Javier Segovia-Aguas

Postdoctoral Researcher

Universitat Pompeu Fabra
RLeap Group
email: javier/dot/segovia/at/upf/dot/edu

Short Bio

I am an Artificial Intelligence (AI) Postdoctoral Researcher in the RLeap project at UPF. Inspired by program synthesis community, I explored during my PhD the potential of classical planning techniques to generate algorithm-like solutions that generalize over multiple problems. Program synthesis is one of the main problems in AI usually approached with logic and Machine Learning, so now I am working on how to make planning and learning benefit from each other, and extending these ideas to learning high-level models from raw data and computing hierarchical and general solutions. This research may has an impact in other fields such as robotics, software engineering, cybersecurity, and even computational biology.


  • EurAI Artificial Intelligence Dissertation Award 2018
    Thesis: Program Synthesis for Generalized Planning
  • IJCAI16 Distinguished Paper Award
    Paper: Hierarchical Finite State Controllers for Generalized Planning

Community Service

  • Chair of AAAI-20 workshop on Generalization in Planning
Journal Reviewer
  • Artificial Intelligence Journal (AIJ)
  • Machine Learning Journal (MLJ)
  • Journal of Artificial Intelligence Research (JAIR)
  • Mechatronics
Conference Program Committee
  • AAAI (2020)
  • IJCAI (2020)
  • ICAPS (2019-20)
  • ICRA (2020)
  • RO-MAN (2020)
  • AAAI - Student Abstracts (2019-21)
Conference Subreviewer
  • ICAPS (2018)
  • AAAI (2018)
  • IJCAI (2017)


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© Javier Segovia-Aguas 2019