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:
Below are the projects where I am (have been) involved in
Land MAnagement: Assessment, Research, Knowledge base
H2020 Project, SFS-2014-635201
Learning from Massive, Incompletely annotated, and Structured Data.
FP7 Project, FET-Open-Xtrack, ICT-2013-612944
In Slovene: Tehnologije znanja.
In Slovene: Integrativne raziskave evolucije spolnega dimorfizma
In Slovene: Uporaba proteomskih in aptamernih pristopov za študij interakcij gostitelj-mikroorganizem pri stafilokoknih in klostridijskih okužbah.
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
- Nikola Simidjievski
- Jovan Tanevski
- Vladimir Kuzmanovski
- Jurica Levatić
- Aljaž Osojnik
- Martin Breskvar
- Matej Petković
- Stevanče Nikoloski
- 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
A list of edited and conference proceedings
A list of invited talks and lectures.
A list of edited journals.
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 probmot.ijs.si
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 clus.sourceforge.net