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Wolfgang Schreiner
Johannes Kepler University
Linz, Austria

Wolfgang Schreiner is an associate professor at the Research Institute for Symbolic Computation (RISC) of the Johannes Kepler University Linz in Austria. His research areas are formal methods; concurrency; and parallel, distributed, and grid computing.

Wolfgang Schreiner was born in 1967 in Austria. In 1994, he earned his PhD at the Johannes Kepler University Linz under the auspices of the federal president with a thesis on parallel functional programming for computer algebra. In 2001, he earned habilitation in practical computer science for his work on parallel software and algorithms for symbolic computation. From 2001 to 2004, he was the director of the degree program "Engineering for Computer-based Learning" at the Upper Austria University of Applied Sciences campus Hagenberg, where he still serves as a lecturer. Since 2004, he has been an associate professor at the RISC institute, where he served as vice-chair from 2004 to 2007.

During his career, Schreiner has participated in and directed various research projects funded by the Austrian Science Foundation, the Austrian Ministry for Science and Research, and the European Union. These projects include "Distributed Supercomputing in the Grid," "MathBroker I+II: Brokering Distributed Mathematical Services," and "HPGP: High-Performance Generic Programming." He has developed various software systems such as the para-functional language compiler pD, the parallel computer algebra software Distributed Maple, and the proving assistant RISC ProofNavigator.

Currently, Schreiner is participating in the doctoral program for computational mathematics at the Johannes Kepler University with a project on formally specified computer algebra software. He is also building the RISC ProgramExplorer, a software environment for program specification, exploration, and verification.


Read our Q&A with Wolfgang Schreiner here.


Learning Bayesian network parameters from small data sets
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Bayesian networks (BNs) represent a powerful statistical tool for uncertainty analysis with applications in many areas, for example, medical diagnosis. Since data is often not sufficiently available to accurately learn the parameters of a BN by th...


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Many applications in operations research require the simultaneous optimization of multiple (typically mutually opposing) objective functions. Since the general problem is usually computationally intractable, bicriteria optimization restricts itsel...


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Schrammel P., Kroening D., Brain M., Martins R., Teige T., Bienmüller T.  Formal Aspects of Computing 29(5): 911-931, 2017. Type: Article

Bounded model checking is employed in tools for the formal verification of C programs such as the C bounded model checker (CBMC). The key idea of this technique is to unwind unbounded program loops to a fixed depth and to translate the resulting p...


Uncertainty and reduction of variable precision multigranulation fuzzy rough sets based on three-way decisions
Feng T., Fan H., Mi J.  International Journal of Approximate Reasoning 85(C): 36-58, 2017. Type: Article

To formalize decision making in information systems with incomplete data, object attributes may be represented as fuzzy sets. Rough sets abstract such systems by two sets that represent the lower and the upper approximation of which objects satisf...


Proof checking and logic programming
Miller D.  Formal Aspects of Computing 29(3): 383-399, 2017. Type: Article

A proof checker is a program that takes a claimed proof of a logic formula and decides whether this claim is true; in applications such as program verification and automated reasoning, it is thus not necessary to trust the (potentially big) prover...


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