2013 CRA-W
Graduate Cohort Workshop
Finding a Research Topic
Carla Brodley
Professor and Chair, Department of Computer Science
Tufts University
(with credits to Lori Pollack and Padma Raghavan)
Academic
History
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Started graduate school, UMASS…………….Fall 1988
Ph.D. awarded………………………………….Aug 1994
Started as Assistant Professor, Purdue….….Nov 1994
Promoted to Assoc. Prof. w/ tenure ………Spring 2000
Started as a Full Professor, Tufts ..………..…Fall 2004
Department Chair, Tufts……………………….Fall 2010
The Thesis Equation
Topic + Advisor = Dissertation
What is (CS) Research?
• the systematic investigation into and study of
materials, sources, etc., in order to establish
facts and reach new conclusions
Oxford dictionary
– Experimental scientific research:
• Observe a problem
• Formulate a hypothesis
• Develop a strategy to solve problem
based on hypothesis
• Perform experiments and demonstrate
conclusive evidence
• Interpret results
What is (CS) Research?
• the systematic investigation into and study of
materials, sources, etc., in order to establish
facts and reach new conclusions
Oxford dictionary
– Theoretical scientific research:
• Identify an open question
• Formulate a hypothesis
• Prove hypothesis
Research is not knowing the answer or how to get it
What is CS Research?
Example from Machine Learning
Classification
k-Nearest Neighbor
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Classification
k-Nearest Neighbor
oo
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oooo
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?
oo
xxx
x
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x xx
x
Classification
k-Nearest Neighbor
o
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?x
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xx
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oo
Assign majority class of the k nearest neighbors
What is CS Research?
Example from Machine Learning
• Observe a problem: Performance of k-NN is little
better than random guessing for a particular dataset
• Hypothesis: Classification accuracy will improve if I
can find and eliminate irrelevant and noisy features.
• Strategy: Develop a feature selection algorithm:
eliminate features with low correlation with the
class label
• Evaluation/Evidence: Implement and compare
accuracy of original k-NN to new feature selection kNN across a large number of data sets.
• Interpret results: Feature selection improves
performance in M of the N datasets, …next
steps?????
So, what isn’t PhD research?
How do I choose a topic
area for my research?
• Whose interest do you need to grab?
– You
– Your advisor
– Your research community
• Love your topic!
– Sets the course for your next 2-3 years
– Determines, in part, opportunities offered
to you upon graduation
– May work in same/related area for years
More Things to Consider
• What are your strengths? weaknesses?
– Programming, design, data analysis, proofs
– Key insights versus long/detailed
verification/simulation
• What drives you? bores you?
– Technology, puzzles, applications, interdisciplinary
• Do you (i.e., your advisor) have funding for
you to work in the area?
– Working as a TA
– Working as an RA
– Having university/college, government, industry,
etc… fellowship/scholarship/grant
Which comes first?
Advisor or Topic Area?
• For many people “advisor before topic”
– Meet faculty member with compelling research
interests
• For some people “topic before advisor”
– Need a guide in an area already of great interest
to you
• Want an advisor
– Knowledgeable about your topic
• Interdisciplinary topics may require >1 advisor
– With compatible working style (e.g., solo vs team)
– With lots of research ideas
– With strong interest in working with PhD students
Focusing from Area to 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
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 picking a
topic is often a low point
– Even for the most successful
students
• Why?
– Going from what you knowcoursework, to something newresearch
– It is very important
– There is no *one* ideal way, but
many good ways
7 Ways to Identify a
Good Research Problem
1) Flash of Brillance
• 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 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, fruitless, badly-motivated,…
– Several students may be working on
the same/related problem
3) The Extended Course Project
• You take a project course that gives you
a new perspective
• The project/paper combines your
research project with the course project
– One (and ½) project does double duty
• Warnings:
– This can distract from your research
if you can’t find a related
project/paper
4) Redo … Reinvent
• You work on some projects
– Re-implement or re-do; Evaluate
– 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
5) Analyze Data
• You participate in more senior
student’s evaluation study:
– Help with data collection and analysis
– Identify open challenges
• You have now discovered a topic
• Warnings:
– You will have to agree on who works on
identified open challenges
– It may not be a topic worthy of a doctoral
thesis
6) The Stapler
• You work on a number of small topics
that turn into a series of conference
papers
• You figure out somehow how to tie it
all together, create a chapter from
each paper, and put a BIG staple
through it
• Warning:
– May be hard/impossible to find the tie
7) The Synthesis Model
• You read papers from other subfields in
computer science or a related field
• Look for places to apply insight from another
(sub)field to your own
– E.g., machine learning to compiler optimizations
• Warnings:
– You can read a lot of papers and not find a
connection
– Or realize someone has done it already!
– Or you have not made a significant impact in either
field
Tips and Suggestions
• Topic + advisor are both important
• Keep a research ideas “journal” (wiki)
• Keep an annotated bibliography
(bibtex)
• Follow your interests and passion
– Key driver for success and impact
• Are you eager to get to work, continue
working?
• If not really interested, adapt
– Tedium or actual lack of interest and motivation?
When you’re stuck at the start
• Read/present papers regularly to find
open research issues
– Practice summarizing, synthesizing &
comparing sets of papers
– Write your own slides for presentations
• Work with a senior PhD student on
their research
• Try something….
• Get feedback and ideas from others:
conferences, research internships,
advisor’s idea
When you’re still stuck…
• Read a PhD thesis in your area
– Often contain an ‘open problems’ or ‘future work’
section
• Read your advisor’s grant proposals
• Attend PhD oral exams and thesis defenses
– Understand how to formulate problems
– Understand what constitutes a problem solution
• Assess your progress, with your advisor
– Set goals per semester - Have you ruled out an
area? converged on an area? Chosen a topic for
an exploratory research project?
When you’re stuck again
• Divide your topic into milestones, and
develop a plan to work on them one-byone
– Reward yourself when you finish a milestone 
– Publications and/or posters as milestones
– Vary what you do during the day, but set aside
blocks of time for each activity
• Assess your progress regularly, with
your advisor
– Have you submitted a workshop paper? A term
project with documentation? A poster at a
conference? A talk at a regional conf?
When you’re really really stuck
• Change research topics?
– May move you out of your advisor’s comfort zone
of expertise
– Starting from “scratch” (e.g., need to learn the
related work in a new area)
• Change research advisor?
– May go through ‘shakedown’ period again
– May or may not be better off
• But change can be invigorating
– What’s hard? Need to recognize when things are
not working out and take action
– Weigh consequences of changing and not changing
The Six Questions….
(from Paul Utgoff)
• What research issue(s) interest you most? Why?
• Who else has worked in this vein? What did they
accomplish? What can't they do?
• What kind of progress would you like to see? Why?
• Do you have an idea for making some such progress?
Explain.
• What do you expect to discover from your
investigation?
• How will your expected result(s) affect the research
community?
So how did I find my topic?
• At ICML1990, I was irritated by
– “Yet Another Learning Algorithm (YALA)”
– Strategic selection of UCI benchmark datasets
to show YALA’s superiority
• My idea: Given a dataset, select the “best”
algorithm automatically for that dataset
• My next observation: Why should we assume all
parts of the data space have the same bias?
• My next idea: Recursive automatic bias selection
Identify a research topic
and get started!
Great relevant article in ACM Crossroads,
“How to Succeed in Graduate School: A
Guide for Students and Advisors”, (part I,
Dec 1994; part II, Feb 1995), available in
ACM Digital Library