Friday, January 31, 2020

How HEC Kills Creativity and How PEC Kills Creativity

How HEC and PEC kill creativity! They kill creativity by targeting the roots from where creativity and innovation originate. Creativity, innovation, new paradigms, new frameworks and and new ideas originate from out-of-the-box thinking, lateral thinking, inter-disciplinary aha experiences. However, HEC/PEC's notion of quality works in the reverse direction; they force faculty, curriculum as well as research to be imprisoned in rigidly defined boxes or cells! They compartmentalize knowledge and think that knowledge can be captured in boxes and in silos designated by the names of the disciplines, programs or departments. This helps the bureaucracy in imprisoning the faculty into existing rigidly defined boxes i.e. the name of the discipline written on the degree or the name of the department on the transcript. This box then becomes the territory on which turf wars are fought for future promotions and fancy designations of professors, chairpersons, and deans. It is on this turf that departmental politics is played out. This imprisoning of knowledge in silos has nothing to do with academics or the quality of higher education.
This is also discussed in my blogpost What does it Mean to Have a PhD: Myths of Specialization and Departmental Expertise
[https://syedirfanhyder.blogspot.com/2014/10/Myths-Definition-of-PhD-Specialization-Expertise.html]



Strictly speaking no discipline and no degree program can be confined into a strict boundary. Knowledge knows no boundaries. Names of disciplines and subjects are artificially created, and the subject boundaries are specified only for administrative convenience for classifying, categorization and logistics. The issue becomes very clear when you force a book to be placed in a particular shelf of the library under a particular Library of Congress (or Dewey's Decimal) Classification. It is clear that knowledge has multidimensional and hyperlinked connections to multiple areas. No wonder that hyperlinked Wikipedia had won out over centuries old sequential-compartmentalized knowledge structure of Encyclopaedia Brittanica. 

Knowledge, innovation and creativity abhors boundaries. In fact, all new exciting discoveries and inventions and progress take place when the boundaries are dismantled. Creativity is the result of cross disciplinary and interdisciplinary linkages. Creativity is the result of lateral thinking across conventional modes of thoughts and disciplines. It is only when you apply the paradigm from one discipline to ideas in to another unrelated discipline that new connections are formed and new ideas germinate.  The current information age and the inventions and innovations in technologies has progressively demolished the boundaries of disciplines, industries, and old classifications.
  • Businesses merged and went from office buildings into the cloud
  • Supply chains went from transportation and logistics to virtual movements from one inventory to another in the cloud
  • Social networks went from physical interactions to virtual interactions in the cloud
  • Neuroscience merged neuro-psychology with computers
  • Orthopedics integration with mechanical prosthetic 
  • AI merged intelligence and cognition and psychology with computer technology.
  • Medical imaging changing medical diagnosis future for ever. 
  • Biochemistry merging into electronic equipment in the medical technologies and equipement. 
  • Philosophy in the social network design 
  • Google using ontology and epistemology from philosophy and incorporating its ideas into data structures that has led to the phenomenal growth of the huge enterprise.
  • Facebook using graph theory to rule the social networks.
  • Politics and public opinion controlled through data analytics. Cambridge Analytica managed to sway the US elections through the investment of a few tens of thousands of dollars. 
  • Flying cars represent the convergence of mechanical and automotive engineering with avionics, computerization and AI.
  • Same is the case for self driving cars and unmanned drones. 
All huge global problems are multidimensional problems and are multi-disciplinary and inter-disciplinary. The global warming which is created by the coincidence of issues created through unbridled progress, capitalism, economic progress, poverty, rich-poor divide, resource exploitation and so on. Similarly, depletion of water resources, extinction of species, changing climate patterns, forests depletion, food shortages, health and virus outbreaks, and so on are issues on the global scale that require multi-disciplinary solutions.

HEC stipulations will not allow an MS Material Engineering with an MBA and stupendous experience of management to supervise Engineering Management or Management. The case in point is Sunder Pichai who is CEO of Google (Alphabet) who has MS in Materials Engineering from Stanford and  MBA from Wharton, and a stupendous over 15 years experience in Google. However, HEC of Pakistan will not allow Sunder Pichai to supervise MS Engineering Management.

HEC will not allow PhD in Electrical Engineering with vast experience in top engineering firm like IBM of over 25 years to supervise PhD in Engineering Management or PhD in Management. For instance, Arvind Krishna who has PhD in Electrical Engineering from University of Illinois at Urbana-Champaign and over 30 years of Engineering management at IBM which is a multi-national technology company that is spread over 170 countries and one of world's largest employers with over 350,000 employees. He was recently promoted as IBM's CEO. However, HEC will not allow even Arvind Krishna to supervise PhD in Engineering Management because his degree does not mention "Engineering Management" as the name of the discipline.
Noam Chomsky is an American linguistphilosophercognitive scientisthistorian,[b][c] social critic, and political activist. Sometimes called "the father of modern linguistics",[d] Chomsky is also a major figure in analytic philosophy and one of the founders of the field of cognitive science. He holds a joint appointment as Institute Professor Emeritus at the Massachusetts Institute of Technology (MIT) and Laureate Professor at the University of Arizona, and is the author of more than 100 books on topics such as linguistics, war, politics, and mass media. Starting with his PhD in Linguistics in 1955, and his work on universal grammar, he is considered as the founder of cognitive psychology and Chomsky Hierarchy is considered as a pivotal part of computer science core subject Automata. His multidisciplinary work in Artificial Intelligence and other fields made him the most cited scholar for over 30 years in late 20th century. [From Wikipedia].
However, HEC rules will not allow Noam Chomsky to be counted in any faculty other than linguistics. Just because his degree or transcript would be saying that his PhD in 1955 states him to belong to the field of Linguistics. What a pity!

It is for this reason that many of the universities do not state the name of the discipline or the department name on their PhD degrees, including University of Texas at Austin, University of Massachusetts, Wayne State University, Northwestern University, etc.

PhD is Doctorate in Philosophy; it is the philosophy of creation of new knowledge in a particular field.

However, HEC and PEC think that erecting huge impregnable walls will ensure quality. Instead of building quality these restrictions kill creativity, vitality and growth of the disciplines as can be seen by the engineering disciplines around the country since the signing of the Washington Accord which imposed OBA (Outcomes Based Accreditation) accompanied by the draconian implementation of the silos mentality in the engineering disciplines. The growth of academic programs in engineering has been stultified and the disciplines are withering away. PEC does not even count the faculty unless the name of the degree matches the name of the department. No mechanical engineering PhD can be counted in Electrical Engineering department and vice versa although Robotics, Flying Cars, and industry automation are multi-disciplinary working on the convergence of electrical engineering with mechanical engineering with avionics engineering and computer science.

Melbourne University definition of PhD says:
"The degree of Doctor of Philosophy signifies that the holder has undertaken a substantial piece of original research, which has been conducted and reported by the holder under proper academic supervision and in a research environment for a prescribed period."
Yale University doctoral manual says that a PhD dissertation should:
"demonstrate the student’s mastery of relevant resources and methods and should make an original contribution to knowledge in the field. The originality of a dissertation may consist of the discovery of significant new information or principles of organization, the achievement of a new synthesis, the development of new methods or theories, or the application of established methods to new materials.
It actually makes you an expert in how to extend the frontier of knowledge (original contribution) using established methods of research, and that it gives you the ability to formally communicate this extension of knowledge to the community of researchers working in the area of your specialization, as indicated by your publications.

By implication, if you can extend frontier of knowledge (original contribution) in one area, you have the capacity (given enough time) that you can understand and extend the knowledge in another area also. It is this expertise that makes some of the PhDs outshine others through their prolific publications in diverse areas, and across disciplines. This brings us to the second myth about PhD.

The reason why departmental turf wars are fought on the name of degree is because it protects the faculty of departments in government universities from encroachment from other researchers.


See My Other Posts Related to PhD:


What is PhD?
Why PhD is Difficult: 
Starting with your PhD
Reading Research and Writing your Research
Qualitative Learning from a PhD

See Also:

Tuesday, January 28, 2020

HEC Ranking Fiasco Genesis


University Rankings in Pakistan

Dr. Syed Irfan Hyder, January 5, 2004


This is with reference to the letter to the editor of Dawn, December 31, 2003 where the writer has highlighted the risks of jumping prematurely in the area of university rankings.

Development of a ranking system is a good idea, but coming up with a criteria that is complete, just, fair, valid, reliable and professional is a non-trivial undertaking as explained below. Hurriedly put together experiences of a few academicians in to a ranking system cannot be expected to be fair, valid or even professional.

It is surprising that with all the emphasis on R&D, there is a reluctance in investing in the research and subsequent development of the proposed ranking system. The ranking initiative is therefore headed in the same direction as the Model University Ordinance and Tenure Track initiatives. Recently a questionnaire was sent out by HEC that lacks in completeness as well as sufficiency. It is even without clearly laid out objectives and has typographical and structural mistakes. It is surprising to note the lack of thoroughness even in the booklet on Criteria for Establishing New Institutes and Universities that has been published and widely circulated. Institutions are threatened to be de-recognized if they do not conform to the booklet Criteria in five years!

A proper ranking system for an institution of higher learning should have the following characteristics without which it would not be implement able:
  • Just: It should be applicable to all the institutions of higher learning including public and private universities. It should be general enough to encompass variations in engineering, medical, general sciences, business and other professional disciplines. It should incorporate and accommodate all finer differences such as those between the doctorate in medicine and doctorate in philosophy. It should be able to cater to mixing and matching of different disciplines in some universities as well as directed specializations preferred by some others.  It also must rationalize on the basis of the economic cost paid per student rather than the fee charged per student.
  • Complete: It should measure all the factors that contribute to the quality of the institute. It appears from the recent HEC questionnaire and the HEC criteria for setting up of new universities and institutes, that the questionnaire is biased towards brick and mortar evaluations. Evaluation of the following factors are conspicuous by their absence and must be included and given due weights: Financial discipline, accounting system, costing system, purchasing systems, grading systems, academic monitoring systems, exam and classes scheduling systems, syllabus management systems, fee management systems, faculty management systems, attendance monitoring systems, curriculum improvement systems, student records and student progress evaluation systems, examination systems, students complaints monitoring systems, industry interaction systems, etc.
  • Valid: Ranking system should be valid. That is, it should actually measure what it sets out to measure. Does it incorporate all the aspects of higher education? Does it measure the objectives of higher education for Pakistan? What does our economy require from the institutes of higher education. What is the economic demand for researchers, scientists, consultants, managers, office workers, professionals, academicians, responsible citizens from institutes of higher learning. What should be their proportion coming out from a given discipline. How often and how soon does the graduates switch from their area of specialization to an area of economic opportunity and what are the skills that they take into the new fields that they choose.
  • Criteria Testing and Pilot: Was a pilot done to identify reasonableness of the criteria. Was the criteria applied to a couple of universities to test its applicability, practicality and ability to yield the desired results. What shortcomings were noticed in the pilot application and how were they rectified. Unfortunately in Pakistan, government organizations tend to launch grand survey projects without the necessary pilot studies. One of the prime examples of failed surveys has been the business survey launched in 1998-99 for CBR and NADRA. We are still reeling from the lack of homework and pilot study done for the NIC project. More pertinent example is the Model University Ordinance that was applied without a pilot study.
  • Practical: Criteria should be realistically measurable. Is the asked for information readily available. Is it verifiable. In how many ways can it be interpreted. How much effort does it take to fill out the questionnaire. Would all institutes put a similar amount of effort. What would be the source of their data. Is the source of data reliable, authentic and comparable.
  • Reliable: Does multiple application of the criteria by different teams and different personnel and at different times on the same institute  yield the same result.
  • Professional: Has the questionnaire been prepared using established research methodologies. Selection of the survey technique, selection of the questions, wording and format of the questions, selection of the respondents, estimation of respondents’ effort, estimation of surveyors’ effort, compilation effort, subsequent analysis effort and other related aspects need to be evaluated and addressed by professionals in business and analytical research.

A brief analysis of the issues raised above indicate, that HEC needs to do a major research in developing the criteria before it can be ready to actually start ranking the institutions. The development of the criteria would have to be done in an open and transparent manner with continuous interactions with institutions of higher learning to yield the desired results. 

Dr. Irfan Hyder

What HEC Quality Criteria Did Not Measure

[Written in December 11, 2003]

This was written on the basis of HEC Survey that was circulated by HEC in 2003. Many of these areas are still not being covered.

The survey still relies too much on brick and mortar measurements.

Following important areas for determining the strength of an institute of higher learning and measuring its quality have not been covered in this survey and should be included:
  • Financial control
  • Administrative control
  • Academic control
  • Records Management
  • MIS Support

Financial management and control:

  • Do you have a financial plan
  • Do you have a proper accounting system based on GAAP (Generally Accepted Accounted Principles)?
  • Is the General Ledger automated?
  • Is your accounting system strong enough to compare activity in any university’s account on a monthly basis with the corresponding activity in a previous year? E.g. comparison of monthly expenses in Feb 03 with Feb 02?
  • Do you have an automated budgeting system where revenues and forecasted and expenses are budgeted. Is there a system for variance analysis?
  • Do you have a costing system? Do you have cost centers and analysis of costs according to various cost-centers?
  • Do you have a proper receivables tracking system with aging analysis?
  • Is there a payables forecasting system?

Administrative management and control:

  • Do you have a purchasing system
  • Do you have an inventory management system
  • Do you have an attendance recording system
  • Do you have a payroll system
  • Do you have an HR system

Academic Control

  • Do you have a student attendance recording system?
  • Is there a system for imposing penalty on excessive absences?
  • Do you have a system for tracking the number of student-teacher interaction hours
  • Are the assignments, quizzes and class participations graded and part of the final evaluation
  • Is there transparency regarding student papers. Are the graded papers shown to the students?
  • Do the student know about their semester work grades before the final exam?
  • Do you have an internship system and a system for monitoring and tracking the internships.
  • Do you have a system for periodic exposure to industry through the guest speaker sessions
  • Do you have a grading system
  • Do you have a class scheduling system
  • Do you have an exam scheduling system
  • Do you have a system for anonymous evaluation of teachers by the students.

Records and Registration

  • Do you have an automated registration system?
  • Does your registration system automatically checks the pre-requisites
  • Can you get statistical reports regarding the frequency distribution of Grades: Class-wise, Faculty-wise, Semester-wise, Course-wise, department-wise
  • How long does it take for you to issue a degree or a transcript
  • How long does it take for you to decide about exemptions and eligibility

Why HEC’s Foreign Faculty Hiring Plan failed

This was written in June 2003 when HEC with a fanfare started the program and spent billions of rupees.

Why HEC’s Foreign Faculty Hiring Plan may fail:
  • HEC and its bureaucracy is not equipped to run and monitor projects.
  • HEC and not the employer organization is going to make a decision on who is to be selected and who is not.
  • Similar scheme for hiring foreign IT faculty initiated by MoST under Dr. Atta ur Rahman, a couple of years ago failed to produce the desired results. Many of the hired faculty have left.
  • Government universities who are going to be the beneficiaries are ill-equipped and ill-prepared for the R&D work, else the need for the PhD’s would not have arisen in the first place.
  • Fund is not going to be used for creating the R&D environment. It is the work environment that produces R&D and not the remuneration of the researcher. The researcher requires proper seating and research environment to deliver.
  • Assimilation and change management problem: Heavily politicized and established faculty in the public universities will ensure that the new faculty do not assimilate and do not get a chance to work.
  • Discrepancy in remunerations for lateral entrants and existing faculty would create fissures for which there appears inadequate preparation.
  • The policy is going to fail because the change management is not something that HEC understands. There would be resistance to change and as the experience of model university ordinance shows, HEC has no strategy for encountering the problems of assimilation.
  • Government with its bureaucracy is utterly unsuited for this task.


Sunday, January 26, 2020

Different Types of Research Contributions and Challenges in Defense


Paper Writing and Research Strategies

 [Copied from the net in 2012. Source unknown]

Introduction

Here is a brief reminder of how to write papers and what kinds of papers to write. Please also see the other materials on our web site on paper structure.
What matters most about a paper is what the reader gets out of it, not how much work you put into it. That is, after reading your paper, the reader should feel that he knows something new, has a better insight into something, or has a better idea of how to do something.
Each paper you write should stick to one main idea, and that idea should be clear enough that you can formulate it in a single sentence. If you can't tell someone else what the main contribution of your paper is in a single sentence, you haven't worked it out enough.
Writing papers doesn't need to be all that hard because papers fall into a small number of common categories. Most of them involve some problem that needs to be solved or some observation that needs to be explained; for the paper to be accepted, it's important that this problem or observation is interesting in the first place.
Solving problems that aren’t interesting to reviewers won't get you published. It is therefore important that you keep track of the literature and figure out what topics are likely going to be of interest to reviewers.
In writing your paper, keep in mind that many reviewers are graduate students like yourself. Think about how you approached your last few paper reviews. How well did you yourself know the literature? Did you give the authors the benefit of the doubt when you didn’t understand something? Did you ask for more experiments?

Paper Types

Now let's look at common different paper types. I have given these papers short, catchy names because you should be able to identify clearly what kind of paper you are writing.

"I reviewed the literature about solutions to problem X." (Literature Review)

A literature review should pick a well-defined research area, something that is neither too large nor too small. An area might be something in which there are 100-200 research papers total that you need to read.
A literature review is a little bit like an experimental paper. It usually starts with a question, then looks at data, and then draws its conclusions. The question it asks is usually of the form “what are the unsolved problems” or “where are there areas for improvement”. In the conclusions, you should answer these questions. They form the basis for further research.
In fact, when you start on your thesis, you need to do a good literature review anyway, and you need to incorporate it into your thesis. If you can get this kind of review published, that's really good. However, publishable review papers are a lot of work and usually require upwards of a hundred original references to be organized and analyzed. Nevertheless, you should at least aim for a group seminar on your chosen topic.

"I looked at the best ways of solving problem X, benchmarked, and compared them." (Benchmark Paper)

A benchmarking paper asks the question: “given these standard methods, how do they perform under different conditions?” It isn’t necessarily for finding “the best” of the methods (usually, there is no uniformly best method), but for identifying tradeoffs between different methods.

A significant component of a benchmarking paper is the datasets. If there are standard datasets and standard algorithms, and they simply haven’t been benchmarked against each other, then it’s easy to do that. Otherwise, you may have to create your own benchmark dataset. This can be a lot of work, but it can also be rewarding: if your dataset is novel and interesting, other people will use it and cite your benchmarks.
This is often a good paper to write at the start of your thesis work: you gain experience with the problem you are trying to solve, you implement algorithms that you need to implement as control experiments later, and you collect and/or create databases you can evaluate on. Papers like this have the potential of getting highly cited. They are also conceptually fairly simple and you are almost guaranteed to get a useful result. However, they are a lot of work to do well.

"I came up with a new/better/interesting way for solving problem X." (Method Paper)

This is perhaps the most common paper people write; it's usually the kind of paper that takes an engineering view of the world. You usually need to show experimentally that your method is better than existing methods, at least under some circumstances or on some kinds of data (rarely, a complexity analysis may be sufficient). Here, "better" may mean one of several things:
  1. It yields lower error rates.
  2. It is faster.
  3. It is simpler to implement.
Additional useful distinguishing features can be the following, but they are rarely sufficient by themselves:
  1. It is easier to understand.
  2. It is theoretically better justified.
  3. It is available in open source form.
The required experiments make these kinds of papers similar to benchmarking papers, but there are some differences. While the goal of a benchmarking paper is to gain insights into existing methods (data sets represent a wide range of conditions, authors are supposed to have no preferences), the goal of the experiments of a "new method" paper is to show that the method is actually better. But that also means that the reviewers will take a more adversarial view of your paper, meaning, they will try to poke holes into your arguments. Key questions they will ask are:
  1. Is the problem you're solving interesting at all?
  2. Are the conditions under which your method is better than other methods too restrictive?
  3. Are the advantages of your method sufficient large?
  4. Does your method have so many parameters that it is hard to implement?
  5. Did you tune your method to the specific dataset, i.e. do the results generalize?
  6. Is the comparison fair? I.e., did you unfairly bias the data or preprocessing in favor of your method?
Generally, when you write up your paper, you need to think about the list of objections that reviewers might make, and you need to have answers to them.
"I performed observations/measurements on X" (Research Paper)
This is the more standard scientific research paper; you don’t create something and you don’t invent a new method for doing something, instead you observe and interpret existing systems. In some way, it is like a benchmark paper.
These papers are, on balance, easier to write than method papers, because it is usually easier to get publishable results out of observations. There are a lot of opportunities for these kinds of papers in computer vision, pattern recognition, social network analysis, etc.

"I have a new mathematical model/theory for observation X." (Model Paper)

Mathematical modeling or theory is a very different approach from experimental approaches, but the basic rules still apply: your result needs to be novel, interesting, relevant, and useful somehow. Generally, mathematical models or theories should make predictions. Some of those predictions, you can compare against existing data, others may suggest new experiments.

"I found a new/different interpretation of observation X." (Response Paper)

These papers are generally responses to the literature, where you read a paper and decide that its conclusions, model, or theory aren't quite right. These papers tend to be shorter and fairly easy to write (because someone else has already done all the background research). They may involve experiments illustrating the point you are trying to make. They are often good papers to write, but they are difficult to plan for because, of course, you don't know when you'll come across a paper you can respond to.

"A new framework/'theory' for X." (Framework Paper)

These papers often don't involve any experiments, theory, or serious review of the literature. Instead, they provide a view of a field, observations, or data. If you hit the "Zeitgeist" just right, these kinds of papers can be spectacularly successful and highly cited, in particular if you have already made a name for yourself. However, most of them end up just languishing at a conference.

Preparation for Ph.D.

Although you may be enrolled in a Ph.D. program, you may first need to learn how to work with the literature and how to conduct experimental research. At some other universities, this phase is delimited from the actual start of the Ph.D. program by the submission and presentation of a formal Ph.D. proposal. We don’t have that requirement at our university, but the two phases really still exist.
  1. Seminars and Projects. Before you can write reviews and papers, perform benchmarks, and perform experiments, you need to learn these things. As part of our graduate program, you should learn reviewing as part of your seminar course, and benchmarking and/or experimenting is commonly a project topic. The seminar and project are the main time during which someone will look at your work and give you detailed feedback. They can do this because it’s area and research they understand well. Later, your advisor will be able to give you much less input and help on your Ph.D. project because they will know fewer of the details of your Ph.D. work.
  2. Collaborate. Another way of gaining experience with reviews and research work is to collaborate on research and papers with others. This may include detailed proof-reading of their papers, contributing to their writing, taking over experimental work from them using their methods, or applying special skills you have to their problems. When collaborating, think not only about the obvious benefit (like maybe another publication), but also what you can learn from the collaboration.
  3. Small Projects. If you need more experience or still don’t know what exactly you want to do for your Ph.D., in addition to collaborations, you can also take on smaller projects. These are projects similar to what your advisor might give you in a project course, but they are not formal parts of your course requirements and you don’t get ECTS points for them. You should still aim for getting publications out of them. There’s usually a long list of such small projects to choose from.
The previous steps are preparatory for your Ph.D. You really shouldn’t spend too much time on them because you don’t want to spend too much time on your Ph.D. At some point, when you have a reasonable handle on how to work with the literature, how to run experiments, and what you want to do, you need to pick a Ph.D. topic. That’s when your Ph.D. studies usually start.

Ph.D. Research

Actual Ph.D. research, like all research, really means three steps:
  1. familiarizing yourself with the literature
  2. getting familiar with the research methods and tools
  3. conducting actual research
Note that you don’t usually start this process from zero; presumably, you have already done a little bit of work in the area--maybe as part of collaborations or projects--simply to determine that you actually are interested in it.
Each of these steps should produce some output, in the form of a review, a benchmark, and research contributions.
  1. Review/Proposal. Start by writing a review paper of the field your thesis is in. You should plan on presenting this at a seminar. You can actually do this for seminar credit. Sometimes, you can publish this as a review article, although that usually involves a lot more work. This should also give you a good idea of what the open problems are and how success is measured in your area. A good review with meaningful conclusions is basically equivalent to a Ph.D. proposal.
  2. Benchmark. Next, implement algorithms, collect datasets, and perform benchmarks. You can do this as part of a project course. If your results warrant it, this is fairly easy to publish at least at a conference. This will give you better understanding of the data and get you set up for your own experiments.
  3. Research Contributions. There are actually two major kinds of research contributions: new methods or observational studies. New Methods. Then you start implementing your own ideas and methods, benchmark them against the standard methods, and start writing up the results, resulting in several publications. Observational Studies. You can also make part or all of your thesis observational studies. You should consider this possibility seriously because it can be easier than “new methods” work. However, while for engineering and methods research, the primary deliverable is something that works better, for observational studies, scientific reasoning and understanding is more of a challenge. (The “benchmarks” for observational studies are replication of other people’s observations.)
This division and three step approach are useful, and it’s the way researchers generally approach starting research in a new field. But it is not formally required and many students end up working on a lot of different projects and ideas and then at some point putting everything together into a thesis. Each of project implicitly still requires a literature review, benchmarks, and new contributions, but they happen in different orders and perhaps aren’t made explicit in the form of publications. There is nothing wrong with that if that works for you and if you get a good thesis out of it. However, if you are limited in the amount of time you have for your Ph.D., if you feel lost or are concerned about completing your Ph.D., then you should consider picking a single topic and focusing on the three steps for research.

Scholarships and Ph.D. Proposals

Some scholarship organizations will require you to submit a Ph.D. proposal in order to get the scholarship. That is a useful starting point, but it is rarely as detailed as a true Ph.D. proposal and literature review should be (although the more detailed you make it, the less work and uncertainty you will have later). Furthermore, after getting the scholarship, you may still need to spend some time with seminars, projects, etc. to get more familiar with research methodology.
This means that you shouldn’t assume that your Ph.D. proposal is finished just because you submitted something with the scholarship application. On the other hand, scholarship agencies are generally fairly flexible about proposals and you can change them later as you learn more about a research area.

Problems You Will Encounter

Of course, in real life, things don't necessarily work as smoothly. Common problems are:
  1. The literature seems to confusing and you can't make a coherent review or presentation out of it. This means that you really don't understand the field well enough yet to even start your research work. When you get serious about your Ph.D. thesis (or research in general), you should have a good answer to the question of "what are the open questions in your area", "what are promising ways of attacking them", and "what are the most common ways in which people have tried to solve these problems before and why have they failed." As part of this, you should become more of an expert on your chosen area than your Ph.D. advisor.
  2. The benchmarking doesn't yield meaningful results. One of the most important skills you need to learn as part of your Ph.D. is how to conduct meaningful experiments. This means asking the right questions, choosing the right datasets, performing the right measurements, and presenting the results in a meaningful and compelling way. Your Ph.D. advisor can help you and give you feedback, although it will largely be of the kind of feedback you would get from a reviewer: he will point out problems and limitations with your approach. Fixing those problems is your job. Your advisor may be able to give you a little input on that, but benchmarks and data sets are very hands-on problems, so that kind of input is limited.
  3. Your new method isn't working better than the old one. Perhaps your idea just isn't a good one, or maybe the dataset you are testing it on isn't good for showcasing its advantages. This is the kind of thing your advisor can help most with: he can provide ideas for other, new approaches and suggest other ways of evaluating your method. However, you shouldn't just abandon an approach if it isn't working; you probably invested a lot of time in trying to make it work and you should try to learn from the experience and understand why it isn't working. If you just keep trying one idea after another and never figure out why they are failing, you are going to make very slow progress. On the other hand, if you learn from your failures, even if you don't get a publication out of it, you are still making progress.

Other Strategies

Here are some other strategies you need to keep in mind:
  1. Talk with other people about your work. It doesn't matter whether they are experts in your area or not. One of the most important parts about talking to other people is that you learn to explain your work better. If you are lucky, they will give you useful feedeback.
  2. When you talk to your advisor, have clear and concise explanations of what you have done, and have specific questions you ask. If you don't, your advisor simply can't help you very much.
  3. In addition to your primary work, do some other things: collaborate with others on projects (contributing the expertise you have obtained from your primary research), and also take on some small and easy additional side-projects.
  4. Pick your research area so that there are lots of opportunities for collaboration and additional projects.


Sunday, January 19, 2020

Duration of Degree Program as a Measure of Quality

Do you know that Allama Iqbal's PhD thesis would never have passed HEC's quality criteria of minimum duration of three years for a PhD!

HEC's notion of quality is bureaucratic which ignores the quality standards of top universities. In USA, duration of a program is measured by the completion of credit hours and other academic requirements, not by passage of years. However, HEC in Pakistan is still confused between annual system and USA's credit hours.

Top Universities in USA have no such rules of calendar years. There is no restriction on minimum number of years for any program, only a restriction on minimum number of credit hours, which are measured academically, and not bureaucratically. At universities like Cornell, you can complete your MS in 9 months if you complete the desired credits. You can also complete your PHD in two or even less years if you meet the academic requirements. You can also complete your bachelor's in 3 years. You just need to complete the credit hours and other requirements.

Quality according to HEC is a bunch of paperwork and not Academics.

When I asked this question to Dr Mukhtar in a meeting of vice chancellors in Islamabad when he was Chairman HEC, he said that because a university (in timbuktu or Chee Chun ki Maliyan) was abusing this therefore HEC had to impose this minimum duration rule for ALL universities. This was a typical bureaucratic reply based on the presumption that "you are guilty unless proven innocent". Their argument goes like this: Because there are so many thieves in Pakistan therefore every one must be considered a thief unless proven innocent! Instead of catching the criminal which is cumbersome, bureaucracy takes the easy way out: Make the life of every one difficult!
Bureaucrats also know that making another rule doesn't stop criminals from circumventing the new rule. But, more such rules increase the power of bureaucracy and make them more important,and can provide justification for getting them more funding, and more privileges.
Do you know that Allama Iqbal's PhD thesis would never have passed HEC's quality criteria of minimum duration of three years for a PhD! 

In 1905, Iqbal travelled to England for educational purpose. Iqbal qualified for a scholarship from Trinity College, University of Cambridge and obtained Bachelor of Arts in 1906, and in the same year he was called to the bar as a barrister at Lincoln's Inn. In 1907, Iqbal moved to Germany to pursue his doctoral studies, and earned a Doctor of Philosophy degree from the Ludwig Maximilian University of Munich in 1908. Working under the guidance of Friedrich Hommel, Iqbal's doctoral thesis was entitled The Development of Metaphysics in Persia.[12][32][33][34]
[PS is from Wikipedia]

Iqbal arrived in Germany between 17 to 20 July 1907. Munich University issued call for defense on 21st July. Defense was held on 10th November 1907.

http://www.allamaiqbal.com/publications/journals/review/apr89/3.htm

http://riffathassan.info/writing/Iqbal_Studies/About_Iqbal%27s_Doctoral_Thesis.pdf