Research students

A list of AIAI research students

PhD Student   Research Topic
Ibraham Ahmed
Ibrahim Ahmed Network Security and Multi-Agent Modeling
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.
F Christianos
Filippos Christianos Coordinated Exploration in Multi-Agent Deep Reinforcement Learning
Mark Chevallier
Mark Chevallier Formal Verification of Machine Learning Properties.
  Benjamin Clavie  
Can Cui Agent Based Behaviour Analysis System with Interaction Model.
Paulius Dilkas
Paulius Dilkas Explainability in autonomous agents: interpretable models, abstraction, and beyond
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
Anton Fuxjaeger
Anton Fuxjaeger Deep networks, graphical models, knowledge compilation, Inference and structure leaning. To be more precise, my research will focus on neural-symbolic; the theoretical foundation of deep neural networks and how to combine them with symbolic reasoning. 
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. 

Xue Li
Xue Li Evolving Logical Theories by Combining Conceptual Change and Belief Revision.
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.
Francisco Jose Quesada Real Dynamic, Focussed Data Matching.
Arrasy Rahman
Arrasy Rahman Deep Reinforcement Learning Algorithms for Open Multiagent Systems
AIAI student2
Lukas Schäfer Collaborative Exploration in Multi-Agent Reinforcement Learning using Intrinsic Curiosity
  Luca Trani  
  Ole Weidner  
Y Xie
Yifei Xie My research focuses on performance optimization of distributed system.
  Cheng-lin Yang  
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.