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.

Awards

  • 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


Organizer
  • 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)

Publications

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© Javier Segovia-Aguas 2019