Holger's Research

My research is currently focused on topics in Algorithmics, Bioinformatics, Computational Intelligence and Computer Music. A fairly current list of my research publications with links to electronic versions of most papers is available here.


Hard combinatorial problems arise in many areas of computing science and its applications. I am interested in algorithmic methods for solving these problems as efficiently and robustly as possible. In particular, I study a fundamental approach called stochastic local search (SLS), which underlies many of the best-performing algorithms for combinatorial problem solving. Much of my work on SLS algorithms is focused on the propositional satisfiability problem (SAT) and its optimisation variant, MAX-SAT, two well-known and conceptually simple combinatorial problems.

Besides developing better SLS algorithms for these and other problems, my research is focussed on obtaining an improved understanding of SLS behaviour and performance, which is often achieved by means of advanced empirical analysis techniques. These techniques form the basis for the area of empirical algorithmics, which complements and extends established theoretical methods for the analysis of algorithms with principles and techniques based on an empirical science approach.


Most of my bioinformatics research is concerned with computational methods for biomolecular structure prediction and design, with a focus on RNA and DNA secondary structure. Together with my students and collaborators, I have developed high-performance algorithms for the following problems:

  • RNA secondary structure design,
  • RNA secondary structure prediction with pseudoknots, and
  • DNA word design and HP protein structure prediction.
I am also actively interested in the role of RNA structure in splicing, in regulatory interactions and networks, gene expression analysis and motif discovery.

Computational Intelligence (aka AI):

Most of my work in computational intelligence is closely related to my research in empirical algorithmics, and focused on solving hard AI problems, including propositional satisfiability (SAT) and maximum satisfiability (MAX-SAT), constraint satisfaction problems (CSPs), as well as scheduling problems. I have also done some work on linear planning, and I have been involved in work preference representation and reasoning using ceteris paribus networks (CP nets).

Furthermore, I have recently started working in an area I call human-centred information management (HCIM), which is concerned with methods and systems for organising and retrieving information, such as text documents or audio content, in a way that is intuitive and efficient for human users.

Computer Music:

Research in this highly interdisciplinary area combines different aspects of computing science and music; my work is mainly on the following topics:

  • composition, variation and analysis of music;
  • music representation issues and formalisms;
  • music programming languages and environments;
  • music information retrieval.
I am one of the originators of GUIDO Music Notation, a rich, human-readable symbolic music representation format. I was also the director of the SALIERI Project, an academic effort for studying algorithmic aspects in music launched in 1993 at Darmstadt University of Technology. In the context of this project, our team developed the SALIERI System, a powerful interactive software environment for the score-level manipulation of musical material.

Other research interests:

I am interested in many other areas, including:

  • theoretical computer science (formal languages, automata, complexity, ...)
  • parallel distributed processing,
  • neurobiology and biocybernetics,
  • meta-mathematics and formal Logic;
but, rather unfortunately, there is not enough time for me to actively pursue these interests at the present time.

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© 2004 Holger H. Hoos - last update 2005/01/01