Alessandro Sebastian Podda
Alessandro Sebastian Podda is a post-doc researcher at the Department of Mathematics and Computer Science of the University of Cagliari. He got a PhD in Mathematics and Informatics, supported by a grant from RAS (Autonomous Region of Sardinia), with a thesis entitled "Behavioural contracts: from centralized to decentralized implementations". In 2017, he has been visiting PhD student at the Laboratory of Cryptography and Industrial Mathematics of the University of Trento. Previously, he got a BSc and a MSc in Informatics (both with honours) at the University of Cagliari.
Currently, he is a member of the Artificial Intelligence and Big Data Laboratory and the Blockchain Laboratory of the University of Cagliari. He is also technical director and solution architect of the Doutdes and Sardcoin projects, and participates in numerous research projects including AlmostAnOracle, Safespotter and Mister.
To date, he has been the co-author of several journal articles and scientific conference papers, as well as reviewer for different top-tier international journals.
artificial intelligence, deep learning, information security, blockchain, smart cities
Most relevant publications
S. Carta, A. Ferreira, A. S. Podda, D. Reforgiato Recupero, and A. Sanna. Multi-DQN: an Ensemble of Deep Q-Learning Agents for Stock Market Forecasting, 2021. In Elsevier: Expert Systems With Applications, vol. 164. pdf here (Impact Factor 5.45)
S. Barra, S. Carta, A. Corriga, A. S. Podda, and D. Reforgiato Recupero. Deep Learning and Time Series-to-Image Encoding for Financial Forecasting, 2020. In IEEE/CAA: Journal of Automatica Sinica, vol. 7, n. 3. pdf here (Impact Factor 5.13)
S. Carta, S. Consoli, A. S. Podda, D. Reforgiato Recupero, and M. M. Stanciu. Ensembling and Dynamic Asset Selection for Risk-Controlled Statistical Arbitrage, 2021. To appear in IEEE Access (Impact Factor 3.75)
S. Carta, A. Corriga, A. Ferreira, A. S. Podda, and D. Reforgiato Recupero. A Multi-Layer and Multi-Ensemble Stock Trader Using Deep Learning and Deep Reinforcement Learning, 2021. In Applied Intelligence, vol. 51, n.2. pdf here (Impact Factor 3.32)
The full list of formal publications (16) is available here.