Sašo Džeroski

Sašo Džeroski

Scientific Councillor

Jozef Stefan Institute, Department of Knowledge Technologies


Jozef Stefan International Postgraduate School

Scientific Councillor

Centre of Excellence for Integrated Approaches in Chemistry and Biology of Proteins

Foreign Member

Macedonian Academy of Sciences and Arts



In the last ten years, I built an informal research group, which at present includes 17 researchers. We develop methods for constraint-based data mining, predicting structured outputs, and automated modelling of dynamics systems and apply them to problems from systems biology and ecology.I coordinated the project IQ, which produced a general framework for data mining, leading to representational ontologies for datatypes and data mining) and methodological advances (methods for predicting structured outputs based on constrained predictive clustering). I participated in two EU-funded projects in the area of systems biology (E.E.T. Pipeline, PHAGOSYS).

I am currently coordinating the project MAESTRA, which is developing (among other) methods for predicting structured outputs. I have also proposed the process-based approach to automated modelling of dynamic systems and edited an overview on the area where the approach belongs (computational scientific discovery).

My research interests and the research interests of my group include:

  • Data Mining / Machine Learning and their Applications in: Environmental Sciences (Ecological Modeling, Systems Ecology); Language Resources and Technologies; Life Sciences (Bioinformatics, Systems Biology); Medicine (Diagnostics and Prognostics),
  • Combining Classifiers, Ensemble Learning and Meta-Learning,
  • Computational Scientific Discovery, Automated Modeling of Dynamic Systems,
  • Constraint-Based Data Mining, Inductive Databases, Inductive Queries,
  • Inductive Logic Programming, Relational Data Mining,
  • Mining Data Streams, Data Science, Big Data Analytics,
  • Structured Output Prediction, Semi-Supervised Learning.


Below are the projects where I am (have been) involved in


(principal investigator)



Land MAnagement: Assessment, Research, Knowledge base

H2020 Project, SFS-2014-635201



Human Brain Project

FP7 Project, FET-Flagship, ICT-2013-604102


2014-2016, Coordinator

Learning from Massive, Incompletely annotated, and Structured Data.

FP7 Project, FET-Open-Xtrack, ICT-2013-612944


Knowledge technologies

2004-2008, 2009-2014, 2015-2020

In Slovene: Tehnologije znanja.

Integrative research of sexual dimorphism evolution


In Slovene: Integrativne raziskave evolucije spolnega dimorfizma

Proteomic and aptamer-based approaches for study of host-pathogen interactions in staphylococcal and clostridial infections


In Slovene: Uporaba proteomskih in aptamernih pristopov za študij interakcij gostitelj-mikroorganizem pri stafilokoknih in klostridijskih okužbah.

Research Group

I currently lead a research group of approximately 17 members within the Department of Knowledge Technologies at the Jožef Stefan Institute.


  • Ljupčo Todorovski
  • Marko Debeljak
  • Bernard Ženko
  • Dragi Kocev
  • Panče Panov
  • Aneta Trajanov


  • Tina Аnžič

Phd. Students

  • Nikola Simidjievski
  • Jovan Tanevski
  • Vladimir Kuzmanovski
  • Jurica Levatić
  • Aljaž Osojnik
  • Martin Breskvar
  • Matej Petković

MSc. Students

  • Nina Vidmar
  • Tomaž Stepišnik-Perdih
  • Vanja Mileski


  • Valentin Gjorgjioski
  • Darko Aleksovski
  • Darko Čerepnalkoski
  • Elena Ikonomovska
  • Daniela Stojanova
  • Ivica Slavkov
  • Aleksandar Pečkov
  • Katerina Taškova
  • Gjorgji Madjarov
  • Ivica Dimitrovski
  • Nataša Atanasova



A curated list of articles in journals, book chapters and conference/workshop proceedings


A list of edited and authored monographs

Conference Proceedings

A list of edited and conference proceedings

Scientific Activity

Invited talks

A list of invited talks and lectures.

Editing journals

A list of edited journals.

Organizing events

A list of conferences and events.



In the core of the process-based modeling approach is a formalism for representing models of dynamical systems as well as knowledge for modeling dynamical systems in a particular domain of interest. The process-based model formalism allows for representing models of dynamical systems at two levels of abstraction. At the higher level, the model is represented as sets of processes that govern the dynamics of the observed system and entities involved in the processes. At the lower level, each process includes a model of its dynamical influence on the variables of the observed system. The process-based modeling software can automatically combine the models of individual processes into a set of coupled differential equations used to simulate the behavior of the observed system. Thus, process-based models at the higher abstraction level reveal the structure of the observed systems in terms of entities and process interactions among them, providing explanation of the model behavior obtained by a lower-level declaration of the model equations.

Available at


Clus is a decision tree and rule induction system that implements the predictive clustering framework. This framework unifies unsupervised clustering and predictive modeling and allows for a natural extension to more complex prediction settings such as multi-task learning and multi-label classification. While most decision tree learners induce classification or regression trees, Clus generalizes this approach by learning trees that are interpreted as cluster hierarchies. We call such trees predictive clustering trees or PCTs.

Available at