Research students

A list of AIAI research students

PhD Student		Research Topic
	Ibrahim Ahmed	Network Security and Multi-Agent Modeling.
	Claire Barale	Enabling Ethical Human-AI Reasoning in International Law
	Jake Barrett	Algorithmic design and multi-agent behavioural analysis for democratic innovations.
	Cillian Brewit t	Systematic Analysis and Comparison of Agent Modelling Methods.
	Andreas Bueff	Abstraction in Probabilistic Reasoning.
	Juan Casanova	Faulty ontology detection and repair.
	Mark Chevallier	Formal Verification of Machine Learning Properties.
	Filippos Christianos	Coordinated Exploration in Multi-Agent Deep Reinforcement Learning.
	Paulius Dilkas	Explainability in autonomous agents: interpretable models, abstraction, and beyond.
	Mhairi Dunion	Algorithms for Multi-Agent Reinforcement Learning in Complex Environments.
	Jona Feldstein	I am interested the unification of relational models and probabilistic AI, with a focus on property based testing in probabilistic programming.
	Thomas Fletcher	Inferential Data Modelling in a Query-Answering System My research involves automatic identification of statistical features of text queries and associated datasets followed by application of consequently appropriate statistical models selected from a wide-ranging catalogue; this is within the context of an inference-based interactive system aimed at answering general-domain data-intensive queries with multiple types of numerical, visual and/or text outputs (e.g. value predictions, specific statistics, specific graphs, fit features descriptions and formal hypothesis statements).
	Elliot Fosong	Model Criticism in Multi-Agent Systems.
	Jorge Gaete Villegas	My research focuses on Explainable AI in the Healthcare domain.
	Nick Hoernle	My PhD focuses on modeling, understanding and supporting collaborative work with a special focus on learning environments. I am interested in probabilistic models of group collaboration and the design of intelligent agents that use these models to support, incentivise and motivate groups of people in their endeavours. I work with mixed-reality exploratory learning environments, badge incentives on StackExchange (and other large scale collaborative projects) and MOOCs where students collaborate in online forums.
	Chang Luo	Machine learning and its applications in finance, especially graph representation learning and complex network analysis.
	Miguel Angel Mendez Lucero	An approach to explainable Artificial Intelligence using Adaptive Causal Models.
	Ionela Georgiana (Gini) Mocanu	My research focuses on integrating PAC semantics with the SMT and modal logic. I am also interested in connecting semiring programming with semiring compositionality at the level of knowledge compilation.
	Imogen Morris	Formalising mathematical proofs with the aid of the proof assistant Isabelle.
	Jake Palmer	Formalising and verifying voting methods using interactive theorem proving in Isabelle/HOL
	Giannis Papantonis	Causal Modelling and Explainability in Machine Learning.
	Georgios Papoudakis	Modelling in Multi-Agent Systems Using Representation Learning.
	Adarsh Prabhakaran	Mathematical modelling of complex systems with a focus on spreading phenomenon and propagation of non-contagious diseases.
	Arrasy Rahman	Deep Reinforcement Learning Algorithms for Open Multiagent Systems.
	Ameer Saadat- Yazdi	Using knowledge graphs and argument mining for explainable decision making applications
	Lukas Schäfer	Sample Efficiency and Generalisation in Multi-Agent Reinforcement Learning
	Filip Smola	Formalising correct process composition in Isabelle/HOL and exploring its applications to complex domains.
	James Vaughan	Applications of Network Science and Computational Creativity to Mechanical Theorem Proving.
	Ole Weidner	Design and Implementation of a Telemetry Platform for High-Performance Computing Environments.
	Yifei Xie	My research focuses on performance optimization of distributed system.
	Rui Zhao	Presenting a formal model for data governance rules to allow reasoning on processing graphs, in order to a) check and track rule compliance b) attach composed rules to output data for future processing.
	Jiawei Zheng	Complex event processing in BPM, IoT and Blockchain.
	Ricky Zhu	Probabilistic knowledge representation and reasoning.

This article was published on 17 Nov, 2021

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Research students