Faculty & Research
Areas of Excellence: a 360° expertise for an impactful Research
AI, Data Science & Business
Data Science for insights & value creation
Objectives
Data science provides the essential tools for extracting knowledge from the data and supporting decisions both at individual and organisation levels. Research in this subarea covers the development and application of classical statistical modelling, data mining, machine learning, optimisation and operations research, social media analytics, and web analytics.
Ongoing research projects include:
- How to detect signs of depression on social media?
- How to predict with deep learning?
- How to use data streaming to make decision in financial markets?
- How to use machine learning methods to explain start-up failures?
CHAUDHURI, N., G. GUPTA, V. VAMSI, I. BOSE, "On the platform but will they buy? Predicting customers' purchase behavior using deep learning", Decision Support Systems, October 2021, vol. 149
CHAUDHURI, N., I. BOSE, "Exploring the Role of Deep Neural Networks for Post-disaster Decision Support", Decision Support Systems, March 2020, vol. 130
ISHIZAKA, A., V. PEREIRA, S. SIRAJ, "AHPSort-GAIA: a visualisation tool for the sorting of alternative in AHP portrayed through a case in the food and drink industry", Annals of Operations Research, May 2021
SBRANA, G., A.SILVESTRINI, "Forecasting with the damped trend model using the structural approach", International Journal of Production Economics, August 2020, vol. 226
SBRANA, G., A.SILVESTRINI, "Random switching exponential smoothing: a new estimation approach", International Journal of Production Economics, May 2019, vol. 211
ZOUACHE, D., A.MOUSSAOUI, F.BEN ABDELAZIZ, "A cooperative swarm intelligence algorithm for multi-objective discrete optimization with application to the knapsack problem", European Journal of Operational Research, January 2018, vol. 264, no. 1
Pedagogy
Examples of courses linked to this sub-Area include: "Data Analytics for Strategic Decisions", "Financial Data & Analytics", "Data Science for Finance", "Big Data for Finance", "Machine Learning and Artificial Intelligence in Finance",", "Unstructured Data Analysis", "Machine Learning and Artificial Intelligence for Business"
FNEGE Medias: Giacomo Sbrana discusses making forecasts with damped model tendency using a structural approach. Watch video
Professors: Fouad Ben Abdelaziz, Indranil Bose, Ismail Erzurumlu, Fabio Fonti, Gaurav Gupta, Vincent Lacoste, Vijay Pereira, Giacomo Sbrana, Laura Trinchera, Jian Wu.
Several projects in the sub-AE are the result of collaborations with researchers from international institutions such as: University of Macedonia (GR), Indian Institute of Management Calcutta ( IN), University of Portsmouth (UK), Université Paris Nanterre-Laboratoire Modal'X (FR), Bank of Italy (IT), Universiy of Lugano (CH), Conservatoire national des arts et métiers (CNAM), Indian Institute of Technology Bombay (IN), University of Bahrain (BH), Shiv Nadar University (IN), University of Deusto (ES)
Coordination
TRINCHERA Laura