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Research interests

I am an Assistant Professor of Business Analytics. My primary theoretical interest is in sports analytics and more generally, the practical implementation of predictive analytics in organizations for data-driven decision-making. My primary methodological interest is in predictive analytics and complex networks. Within sports analytics, I focus on using detailed observational data from various sources, from match databases to detailed sensor-data from training sessions, to study issues related to talent identification and career management, teamwork and managing physical fitness and injury prevention. These issues often require the use of predictive analytics, which is substantially different from classical explanatory statistics (Shmueli & Koppius, 2011) and therefore the methodology of implementing predictive analytics in organizations is a strong research interest as well. Specific issues here include feature engineering to improve the decision-model-linkage as well as developing tools to detect algorithmic bias. Finally, the methods I develop and use from predictive analytics and complex networks have broader applicability than just sports. I actively work on translating these advanced methods to business problems related to sustainability. One area in particular that I am working on, revolves around using detailed sensor-data from ‘smart cargo’ to enable better coordination in supply chain networks, thus lowering their footprint.


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