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Helpful guide providing strategies and considerations for finding a research topic in academic settings. Explore the importance of selecting the right topic aligned with your interests and goals while considering potential pitfalls and key factors like advisor compatibility and topic scope. Get tips on staying motivated and setting clear goals to streamline the topic selection process effectively.
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Finding a Research Topic With credits to: Mary Jane Irwin and Padma Raghavan, CSE Penn State and Kathy Yelick, EECS UC Berkeley
The ThesisEquation Topic + Advisor = Dissertation
Area vs Topic • Area = subfield • E.g., architecture, theory, programming languages, high performance computing, or multidisciplinary, e.g., computational science • Is it important? Timely? Jobs in the area? • Topic = specific open problems in subfield • Theory: faster algorithm • AI: Improving a machine learning algorithm • SE: Introducing a new way to test programs
Topic Scale and Scope • Scale • Should have more than one open problem, or solving one should lead to another • Should lead to more than one result/finding, some big, some smaller • Scope • Too narrow, e.g., just analysis no experiment, many not leave enough room • Too broad, e.g., data mining, for what? why? too open ended
Selecting a Topic • Moving from coursework to finding a topic is often a low point • Even for the most successful students • Why? • Going from what you know-coursework, to something new-research! • It is very important! • There is no *one* ideal way, but many good ways
Selecting a Topic Is Important! • It sets the course for the next two (or three) years of your life • It will define the area for your job search • You may be working in the same area (or a derivative) for years after • It is uncommon to completely switch areas • It is common to extend and add nearby areas
Things to Consider • What kind of job are you interested in? • research univ, teaching univ, gov’t lab, or industry • What are your strengths? Weaknesses? • Programming, design, data analysis, proofs? • Key insights vs. long/detailed system building, verification/simulation • A combination? • Narrow, broad, multidisciplinary ?
Topic vs Advisor Topic ?= Advisor • They are distinct but related choices • At times hard to separate topic from advisor • Multidisciplinary topic may need co-advisors, etc.
Things to Consider • Do you have a “preassigned” research advisor or do you have to find one? • Does your advisor know anything about the topic? What is your advisor’s style? • Are you more comfortable working as part of a team or alone?
1) A Flash of Brilliance • You wake up one day with a new insight/idea • New approach to solve an important open problem • Warnings: • This rarely happens if at all • Even if it does, you may not be able to find an advisor who agrees
2) The Term Project + • You take a project course that gives you a new perspective • E.g., theory for systems and vice versa • The project/paper combines your research project with the course project • Warnings: • This may be too incremental
3) Re-do & Re-invent • You work on some projects • Re-implement or re-do • Identify an improvement, algorithm, proof • You have now discovered a topic • Warnings: • You may be without “a topic” for a long time • It may not be a topic worthy of a doctoral thesis
4) The Apprentice • Your advisor has a list of topics • Suggests one (or more!) that you can work on or helps you find one • Can save you a lot of time/anxiety • Warnings: • Don’t work on something you find boring, badly-motivated,… • Several students may be working on the same/related problem
5) papers = Thesis • You work on a number of small topics that turn into a series of conference papers • E.g., you figure out how to apply a technique (e.g., branch and bound) to optimize performance tradeoffs • Warnings: • May be hard to tie into a thesis • May not have enough impact
6) Idea From A B • You read some papers from other subfields/fields • Apply this insight to your (sub)field to your own • E.g., graph partitioning to compiler optimizations • Warnings: • You can read a lot of papers and not find a connection • Or realize someone has done it already!
* … Combine, compose • Try any combination of these ideas • But, focus on tangible progress, milestones • Warnings: • It can take a lot of time without any results!
Some Tips • Research topic and advisor are both important • Keep an ‘ideas’ notebook; these could turn into research papers later • Follow your interests and passion • Key driver for success and impact • Are you eager to get to work, continue working? • If not really interested, correct and adapt • But, differentiate between tedium versus real lack of interest and motivation
Set Goals/Take Stock • Set goals for a topic-finding-semester • E.g. Selecting and trying 2 of 6 strategies • Assess your progress • Are you converging to an area? • Or have you ruled out an area? • Have you got a workshop paper or term project+ done? • Adapt your strategy
When You’re Stuck …. • Serve as an apprentice to a senior PhD student in your group • Keep working on something • Get feedback and ideas from others • Attend a really good conference in an area of interest • Do a industry/government lab internship
Good things to do … • Read papers in your area of interest • Write an annotated bibliography • Present possible extensions/improvements to each • Read a PhD thesis or two (or three) • Attend oral exams, thesis defense of others students • Read your advisor’s grant proposal(s)
Take Risks ! • Switching areas/advisors can be risky • May move you outside your advisor’s area of expertise • You don’t know the related work • You are starting from scratch • But it can be very refreshing! • Recognize when your project isn’t working • It is hard to publish negative results
Take Risks ! • Take some risks in your research • Choose problems that are significant • Higher risk to solution • Higher reward for solution • But, balance • High risk ---may not have solution, negative results cannot be published
Find a Topic and Forge Ahead! Questions Comments Discussions