Marek Grzes

Dr Marek Grzes is a Senior Lecturer in the School of Computing at the University of Kent.

Marek received his PhD in Computer Science from the University of York (UK) in 2010 for his work on reinforcement learning. Subsequently, he held research positions in Scotland and Canada before joining the School of Computing at Kent in 2015 as a lecturer. Marek is interested in both fundamental and applied machine learning research. The practical applications of artificial intelligence that he has been exploring in recent years are natural language processing, computational creativity, cybersecurity, and social economics, among others. Marek also teaches modules related to data analysis and artificial intelligence. Visit Mark's personal website for more information about his work.  

Academic Genealogy

Please find a fairly deep tree of Marek's academic ancestors here.

Marek belongs to the following research groups:

• Artificial Intelligence and Data Science Group

Marek's current research efforts focus on various aspects of machine learning and artificial intelligence: Markov decision processes, reinforcement learning, neural networks, decision trees, natural language processing, chatbots, and probabilistic inference. For several years, Marek was focusing mostly on reinforcement learning where the goal is to learn intelligent behaviour using data or sampling from a real or a simulated environment. For example, an algorithm can learn how to play a game using a set of past games, or it can play against a human or a simulator in order to improve its behaviour. His work on sequential decision-making included also planning where an algorithm knows the parameters (dynamics) of the game, and it can perform a direct optimisation of its own strategy. In his past life, Marek invested part of his professional time in research on nature-inspired algorithms for optimisation and machine learning.

Please find a full list of Marek's publications here.   

In the School of Computing, we engage in collaborative teaching where most modules are shared between at least two academics. Marek is involved in teaching the following modules:

• COMP6004 and COMP7019: Machine Learning Algorithms with Christos Efstratiou
• COMP7004: Data Mining and Knowledge Discovery with Alex Freitas
• COMP6028: Natural Language Processing with Zhongtian Sun

Reinforcement learning; AI; deep learning; neural networks; applications of AI; data science  

PhD Students

• Olivier Belan - PhD student
• Aidin Kazempour - PhD student
• Andrew Swinn - PhD student
• Cosmo De Bonis-Campbell - PhD student (Eleni Matechou main supervisor)
• Giuseppe Joulianou - PhD student (Viktorija Makarovaite main supervisor)

Past Research Students

PhD

• Théophile Champion, PhD, 2026, now Ingénieur Décisionnel Senior at Motor.AI in Berlin, Germany
  Thesis title: Scaling up Active Inference
• Peter Clapham, PhD, 2026
  Thesis title: A Study of Representation Learning without Encoders
• Piotr Sawicki, PhD, 2025, now Assistant Lecturer at the University of Kent
  Thesis title: The Digital Muse: Advancing LLM-Based Methods for Poetry Generation and Automated Evaluation
• Lee Harris, PhD, 2025, now KTP Research Associate at the University of Kent
  Thesis title: Exploring Feedforward Neural Network Explainability Using The Layerwise Relevance Propagation Framework
• Lisa Bonheme, PhD, 2023, now Postdoctoral Researcher at the World Health Organisation
  Thesis title: The Polarised Regime of Variational Autoencoders
• Farhana Ferdousi Liza, PhD, 2019, now Lecturer at the University of East Anglia
  Thesis title: Improving Training of Deep Neural Network Sequence Models

MSc by Research

• Simona Paraskevopoulou, MSc by Research, 2026
  Thesis title: Comparing Multimodal And Unimodal Representation Learning For Rare Cancer Prediction
• Adrian Wesek, MSc by Research, 2019, now Research Assistant at Imperial College London
  Thesis title: A Comprehensive Study of State-of-the-Art Word Embedding Algorithms for Natural Language Generation

An up-to-date account of Marek's professional activities can be found here.