I am a Postdoctoral Researcher in AI

at the Department of Computing, Imperial College London.

Assumption-Based Argumentation at AAMAS

I have 2 papers accepted for publication at AAMAS 2019, a premier AI conference.

Resolving Conflicts in Clinical Guidelines using Argumentation

Paper on medical decision support using Assumption-Based Argumentation with Preferences.

Complexity Results and Algorithms for Bipolar Argumentation

Paper on complexity and implementations of Bipolar Assumption-Based Argumentation.

Argumentation for Explainable Scheduling

I have a paper accepted for publication at AAAI 2019, a premier AI conference.

Distinguished Reviewer

I was recognised as a very distinguished PC member at IJCAI-ECAI 2018, a premier AI conference.

Research

I work on the following

Explainable AI (XAI)

I am particularly interested in explainable reasoning using symbolic AI, in applications ranging from optimisation to medical decision support.

mAchine arguIng

I am part of the mAchine arguIng consortium.
We build Transparent Argumentative Intelligence for explainable AI.

ROAD2H

EPSRC project: Resource Optimisation, Argumentation, Decision support and Knowledge Transfer to create Value via Learning Health Systems.

I apply computational argumentation methods to explainable reasoning with clinical guidelines and integration with optimisation.

Analysing the Passage of UK Parliament Bills

In collaboration with Thomson Reuters, Data Science Institute and Imperial College Business School.

I have driven the development of a methodology to explain primary legislation outcomes.

Qualifications

  • PhD Research in Computing (Artificial Intelligence), Imperial College London. 2017

Contact Information

  • Address

    Room 433
    Department of Computing, Imperial College London
    180 Queen's Gate, SW7 2AZ, London, UK
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