Storytelling 101: Writing Tips for Academics

Little did I know that a Ph.D. in computer science would teach me more about how to write than any other previous experience I’d had.  Indeed, research isn’t only about making new discoveries, but also about expressing those discoveries so that other people can appreciate them and their significance.

Below, I’ll present some thoughts on writing that I’ve picked up over the years and continually aim (and encourage students) to put into practice. I’ll focus on story-telling, presentation, and efficiency: how to write your paper so that it clearly, cleanly, and efficiently tells a good story.

What this post is not about.  At least in this post, I won’t write about grammar—there is too much English grammar to cover in a single post, and there are plenty of good courses one can take to learn grammar.  I also won’t talk about style (common phrases) or usage (e.g., commonly misused words).  For now, my only advice on the topic of learning English grammar and style—both for native speakers and foreigners—is to read daily.  Reading the New York Times, or The Economist are great places to start: the writing is generally of high quality, and you’ll also have something interesting to talk about outside of the lab.  I have the impression that foreign students can tend towards reading their local news from home; that’s understandable, but I would encourage those students to also read periodicals and publications that are written in the style of English that people are used to reading in the Western world (indeed, English in different parts of the world has different tones, styles, idioms, and turns of phrase).

Why Does Writing Well Matter?

Writing is part and parcel of the research process.  Writing communicates your research results, and—perhaps contrary to intuition—writing well is even more important when the results that one wants to present are “strong” (after all, if the results are that great, you should want as many people as possible to benefit from understanding them).  But writing isn’t just something that happens at when the research is “done”; rather, writing should occur throughout the course of a research project.  Writing and thinking are tightly coupled—writing is a reflection of our own thoughts, and writing well can actually sometimes clarify our own thinking about a problem and lead us to a solution.

Communicating your results. A research finding or discovery that others cannot understand or appreciate is as good as no discovery in the first place.  A well-written paper with a half-baked idea that people can understand and appreciate will often be accepted to a conference over a paper with a (possibly) stronger result that nobody can understand or appreciate.  Cynics may dismiss this as “unfair”, as “laziness” on the reviewer’s part for not reading the details thoroughly, or as “accepting a paper because it was well-written” (insinuating that the results themselves might not be that strong).  The fact of the matter is that none of this matters: Independently of the quality of the research, the results must be written well, or people will not be able to appreciate the value of your work. Writing well is perhaps one of the most important skills that budding researchers must attain; it is equally important as learning how to conduct the research.

Clarifying your own thinking.  Writing is a reflection of your thinking.  You should never be content with vague language or imprecise descriptions.  Muddy writing often reflects muddy thinking.  An attempt to write down a concept, idea, or solution can actually help you realize that you don’t know what you are talking about!  (And, help you crystallize your thinking.)  You may have had the experience of realizing you don’t understand something as well as you thought you did when someone asks you to explain something and you have trouble.  Writing achieves the same effect: as you attempt to write something down, you may realize that you don’t know why you are working on something, why a particular phenomenon appears, and so forth.  If you don’t force yourself to try to write down your explanations and observations, you may never discover that you don’t know what you’re doing until it’s too late.  Therefore, start writing early in the course of a research project.  Most of the early text you write may not find its way into the final version of the paper, but taking the time to express your ideas on paper can help you crystallize your thoughts and guide your research.

Tell Me a Story

Every paper should tell a story.  For a given research result, there are many stories that a paper could tell, and it’s important to think about what the story should be.  This decision should be made early on in the writing process.  The story can be changed later on, to match the strongest results in the paper, or if the context for the research changes (for example, sometimes when working on a research problem, people can find it necessary to re-formulate the problem that they are solving).  But, it’s always good to figure out a story before you go too far down the path.

Determining the Big Picture: Composition and “Flow”. Composition is the structure and flow of your paper writeup, and how individual paragraphs fit together.  When people talk about the “flow” of a piece of writing, they’re talking about composition: how sentences flow together to form paragraphs, how paragraphs flow together to form sections, and how sections flow together to form a paper.  Composition (or “flow”) is the most important aspect of technical writing.  I’ve found that reviewers (and readers) are often forgiving of the occasional grammatical error, especially if a paper was written by non-native English speakers.  The human brain is pretty good at applying “error correction” on grammar.  Correcting “flow”, on the other hand, is extremely hard for a reader to do.  Often, the reader will complain that they “can’t understand where [the paper] is going” and simply give up.  Grammar is also easier to teach (and learn) than composition because there are fixed rules for grammar; composition is more of an art form, and it does involve a bit of intuition.  It involves thinking about the big picture, the story that you want to tell, and how the pieces fit together.  It takes practice to develop intuition for composition, and I’ve found that native English speakers are not necessarily any better at composition than foreigners are (that’s good news for Ph.D. students coming from abroad: the playing field is actually more level than it appears).  Even though composition is a bit of an art form, I’ve found that the following tips can be incredibly helpful for improving flow:

  • Build the scaffolding before filling in the details. Write sections and topic sentences first.  There is always the temptation to simply jump in and start writing full paragraphs.  This rarely ends well.  Nobody would build a house before first drawing plans.  After the plans comes a foundation, and scaffolding.  Only after the structure is in place can many of the details be filled in.  You should think about writing a paper in the same way: before you can fill in the details, you need to figure out how you want the paper to read when its done; this is your outline.  Start at a high level, by outlining sections.  Once you have an outline of sections, you can begin to slowly fill in more details: outline each paragraph within a section by writing only the topic sentence for the paragraph.  See whether your story makes sense if you read each topic sentence in sequence.  Your “paper” should roughly make sense if you read these topic sentences in sequence (in fact, some reviewers or readers may try to read your paper in exactly this fashion, by skimming topic sentences!).  If reading your topic sentences in sequence doesn’t make sense, something is wrong with your flow.   Determining which topic sentences to write takes a bit of practice, but I find that making a bulleted list of points that you want to include in the section and then clustering those points into related points and topics can help identify the topics you want to cover (and, hence, the topic sentences you want to write).  If you find that the topics you want to cover are not all related to one another, you may need to create additional subsections.  Once you have topic sentences, you can fill in paragraphs, which are groups of closely (and logically) related sentences.  If your sentences aren’t closely related, they don’t belong in the same paragraph and you may need to consider adding a new topic (and topic sentence).
  • Assess balance and budget pages accordingly.  Once you have your outline, it’s important to budget pages, to figure out how much you plan to write about each topic.  Topics of equal importance or equal relevance should receive “balanced” coverage in the paper.  If your system has two components of relatively equal importance, then the sections covering those components should be of approximately equal length in the paper.  Once you have your outline (from the previous step), you can assess how much space you want to budget for each point.  If some points in a section are more important than others (and hence require more page budget), you can go back and think about how to re-balance your writeup (e.g., by creating a new devoted section to the topic that’s more important, shortening or eliminating the less important point, and so forth).
  • Follow convention.  Continuing the analogy of a house or building, many designs follow convention: a restroom in a restaurant is often in the back.  A powder room in a house is often near the front door.  These common “design patterns” make life easier for us when we visit a new building or house.  Similarly, your reader is a visitor of your thought process.  A reader who has read (and written) many research papers will similarly expect the structure of your paper to follow certain conventions.  These conventions differ by field: some fields write papers completely in the passive voice, whereas others use active voice.  Some fields prefer related work sections at the beginning of the paper, and others prefer related work at the end.  Some fields expect an introduction that spends time framing the problem, and others expect an introduction that starts with a clear statement of the main result.  There may be an expectation of (or aversion) to mathematics in the main line of the paper.  Within a section, people may expect the introduction to flow in a particular way (in computer science, Jim Kurose’s description of how to write an intro is the best example I’ve seen for writing a conventional paper introduction).
  • Signpost your paper.  Even if your paper plan and outline make perfect sense to you, the reader doesn’t start out “on the same page” as you.  Even if the builders have built a house completely according to the blueprints, even if the design of the house follows convention, visitors still typically appreciate an explanation of where the bathroom is.  The reader needs instructions for how to read the paper and a clear view for how the paper and story will proceed.  Signposts throughout the paper help the reader understand how the paper is organized.  Examples of signposts include: an outline of the paper at the end of the introduction (“The rest of this paper proceeds as follows.”), a preamble to each section (“In this section, we discuss…”), declarative subsection titles, and (within reason) bold paragraph headings (such as those in this blog post).

Make a Good Impression

A reader forms an impression about your research based on your presentation of the work.  Just as people quickly form first impressions about other people that may be difficult to subsequently correct, your reader will quickly form a first impression of your work based on a quick read of the title, abstract, and introduction and perhaps a quick skim through other parts of the paper.  Therefore, it is important to make a good impression by writing a clear abstract and introduction (and also ensuring that the rest of the paper also is commensurate with the impression that you create).

Spend a lot of time on your introduction; start early.  The introduction summarizes the story of your paper (now, it should be doubly clear why you need a story!) and is the most important part of your paper.  Many people won’t read past the introduction, even if they find the work interesting.  Yet, if people don’t understand your “story” (the problem you’re solving, its importance, and your approach to solving it), they will never make it past the introduction into the details of the paper.  There are two schools of thought for writing the introduction: if you write it first, you ensure that you have a clear story.  If you write it last, then your introduction will clearly reflect the ultimate story and be bolstered by your results.  My advice is to do both.  Writing the introduction is probably the most difficult part of writing the paper, which may come as a surprise to new students.  After all, the introduction is high-level and often doesn’t reflect the nitty gritty details of how difficult that experiment was to run, and how much toil went into writing the system.  To the contrary, distilling your work from those details so that the rest of the world can digest the takeaways—stepping out of the weeds and trying to figure out why your work matters—can take more effort than anything.  The same results can be presented in many different ways, so you will likely need to perform many iterations on your story.  Typically, you can “test” your story out on other colleagues and readers with a draft introduction in parallel with completing the rest of your work.

Write the introduction first—and last. I typically try (and encourage students) to write a draft of the paper’s introduction before writing other parts of the paper.  You will most likely eventually discard this draft completely, since it’s very hard to predict what one should be emphasizing in the introduction before the “meat” of the paper is done.  Yet, writing an early introduction draft allows a researcher to put the work in context, and to argue (1) what the problem is, (2) why it is hard, and (3) why the solution (if it is achieved) will be interesting to readers.  If an introduction draft can’t articulate answers to these questions, even at an early stage in the research, then the work likely needs some focus, as well.  When you write the second version of your introduction (after the rest of the paper is written), you are in a good position to refine your story, making sure that any claims you make are clearly supported by your results and data.  Your results should be stated as clearly and specifically as possible.  You should neither understate your results (which might cause the reader to lose interest and stop reading) or overstate them (which might cause the reader to be annoyed or “let down” when they dig in and find that you don’t deliver on what you’ve promised).

Perform some “landscaping” on your paper.  Your paper should be pleasing to the eye.  Care in preparation of the paper suggests that you also cared about the research itself.   Conversely, if the preparation of the paper is sloppy, how can the reader trust that you were not similarly sloppy in writing code, setting up and running your experiments, and analyzing your results?  Therefore, take some time to make your paper look nice. Here are a few quick tips on “landscaping” your paper.

  • Eliminate widows.  Section headings at the bottoms of columns or pages look sloppy.  Depending on your word-processing tool, there are good ways to get rid of widows.  In Latex, you can always use a “\vfill” to kick the section heading to the top of the next column.  Similarly, widows on paragraphs (paragraphs that end with a single word on the last line) look sloppy.  Typically you can eliminate such widows by wordsmithing (see below on omitting needless words).  Latex also has macros such as “\widowpenalty” that help ensure that the text layout avoids widows whenever possible.
  • Place (and create) signposts, figures, and graphs to create whitespace and avoid “walls of text”.  Reading long installments of technical writing is tiring, and seeing a page of uninterrupted technical writing can psychologically tire the reader before they even begin to read.  Create whitespace and take other steps to break up large chunks of text.  Use paragraph headings to break up long strings of text (ensuring that your headings match the topics you’ve outlined, of course).  Try to space figures, tables, and graphs throughout the paper so that most pages have a mix of figures and text.  If you don’t have a figure to break up the text, consider making one.  Often, a table summarizing results can be useful.  Or, you might find that a figure (or block of pseudocode) describes a concept better than a paragraph in your writeup.  Take steps to ensure that your writeup is not a long stream of text.
  • Check your spelling.  Run a spell checker.  Every word-processing tool has one, and a writeup that is rife with spelling errors reflects carelessness.  Again, if you can’t be bothered to check your spelling, why should a reader trust that you spent time debugging your experiment or code?  While a reader can forgive grammatical errors, especially when the writeup comes from a non-native English speaker, anyone can run a spell checker, so spelling errors simply reflect carelessness.  That said, not all spell-checkers are perfect, and sometimes typographical errors actually manifest as real words; spell checkers won’t catch those, so they should be used only as a first pass.
  • Make the last page look decent.  If you’ve edited your text down to remove redundancy, you may find that you have some extra space at the end of your paper.  Try to adjust your formatting so that the last page of the paper looks decent.  If your paper is double-column and you end with references, balance the references across the two columns so that both columns are both equally (partially) filled.  Latex has a “\balance” command that you can use to balance the columns on the last page of your writeup.  If you have a bit more time, you can also make small adjustments to margins, space between columns, space around figures, sizes of figures, and so forth to ensure that the last page doesn’t look like you just suddenly decided to stop writing.

Be Efficient

“I’m sorry I have had to write you such a long letter, but I did not have time to write you a short one.” –Blaise Pascal

After you’ve made a good impression, you still need to engage the reader at least long enough to convey your main message.  You should assume that the reader is busy, has a short attention span, and (unlike the situation when they are, say, reading a news article or novel) simply wants to learn what you’ve done as quickly as possible and then move on.  In contrast to writing a novel, your goal is not to hold the reader in suspense or to illustrate your command of vocabulary or literary construction.  Rather, the goal is to transfer information as efficiently as possible.

Tailor the length of your paper to the information content.  People sometimes feel compelled to stuff a paper to the conference’s or journal’s page limit (after all, if the paper is too short, isn’t there “more work to do”?).  I’ve had a few papers that do fall short of the page limit, although I’ve found that this scenario is the exception rather than the rule (see Blaise Pascal: it takes time to shorten a paper!).   Remember that your goal is to efficiently transfer information. Therefore, economize on words.  Do not add unnecessary sentences, paragraphs, or other content that do not support a topic or point that you’ve explicitly outlined.

Keep words simple and sentences short. The easier it is to read your paper, the more likely a reader will keep reading.  Therefore, don’t make the reader work harder than they have to.  On a sentence level, follow Strunk and White’s most indispensable advice: Omit needless words (e.g., “to” as opposed to “in order to”, “optimizing” instead of “the problem of optimizing”, “This module…” instead of “This is a module that”, “more quickly” instead of “in a shorter time”, “whether” instead of “the question as to whether”).   Avoid padding phrases such as “the fact that” and unnecessary adverbs (e.g., “eliminated” instead of “totally eliminated”, “fast” instead of “very fast”).  Keep words simple (e.g., “use” instead of “utilize”, “new” instead of “novel”), and keep sentences short.

Eliminate redundancy.  Always make at least one (and probably several) editing passes over your paper to cut out redundancy.  You likely (and hopefully) did not write your paper in a linear fashion, and different co-authors may have written different sections or pieces of the paper.  It’s likely that you’ve stated the same point multiple times throughout the paper.  Cutting redundancy takes practice, and it sometimes requires divorcing the editing process from your ego (after all, isn’t that sentence you wrote beautiful?  It’d be a shame to cut it, right?).  If you need help cutting, ask someone else (a co-author or colleague) to help you—they’re not as married to your text as you are.

Be as specific and precise as you can.  Precision is especially important when describing results, and when writing up an experiment or evaluation section.  For example, a good rule of thumb for writing up an evaluation section is:  Would someone be able to reproduce your experiment from your description?  If not, you need to be more clear.  In an evaluation section, clearly state your assumptions; in what context(s) do your results hold?  How general are they?  Clearly describe your evaluation setup, as it sets the context for the results themselves.  What machines did you use to run the evaluation?  What data did you gather to support your conclusions?  What algorithms did you use to analyze the data?  The evaluation setup should read like a recipe.  Someone reading it should be able to reproduce what you’ve done, at least well enough to understand the context of the results.  Similarly, the results should be clearly and precisely described.  Avoid writing sentences such as “This optimization significantly reduced the system’s running time.”  Such vague statements frustrate the reader.  Instead, substitute precise and specific information: “This optimization reduced the system’s running time by 20%”.

Use clear and consistent terminology.  Unclear, vague, and inconsistent terminology is incredibly frustrating and also typically reflects muddy thinking.  If you call something a “node” in one part of your paper, a “host” in another part of the paper, a “client” in another part, and a “machine” in yet another part, people will have absolutely no clue that all of these terms are in fact referring to the same thing.  Before you get too far along in the writing process, clearly define your lexicon.  Defining your terms up front can help clarify your own thinking, by helping you to identify and articulate the important concepts that you will present.  Ultimately, this clarity will be reflected in your writeup.

Reduction to Practice: Write Every Day

Writing a technical paper involves clearly presenting a good story, as efficiently as possible.  The above tips should help you compose your story and ensure that it “flows” and ultimately produce a final result that leaves a good impression and communicates your results as efficiently as possible.  But, simply reading this post won’t make you a good writer: putting these tips into practice takes tireless repetition.  Try writing something every day to practice these tips.  Keep a “running draft” of your research paper.  Start a blog and practice composing posts that present shorter and simpler ideas and thoughts than a full research paper.  Keep a diary or notebook.  Ultimately, becoming a good writer requires relentless, daily practice, with continual attention to the above tips.

You and Your Research Proposal

It’s that time of year again, when researchers young and old gear up to write research proposals. Graduate school hopefuls are preparing research statements, Ph.D. students are writing fellowship applications, students who are trying to graduate are writing thesis proposals, and professors are writing grant applications to funding agencies.  At the core of each of these activities is a single kernel: a research proposal.  Since research proposals show up in many forms throughout one’s research career, figuring out how to write a good one is one of the most important skills that a researcher can learn (and hone).  I think it’s also important to embrace the process—as a researcher you’ll be writing a lot of proposals, so learning to enjoy the process (and becoming good at it) is an important part of one’s happiness (and, hence, ultimate success) as a researcher.

I was initially motivated to write this post as advice for Ph.D. students applying for fellowships, as part of the Intro to the Ph.D. class I’m teaching—we had a long discussion about fellowship applications last week, and some of the content in this post crystallizes that discussion.  As I thought about it, however, I realized that much of the advice that applies to writing a fellowship application can be applied to writing a good research proposal in general, so I’ve decided to generalize the advice that we discussed.

What is a research proposal?

A research proposal is exactly what it sounds like: it is a proposal to perform a certain research project.  It is a plan that is typically non-binding.  In the words of Eisenhower: “Plans are worthless, but planning is everything.”  Your research proposal will thus outline a vision and a plan of work for a research agenda that spans generally three to five years.  This timeframe appears to be common across almost every form of research proposal.  Shorter timeframes generally do not account for the fact that research is an inherently uncertain process and ideas take time to germinate (i.e., if you knew you could complete the work in one or two years, it likely wouldn’t be a research project).  Timeframes that are longer than five years are impossible to plan—generally, there is far too much uncertainty in life, in the world around us (technologies, other discoveries and advances, etc.) to say anything credible about reasonable problems to work on ten years from now.

In all likelihood, nobody will hold you to the plan that you outline in a research proposal.  It is well understood (at least by your colleagues who will review your proposal) that research can take unintended twists and turns, and you may find an exciting thread to pursue mid-course that you could not have imagined or foreseen at the beginning of your journey.  Therefore, as long as you do not stray too far from your original mission, you generally have the flexibility to adapt your research as you proceed.   There is definite value in specificity (I always try to outline specific paper-sized tasks in a proposal), but the broader vision is equally important: The specifics of your proposal (and how or whether certain aspects of your proposal are ultimately executed) are likely to evolve, but the vision is likely to stay roughly the same over the course of the project.

For these reasons, I think the process of writing a research proposal can be tremendously fun.  It is also a crucial part of the research process.    Writing a research proposal is an opportunity to think more broadly about a research agenda, and to be introspective about what problems you think are really important.  Because it is an opportunity to think as far as five years in advance, you can think about the bigger problems that you really want to solve and the best ways to go about solving them.  Because you have a longer time period to solve a problem, you can think about the best methods to solve the problems with and the best people to work on those problems with—even if you don’t know everything about those methods now or aren’t working with those people yet.  Thinking in this unconstrained fashion about bigger problems on a three-to-five year arc allows us as researchers to think beyond the next paper and consider how the work we do fits together into a larger picture. It is a lot of fun.

A recipe for a winning proposal

Of course, we should be clear that success is never guaranteed.  Many factors will affect your proposal’s chances of success, including some factors that are completely out of your control.  A research proposal is often evaluated by a committee.  In the case of a fellowship or a grant proposal, that committee may be made up of people of varying levels of experience and familiarity with your area of research.  They also bring their own biases and assumptions, some of which may be incorrect but be nonetheless held with strong conviction.  You cannot control who reviews your proposal or whether their pre-conceived biases will clash with your vision of a better world.  Fortunately, a few simple steps can dramatically increase your chances of success.

Get to the point.  I remember when I asked a colleague to read my NSF CAREER proposal.  He said, “Here are some comments. Sorry, these are based on only about 30 minutes of reading, but that’s probably the most any panelist will spend reading the proposal, anyway.”  You may find it disheartening that you spend hours, days, or weeks putting together a research proposal, only to have its fate turn on the whims of a reviewer who spends 30 minutes or less on your proposal before rushing off to teach a class or returning to tomorrow’s paper deadline.  That’s life.  Remember that there is more to the process of proposal writing than what one person ultimately thinks of the proposed work (see above on the benefits of the proposal-writing process).  What’s more, if you can’t capture someone’s attention in a few minutes of reading, chances are you need to work on distilling your message more.  You should be able to draw the reader in with just a few sentences or a paragraph at most.  A reviewer is obligated to start reading your proposal, but they are not obligated to finish reading it—get to the point as fast as possible (more below on what your point should be), and entice the reader to continue.  Make it easy for the reader to digest the key points; use bullets and bold headings as necessary (this post is an example of such a writing style).

Tell a story with vivid contrasts.  Everyone loves a good story.  If you want people to enjoy reading your research proposal, then the proposal should tell a good story.  The story, of course, needs to be of a certain type, and written in a certain style (I would not recommend the “mystery novel” approach, for example).  One of my favorite recipes for telling a story is to set up the problem context, explain why the problem is important and hard to solve, and then draw a succinct, stark contrast between your approach and every other previous approach.  For example, many research proposals I wrote about email spam filtering followed this recipe: (1) spam filtering is an important problem; (2) everyone else has been trying to filter spam by looking at the content of email messages; (3) in contrast, I will develop spam filters that discriminate good from bad based on the network traffic, without looking at the content of the messages at all.  I’d then proceed to explain why this was a promising approach and likely to result in new breakthroughs (which it ultimately did).  Your research proposal can and should also follow this recipe.  Tell the reader what sets your work apart, and why it’s likely to succeed where others have failed or otherwise come up short.  Effectively, you are painting your work as so promising and so different from everyone else’s approach that it would be foolish not to fund the proposed work, because nobody else is going to do the work, and not doing it could result in a missed opportunity for a breakthrough.

Answer the Four “Whys”: Why Important, Why Hard, Why Now, Why You?  I think every proposal should answer the following four questions.  Every proposal I write aims to answer these questions, and when I review a proposal, I also look for the answers to these questions:

  • Why is the problem important?  I heard a professor at MIT once tell a fellow Ph.D. student, “There are an infinite number of hard problems.  You might as well work on one that’s worth solving.”  In your proposal, it is important to convince the reader that there is a problem that needs to be solved, and, if your research is successful, it will result in solutions that will make the world a better place for some people.  You might be ridding the world of a certain kind of cyberattack, designing better user interfaces for some class of technology, making it easier to debug software programs, or something else.  There are many ways to advance knowledge and make the world a better place in doing so.  You should first convince the reader that there is a problem out there that needs to be solved—in fact, you should convince the reader that the problem is too important to be left unsolved.
  • Why is the problem hard?  I’ll follow up on this more in a later post, but you should beware of industry “bulldozers”.  The problem that you are working on should require deep thinking and insights, and possibly the application of tools and techniques from multiple disciplines.  It should require a level of thinking that goes beyond the next couple of months (quarterly deadlines, etc.).  If your problem does not pass this test, it’s likely that industry could solve your problem, and they could likely solve it better.  Industry has the capability to hire armies of software engineers to rapidly churn out code.  If the solution to the problem that you propose is a “simple matter of engineering”, and the problem is worth solving, then there is a strong risk that industry will solve the problem better and more quickly.  Convince the reader that the problem that you are working on cannot (or will not) be solved by industry, and that investing money in research on the problem is the best (or only) way to solve the problem.
  • Why now? Most research problems are not entirely new. You may think that you are the first to propose a particular problem.  In many cases, however, problems are longstanding, and many people have proposed variants of the same problem before.  For example, to return to the example of spam filtering: people had been working on the problem for at least ten years; why now is there a possibility to make headway on an old problem, where many others had attempted the same problem?   The answer to this question may be a recent technological advance (e.g., the ability to monitor traffic at high speeds); it might also be the emergence of new technologies in other areas that bring new “hammers” to an old nail (e.g., a new machine learning algorithm that makes an old approach more tractable, efficient, or accurate).  Regardless of what creates the “perfect storm” for doing the research at this time, you should aim to convince the reader that “things are different this time” because of recent advances, changes, etc., and that you’re equipped to take advantage of these new opportunities.
  • Why you?  I think this is perhaps one of the most important elements of a proposal, and one that is commonly forgotten.  Why are you the right person to carry out this research?  You may have convinced the reader that you have identified a hard problem that is worth solving, but if you are a networking researcher who has identified a hard and worthwhile problem in complexity theory (or vice versa), you will have a very hard time convincing the reader that your proposal should be funded.  You must establish credibility, and convince the reader that you are qualified (and, ideally, uniquely qualified) to carry out the work that you have proposed.  Establish your “secret weapon” that you will use to solve the problem that other people don’t have (e.g., domain expertise, a certain body of knowledge, collaborations with people in the appropriate discipline).  Tie back to successes from your own previous work, where possible, and establish bridges between your old (successful) work and the new work that you are proposing to do.  This aspect is where some delicate balancing comes in: You should lean on your past record to establish credibility for the proposed work, yet the proposed work should be visionary enough to encompass three-to-five years of future work.  One way to do this is to include some preliminary work in the proposal to demonstrate that your vision is feasible and that you are qualified to carry it out.  It’s worth noting that this is not the time to be modest.  You’re not talking with friends at a cocktail party; you are selling yourself and your research.  If you don’t sell your work, someone else is going to sell their work, and their sales job may edge your proposal out.  We can be cynical about the need to give a sales pitch and promote ourselves, but the fact of the matter is that if you don’t do it, the other researchers who are competing for the same fellowships, grants, etc. will anyway, so you might as well put your best foot forward and so that your proposed work can be judged on a level playing field.

Be meticulous.  Make sure your proposal is accepted or rejected for the appropriate reasons. Absolutely do not forget to include all mandatory sections of the proposal (e.g., the National Science Foundation takes education, diversity, and outreach extremely seriously; leaving out discussion of these aspects is almost certainly a showstopper for your NSF proposal).  Don’t forget to read the fine print about certain things that reviewers expect to see; if a call for proposal explicitly asks questions, be sure to answer them.  Perhaps most importantly, spell check your proposal, and have it read by a native English speaker before you submit it.  Sure, we all have the occasional typo, but more than one or two typos suggests extreme carelessness and sloppiness.  How can someone trust you to conduct your experiments carefully if you can’t even be meticulous with a short research proposal?  Can someone trust your code or research results if he or she can’t trust your ability to proofread a short, simple document?  Do not convey carelessness, ever.  It is a surefire way to put off reviewers and set you back significantly.  Running a spell-check is super easy, so there is absolutely no excuse for spelling errors.

Have fun, and enjoy the process. Hopefully these pointers will help you in the proposal-writing process.  Not every proposal will win the fellowship or get funding. Remember that there are always factors that you cannot control.   Like many things in life, the process is often as important as the outcome, and with these tips, hopefully the process of proposal writing can be both enlightening and fun.

[Update (October 23, 2013): From Craig Partridge]

I had a great chat with Craig Partridge, one of the Internet’s luminaries, chief scientist at BBN, and an all-around great researcher.  Craig has spent a large part of his career at BBN, which submits proposals for government contracts regularly.  He rightly pointed out that much of what I wrote above is accurate for academics who are applying to funding agencies like the National Science Foundation, but also that some funding agencies are extremely regimented about the format and content of a proposal.  He offered the following tips for the proposal writing process, which researchers at BBN regularly practice when replying to government funding solicitations.  Thanks, Craig!

“Two thoughts to the recipe for a winning proposal.

  1.  A best practice for being meticulous is to create a checklist.  Scour the solicitation for words like “must” and “should” and  “required” and make those sentences into a checklist.   Before you  submit your proposal, confirm that every item on your checklist is in the proposal.
  2.  Get outside reviewers.  The common practice among corporate research   proposers is to use a “pink team” and a “red team”.  A pink team  reads the solicitation and an outline of your proposal about 6 weeks  before it is due and tells you where it sees intellectual or practical  gaps in the outline.  It is an early chance to find problems.  A red team reads the proposal (preferably with your checklist and the solicitation  as supporting material) about a week before it is due and gives you a list  of problems that need to be fixed before submission.”