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Andrea De Lorenzo

Assistant Professor of Computer Engineering

University of Trieste

Biography

Andrea De Lorenzo is assistant professor at the University of Trieste. His research interests include evolutionary computation, application of machine learning techniques to engineering, computer security problems, information extraction and natural language processing.

He won the silver medal at the 13th Human-Competitive Awards, 2016, for Human-Competitive Results produced by Genetic and Evolutionary Computation. He serves as reviewers for many international journals closely related to his research interests; he is a member of the scientific/program committee of the most important conferences on evolutionary computation.

He authored and co-authored more than 40 peer-reviewed articles on international journals or conferences, with more than 20 coauthors. According to Google Scholar, his h-index is 11 and his works received more than 300 citations in the last 5 years (November 2019).

Interests

  • Artificial Intelligence
  • Computer Security
  • Genetic Programming
  • Information Retrieval

Education

  • PhD in Information Engineering, 2014

    University of Trieste

  • MEng in Computer Engineering, 2010

    University of Trieste

  • BSc in Computer Engineering, 2006

    University of Trieste

Recent Publications

Communication in Decision Making: Competition favors Inequality

We consider a multi-agent system in which the individual goal is to collect resources, but where the amount of collected resources …

Design, Validation, and Case Studies of 2D-VSR-Sim, an Optimization-friendly Simulator of 2-D Voxel-based Soft Robots

Voxel-based soft robots (VSRs) are aggregations of soft blocks whose design is amenable to optimization. We here present a software, …

Evolution of Distributed Neural Controllers for Voxel-based Soft Robots

Voxel-based soft robots (VSRs) are aggregations of elastic, cubic blocks that have sparkled the interest of Robotics and Artificial …

Interactive Example-Based Finding of Text Items

We consider the problem of identifying within a given document all text items which follow a certain pattern to be specified by a user. …

Learning a Formula of Interpretability to Learn Interpretable Formulas

Many risk-sensitive applications require Machine Learning (ML) models to be interpretable. Attempts to obtain interpretable models …

Contact

  • +39 040 558 3419
  • Via Alfonso Valerio, 10, Trieste, TS 34100
  • Enter building C2, take the stairs to building C3, office C3_2.54 on floor 2
  • Monday 10:00 to 13:00
    Wednesday 09:00 to 10:00