2010 07-26-interdisciplinary research and learning

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2010 07-26-interdisciplinary research and learning

  1. 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. 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. 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. 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. 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. 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. 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. 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. 9. Price Sequence Generated by Computer Simulations 7/26/2010 NCCU 9
  10. 10. Price Sequence Generated by Computer Simulations 7/26/2010 NCCU 10
  11. 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. 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. 13. Data Compression Techniques for Stock Market Prediction (Azhar, Badros, Glodjo, Kao, Reif, 1994) 7/26/2010 NCCU 13
  14. 14. Data Compression Techniques for Stock Market Prediction (Azhar, Badros, Glodjo, Kao, Reif, 1994) 7/26/2010 NCCU 14
  15. 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. 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. 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. 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. 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. 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. 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. 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. 23. Tracking an Index 7/26/2010 NCCU 23
  24. 24. Outperforming an Index 7/26/2010 NCCU 24
  25. 25. Sacrificing Return for Less Volatility 7/26/2010 NCCU 25
  26. 26. Sacrificing Return for Less Volatility 7/26/2010 NCCU 26
  27. 27. Designing Proxies for Stock Market Indices Is Computational Hard! (Kao and Tate, 1999) 7/26/2010 NCCU 27
  28. 28. Designing Proxies for Stock Market Indices Is Computational Hard! (Kao and Tate, 1999) 7/26/2010 NCCU 28
  29. 29. Designing Proxies for Stock Market Indices Is Computational Hard! (Kao and Tate, 1999) 7/26/2010 NCCU 29
  30. 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. 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. 32. DNA Tiles TILE GCATCG CGTAGC 7/26/2010 NCCU 32
  33. 33. Algorithmic DNA Self-Assembly Program and Input = Tiles + Lab Steps Output = Shape + Pattern 7/26/2010 NCCU 33
  34. 34. Examples of DNA Tiles (Holliday, 1964) exchange of genetic information in yeast 7/26/2010 NCCU 34
  35. 35. Examples of DNA Tiles aaa a TILE aaa a 7/26/2010 NCCU 35
  36. 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. 37. Examples of DNA Tiles (Park, Pistol, Ahn, Reif, Lebeck, Dwyer, and LaBean, 2006) 7/26/2010 NCCU 37
  38. 38. Examples of DNA Tiles (Winfree’s Group, Cal Tech) 7/26/2010 NCCU 38
  39. 39. Examples of DNA Tiles Sierpinski Triangle (Rothemund, Papadakis, Winfree, 2004) 7/26/2010 NCCU 39
  40. 40. Self-Assembly for Binary Counters (Winfree, 2000) 7/26/2010 NCCU 40
  41. 41. 2D Self-Assembly for Turing Machines (Winfree, Yang, and Seeman, 1998) 7/26/2010 NCCU 41
  42. 42. Self-Assembly for Circuit Patterns (Cook, Rothemund, Winfree, 2003) 7/26/2010 NCCU 42
  43. 43. Clonable DNA Octahedron (Shih, Quispe, Joyce, 2004) one 1,669-mer + five 40-mers 7/26/2010 NCCU 43
  44. 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. 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. 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. 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. 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. 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. 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. 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. 52. The End Thank you! Any further comments or questions? 7/26/2010 NCCU 52
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