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Assistant Professor in the Department of Computer ScienceMCS 2106 



I am an interdisciplinary researcher mainly working with methods from machine learning (ML) and artificial intelligence (AI). By building computational models of complex (often cognitive) phenomena I attempt to 1) better understand these phenomena, 2) provide valuable tools for domain experts and end users, and 3) improve the state of the art in ML/AI. Interpretability and robustness is crucial to gain insights and incorporate expert knowledge into the systems.

A focus of my research is on modelling musical structure and perception as a particularly rich and challenging problem. Beyond that, my research includes topics such as the dynamics of communication between agents and the formation of latent/mental representations.

More broadly, I am interested in the development and application of intelligent and autonomous systems, including their social and ethical implications as well as the resulting challenges in arts, legislation and policy making.

Short Bio

Before joining Durham University as an Assistant Professor in Computer Science, I worked as a Postdoc in the Digital and Cognitive Musicology Lab at EPFL, Switzerland (2018–2021). I did my PhD in the Machine Learning and Robotics Lab (now Learning and Intelligent Systems Lab) in Stuttgart/Berlin, Germany (2012–2017) after studying Physics and Philosophy at FU Berlin.

Research interests

  • Machine Learning & Artificial Intelligence
  •     • Probabilistic Modelling (Bayesian inference, graphical models, artificial grammars, Monte-Carlo methods, approximate inference)
  •     • Neuro-Symbolic Modelling (end-to-end differentiable parsing algorithms, deep neural networks, structured differentiable models)
  •     • Structure Learning (feature discovery, structure learning in graphical models, parsing algorithms)
  •     • Planning & Decision Making (reinforcement learning, classical planning, Monte-Carlo tree search, heuristic search, active learning)
  • Cognitive Modelling
  •     • Music Cognition (perception of harmony & voice leading, hierarchical metrical structure, rhythm, expectation and surprise)
  •     • Communication & Interaction (emergence of symbols in communication, cultural evolution, iterated learning paradigm)
  • Applications
  •     • Music (music analysis & musical form, new interfaces for musical expression and education)
  •     • Ethical AI (moral reasoning & autonomous systems)
  •     • Medicine (3D medical image analysis (CT/MRI) & semi-automatic segmentation)


Conference Paper

  • Lieck, Robert, Wall, Leona & Rohrmeier, Martin Alois (2021), Discretisation and Continuity: Simulating the Emergence of Symbols in Communication Games, 43: Proceedings of the Annual Meeting of the Cognitive Science Society. Vienna, Austria.
  • Lieck, Robert & Rohrmeier, Martin (2021), Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks, Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021.
  • Thibault Jaccard, Lieck, Robert & Rohrmeier, Martin (2020), AutoScale: Automatic and Dynamic Scale Selection for Live Jazz Improvisation, International Conference on New Interfaces for Musical Expression. Birmingham, United Kingdom.
  • Lieck, Robert & Rohrmeier, Martin (2020), Modelling Hierarchical Key Structure With Pitch Scapes, Proceedings of the 21st International Society for Music Information Retrieval Conference. Montréal, Canada, 811-818.
  • Landsnes, Kristoffer, Mehrabyan, Liana, Wiklund, Victor, Lieck, Robert, Moss, Fabian C & Rohrmeier, Martin (2019), A Model Comparison for Chord Prediction on the Annotated Beethoven Corpus, Proceedings of the 16th Sound \& Music Computing Conference. Málaga, Spain, 4.
  • Langhabel, Jonas, Lieck, Robert, Toussaint, Marc & Rohrmeier, Martin (2017), Feature Discovery for Sequential Prediction of Monophonic Music, Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR). Suzhou, China.
  • Lieck, Robert & Toussaint, Marc (2017), Active Tree Search, ICAPS Workshop on Planning, Search, and Optimization.
  • Lieck, Robert, Ngo, Vien & Toussaint, Marc (2017), Exploiting Variance Information in Monte-Carlo Tree Search, ICAPS Workshop on Heuristics and Search for Domain-independent Planning.
  • Kulick, Johannes, Lieck, Robert & Toussaint, Marc (2016), Cross-Entropy as a Criterion for Robust Interactive Learning of Latent Properties, NIPS Workshop on the Future of Interactive Learning Machines.
  • Lieck, Robert & Toussaint, Marc (2015), Discovering Temporally Extended Features for Reinforcement Learning in Domains with Delayed Causalities, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Presses universitaires de Louvain, 183.
  • Sharma, Gulshan, Ho, Karen, Saevarsson, Stefan, Ramm, Heiko, Lieck, Robert, Zachow, Stefan & Anglin, Carolyn (2012), Knee Pose and Geometry Pre- and Post-Total Knee Arthroplasty Using Computed Tomography, 58th Annual Meeting of the Orthopaedic Research Society (ORS).
  • Saevarsson, Stefan, Sharma, Gulshan, Montgomery, Sigrun, Ho, Karen, Ramm, Heiko, Lieck, Robert, Zachow, Stefan, Hutchison, Carol, Werle, Jason & Anglin, Carolyn (2012), Kinematic Comparison Between Gender Specific and Traditional Femoral Implants, 67th Canadian Orthopaedic Association (COA) Annual Meeting.
  • Saevarsson, Stefan, Sharma, Gulshan B., Montgomery, Spencer J., Ho, Karen, Ramm, Heiko, Lieck, Robert, Zachow, Stefan & Anglin, Carolyn (2011), Kinematic Comparison Between Gender Specific and Traditional Femoral Implants, Proceedings of the 11th Alberta Biomedical Engineering (BME) Conference (Poster). 80.

Doctoral Thesis

Journal Article

Other (Print)

  • Lieck, Robert, Harasim, Daniel & Rohrmeier, Martin (2018). Towards a Unified Model for Harmony and Voice-Leading.
  • Lieck, Robert, Harasim, Daniel & Rohrmeier, Martin (2018). Learning Structured Models of Musical Syntax.
  • Lieck, Robert (2014). Temporally Extended Features: Modeling Delayed Causalities in Reinforcement Learning.


  • Charisi, Vicky, Dennis, Louise, Fisher, Michael, Lieck, Robert, Matthias, Andreas, Slavkovik, Marija, Sombetzki, Janina, Winfield, Alan FT & Yampolskiy, Roman (2017). Towards Moral Autonomous Systems.
  • Kulick, Johannes, Lieck, Robert & Toussaint, Marc (2015). The Advantage of Cross Entropy over Entropy in Iterative Information Gathering.
  • Kulick, Johannes, Lieck, Robert & Toussaint, Marc (2014). Active Learning of Hyperparameters: An Expected Cross Entropy Criterion for Active Model Selection.