.
    • Theory
      • Proving P /= NP
      • Proving P = NP is even worse (likely this thesis will never finish!).
      • Deciding whether P = or /= NP is best of the three (i.e., be flexible)
    • System
      • The initial effort is so large that real issues never get a chance to be looked at.
      • It is important to size the project and evaluate the total effort carefully based on past experiences.
  • Ideas cannot stand without an implementation that competes with commercial products.
    • Chess machine implementation is OK, because there is no commercial competitor.
    • In this sense, Warp hardware is more difficult than software.
    • Floating-point designs that require a high-performance chip implementation to validate the concept would be disastrous.
    • Never need to implement another vector processor!
  • The thesis area is overtaken by technology and environment
    • Technology advances have solved the thesis problem.
      • A clever operating system using no more than 128K memory is not very interesting today.
    • Advisor (or student sometimes) has changed his or her interest
    • Other new projects have better approaches and opportunities
    • Other people have published similar and/or better results.
    • Advisor has a better job elsewhere or the project is over.
    • Lesson: You should always do your thesis as quickly as possible.
  • Totally isolated work
    • No encouragement and support--no one cares about your thesis
      • Can't even find an advisor sometimes
      • Doing a thesis away from CMU is really difficult.
    • System research
      • Lone ranger approach is almost suicidal.
        • No software, systems and application support for evaluation
        • Very difficult to do anything real without feedback from a community
    • Theory research
      • At least global networking is needed.
  • Not knowing when to stop
    • Thesis is not the last research you will do.
    • You can do the same research after your Ph.D. thesis (while making more money).
    • Learn to make reasonable assumptions to restrict the problem
  • Unhealthy competition between student and advisor
    • This is more likely to happen in the theory area.
    • The potential is always there (especially for smart professors with lots of ego). In general if both sides try to be fair, things can always be worked out.
  • Lots of numbers and hacking but no fundamental principles
    • System research has to have more than implementation.
    • Implementation for a thesis research is interesting only if it can be used to validate some theory.
    • This problem should be fixed as early as possible.
  • Things dragged on--wonderful general ideas in the beginning that never get developed into a coherent approach (i.e., heading to a black hole--there is no output)
    • Wrong areas for the student (and perhaps the advisor) with respect to ability and interest
    • Nightmare case--it does no good to anyone.


9. Some other general advice
  • Stay away from areas that have been thoroughly mined by your ancestors.
    • Keep yourself at the very front of a research area so that you have a better chance to hit something big or at least new.
    • After all in research what matters is the work that pushes us into new territories.
    • Make use new advances in other areas
  • Don't avoid thinking
    • Thinking is hard but there is no substitute for it.
  • Psych yourself up for this unique experience of doing a Ph.D. thesis
    • Make yourself believe you are solving the most important problem in the world
    • Remember what worked for you before
      • If you work best when you are competing with others, then create some confrontation.
    • Must be very alert about issues and opportunities
    • Thesis process is sort of artificial (almost a torture in some way)
      • The thesis is judged by a committee (mainly your advisor)
        • More subjective than exams
      • Probably one of the most humiliating experiences for people of this age (advisors should all remember this and be considerate.)
      • The process is not a typical research style--you don't do anything similar to it again even if you will be doing research after the degree.
    • The thesis process can be long and treacherous. (Be prepared for it.)
      • You don't want depression.
    • There are quite a few very competent people who just do not want to go through this.
  • Use forcing functions well to speed up the thesis process
    • Competing with someone else
    • Family pressure
    • Financial pressure
    • A job is waiting
    • Advisor is leaving or project is over
    • Equipment is retiring
  • Never throw away advisor's comments
    • Cox-Denning case
  • Keep good relationship with your advisor (even after you graduate)
    • Good thing to do--no exception almost
    • Relationship is unique.
      • Advisor usually has lots of influence on you in this very important stage of your life. Advisor also appreciates the good research you did with him, and is in general interested in your well-being.
    • Advisor may be your mentor for your entire career.


10. All the effort is worth it (believe it or not)
  • Experience from Ph.D. thesis research is unique. You have learned how to do research. Future research is going to be more interesting because you will know how to do it, so you will have more freedom and fun.
  • Almost all leaders in research have this experience. You will have confidence in your research ability. You will look at things differently than people who did not go through this process. It is very clear that Ph.D. thesis research is still the best way we know of in developing powerful researchers.
  • In summary, it is the best investment for becoming a successful researcher.