Beibei Li

I am a professor of IT & Management. Before I joined CMU, I received my PhD with distinction from Leonard N. Stern School of Business at NYU.

My research interests lie at the intersection of social, behavioral and technical aspects of technology. I am especially interested in the interaction between human decisions and recent technological disruptions in both online and offline markets. Specifically, the ubiquitous adoption and usage of mobile, web and sensor technologies today have completely changed the way individuals behave and make decisions. These technologies have led to the pervasive digitization of individual behavior across digital and physical environments at a very fine-grained level. This information provides us with a new lens through which we can better monitor, understand, and optimize the individual decision making. By looking into these digital footprints of individuals and their interactions with technologies, I am also interested in designing effective strategies for technology platforms and policy makers to improve technology design and economic welfare.

For example, some questions I have looked at in the past include: How do consumers make decisions in an environment of large-scale unstructured social media and crowd-sourced content? How can we leverage such knowledge and design effective policies for online search engines to improve consumer welfare? How do individuals interact with mobile devices, and can mobile platforms leverage customers' online and offline trajectories to better understand their preferences, improve user experience and facilitate platform innovation? Finally, how can policy makers leverage such knowledge to design optimal social and economic incentive to improve market efficiency?

To achieve my goal, I apply an inter-disciplinary approach to combine social and economic theories with cutting-edge AI and Machine Learning algorithms to examine causal effects of technological changes, and to develop models appropriate for counterfactual analysis of policy change while demonstrating high predictive power. For validation, I combine secondary analyses using large-scale datasets from a variety of industries, together with randomized field experiments by partnering with real-world test beds.

My recent research has been published in Management Science, Marketing Science, Information Systems Research, MIS Quarterly and several top IS, Economics, Marketing and CS conferences. I am the recipient of the Anna Loomis McCandless Chair Professorship at Carnegie Mellon University in 2015. I am the winner of the Best Paper Award at the 20th International World Wide Web Conference (WWW 2011), two Best Paper Awards and one Best Impact Paper Award at the International Conference on Information Systems (ICIS 2012, 2019, 2022), three NSF Awards with over $3M total funding, two NBER Research Awards, Amazon Faculty Research Award, Facebook Faculty Research Award, Google Faculty Research Award, Adobe Faculty Research Award, LinkedIn Econ Graph Challenge Award and WCAI Research Award from Wharton Customer Analytics Initiative. I am the winner of the Junior Marketing Researcher Award at the Big Data Marketing Conference 2015. I am also the winner of the INFORMS ISS Sandy Slaughter Early Career Award in 2019, INFORMS ISS Nunamaker-Chen Dissertation Award, the ACM SIGMIS Best Doctoral Dissertation Award and the Herman E. Krooss Doctoral Dissertation Award in 2012-2013. For more information, here is my CV.