Interdisciplinary Research and Learning: Some Experiences and Strategies

Ming-Yang KaoDepartment of Electrical Engineering and Computer Science

Northwestern UniversityEvanston, Illinois

USA

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My Research

Area: algorithms

Interest: I am interested in any problem that has significant algorithmic substance.

Spectrum: from speculative to practical.

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today’s focus

Sample Sub-areas of My Research

• Algorithmic Perspectives for Finance • DNA Self-Assembly• Computational Biology• E-Commerce• Data Security• Graph Algorithms• Online Algorithms• Parallel Algorithms• Discrete Optimization

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today’s examples

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

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Question:• Do historical stock prices contain information

that can be used to predict future stock prices?

Answers:• Economists: No.• Traders: Yes.

Who is right?

Predictability of Stock Markets

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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.

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Towards Understanding the Predictability of Stock Markets from the Perspective of Computational Complexity

(Aspnes, Fischer, Fischer, Kao, Kumar, 2001)

Approach: • Information + Computational ComplexityQuestion: • Is it possible that historical prices contain

desired information but extracting such information is computationally hard?

Answer: • Yes, at least theoretically.

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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

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Price Sequence Generated by Computer Simulations

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Price Sequence Generated by Computer Simulations

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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.

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more about these two topics next

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.

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Data Compression Techniques for Stock Market Prediction

(Azhar, Badros, Glodjo, Kao, Reif, 1994)

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Data Compression Techniques for Stock Market Prediction

(Azhar, Badros, Glodjo, Kao, Reif, 1994)

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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.

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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?

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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 Indexe.g., the Standard and Poor’s 500

Type 3: Equal-Weighted Indexe.g., the Indicator Digest Index

Type 4: Price-Relative Indexe.g., the Value Line Index

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Designing Proxies for Stock Market Indices(Kao and Tate, 1999)

Performance Objectives:1. tracking an index2. outperforming an index 3. sacrificing return for less volatility

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Price-Weighted Index(E.g., the Dow Jones Industrial Average)

B = a set of stocks.b = # of stocks in B.

= the price of the i-th stock at time t.= # of outstanding shares of the i-th stock.

tiS ,

iw

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Value-Weighted Index(E.g., the Standard and Poor’s 500)

B = a set of stocks.b = # of stocks in B.

= the price of the i-th stock at time t.= # of outstanding shares of the i-th stock.

tiS ,

iw

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Equal-Weighted Index(E.g., the Indicator Digest Index)

B = a set of stocks.b = # of stocks in B.

= the price of the i-th stock at time t.= # of outstanding shares of the i-th stock.

tiS ,

iw

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Price-Relative Index(E.g., the Value Line Index)

B = a set of stocks.b = # of stocks in B.

= the price of the i-th stock at time t.= # of outstanding shares of the i-th stock.

tiS ,

iw

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Tracking an Index

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Outperforming an Index

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Sacrificing Return for Less Volatility

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Sacrificing Return for Less Volatility

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Designing Proxies for Stock Market Indices Is Computational Hard!

(Kao and Tate, 1999)

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Designing Proxies for Stock Market Indices Is Computational Hard!

(Kao and Tate, 1999)

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Designing Proxies for Stock Market Indices Is Computational Hard!

(Kao and Tate, 1999)

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Designing Proxies for Stock Market Indices

Suggestions for Projects:

1. Design approximation algorithms.

2. Consider other performance objectives.

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Algorithmic DNA Self-Assembly

1. Nano Technology

Using computation to build nanostructures

2. Computational Technology

Using nanostructures to perform computation

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TILE

G C A T C G

C G T A G C

DNA Tiles

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Algorithmic DNA Self-Assembly

Program and Input = Tiles + Lab Steps Output = Shape + Pattern

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Examples of DNA Tiles(Holliday, 1964)

exchange of genetic information in yeast

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TILE

Examples of DNA Tiles

aaaa

aaaa

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Examples of DNA Tiles(Reif ’s Group, Duke University)

A T A G CT A T C G

T G A T C G G AA C T A G C C T

A C T A G C C TA C T A G C C T

C T A G C C G TG A T C G G C A

G C T T G A C CC G A A C T G G

A G

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G T A C AC A T G T

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Examples of DNA Tiles(Park, Pistol, Ahn, Reif, Lebeck, Dwyer, and LaBean, 2006)

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Examples of DNA Tiles(Winfree’s Group, Cal Tech)

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Examples of DNA Tiles Sierpinski Triangle

(Rothemund, Papadakis, Winfree, 2004)

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Self-Assembly for Binary Counters(Winfree, 2000)

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2D Self-Assembly for Turing Machines(Winfree, Yang, and Seeman, 1998)

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Self-Assembly for Circuit Patterns(Cook, Rothemund, Winfree, 2003)

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Clonable DNA Octahedron(Shih, Quispe, Joyce, 2004)

one 1,669-mer + five 40-mers

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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.

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General Thoughtsabout 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?

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Opportunities in Interdisciplinary Research

Intersections:1. different disciplines 2. different areas of the same disciplineExamples:1. Discrete Math and Continuous Math 2. Nature Inspired Computing3. Economics and Computer Science4. Sociology and Computer Science5. Political Science and Computer Science6. many more …

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Psychological-Intellectual Ingredientsfor 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.

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Multicultural Values for Interdisciplinary Research

1. technical difficulty – e.g., math2. immediate practicality – e.g., systems research3. provable performance guarantees – e.g., theoretical

computer science 4. discovery of facts – e.g., biology5. interpretational power – e.g., economics6. opening up new possibilities – e.g., interdisciplinary

research 7. many more

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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.

Choosing Valuesfor Interdisciplinary Research

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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.

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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.

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The End

Thank you!

Any further comments or questions?

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