Andrea Corriga

Research & Development Engineer at VisioScientiae S.r.l.

Research Interests: Deep Learning for Financial Forecasting

About Me

Andrea Corriga is a Research & Development Engineer and Team Lead at VisioScientiae S.r.l. He got a PhD at the Department of Mathematics and Computer Science of the University of Cagliari supervised by Salvatore Carta and Diego Reforgiato Recupero.

He got a Master Degree in Computer Science in 2018 with a thesis entitle: "A Cross-Platform Application About Precision Farming". His research is currently focused on Deep Learning and Artificial Intelligence for Financial Forecasting.

Andrea was Microsoft Student Partner Team Leader for Sardinia from 2015 to 2018.

Currently in addition to research, Andrea has many Open Source projects on Github and he's the founder of the Unicapp, a cross-platform application for the University of Cagliari that allows the user to see their university booklet.

Publications (6)

All 2021 2020 2019 2018

Document title	Author	Years	Source
A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning	Carta S., Corriga A., Ferreira A., Podda A.S., Recupero D.R.,	2021	Applied Intelligence
HawkEye: A visual framework for agile cross-validation of deep learning approaches in financial forecasting	Carta S., Consoli S., Corriga A., Dapiaggi R., Podda A.S., Reforgiato Recupero D.,	2020	ACM International Conference Proceeding Series
Deep learning and time series-To-image encoding for financial forecasting	Barra S., Carta S.M., Corriga A., Podda A.S., Recupero D.R.,	2020	IEEE/CAA Journal of Automatica Sinica
A holistic auto-configurable ensemble machine learning strategy for financial trading	Carta S., Corriga A., Ferreira A., Recupero D.R., Saia R.,	2019	Computation
A supervised multi-class multi-label word embeddings approach for toxic comment classification	Carta S., Corriga A., Mulas R., Recupero D.R., Saia R.,	2019	IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Leveraging cognitive computing for gender and emotion detection	Corriga A., Cusimano S., Malloci F.M., Marchesi L., Recupero D.R.,	2018	CEUR Workshop Proceedings

Carta S., Corriga A., Ferreira A., Podda A.S., Recupero D.R., A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning (2021), Applied Intelligence

Carta S., Consoli S., Corriga A., Dapiaggi R., Podda A.S., Reforgiato Recupero D., HawkEye: A visual framework for agile cross-validation of deep learning approaches in financial forecasting (2020), ACM International Conference Proceeding Series

Barra S., Carta S.M., Corriga A., Podda A.S., Recupero D.R., Deep learning and time series-To-image encoding for financial forecasting (2020), IEEE/CAA Journal of Automatica Sinica

Carta S., Corriga A., Ferreira A., Recupero D.R., Saia R., A holistic auto-configurable ensemble machine learning strategy for financial trading (2019), Computation

Carta S., Corriga A., Mulas R., Recupero D.R., Saia R., A supervised multi-class multi-label word embeddings approach for toxic comment classification (2019), IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Corriga A., Cusimano S., Malloci F.M., Marchesi L., Recupero D.R., Leveraging cognitive computing for gender and emotion detection (2018), CEUR Workshop Proceedings