This document summarizes Ming-Yang Kao's talk on interdisciplinary research and learning. The talk covered Kao's research areas including algorithms, finance, DNA self-assembly, and more. It provided examples of projects in predicting stock markets using computational complexity and data compression techniques. The talk also discussed designing index-based stock portfolios as a hard computational problem. Finally, it offered general thoughts on opportunities, values, and strategies for interdisciplinary research and learning.

1. Interdisciplinary Research and Learning:
Some Experiences and Strategies
Ming-Yang Kao
Department of Electrical Engineering and Computer Science
Northwestern University
Evanston, Illinois
USA
7/26/2010 NCCU 1

2. My Research
Area: algorithms
Interest: I am interested in any problem
that has significant algorithmic
substance.
Spectrum: from speculative to practical.
today’s focus
7/26/2010 NCCU 2

3. Sample Sub-areas of My Research
• Algorithmic Perspectives for Finance
• DNA Self-Assembly
• Computational Biology
• E-Commerce
today’s examples
• Data Security
• Graph Algorithms
• Online Algorithms
• Parallel Algorithms
• Discrete Optimization
7/26/2010 NCCU 3

4. Outline of the Remainder of the Talk
1. Algorithmic Perspectives for Finance
– three projects
2. DNA Self-Assembly
– if we have time
– general introduction
3. General Thoughts about Interdisciplinary
Research
7/26/2010 NCCU 4

5. Predictability of Stock Markets
Question:
• Do historical stock prices contain information
that can be used to predict future stock
prices?
Answers:
• Economists: No.
• Traders: Yes.
Who is right?
7/26/2010 NCCU 5

6. Do historical stock prices contain information
that can be used to predict future stock prices?
Answers:
• Economists: No.
• Traders: Yes.
Question:
• Who is right?
Limitation of These Answers:
• These two answers are based on the
perspective of information.
7/26/2010 NCCU 6

7. Towards Understanding the Predictability of Stock Markets
from the Perspective of Computational Complexity
(Aspnes, Fischer, Fischer, Kao, Kumar, 2001)
Approach:
• Information + Computational Complexity
Question:
• Is it possible that historical prices contain
desired information but extracting such
information is computationally hard?
Answer:
• Yes, at least theoretically.
7/26/2010 NCCU 7

8. Towards Understanding the Predictability of Stock Markets
from the Perspective of Computational Complexity
(Aspnes, Fischer, Fischer, Kao, Kumar, 2001)
Agent-Based Market Model:
• Traders
• Each trader has a trading strategy based on price
history.
• The stock price is determined by the trades
issued by the traders.
Computer Simulations:
• Price movements generated by the model are
visually realistic.
Mathematical Proof:
• Reducing a computational hard problem to the
problem of predicting the future prices.
7/26/2010 NCCU 8

9. Price Sequence Generated by Computer Simulations
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10. Price Sequence Generated by Computer Simulations
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11. Understanding Market Predictability
Suggestions for Projects:
1. Design your own market models.
2. Experiment with computer simulations.
3. Analyze the computational complexity of predicting
future prices under your models.
4. Write programs for market prediction, portfolio
optimization, or trading algorithms under your models.
more about these
7/26/2010 NCCU two topics next 11

12. Algorithms for Stock Market Prediction
(Azhar, Badros, Glodjo, Kao, Reif, 1994)
Idea: data compression
Intuitions:
• the more predictable the stock prices are;
• the more information the stock prices contain;
• the more patterns the stock prices contain;
• the more compressible the stock prices are.
7/26/2010 NCCU 12

13. Data Compression Techniques
for Stock Market Prediction
(Azhar, Badros, Glodjo, Kao, Reif, 1994)
7/26/2010 NCCU 13

14. Data Compression Techniques
for Stock Market Prediction
(Azhar, Badros, Glodjo, Kao, Reif, 1994)
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15. Data Compression Techniques
for Stock Market Prediction
Suggestions for Projects:
1. Design your own ideas for market predictions
based on data compression.
2. Experiment your algorithms with computer-
simulated data, historical market data, or
real-time market data.
7/26/2010 NCCU 15

16. How to Design Index-Based Portfolios?
Design Process:
Step 1. Pick a market index.
Step 2. Pick a subset of the stocks used for the index.
Step 3. Invest in the subset.
Optimization Objective:
We want our subset of stocks to perform well relative
to the index at least historically.
Question:
How easy or hard is this design task computationally?
7/26/2010 NCCU 16

17. Designing Proxies for Stock Market Indices
(Kao and Tate, 1999)
Type 1: Price-Weighted Index
e.g., the Dow Jones Industrial Average
Type 2: Value-Weighted Index
e.g., the Standard and Poor’s 500
Type 3: Equal-Weighted Index
e.g., the Indicator Digest Index
Type 4: Price-Relative Index
e.g., the Value Line Index
7/26/2010 NCCU 17

18. Designing Proxies for Stock Market Indices
(Kao and Tate, 1999)
Performance Objectives:
1. tracking an index
2. outperforming an index
3. sacrificing return for less volatility
7/26/2010 NCCU 18

19. Price-Weighted Index
(E.g., the Dow Jones Industrial Average)
B = a set of stocks.
b = # of stocks in B.
S i ,t = the price of the i-th stock at time t.
wi = # of outstanding shares of the i-th stock.
7/26/2010 NCCU 19

20. Value-Weighted Index
(E.g., the Standard and Poor’s 500)
B = a set of stocks.
b = # of stocks in B.
S i ,t = the price of the i-th stock at time t.
wi = # of outstanding shares of the i-th stock.
7/26/2010 NCCU 20

21. Equal-Weighted Index
(E.g., the Indicator Digest Index)
B = a set of stocks.
b = # of stocks in B.
S i ,t = the price of the i-th stock at time t.
wi = # of outstanding shares of the i-th stock.
7/26/2010 NCCU 21

22. Price-Relative Index
(E.g., the Value Line Index)
B = a set of stocks.
b = # of stocks in B.
S i ,t = the price of the i-th stock at time t.
wi = # of outstanding shares of the i-th stock.
7/26/2010 NCCU 22

23. Tracking an Index
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24. Outperforming an Index
7/26/2010 NCCU 24

25. Sacrificing Return for Less Volatility
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26. Sacrificing Return for Less Volatility
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27. Designing Proxies for Stock Market Indices
Is Computational Hard!
(Kao and Tate, 1999)
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28. Designing Proxies for Stock Market Indices
Is Computational Hard!
(Kao and Tate, 1999)
7/26/2010 NCCU 28

29. Designing Proxies for Stock Market Indices
Is Computational Hard!
(Kao and Tate, 1999)
7/26/2010 NCCU 29

30. Designing Proxies for Stock Market Indices
Suggestions for Projects:
1. Design approximation algorithms.
2. Consider other performance objectives.
7/26/2010 NCCU 30

31. Algorithmic DNA Self-Assembly
1. Nano Technology
Using computation to build nanostructures
2. Computational Technology
Using nanostructures to perform computation
7/26/2010 NCCU 31

32. DNA Tiles
TILE
GCATCG
CGTAGC
7/26/2010 NCCU 32

33. Algorithmic DNA Self-Assembly
Program and Input = Tiles + Lab Steps Output = Shape + Pattern
7/26/2010 NCCU 33

34. Examples of DNA Tiles
(Holliday, 1964)
exchange of genetic information in yeast
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35. Examples of DNA Tiles
aaa
a
TILE
aaa
a
7/26/2010 NCCU 35

36. Examples of DNA Tiles
(Reif ’s Group, Duke University)
GACAG
ATAG C
ATAG C
TATCG
TATCG
ATG G CG TA
TACCG CAT
AG ATCG AC
TCTAG CTG
ATAGC TGATCGGA GCTTGACC ATAGC
CGGTC TATCG ACTAGCCT CGAACTGG TATCG
ATAGC ACTAGCCT CTAGCCGT GTACA TTCCA
TATCG ACTAGCCT GATCGGCA CATGT
TG AATAG C
ACTTATCG
ACTAG CCT
ACTAG CCT
ATAG C
ATAG C
TATCG
TATCG
TTAG T
7/26/2010 NCCU 36

37. Examples of DNA Tiles
(Park, Pistol, Ahn, Reif, Lebeck, Dwyer, and LaBean, 2006)
7/26/2010 NCCU 37

38. Examples of DNA Tiles
(Winfree’s Group, Cal Tech)
7/26/2010 NCCU 38

39. Examples of DNA Tiles
Sierpinski Triangle
(Rothemund, Papadakis, Winfree, 2004)
7/26/2010 NCCU 39

40. Self-Assembly for Binary Counters
(Winfree, 2000)
7/26/2010 NCCU 40

41. 2D Self-Assembly for Turing Machines
(Winfree, Yang, and Seeman, 1998)
7/26/2010 NCCU 41

42. Self-Assembly for Circuit Patterns
(Cook, Rothemund, Winfree, 2003)
7/26/2010 NCCU 42

43. Clonable DNA Octahedron
(Shih, Quispe, Joyce, 2004)
one 1,669-mer + five 40-mers
7/26/2010 NCCU 43

44. My Works in DNA Self-Assembly
new self-assembly models:
• objective: imitating Mother Nature.
• reason: Mother Nature is extremely capable.
new computational models:
• objective: implementing applications of self-
assembly.
• examples: drug delivery, disease detection.
7/26/2010 NCCU 44

45. General Thoughts
about Interdisciplinary Research
1. Where to look for interdisciplinary research
opportunities?
2. How to interact with (potential) interdisciplinary
collaborators?
3. How to evaluate interdisciplinary research?
4. How to learn interdisciplinary materials?
5. How to teach interdisciplinary materials?
7/26/2010 NCCU 45

46. Opportunities
in Interdisciplinary Research
Intersections:
1. different disciplines
2. different areas of the same discipline
Examples:
1. Discrete Math and Continuous Math
2. Nature Inspired Computing
3. Economics and Computer Science
4. Sociology and Computer Science
5. Political Science and Computer Science
6. many more …
7/26/2010 NCCU 46

47. Psychological-Intellectual Ingredients
for Interdisciplinary Research
Curiosity: e.g.,
• eager to learn new things
Open Mind: e.g.,
• willing to consider values different from our own
Taking Psychological Risks: e.g.,
• willing to show/acknowledge/fix our own
ignorance/prejudice.
• willing to tolerate ignorance/prejudice from others.
7/26/2010 NCCU 47

48. Multicultural Values
for Interdisciplinary Research
1. technical difficulty – e.g., math
2. immediate practicality – e.g., systems research
3. provable performance guarantees – e.g., theoretical
computer science
4. discovery of facts – e.g., biology
5. interpretational power – e.g., economics
6. opening up new possibilities – e.g., interdisciplinary
research
7. many more
7/26/2010 NCCU 48

49. Choosing Values
for Interdisciplinary Research
Which value is right?
• All these objectives are worthy.
Which value do we follow?
• It is sufficient to optimize any one of them.
The more may be the better, but just one would suffice.
Why?
• Research is a collective activity for society.
Each person optimizes her/his preferred objectives. Collectively,
society will optimize all of the objectives.
• Research is a career-long effort for a person.
We optimize different objective at different times. Over a
career, we will benefit from all or most of the objectives.
7/26/2010 NCCU 49

50. Learning Strategies
for Interdisciplinary Research
1. Learn non-CS materials as much as we need to start
working on an interdisciplinary research project.
2. Start working on the project as soon as we can.
Don’t wait!
3. Continue to learn non-CS materials while we are
working on the project.
7/26/2010 NCCU 50

51. Teaching Strategies
for Interdisciplinary Research
1. Recruit students from outside Computer Science.
2. Let them help us and CS students with non-CS
materials
3. We and CS students help them with CS materials.
7/26/2010 NCCU 51

52. The End
Thank you!
Any further comments
or questions?
7/26/2010 NCCU 52