I am a Postdoctoral Researcher in AI

at the Department of Computing, Imperial College London.

I work on Explainable AI, using computational argumentation to design inherently interpretable models and methods that support transparent decision making in fields such as medical reasoning, legal informatics, mathematical optimisation.

I manage researchers, enthusiastically supervise and teach students, collaborate internationally in interdisciplinary research, publish and present my work at top tier international conferences and journals, review and assess the works of others and give the highest quality feedback, organise research events, represent and speak on behalf of my colleagues and fellow AI researchers during institutional and public engagement opportunities.

Activities

I am a Highly Distinguished Program Committee member (one in 4 out of 2818) of IJCAI 2019, a premier AI conference.

I organise Imperial's Explainable AI seminars.

Engagement

I participated at the BCS Machine Intelligence Competition in October and at The Great Exhibition Road Festival in June, with AI-assisted Schedule Explainer.

I participated at the Global Grand Challenges Summit 2019 in September, representing the ROAD2H project.

Assumption-Based Argumentation at AAMAS

I have 3 papers published and presented at AAMAS, a premier AI conference, in May 2019. (24.3% acceptance rate)

Explaining Legislation

I have published Explanations by Arbitrated Argumentative Dispute at Expert Systems with Applications, a premier international journal on applications of intelligent systems.

Research

Explainable AI (XAI)

I am particularly interested in explainable reasoning using symbolic AI, working specifically on computational argumentation for making and explaining decisions using mined data, with applications to mathematical optimisation, clinical decision support, legal reasoning. XAI research currently spans across many of my activities.

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

Project in collaboration with Thomson Reuters, Data Science Institute and Imperial College Business School. I have driven the development of both methodology and a working system to explain primary legislation outcomes.

Other activities

I engaged with UK policy-makers at The Forum: AI & Health on March 3.
I participated at an Explainability Expert Roundtable discussion on Explainable AI, at The Alan Turing Institute.
I am a highly distinguished reviewer for two years in a row at IJCAI (2018 & 2019), a premier AI conference.
I appear in Imperial's Industry issue on Explainable AI.

Publications

I consider these the most important:

Qualifications

Contact Information

  • Address

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