The AIM Research Center on Quantitative Methods in Business – QUANT is a multidisciplinary research center dedicated to quantitative research in management.


Research at QUANT is concerned with analyzing, modelling, simulating and optimizing management systems with quantitative methods, whether they come from an industrial environment, financial markets, information technology or networks.


Particularly, some research conducted at QUANT aims to investigate how AI and related emerging technologies affect business and decision making systems. On the premise that getting business value from AI may be theoretically possible but pragmatically difficult, its members revisit some managerial practices and decision frameworks, in different management field, by involving AI and new technologies



Data Science

“Data” as a subject introduces major economic, cultural and social challenges, from new business models to the protection of personal data. Each of the functions of a business, from finance to HR, can study these challenges from their own viewpoint. But it is relevant to develop a forum where each of these specialties can meet and compare their approach to develop transversal methods and projects, and possibly a common vision. In particular, the research team aims at becoming a vehicle for corporate partnerships with the school, where collecting and using data become central dimensions in business strategy.


Business Analytics

The research team performs basic and applied research for solving complex problems arising in Decision Systems. The focus is on computational intelligence approaches and optimization methodologies tailored to specific practical applications such as finance, real economy, education, logistics/transportation, customer service, manpower planning or yield/revenue management. 


Quantitative Finance

Quantitative Finance covers all applications of quantitative methods to finance (mathematics, statistics, computational methods). The activities of the research team are in different fields including decision under risk and ambiguity, Financial markets and asset pricing, Real options/investment under uncertainty as well as the performance of machine learning models.

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