The General Idea:
- A thesis is a hypothesis or conjecture.
- A PhD dissertation is a lengthy, formal document that argues in defense of a particular thesis. (So many people use the term “thesis” to refer to the document that a current dictionary now includes it as the third meaning of “thesis”).
- Two important adjectives used to describe a dissertation are “original” and “substantial.” The research performed to support a thesis must be both, and the dissertation must show it to be so. In particular, a dissertation highlights original contributions.
- The scientific method means starting with a hypothesis and then collecting evidence to support or deny it. Before one can write a dissertation defending a particular thesis, one must collect evidence that supports it. Thus, the most difficult aspect of writing a dissertation consists of organizing the evidence and associated discussions into a coherent form.
- The essence of a dissertation is critical thinking, not experimental data. Analysis and concepts form the heart of the work.
- A dissertation concentrates on principles: it states the lessons learned, and not merely the facts behind them.
- In general, every statement in a dissertation must be supported either by a reference to published scientific literature or by original work. Moreover, a dissertation does not repeat the details of critical thinking and analysis found in published sources; it uses the results as fact and refers the reader to the source for further details.
- Each sentence in a dissertation must be complete and correct in a grammatical sense. Moreover, a dissertation must satisfy the stringent rules of formal grammar (e.g., no contractions, no colloquialisms, no slurs, no undefined technical jargon, no hidden jokes, and no slang, even when such terms or phrases are in common use in the spoken language). Indeed, the writing in a dissertaton must be crystal clear. Shades of meaning matter; the terminology and prose must make fine distinctions. The words must convey exactly the meaning intended, nothing more and nothing less.
- Each statement in a dissertation must be correct and defensible in a logical and scientific sense. Moreover, the discussions in a dissertation must satisfy the most stringent rules of logic applied to mathematics and science.
Definitions And Terminology:
- Each technical term used in a dissertation must be defined either by a reference to a previously published definition (for standard terms with their usual meaning) or by a precise, unambiguous definition that appears before the term is used (for a new term or a standard term used in an unusual way).
- Each term should be used in one and only one way throughout the dissertation.
- The easiest way to avoid a long series of definitions is to include a statement: “the terminology used throughout this document follows that given in [CITATION].” Then, only define exceptions.
- The introductory chapter can give the intuition (i.e., informal definitions) of terms provided they are defined more precisely later.
Terms And Phrases To Avoid:
- Mostly, they are very often overly used. Use strong words instead. For example, one could say, “Writers abuse adverbs.”
- jokes or puns
- They have no place in a formal document.
- “bad”, “good”, “nice”, “terrible”, “stupid”
- A scientific dissertation does not make moral judgements. Use “incorrect/correct” to refer to factual correctness or errors. Use precise words or phrases to assess quality (e.g., “method A requires less computation than method B”). In general, one should avoid all qualitative judgements.
- “true”, “pure”,
- In the sense of “good” (it is judgemental).
- Nothing is.
- “an ideal solution”
- You’re judging again.
- “today”, “modern times”
- Today is tomorrow’s yesterday.
- How soon? Later tonight? Next decade?
- “we were surprised to learn…”
- Even if you were, so what?
- “seems”, “seemingly”,
- It doesn’t matter how something appears;
- “would seem to show”
- all that matters are the facts.
- “in terms of”
- usually vague
- “based on”, “X-based”, “as the basis of”
- careful; can be vague
- Does not mean “various”; different than what?
- “in light of”
- “lots of”
- vague & colloquial
- “kind of”
- vague & colloquial
- “type of”
- vague & colloquial
- “something like”
- vague & colloquial
- “just about”
- vague & colloquial
- “number of”
- vague; do you mean “some”, “many”, or “most”? A quantative statement is preferable.
- “due to”
- only if you know the statistical probability (if you do, state it quantatively
- “obviously, clearly”
- be careful: obvious/clear to everyone?
- Can have a negative connotation, as in “simpleton”
- “along with”
- Just use “with”
- “actually, really”
- define terms precisely to eliminate the need to clarify
- “the fact that”
- makes it a meta-sentence; rephrase
- “this”, “that”
- As in “This causes concern.” Reason: “this” can refer to the subject of the previous sentence, the entire previous sentence, the entire previous paragraph, the entire previous section, etc. More important, it can be interpreted in the concrete sense or in the meta-sense. For example, in:
“X does Y. This means …”
- the reader can assume “this” refers to Y or to the fact that X does it. Even when restricted (e.g., “this computation…”), the phrase is weak and often ambiguous.
- “You will read about…”
- The second person has no place in a formal dissertation.
- “I will describe…”
- The first person has no place in a formal dissertation. If self-reference is essential, phrase it as “Section 10 describes…”
- “we” as in “we see that”
- A trap to avoid. Reason: almost any sentence can be written to begin with “we” because “we” can refer to: the reader and author, the author and advisor, the author and research team, experimental computer scientists, the entire computer science community, the science community, or some other unspecified group.
- “Hopefully, the program…”
- Computer programs don’t hope, not unless they implement AI systems. By the way, if you are writing an AI thesis, talk to someone else: AI people have their own system of rules.
- “…a famous researcher…”
- It doesn’t matter who said it or who did it. In fact, such statements prejudice the reader.
- Be Careful When Using “few, most, all, any, every”.
- A dissertation is precise. If a sentence says “Most computer systems contain X”, you must be able to defend it. Are you sure you really know the facts? How many computers were built and sold yesterday?
- “must”, “always”
- Who says so?
- “proof”, “prove”
- Would a mathematician agree that it’s a proof?
- Used in the sense of “prove”. To “show” something, you need to provide a formal proof.
- Your mother probably told you the difference.
Knowledge Vs. Data
- The facts that result from an experiment are called “data”. The term “knowledge” implies that the facts have been analyzed, condensed, or combined with facts from other experiments to produce useful information.
Cause and Effect:
- A dissertation must carefully separate cause-effect relationships from simple statistical correlations. For example, even if all computer programs written in Professor X’s lab require more memory than the computer programs written in Professor Y’s lab, it may not have anything to do with the professors or the lab or the programmers (e.g., maybe the people working in professor X’s lab are working on applications that require more memory than the applications in professor Y’s lab).
Commerce and Science:
- In a scientific dissertation, one never draws conclusions about the economic viability or commercial success of an idea/method, nor does one speculate about the history of development or origins of an idea. A scientist must remain objective about the merits of an idea independent of its commercial popularity. In particular, a scientist never assumes that commercial success is a valid measure of merit (many popular products are neither well-designed nor well-engineered). Thus, statements such as “over four hundred vendors make products using technique Y” are irrelevant in a dissertation.
Politics And Science:
- A scientist avoids all political influence when assessing ideas. Obviously, it should not matter whether government bodies, political parties, religious groups, or other organizations endorse an idea. More important and often overlooked, it does not matter whether an idea originated with a scientist who has already won a Nobel prize or a first-year graduate student. One must assess the idea independent of the source.
- In general, every dissertation must define the problem that motivated the research, tell why that problem is important, tell what others have done, describe the new contribution, document the experiments that validate the contribution, and draw conclusions. There is no canonical organization for a dissertation; each is unique. However, novices writing a dissertation in the experimental areas of CS may find the following example a good starting point:
Chapter 1: Introduction
- An overview of the problem; why it is important; a summary of extant work and a statement of your hypothesis or specific question to be explored. Make it readable by anyone.
Chapter 2: Definitions
- New terms only. Make the definitions precise, concise, and unambiguous.
Chapter 3: Conceptual Model
- Describe the central concept underlying your work. Make it a “theme” that ties together all your arguments. It should provide an answer to the question posed in the introduction at a conceptual level. If necessary, add another chapter to give additional reasoning about the problem or its solution.
Chapter 4: Experimental Measurements
- Describe the results of experiments that provide evidence in support of your thesis. Usually experiments either emphasize proof-of-concept (demonstrating the viability of a method/technique) or efficiency (demonstrating that a method/technique provides better performance than those that exist).
Chapter 5: Corollaries And Consequences
- Describe variations, extensions, or other applications of the central idea.
Chapter 6: Conclusions
- Summarize what was learned and how it can be applied. Mention the possibilities for future research.
- A short (few paragraphs) summary of the the dissertation. Describe the problem and the research approach. Emphasize the original contributions.