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Instructor:
 Rawn Shah
Blogger / Social Business Strategist
Forbes.com & IBM
Rawn Shah is an author and Practices Lead on the Social Software Adoption team, IBM Software Group, where he focuses on understanding and measuring the business value, risks, and metrics of social computing methods. He has nearly 300 articles published in various international technology and business publications, and seven books on a range of technical topics. He was Community Program Manager for the 7 million member community of IBM developerWorks, and before that Editor for the SOA and Web2.0 zone on the site. His latest book released in January 2010, Social Networking for Business (Wharton School Publishing) focuses on marrying the business and technical models behind social computing into useful, deployable projects.
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Instructor:
Michael Wu, Ph. D
Principal Scientist, Analytics
Lithium Technologies, Inc.
Michael Wu is the Principal Scientist of Analytics at Lithium Technologies Inc. Michael received his Ph.D. from UC Berkeley’s Biophysics graduate program, where he modeled visual processing within the human brain using math, physics, and machine learning. He is currently applying similar data-driven methodologies to investigate and understand the complex dynamics of the social web. Michael has developed the Facebook Engagement Index (FEI), Community Health Index (CHI) and many predictive social analytics with actionable insights. His R&D work at Lithium has won him the recognition as a 2010 Influential Leader by CRM Magazine.
In addition to the purely empirical methods, Michael also leverages social principles that govern human behavior (from sociology and anthropology, to behavioral economics and psychology, etc.) to decipher the intricate human components of social interactions. Through this combined bottom-up and top-down approach, Michael has developed a sophisticated predictive model of influence and an evaluative framework for understanding gamification. To tackle challenging open problems (like the value of WOM, social ROI, or the loyalty implications of gamification, etc.), Michael collaborates with academicians to conduct research on these unsolved problems.
Michael has been a DOE fellow during his graduate career and was awarded 4 years of full fellowship under the Computational Science Graduate Fellowship. During his fellowship tenure, he has served at the Los Alamos National Lab conducting research in face recognition. Prior to his Ph.D., Michael received his triple major undergraduate degree in Applied Math, Physics, and Molecular & Cell Biology from UC Berkeley.
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