CS 1944: Sophomore Seminar Big Data and Machine Learning

1. CS 1944: Sophomore SeminarBig Data and Machine Learning B. Aditya Prakash Assistant Professor Nov 3, 2015

2. About me • Assistant Professor, CS • Member, Discovery Analytics Center • Previously • Ph.D. in Computer Science, Carnegie Mellon University • B.Techin Computer Science and Engg, Indian Institute of Technology (IIT) – Bombay • Internships at Sprint, Yahoo, Microsoft Research

4. Data contains value and knowledge

5. Data and Business Source: A. Machhanavajjhala

6. Data and Science

7. Data and Government Source: A. Machhanavajjhala

8. Data and Culture Source: A. Machhanavajjhala

10. Good news: Demand for Data Mining

11. How to extract value from data? • Manipulate Data • CS, Domain expertise • Analyze Data • Math, CS, Stat… • Communicate your results • CS, Domain Expertise

12. Communication is important!

13. What is Data Mining? • Given lots of data • Discoverpatterns and models that are: • Valid:hold on new data with some certainty • Useful:should be possible to act on the item • Unexpected: non-obvious to the system • Understandable:humans should be able to interpret the pattern

14. Data Mining Tasks • Descriptive methods • Find human-interpretable patterns that describe the data • Example: Clustering • Predictive methods • Use some variables to predict unknown or future values of other variables • Example: Recommender systems

15. Biology • Physics • Theory & Algo. • Big data • Comp. Systems • Social Science • ML & Stats. • Econ.

16. Data at CS, VT • Knowledge, Information and Data • http://www.cs.vt.edu/undergraduate/tracks/kid • People: Fox, Harrison, Huang, Lu (in NVA), Ramakrishnan(in NVA), Rozovskaya, Prakash

17. Courses • Background in some areas: • CS3414 (Numerical Methods); also prob/stat • 4000 level • 4244 Internet Software Development • 4604 Database Management Systems • 4624 Capstone (Multimedia, Information Access) • 4634 Design of Information (Capstone) • 4804 AI • 4984 Computational Linguistics (Capstone)

18. Discovery Analytics Center

19. My Research

20. Networks are everywhere! Facebook Network [2010] Gene Regulatory Network [Decourty 2008] Human Disease Network [Barabasi 2007] The Internet [2005]

21. What else do they have in common?

22. High School Dating Network Bearman et. al. Am. Jnl. of Sociology, 2004. Image: Mark Newman Blue: Male Pink: Female Interesting observations?

23. The Internet Skewed Degrees Robustness

24. Karate Club Network

25. Dynamical Processes over networks are also everywhere!

26. Why do we care? • Social collaboration • Information Diffusion • Viral Marketing • Epidemiology and Public Health • Cyber Security • Human mobility • Games and Virtual Worlds • Ecology ........

27. Why do we care? (1: Epidemiology) • Dynamical Processes over networks [AJPH 2007] CDC data: Visualization of the first 35 tuberculosis (TB) patients and their 1039 contacts Diseases over contact networks

28. Why do we care? (1: Epidemiology) • Dynamical Processes over networks • Each circle is a hospital • ~3000 hospitals • More than 30,000 patients transferred [US-MEDICARE NETWORK 2005] Problem: Given k units of disinfectant, whom to immunize?

29. Why do we care? (1: Epidemiology) ~6x fewer! [US-MEDICARE NETWORK 2005] CURRENT PRACTICE OUR METHOD Hospital-acquired inf. took 99K+ lives, cost $5B+ (all per year)

30. Why do we care? (2: Online Diffusion) > 800m users, ~$1B revenue [WSJ 2010] ~100m active users > 50m users

31. Why do we care? (2: Online Diffusion) • Dynamical Processes over networks Buy Versace™! Followers Celebrity Social Media Marketing

32. Social  Biological Contagion Automatically learn models Prakash 2014

33. Why do we care? (3: To change the world?) • Dynamical Processes over networks Social networks and Collaborative Action

34. High Impact – Multiple Settings epidemic out-breaks Q. How to squash rumors faster? Q. How do opinions spread? Q. How to market better? products/viruses transmit s/w patches

35. Dynamical Processes = (a lot of) Networks + (some) Time-Series

36. Research Theme ANALYSIS Understanding POLICY/ ACTION Managing DATA Large real-world networks & processes

37. Research Theme – Public Health ANALYSIS Will an epidemic happen? POLICY/ ACTION How to control out-breaks? DATA Modeling # patient transfers

38. Research Theme – Social Media ANALYSIS # cascades in future? POLICY/ ACTION How to market better? DATA Modeling Tweets spreading

39. A Question • How many of you think your friends have more friends than you?  • A recent Facebook study • Examined all of FB’s users: 721 million people with 69 billion friendships. • about 10 percent of the world’s population! • Found that user’s friend count was less than the average friend count of his or her friends, 93 percent of the time. • Users had an average of 190 friends, while their friends averaged 635 friends of their own.

40. Possible Reasons? • You are a loner? • Your friends are extroverts? • There are more extroverts than introverts in the world?

41. Example Average number of friends? Source: S. Strogatz, NYT 2012

42. Example Average number of friends = ( 1 + 3 + 2 + 2 ) / 4 = 2 Source: S. Strogatz, NYT 2012

43. Example Average number of friends = ( 1 + 3 + 2 + 2 ) / 4 = 2 Average number of friends of friends Source: S. Strogatz, NYT 2012

44. Example Average number of friends = ( 1 + 3 + 2 + 2 ) / 4 = 2 Average number of friends of friends = (3 + 1 + 2 + 2 + 3 + 2 + 3 + 2)/8 = ((1x1) + (3x3) + (2x2) + (2x2))/8 Source: S. Strogatz, NYT 2012

45. Example Average number of friends = ( 1 + 3 + 2 + 2 ) / 4 = 2 Average number of friends of friends = (3 + 1 + 2 + 2 + 3 + 2 + 3 + 2)/8 = ((1x1) + (3x3) + (2x2) + (2x2))/8 = 2.25! Source: S. Strogatz, NYT 2012

46. Actually it is (almost) always true! • Proof?

47. Actually it is (almost) always true! • Proof?

48. Actually it is (almost) always true! • Proof?

49. Actually it is (almost) always true! • Proof?

50. Actually it is (almost) always true! Essentially, it is true if there is any spread in # of friends (non-zero variance)! • Proof?