Scope of Research

For PhD level, contribution expected at global/ world level

What is Research

• Quantitative
  • Def: use of mathematical models, softwares do statistical, formulaic or numerical analysis
  • Approach: discovery, analysis, causal determination, prediction: or generalization of findings
  • Results: “This solution is N % better”
• Qualitative
  • Def: use of non-numeric techniques
  • Approach: discovery, analogy, understanding, extrapolation of similar circumstances
  • Results: “This is a new problem and here is how to solve it”
• Pragmatic method & Mixed methods:
  • Generally problem given
  • Combines both quantitative and qualitative approaches
• Advocacy/ participatory research methods
  • Predefined agenda

Approaches to CS Research

• Attack a known problem, find or compare tools to solve it
• Formalize a new problem, then attack it

Why Do We Cite Papers

• Relate our problem to prior work
• Add credibility
• Show that we know what is important in prior work, what is solved and what is open.
• Establish current state-of-the-art

READ HIGH QUALITY PAPER

• Tools
  • Google scholar
  • Microsoft academic research
  • Library
  • Research gate
• Criteria
  • Acceptance rate (for conference/…)
  • Total citations
  • Impact factors
  • Number of readers

How To Read A Paper

• The browse (total 1-2 mins “gut check”)
  • Go through 100-1000 papers a year
  • To see interesting /relevant
  • Abstract + figures + captions
  • Venuer check. Author Check. Citation check.
• The skim (5-10 mins max)
  • Believe the author and assume it’s all true and decide if the paper is actually interesting and worth more exploration
  • Answer:
    • Category: type of paper
    • Context: what other papers is it related to? How related to your work?
    • Contribution
    • Credible
    • Care: will anyone care, given its valid? will it change anything?
    • Cost: time and effort cost you to read it carefully
  • 2-3 sencentences to say the addressed problem/ the new in solution/ how significant the experimentation/theory
• Critical read (1 hr)
  • Read carefully, but ignore details (e.g., proofs)
    • Note assumptions and question them
    • Analyze experiments/conclusions
    • Anslyze figures, diagrams, illustrations, graphs
    • Mark relevant unread references
    • Summary the main thrust and point out likely weakness in assumptions/methodology/experiments
  • Critically read
    • Understand
      • The problem
      • The proposed solution
      • The competing approaches/designs
      • The evaluation methodology
      • The comparison to state-of-the-art
    • Answer questions:
      • Is the problem carefully stated/ formulated?
      • Is it a meaningful/ real problem?
      • Is it the RIGHT problem (formulation vs description)?
      • Make a list of assumptions?
      • Novel about the problem formulation, if any
      • What well known problems are related or the same?
      • Are there simple solutions the authors do not seem to have considered?
      • Consider every divide, matrix op, function inversion, minimization (maximization) makes sense to you? Are assumptions justified?
      • What are the limitations of the soluation?
      • Is the logic of the paper clear and justifiable, given the assumptions?
      • Do all the pieces of their work fit together logically?
      • Has the right theorem been proven?
      • Are theorems logically supported? Are proofs given? Cited? If cited, how credible a source
      • If data is presented, did they gather the right data to substantiate their argument and did the data appear in the correct manner?
      • Did they have enough data to make a statistically sound decision?
      • Did they interpret the data in a reasonable manner?
      • Would other data be more compelling?
      • Did they compare with actual state-of-the-art performance?
      • What were the results? Did they do what they set out to do?
      • On what dimension did they advance the art?
      • Have the authors been cutting corners?
      • Would results be reproducable?
      • Problematic experimental setup?
      • Confounding factors
      • Unrealistic or artificial benchmarks?
      • Methodological misunderstanding?
      • Do the numbers add up?
      • Are the generalizations valid?
      • Are the claims modest enough?
• Creatively read
  • What is the novelty of the paper?
  • Does it open up new directions?
  • What are the good ideas in this paper? Can they can extended or generalized?
  • Could their ideas be combined with other approaches / your work?
  • If there were other assumptions made, could that improve the approach? Could you ensure those assumptions were true for some problems?
  • Are there better numerical methods than what they are doing?
  • Are there possible improvements that make important practical differences?
  • If you were going to start doing research from this paper, what would be the next thing you would do?

Literatrue comprehension

• Active reading
• Literature map: https://tobloef.com/text2mindmap/
  • write the title of your research on top
  • main topics relavent to your research underneath
  • Associate the papers you read with each of the topics