ESSEC METALAB

RESEARCH

Expanding Scientific Knowledge

ESSEC’s approach to AI, Data Science and Technology is original and reflects our strengths and tradition of excellence of research in Data Science, Economics, Management, Law and Social Sciences. Through their academic research, the 160 Research Professors at ESSEC improve society's understanding of the profound transformations caused by the advancement of science. They all prove everyday that social, economic and management sciences play a key role in fostering scientific and human progress.

Workplace & Business models

AI & Business Data Science

AI Policy & Regulation

Culture & Sustainability

MATHEMATICAL PROGRAMMING FORMULATIONS FOR THE COLLAPSED K-CORE PROBLEM

[ARTICLE] This research aims to identify the most critical users in a social network whose removal would most significantly disrupt ...

UNIVERSAL ROBUST REGRESSION VIA MAXIMUM MEAN DISCREPANCY

[ARTICLE] This study tackles data outliers in modern datasets by introducing a new method for robust estimation in various regression ...

ESTIMATION OF COPULAS VIA MAXIMUM MEAN DISCREPANCY

[ARTICLE] This research proposes a new method (MMD) for analyzing data modeled by "copulas," which are statistical tools used to ...

THE GENERALIZED CLOSE ENOUGH TRAVELING SALESMAN PROBLEM

[ARTICLE] This study extends the classic "traveling salesman" problem by allowing for variable-sized targets and rewards, aiming to find the ...

TWO EXTENDED FORMULATIONS FOR THE VIRTUAL NETWORK FUNCTION PLACEMENT AND ROUTING PROBLEM

[ARTICLE] This paper tackles how to efficiently route data traffic across a network, considering costs and specific service needs, using ...

HOW DO EMPIRICAL ESTIMATORS OF POPULAR RISK MEASURES IMPACT PRO-CYCLICALITY?

[ARTICLE] This paper explores how the methods used to calculate financial risk can sometimes overestimate risk during downturns and underestimate ...

VARIABLE SELECTION, MONOTONE LIKELIHOOD RATIO AND GROUP SPARSITY

[ARTICLE] This study explores methodologies for selecting crucial variables amidst a multitude of options. It presents both optimal solutions and ...

A SURVEY ON BILEVEL OPTIMIZATION UNDER UNCERTAINTY

[ARTICLE] This survey explores bilevel optimization under uncertainty, examining challenges in hierarchical decision-making and discussing approaches like stochastic techniques, while ...

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