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Laboratory for Innovation Science at Harvard

Researchers at LISH look at the way incentives and governance work in innovation, explore novel approaches to problem-solving and investigate how knowledge is produced and disseminated. LISH’s main partners include the Harvard Medical School, the Broad Institute, NASA, and P&G deploying advanced field-based experiments.

Faculty

LISH is led by a team of principal investigators from diverse backgrounds: 

  • Karim R. Lakhani Karim Lakhani headshot Karim R. Lakhani , Dorothy and Michael Hintze Professor of Business Administration. He is the founder and co-director of LISH and holds a PHD from MIT. 
  • Eva C. Guinan, Professor, Radiation Oncology, Harvard Medical School. She is the director of the Harvard Catalyst Translational Innovation program. Eva earned her MD from Harvard Medical School.
  • Kyle R. Myers Kyle R. Myers Assistant Professor of Business Administration. He has a PhD from the University of Pennsylvania. He studies the economics of the rate and direction of innovation. 
  • Alberto F. Cavallo Alberto Cavallo Headshot Alberto Cavallo , Thomas S. Murphy Professor of Business Administration. Pioneer of measuring inflation, he is the creator of Inflacion Verdadera and co-founder of The Billion Prices and PriceStats. Alberto earned a PhD in Economics from Harvard University.
  • Alexander MacKay Alexander MacKay Headshot Private: Alexander MacKay , Assistant Professor of Business Administration. He holds a PhD in economics from the University of Chicago.
  • Iavor I. Bojinov Iavor Bojinov Headshot Iavor Bojinov , Assistant Professor of Business Administration. He is a Richard Hodgson Fellow and holds a PhD from Harvard University. 
  • Edward McFowland III Edward McFowland III , Assistant Professor of Business Administration in the Technology and Operations Unit of HBS. Edward holds a PhD from Carnegie Mellon University. 
  • Frank Nagle Frank Nagle Headshot Frank Nagle , Assistant Professor of Business Administration. Frank earned his DBA in Technology and Operations Management from Harvard Business School
  • Jacqueline Ng Lane Jackie Lane Headshot Jackie Lane , Assistant Professor of Business Administration. She holds a PhD from Northwestern University and studies the impact of new technologies on performance outcomes. 

Activities/Research Focus

The Lab focuses on three key areas of investigation:

Open Innovation: 

  • How can organizations use contests, communities, and labor markets to solve complex problems? 
  • How does open innovation transform access to talent? 
  • What does the future of work look like in an era of open talent? 

Science of Science: 

  • What are the drivers, behaviors, and motivation underpinning innovative work? 
  • What makes a successful Lab? 
  • How can science best be applied in solving business challenges?

Data Science and AI: 

  • How is a data-first company built? 
  • What is the role of data science in a firm’s operating model? 
  • How can AI factories be used in organizations? 
  • What impact do algorithms have on the innovation process?

Incubating within LISH are two other labs:

Pricing Lab, PIs: Alberto F. Cavallo Alberto Cavallo Headshot Alberto Cavallo and Alexander MacKay Alexander MacKay Headshot Private: Alexander MacKay

The mission of the Pricing Lab is to conduct research on firm-level pricing decisions, underlying pricing capabilities, and the impacts of pricing on firm performance and the overall economy. The Pricing Lab promotes an understanding of current technologies and related policies through a combination of theoretical and empirical methods. 

Data Science and AI Operations Lab, PIs: Iavor I. Bojinov Iavor Bojinov Headshot Iavor Bojinov and Edward McFowland III Edward McFowland III

The Data Science & AI Operations Lab focuses on enabling organizations to overcome the methodological and operational challenges that arise when employing data science for data-driven decision making. Successful applications of data science require the correct methodology, technology, process, and organizational culture to develop the capability. Methodological challenges arise because the statistical theory underpinning modern data science was developed for contexts and applications that are significantly different from the current business use cases.

Find out more about LISH here.

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