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Finding a Research Topic

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  1. Finding a Research Topic Padma Raghavan CSE Penn State With credits to: Mary Jane Irwin, CSE Penn State and Kathy Yelick, EECS UC Berkeley

  2. The ThesisEquation Topic + Advisor = Dissertation

  3. Area vs Topic • Area = subfield • architecture, theory, AI, high performance computing, or interdiscplinary • Is it important? Timely? Jobs in the area? • Topic = specific open problems in subfield • Theory: provably better algorithm • AI: Improving a machine learning algorithm • Architecture: multicore cache design • HPC: parallel algorithm, scheduling scheme • Interdisciplinary: computer simulation of tumor growth

  4. 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

  5. First publication Passing exams Picking a Topic, Moving from coursework to research Adapted from: Carla Ellis, Duke

  6. Selecting a Topic • Moving from coursework to picking a topic is often a low point • Even for the most successful students • Even for men (but they may not say so!) • 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

  7. 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

  8. Things to Consider • What kind of job are you interested in? • Top-20 research univ, teaching, 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 ?

  9. Topic vs Advisor Topic ?= Advisor • They are distinct but related choices • At times hard to separate topic from advisor • Interdisciplinary topic may need co-advisors, etc.

  10. Things to Consider • Do you have a “preassigned” research advisor or do you have to find one? • How can your research be supported? • By working as a TA • By working as an RA for your advisor • By having a university/college or NSF fellowship

  11. More Things to Consider • 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?

  12. Some Ways to Find a Topic

  13. 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

  14. 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

  15. 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 • It may be seen as incremental

  16. 4) The Apprentice • Your advisor has a list of topics • Suggests one (or more!) that you can work on • 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

  17. 5) 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

  18. 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!

  19. * … 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!

  20. 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

  21. 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

  22. 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 good conference on a hot topic • Grand challenge conferences, CRA-W Summer Schools • Do a industry/government lab internship

  23. When You’re Stuck … • 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)

  24. 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

  25. 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

  26. Find a Topic and Forge Ahead! Questions Comments Discussions