Presenting Research

1. Presenting Research Dr. Anjum Naveed & Dr. Peter Bloodsworth

2. Discussion: What is research?

3. Discussion: How can research outcomes be communicated to others?

4. How to Make a Persuasive Argument (researcher’s perspective)

5. Getting Started • Before you start: • Have your literature review handy • Be very clear regarding what you are trying to prove • Re-read your problem statement / research hypothesis • List the contributions that you want to claim in your thesis • For each point in your list you will need evidence to convince others that you have achieved it • People won’t just take your word for it • The most common reason for a rejected paper is getting this wrong

6. Learn From Others • Use your literature review • How have others doing similar work to you evaluated their research? • What metrics did they use and why? • Are there any common data sets that are used widely? • How much proof is normally expected / presented? • Some fields require more proof than others

7. Compared to What? • Are there any systems that you can compare your work with? • Caution 1 : It might sound clever to pick a legacy system to compare against • Your system will look better – right? • WRONG!! : Researchers will spot this straight away and it will harm your credibility in the future • Caution 2 : Be sure that you make fair comparisons • Don’t deliberately pick an inappropriate system to compare against • Don’t choose a data set that favours your system • Don’t make sweeping statements with limited proof!

8. Explicitly State Assumptions and Initial Conditions • Carefully write down the assumptions that you have made • Are they reasonable? • Could others question them? • How would you answer tough questions? • Results need to be bullet-proof • What initial conditions were set? • Could they bias the results? • What have you done to avoid this? • Could other researchers repeat your tests to verify the results?

9. Types of Evaluation • Two main types of evaluation: • Quantitative Evaluation • Qualitative Evaluation • The choice / mix of evaluation techniques depends on your thesis topic • Generally quantitative results allow us to make stronger claims • We have to be more careful when taking a qualitative approach can’t claim too much • Ask for advice from your supervisor before starting on this • Try to write an early paper to get some feedback on your evaluation technique

10. Quantitative Evaluation • Numerical comparisons • System X performs 10% more accurately than System Y • The algorithm is 90% effective in classifying brain tumors • Makes use of statistical and other mathematical techniques • Includes formal mathematical proof • Logical proof and Model checking • Regression to identify trends • Is very powerful but care needs to be taken because errors can be very costly • Examiners tend to be very numerate and will spot mistakes

11. Simulating Results • In some research areas you won’t have access to the required resources needed for testing • In such cases simulating or modeling can help us to generate quantitative results • Caution : The results will only be as good as our simulation / model!! • Use established simulation / modeling techniques and packages wherever possible • Carefully show that your simulation / model is accurate and that its configuration doesn’t introduce bias • Run several experiments and gradually increase complexity

12. What Can Go Wrong? • Claiming too much, justifying too little • Using an inappropriate mathematical technique which introduces bias to results • Making a basic mathematical error • Selecting data that isn’t representative of your problem domain • Choosing data which is biased in some way • Not doing enough testing • Not having a large enough data sample • Misinterpreting results – missing the point or drawing wrong conclusions

13. What Can Go Wrong? • Not using metrics that are expected in your domain • Misunderstanding metrics and applying incorrectly • Choosing the wrong simulation tool and trying to force it to fit your problem • Badly configuring your simulation / model so that it doesn’t really describe your problem • Doing things in a hurry at the last minute increases all of the above risks – take your time!!

14. Qualitative Evaluation • This is not numerical • Is more descriptive • May involve a criteria for success • Create a list of necessary features that your system needs to show to be deemed a success • Use a range of tests to show how the system behaves in response to stimuli • Try to anticipate the possible inputs to the system • Create a real use-case and make it the focus of your evaluation

15. What Can Go Wrong • You claim too much – remember that qualitative results give you less real evidence • You make sweeping statements that you don’t properly justify (avoid words like: generic, optimal , etc) • You cover only a small range of possible inputs • You create a basic prototype and try to claim that it shows much more than it does • You don’t do enough testing • You set biased tests in some way without noticing • Your criteria is too limited to really test your work