Research students

A list of AIAI research students

PhD Student   Research Topic
Ibraham Ahmed
Ibrahim Ahmed Network Security and Multi-Agent Modeling.
Claire Barale
Claire Barale Enabling Ethical Human-AI Reasoning in International Law
J Barrett
Jake Barrett Algorithmic design and multi-agent behavioural analysis for democratic innovations.
Cillian Brewitt
Cillian Brewitt Systematic Analysis and Comparison of Agent Modelling Methods.
Andreas Bueff Abstraction in Probabilistic Reasoning.
Juan Casanova Faulty ontology detection and repair.
Mark Chevallier
Mark Chevallier Formal Verification of Machine Learning Properties.
F Christianos
Filippos Christianos Coordinated Exploration in Multi-Agent Deep Reinforcement Learning.
Paulius Dilkas
Paulius Dilkas Explainability in autonomous agents: interpretable models, abstraction, and beyond.
M Dunion
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.
aiai student1
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).

E Fosong


Elliot Fosong Model Criticism in Multi-Agent Systems.
Jorge Gaete
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. 

Zonglin Ji
Zonglin Ji Understanding the Intensive Care Patients' Clinical Pathways with AI.
Patrick Kage
Patrick Kage My research focuses on how we can train models for downstream tasks with incomplete or inaccurate information, borrowing from semi-supervised learning and explainability methods. 
AIAI student4
Chang Luo Machine learning and its applications in finance, especially graph representation learning and complex network analysis.
Miguel Angel Mendez Lucero
Miguel Angel Mendez Lucero

An approach to explainable Artificial Intelligence using Adaptive Causal Models.

Ionela Georgiana Mocanu
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
Jake Palmer Formalising and verifying voting methods using interactive theorem proving in Isabelle/HOL
Giannis Papantonis
Giannis Papantonis Causal Modelling and Explainability in Machine Learning.
G Papoudakis
Georgios Papoudakis Modelling in Multi-Agent Systems Using Representation Learning.
AIAI student3
Adarsh Prabhakaran Mathematical modelling of complex systems with a focus on spreading phenomenon and propagation of non-contagious diseases.
Arrasy Rahman
Arrasy Rahman Deep Reinforcement Learning Algorithms for Open Multiagent Systems.
  Ameer Saadat- Yazdi Using knowledge graphs and argument mining for explainable decision making applications
AIAI student2
Lukas Schäfer Sample Efficiency and Generalisation in Multi-Agent Reinforcement Learning
Richard Schmoetten
Richard Schmoetten Formalisation and Interactive Theorem Proving in Physics.
F Smola
Filip Smola Formalising correct process composition in Isabelle/HOL and exploring its applications to complex domains.
J Vaughan
James Vaughan Applications of Network Science and Computational Creativity to Mechanical Theorem Proving.
O Weidner
Ole Weidner

Design and Implementation of a Telemetry Platform for High-Performance Computing Environments.

Y Xie
Yifei Xie My research focuses on performance optimization of distributed system.
Rui Zhao
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.