Find Academic Supervisors

• Combinatorial Optimization
• Polynomially Solvable Special Cases
• Assignment Problems
• +6 more

Eranda Dragoti-Cela is an Associate Professor at the Department of Optimization and Discrete Mathematics, Graz University of Technology. She specializes in combinatorial optimization, focusing on polynomially solvable special cases of NP-hard problems like the Quadratic Assignment Problem (QAP), portfolio optimization, and algorithmic graph theory. Education: M.Sc. in Applied Mathematics, University of Tirana (1992) Ph.D. in Technical Sciences, Graz University of Technology (1995) Habilitation in Applied Mathematics (2001) Her research spans combinatorial optimization (QAP, assignment problems, location problems), portfolio optimization (risk-return models, ordinal information integration), and graph theory (edge intersection graphs, network flows). Recent work includes linearization of quadratic shortest path problems and data arrangement on trees. She supervises bachelor's and master's theses in discrete mathematics and teaches courses like Combinatorial Optimization 1 and Risk Theory and Management , emphasizing algorithmic rigor and practical applications in finance and engineering.

• Applied Mathematics
• Discrete Mathematics
• Optimization
• +1 more

Elisabeth Gassner is a Lecturer in the Institute of Discrete Mathematics at Graz University of Technology (TU Graz). She holds a teaching authorization (Lehrbefugnis) in Applied Mathematics and is associated with research activities at the institute. Her work primarily focuses on discrete mathematical structures, optimization problems, and theoretical computer science. Contact: Email: elisabeth.gassner@tugraz.at

• Mathematical Ability
• Cognitive Neuroscience
• Giftedness Research
• +5 more

Prof. Roland Grabner , holding the chair in Giftedness Research at the Institute of Psychology, Faculty of Natural Sciences, University of Graz, Austria, since 2014, previously served as a W3 Professor in Educational Psychology at Göttingen University (2012-2014). His research integrates cognitive neuroscience and mathematics education, focusing on individual differences in mathematical ability, bilingual learning effects, and neurostimulation applications for educational enhancement. Current research spans neural mechanisms in arithmetic learning and teacher professionalization Principal investigator in FWF-funded projects on non-invasive brain stimulation Key contributor to the Brain and Behavior research network His scientific focus areas include: Cognitive neuroscience of mathematical learning Giftedness and expertise development Language-switching costs in education Transcranial electrical stimulation (tES) applications Intelligence-creativity interactions Neuroimaging in educational contexts Analysis of his 15 most recent publications reveals strong emphasis on: Arithmetic cognition neural correlates Language dependency in knowledge acquisition Creativity assessment methodologies Brain stimulation for learning enhancement Interference effects in mathematical reasoning Neuroeducational intervention design Awarded both the INGE St Research Prize (2007) for mathematics education publications and the Karpow Chess Academy Science Prize (2007) for chess expertise studies, Grabner serves on multiple educational advisory boards including: Scientific Board of FUTURE EDUCATION network Austrian Center for Giftedness and Talent Research International Panel of Experts for Gifted Education (iPEGE) Steierische Gesellschaft für Psychologie (StGP)

Simon FinsterView profile

Assistant Professor

• Microeconomic Theory
• Auction Theory
• Market Design
• +4 more

Simon Finster is an Assistant Professor in the Department of Economics at Johannes Kepler University Linz, specializing in microeconomic theory, auction and market design, energy economics, and industrial organization. He joined JKU Linz in October 2025 after serving as a postdoc at CREST/Inria FairPlay and completing his PhD at the University of Oxford, Nuffield College under Paul Klemperer's supervision. His research focuses on developing frameworks for preferences over how goods should be partitioned between buyers in indivisible goods markets, devising pooled testing mechanisms for infectious diseases in populations with heterogeneous welfare weights, and reconciling welfare and revenue concerns in quasi-Fisher markets. He approaches market design through both theoretical analysis and practical implementation in lab and field settings. Finster is actively involved in the academic community as a co-organizer of GAIMSS (Games and Artificial Intelligence Multidisciplinary Summer School), which brings together researchers from mathematics, computer science, and economics. The 2025 edition will be held at Université Toulouse Capitole with distinguished speakers including José Correa, Penélope Hernández, Tristan Tomala, and Paul Dütting. His recent publications accepted in top venues include work on selling multiple complements with packaging costs, equitable auctions, and competitive and revenue-optimal pricing with budgets. These publications reflect his focus on bridging theoretical market design with practical applications. At JKU Linz, he teaches 'Public Finance, Environment and Health' and maintains an active research program with presentations at major international conferences across Europe and North America. His work demonstrates a strong commitment to applying economic theory to solve real-world problems while advancing theoretical understanding in microeconomics and game theory.

Jan VrabliczView profile

Researcher

• Visual Analytics
• Industrial Manufacturing Data
• Knowledge-Assisted Systems
• +3 more

Jan Vrablicz serves as a Junior Researcher at the Josef Ressel Center for Knowledge-Assisted Visual Analytics for Industrial Manufacturing Data within the University of Applied Sciences St. Pölten, Austria. His role focuses on developing visual analytics methodologies that integrate domain knowledge to enhance data-driven decision-making in industrial manufacturing contexts, particularly addressing challenges in injection molding processes through semantic technologies and interactive causal analysis frameworks. His research interests span Visual Analytics, Industrial Manufacturing Data, Knowledge-Assisted Systems, Causal Analysis, Injection Molding, and Semantic Systems. This work emphasizes practical applications where visual interfaces bridge complex manufacturing data and operational insights, targeting transparency in AI-driven industrial systems. Current projects investigate knowledge-assisted causal modeling to optimize production quality and efficiency in metal casting environments. Recent 2025 publications demonstrate a clear research trajectory: integrating semantic technologies with visual analytics for industrial data interpretation, specifically in injection molding. Key trends include problem characterization methodologies for causal analysis, knowledge-assisted interactive systems, and semantic transparency frameworks—all targeting real-world manufacturing data challenges with applied computer science solutions. No information regarding student advising or research grants was found in available sources. The Josef Ressel Center functions as his primary research ecosystem, a dedicated unit advancing visual analytics for industrial manufacturing through collaborations between computer scientists, engineers, and industry partners. The center specializes in developing knowledge-assisted tools that transform complex production data into actionable visual insights for manufacturing professionals.

• Assembly Tasks
• Smart Factory Systems
• Cloud Computing
• +2 more

Johann Heinzelreiter serves as an FH-Prof. DI at the University of Applied Sciences Hagenberg, actively contributing to the Assistive Technology Lab and Center of Excellence for Smart Production . His research spans industrial automation, smart factory systems, and cloud computing applications in production environments. His research interests focus on assembly task modeling , low-cost tracking systems for industrial environments, and human-centered workplace design . Heinzelreiter develops practical solutions for real-world manufacturing challenges, particularly in optimizing human-machine collaboration on shop floors through the General Assembly Task Model (GATM) framework. Publication analysis reveals consistent contributions to industrial informatics, with recent work emphasizing smart factory implementation (2019-2020), cloud-based optimization (2014), and digital identity management (2015). His research demonstrates strong industry relevance through COIN Cooperation & Innovation projects. With an h-index of 34, Heinzelreiter has secured significant research funding through multiple COIN projects: Human Centered Workplace (2016-2021) - Co-Investigator Themis - Conserve Your Digital Life (2013-2015) - Principal Investigator BackmeUp - Offline Datensicherung Web 2.0 (2010-2012) - Principal Investigator He collaborates extensively with researchers including Pimminger, Kurschl, and Augstein across production environments and assistive technology domains. His work bridges academic research with practical industrial applications through the Smart Automation and Robotics initiative.

• Building Construction
• Energy Optimization
• Cost Optimization
• +2 more

Stefan Hinterholzer serves as a Professor at the University of Applied Sciences Upper Austria, based at the Research Center Hagenberg. His research bridges civil engineering and information systems, specializing in computational optimization of building construction processes for energy efficiency and cost reduction. His primary research domains include: Building Construction Design Optimization Energy Demand Modeling and Reduction Construction Cost Minimization Automated Construction Planning Systems Business Process Management for Knowledge Work Hinterholzer pioneered the “Bauoptimizer” simulation tool for architectural and material selection, enabling dual optimization of energy consumption and construction expenditures. His work establishes standardized reference frameworks for quantitative assessment in construction planning, addressing complex trade-offs between environmental impact and financial constraints through algorithmic exploration of design spaces. Analysis of his 2010-2014 publications reveals consistent application of computer-aided systems theory to sustainable construction challenges. His research demonstrates how automated simulation can navigate multi-dimensional optimization problems in building design, significantly advancing methodologies for eco-efficient construction planning within constrained computational search spaces. Professional activities include peer-review services for Wirtschaftsinformatik 2005 (eEconomy/eGovernment conference) and presentations on Business Process Management integration with enterprise software. His interdisciplinary approach connects construction engineering with information systems through practical computational solutions.

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations
• +3 more

Herbert Egger is a Professor of Numerical Analysis and Scientific Computing at Johannes Kepler University (JKU) Linz and heads the Institute of Numerical Mathematics. He is also a Group Leader at RICAM (Johann Radon Institute for Computational and Applied Mathematics) and Scientific Director at the Austrian Academy of Sciences. His research focuses on advanced numerical methods for partial differential equations, including structure-preserving discretizations, inverse problems, and computational electromagnetics. Current projects include SFB F90-N (CREATOR) for electric machine co-simulation, SPP 2256 for phase-field modeling in additive manufacturing, and TRR 146 on multiscale soft matter systems. Key research areas: mixed/hybrid finite element methods, energy-based modeling, model order reduction, radiative transfer, and inverse problem stabilization. His recent work emphasizes nonlinear magnetostatics, phase separation models, and efficient solvers for time-dependent systems. Egger collaborates internationally with institutions like TU Eindhoven and TU Darmstadt.

• Mathematics Education
• Procedural Knowledge Assessment
• Mathematical Modeling in Education
• +4 more

Christoph Ableitinger is an Associate Professor at the Department of Mathematics , Faculty of Mathematics , University of Vienna , with a dual affiliation to the Center for Teacher Education . His academic profile focuses on the intersection of theory and practice in mathematics education, particularly through his roles in curriculum development and teacher training programs. Broadly investigates mathematical procedural knowledge and its assessment Examines technology integration in secondary mathematics instruction Develops modeling seminars and educational outreach initiatives Studies teacher-student belief systems in mathematical contexts Researches the impact of standardized testing on educational practices Recent publications highlight trends in analyzing student errors, optimizing technology use in classrooms, and bridging university mathematics with secondary school requirements through modeling activities. His work offers valuable insights into improving mathematical understanding and instructional effectiveness across educational systems.

• Artificial Intelligence
• Artificial Neural Networks
• Information Systems
• +3 more

Konstantin Schekotihin is an Associate Professor at the Department of Artificial Intelligence and Cybersecurity, Alpen-Adria University of Klagenfurt. His research focuses on artificial intelligence, machine learning, and semantic technologies with applications in industrial systems and semiconductor manufacturing. Reinforcement learning for industrial scheduling Answer Set Programming (ASP) and stream reasoning Failure analysis automation and ontology engineering Neuro-symbolic AI integration Knowledge-based systems in manufacturing Recent publications emphasize AI-driven optimization in semiconductor production, decomposition strategies for scheduling problems, and multi-agent systems for workflow management. His work combines symbolic reasoning with machine learning to address complex industrial challenges. Contact: Konstantin.Schekotihin@aau.at

Christoph ErathView profile

Professor

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +4 more

Christoph Erath is a Professor of Mathematics at the University College of Teacher Education Vorarlberg (PH Vorarlberg) in Austria, serving as Director of Secondary Education and Subject Didactics. He holds a Habilitation (Privatdozent) in Applied Mathematics from TU Wien and maintains active research and teaching responsibilities. His educational background includes: PhD in Mathematics (Dr. rer. nat., summa cum laude) from Ulm University, Germany (2010) Master's degree in Technical Mathematics (Dipl.-Ing., with honor) from TU Wien, Austria (2005) Professor Erath's research focuses on advanced numerical methods for partial differential equations , with expertise spanning Finite Element Methods, Boundary Element Methods, and Discontinuous Galerkin Methods. His theoretical work emphasizes a priori and a posteriori error analysis, robustness, and adaptive mesh refinement strategies. He has developed numerical schemes for climate modeling and created innovative teaching formats through collaboration with local companies, bridging theoretical mathematics with practical classroom applications. His publication record demonstrates consistent advancement in coupling different numerical methods with rigorous error analysis. Recent work has expanded into educational applications, examining how fundamental mathematical competencies interact with emerging technologies like artificial intelligence in school settings, reflecting his dual commitment to theoretical mathematics and pedagogical innovation. Scientific recognition includes: Award from Ulmer Universitätsgesellschaft for PhD Thesis (2012) PhD scholarship from Baden-Württemberg, Germany (2007-2010) Award from Austrian Mathematical Society for Diploma Thesis (2006) Professor Erath actively supervises graduate theses in mathematical fields and has led research teams including postdocs and PhD students. His teaching portfolio includes core courses in Applied Mathematics, Analysis, and Mathematical Methods for teacher education students. He maintains strong research connections with institutions including TU Wien, TU Darmstadt, and the University of Colorado, contributing significantly to both computational mathematics and mathematics education methodology.

Olga Mula HernandezView profile

Professor

• Numerical Analysis of Partial Differential Equations
• Mathematical Foundations of Scientific Machine Learning
• High-Dimensional and Nonlinear Approximation
• +2 more

Olga Mula Hernandez is a Professor of Mathematics at the University of Vienna since September 2025, holding the Chair of Computational Partial Differential Equations. Previously, she served as an Associate Professor at Eindhoven University of Technology (2022-2025) and Assistant Professor at University Paris-Dauphine (2015-2022). Her educational background includes a double master's degree in Applied Mathematics and Nuclear Engineering from École Polytechnique (France) and Escuela Politécnica (Madrid) (2005-2011), followed by a PhD in Applied Mathematics from Sorbonne University (2011-2014). She also completed a postdoctoral fellowship at RWTH Aachen (2014-2015). Professor Mula's research centers on numerical analysis of partial differential equations with data-driven integration. Her work spans mathematical foundations of scientific machine learning (including PINNs), high-dimensional nonlinear approximation for PDE solvers, data assimilation and inverse problems, numerical optimal transport, and structure-preserving numerical schemes. She develops explainable, data-efficient computational methods that leverage physical insights rather than relying on black-box machine learning approaches. Her methodologies address critical applications across 3D-printing, haemodynamics, environmental pollution modeling, epidemiology, and nuclear engineering, where accurate physics-based simulations are essential for real-world problem solving.

• Global Optimization
• Numerical Analysis
• Data Analysis
• +3 more

Waltraud Huyer is a faculty member at the Department of Mathematics, Faculty of Mathematics, University of Vienna. Her office is located in Room 04.120 at Oskar-Morgenstern-Platz 1, 1090 Wien, Austria. She actively teaches courses including Numerical Mathematics and Introduction to Mathematics for both undergraduate and secondary school teacher accreditation programs. Her research spans Global and Local Optimization , Numerical Analysis , Data Analysis , Protein Folding , and Population Dynamics with structured populations. She develops advanced optimization algorithms like SNOBFIT for noisy environments and MINQ8 for quadratic programming, while applying mathematical techniques to biological problems such as protein structure prediction and age-structured population modeling. Analysis of her 14 most recent publications (1994-2018) reveals three dominant research thrusts: (1) Algorithmic development in global optimization (7 papers), including multilevel coordinate search and exact penalty functions; (2) Mathematical biology applications (5 papers), particularly in protein folding and population dynamics; (3) Computational verification methods (2 papers) for linear systems and feasibility problems. Her work consistently bridges theoretical mathematics with practical computational implementations. Her scientific contributions include widely-used algorithms implemented in MATLAB, such as the MCS global optimizer and SNOBFIT for noisy optimization, available through the University of Vienna's software repository. Huyer supervises teaching activities for foundational mathematics courses, emphasizing practical computational skills using MATLAB for numerical analysis and regression problems. Her teaching materials include detailed exercise sets with structured assessment criteria requiring both theoretical understanding and programming implementation.