Special Sessions

The Special Session on Big data in Economics and Finance

Organized by the Big Data and Forecasting of Economic Developments project (bigNOMICS) of the Centre for Advanced Studies of the European Commission, Joint Research Centre.

Chairs: Sergio Consoli, Luca Barbaglia, Luca Tiozzo Pezzoli

  • Novel data sources for economic analysis
  • Natural Language Processing, semantics and sentiment analysis to build economic indicators
  • Big data and advanced Machine Learning methods covering economics, finance, businesses
  • Economic time series analysis and forecasting
  • Knowledge-base, Information Retrieval, Cognitive Computing for prediction and understanding of the economy
  • Insights mining in business economics and financial services


The Industrial Session

Chair: Giovanni Giuffrida – Neodata.

The Special Session on Explainable Artificial Intelligence (XAI)

Explainability is essential for users to effectively understand, trust, and manage powerful artificial intelligence applications.


The Special Session on Multi-Objective Optimization (MOO) & Multi Criteria Decision Aiding (MCDA)

  • Comparative studies of various many-objective optimisation techniques;
  • Designing and constructing many-objective benchmark test problems;
  • Designing quality/performance metrics for many-objective solutions/algorithms;
  • Development of meta-heuristic algorithms for many-objective optimisation problems;
  • Evolutionary many-objective optimisation methods in search-based software engineering;
  • Evolutionary many-objective optimisation methods applied to real-world problems;
  • Exact methods from mathematical programming for many-objective optimisation problems;
  • Many-objective optimisation in bi-level optimisation problems;
  • Many-objective optimisation in combinatorial/discrete optimisation problems;
  • Many-objective optimisation in computational expensive optimisation problems;
  • Many-objective optimisation in constrained optimisation problems;
  • Many-objective optimisation in dynamic environments;
  • Many-objective optimisation in large-scale optimisation problems;
  • Objective reduction techniques;
  • Preference articulation in many-objective optimisation;
  • Preference-based search in many-objective optimisation;
  • Study of parameter sensitivity in many-objective optimisation;
  • Theoretical analysis and developments in many-objective optimisation;
  • Visualisation for decision-making in many-objective optimisation;
  • Visualisation for many-objective solution sets;
  • Visualisation for search process of meta-heuristic algorithms.
  • Multi-objective Optimization: new algorithms and concrete applications
  • Industrial problems, transportation and logistics problems
  • contributions to theoretical aspects of Multi-Objective Optimization (MOO) and Multi-Criteria Decision Aiding (MCDA)
  • descriptions of actual application cases.
  • software contributions to MOO or MCDA.
  • inter-disciplinary research, presenting the contributions of MOO and/or MCDA to other scientific disciplines, or integrating other disciplines into MOO or/and MCDA
  • decision aiding and multi-objective optimization for sustainability.

The 7 Special Sessions on Machine Learning

  • Multi-Task Learning
  • Reinforcement Learning
  • Deep Learning
  • Generative Adversarial Networks
  • Deep Neuroevolution
  • Networks with Memory
  • Learning from Less Data and Building Smaller Models

The 7 Special Session on Data Science and Artificial Intelligence

  • Simulation Environments to understand how AI Systems Learn
  • Chatbots and Conversational Agents
  • Data Science at Scale & Data in the Cloud
  • Urban Informatics & Data-Driven Modelling of Complex Systems
  • Data-centric Engineering
  • Data Security, Traceability of Information & GDPR
  • Economic Data Science