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Researcher
Laimighofer, Johannes is a researcher at the Institute of Statistics (iSTAT) within the University of Natural Resources and Life Sciences, Vienna. His work spans statistical methodologies applied to environmental and life sciences. His research interests include: Environmental data analysis Climate modeling Applied statistics
Researcher
Dr. Marcus Wurzer is a researcher in the Department of Statistics and Mathematics at WU Vienna. His work focuses on social stratification, education policy, demography, and microsimulation modeling. He has contributed to studies on gender segregation in educational fields, demographic projections in heterogeneous societies, and pension system dynamics. Key projects include the development of dynamic microsimulation models for Austrian labor markets and pension systems. Education background not explicitly detailed in text, but his academic contributions span interdisciplinary research in statistics, sociology, and economics. He received the 'Preis für innovative Lehre' award in 2007 for his teaching concept 'Statistik integrativ', collaborating with colleagues at WU Vienna. His research activities include presentations at international conferences on topics like socio-economic segregation in teacher training programs and graphical models in microsimulation. He has led a major research project from 2007–2014 focused on developing microsimulation tools for long-term career trajectory projections.
Researcher
Neda Ghiassi is an affiliated researcher at TU Wien's E259 - Institut für Architekturwissenschaften , focusing on urban energy modeling and computational frameworks. Her work integrates Geographic Information Systems (GIS), data analysis, and interdisciplinary approaches to address urban energy challenges. Key projects include MOTIVE (vacuum glass integration in construction) and EMULATE (urban energy assessment tools). She has published extensively on topics like high-resolution energy modeling, building product data handling, and cluster analysis for urban systems. Her research emphasizes sustainable urban development through innovative computational methods, bridging technical, social, and policy dimensions. She collaborates on projects funded by Research Studios Austria and contributes to teaching via research-guided courses. Notable outputs include a doctoral thesis on an hourglass model for urban energy systems and reports detailing project advancements in energy efficiency and building technology. Her expertise spans technical documentation, multi-stakeholder data collaboration, and scenario-based modeling. Current work explores human-centric energy modeling (MAIN_STREAM) and semantic web approaches for building product data. No awards are explicitly listed, but her contributions reflect significant engagement in academic and applied research.
Researcher
Michael Konstantin Heckmann holds an M.Sc. and works as a researcher at the University of Applied Sciences Upper Austria, specifically within the Research Center Hagenberg and the Center of Excellence for Smart Production. His work focuses on Digital Transformation and Information & Communications Technology with particular emphasis on optimization algorithms for production systems. His research interests center around dynamic production scheduling, genetic algorithms, convergence analysis, and surrogate modeling for tolerance chain analysis. Heckmann has made significant contributions to the field of mathematical optimization as applied to manufacturing systems, with particular focus on how algorithms behave in dynamic environments where production requirements change over time. His scholarly output demonstrates consistent focus on optimization problems in production scheduling, with recent work examining how genetic algorithms converge when applied to dynamic production environments and how learning techniques can be incorporated for self-adaptation in scheduling systems. His research also extends to precision engineering applications through surrogate modeling for tolerance chain analysis. h-index: 2 Citations: 1 Heckmann has been actively involved in research collaborations, serving as a Co-Investigator in the Josef Ressel Center for Adaptive Optimization in Dynamic Environments project (2019-2024), working alongside researchers including Stefan Wagner, Bernhard Werth, and Michael Affenzeller. His work bridges theoretical optimization techniques with practical manufacturing applications.
Research Fellow
Tomáš Skřivan serves as a Research Fellow at the Hoskinson Center for Formal Mathematics , Carnegie Mellon University. His work bridges formal mathematics with practical scientific computing through the development of the SciLean library in Lean 4, targeting enhanced reliability in machine learning and simulation software. Skřivan's research spans interdisciplinary domains with core emphases on: Physics-based simulation of fluid dynamics and wave phenomena Computer graphics algorithms for light transport and rendering Formal verification techniques applied to numerical methods Mathematical modeling of viscoelastic materials His publication trajectory since 2016 reveals evolving expertise from computational fluid dynamics (water wave simulation, viscoelastic modeling) toward formal methods in scientific computing, consistently merging theoretical rigor with practical implementation. Recent work on SciLean represents a strategic pivot toward verified software foundations. As a key contributor to the Hoskinson Center's mission, Skřivan collaborates on projects leveraging proof assistants to eliminate errors in scientific code. The center, established through Charles Hoskinson's support, pioneers mathematically guaranteed correctness in computational science through formal verification frameworks.
Professor
Erika Hausenblas holds the position of Chair of Applied Mathematics. Her research focuses on stochastic processes, computational mathematics, and their applications in fluid dynamics and mathematical biology. She actively participates in international conferences such as the World Congress in Probability and Statistics and the Symposium on Sparsity and Singular Structures. Her work spans theoretical developments in stochastic partial differential equations and practical applications in ecological modeling and financial mathematics. Research Interests: Stochastic Analysis, Numerical Methods for SDEs, Fluid Dynamics, and Mathematical Biology. Recent Activities: Invited talks at CRC 1283 and participation in workshops on aggregation-diffusion equations.
Professor
Peter Kirschenhofer holds the Chair of Mathematics, Statistics and Geometry at the Graz University of Technology, within the Faculty of Mathematics, Physics and Geodesy. His research focuses on number theory, combinatorics, discrete mathematics, and polynomial analysis with applications to data structures. He has published 15 research outputs between 2005 and 2019, including peer-reviewed articles in journals like Monatshefte für Mathematik and INTEGERS . His work often explores distribution results of polynomials, Catalan sequences, and theoretical mathematical frameworks. Kirschenhofer has actively participated in academic events such as the 'Numeration and Substitution 2012' conference and the 'Seminar des steirischen Doktoratskollegs'. He contributed to academic recognition efforts, including a laudatio for Wilfried Imrich and an obituary for Gerd Baron. His research aligns with the university's focus on discrete mathematics and theoretical computer science.
Professor
Lukas Spiegelhofer holds the academic rank of Professor in a position associated with the Chair of Mathematics, Statistics and Geometry. His research focuses on number theory, combinatorics on words, and mathematical analysis with particular emphasis on automatic sequences, digit statistics, and entropy-related problems. His work has been published in prestigious journals such as Israel Journal of Mathematics , Journal of the Australian Mathematical Society , and Journal d'analyse mathématique . Recent research explores topics like synchronization of automatic sequences, digit sum collisions across bases, and gap distributions in the Thue-Morse word. Spiegelhofer actively participates in academic activities including invited lectures at conferences such as the 2024 Thue-Morse workshop and combinatorics on words symposium. His research collaborations span international institutions and focus on interdisciplinary areas combining number theory with dynamical systems.
Researcher
Peter Scheibelhofer is a researcher at the Institute of Statistics, affiliated with the Technical University of Graz. His work focuses on statistical methodologies and their applications in various domains. Research Interests : Statistics Data Analysis Stochastic Processes Mathematical Modeling Probability Theory Computational Statistics
Professor
Siegfried Hörmann is a Professor at the Institute of Statistics within Graz University of Technology. His research focuses on applied statistics and related disciplines in statistical modeling and data analysis. Contact: Email: shoermann@tugraz.at Phone: +43 316 873-6476 Fax: 6977 Office: Room NT03110, Kopernikusgasse 24/III, 8010 Graz, Austria
Assistant Professor
Matthias Neumann is an Assistant Professor at the Institute of Statistics, Graz University of Technology . He completed his PhD in 2020 at Ulm University under Prof. Volker Schmidt, earning the PhD prize of Ulm University . His research focuses on stochastic 3D modeling and statistical analysis of micro- and nanostructures for functional materials, including battery electrodes , fuel cells , and paper-based materials . He has received start-up funding from ProTrainU (2020-2022) and served as principal investigator in the POLiS Cluster of Excellence (2022-2023). Research Interests: His work integrates mathematical morphology , machine learning , and spatial statistics to develop methods for microstructure quantification , estimation of geometrical descriptors (e.g., tortuosity, constrictivity), and data-driven models linking morphology to effective physical properties . He utilizes random fields , point processes , and copulas for virtual microstructure generation and parameter estimation. Teaching: He lectures on Applied Statistics , Statistical Modeling , and Mathematical Statistics at Graz University of Technology, with prior teaching experience at Ulm University in Multivariate Stochastic Modeling , Point Processes , and Spatial Statistics . Scientific Achievements: PhD prize of Ulm University (2020) ProTrainU start-up funding (2020-2022) POLiS Cluster of Excellence grant (2022-2023) Publications: His 15 most recent articles (2023-2025) emphasize machine learning techniques for microstructure segmentation , stochastic 3D modeling of nanoporous materials , and data-driven quantification of transport-property relationships . Key topics include random forests , neural networks , and R-vine copulas applied to fuel cells , sodium-ion batteries , and polymer electrolytes .
Professor
Arne Nothdurft is a University Professor for Forest Monitoring at the University of Natural Resources and Life Sciences (BOKU) in Vienna, Austria, where he chairs the Institute of Forest Growth within the Department of Forest and Soil Sciences. With a career spanning over two decades in forest research and academic leadership, he has established himself as a leading expert in advanced forest inventory techniques and forest growth modeling. Professor Nothdurft's research focuses on the application of cutting-edge technologies in forest monitoring, particularly LiDAR and personal laser scanning systems for forest inventory. His work bridges the gap between traditional forestry practices and modern digital solutions, with emphasis on mixed species forest management, climate change adaptation, and the development of smart forestry systems. His research interests encompass forest growth modeling, tree species classification using point cloud data, and the development of spatial prediction models for forest inventory parameters. His recent publications demonstrate a strong trend toward integrating artificial intelligence with forestry applications, particularly in the analysis of 3D point cloud data from laser scanning technologies. His work spans both theoretical advancements in spatial statistics and practical applications for forest managers, with a particular focus on improving the accuracy and efficiency of forest inventory systems. Thurn und Taxis Förderpreis für die Forstwissenschaft (2008) Professor Nothdurft has supervised numerous master's and doctoral theses, primarily focused on the application of laser scanning technologies in forestry, forest inventory optimization, and growth modeling. His research is supported by multiple ongoing projects funded by Austrian research agencies and federal ministries, with a strong emphasis on practical applications for forest management. He leads the Institute of Forest Growth, which maintains the Lehrforst Rosalia long-term forest monitoring site, and collaborates extensively with the Institute of Forest Engineering on smart forestry initiatives.
Researcher
Andreas Tockner is a researcher at the Institute of Forest Growth, part of the Department of Ecosystem Management, Climate and Biodiversity at the University of Natural Resources and Life Sciences, Vienna (BOKU). He holds a Dipl.-Ing. and B.Sc. degree and is currently pursuing PhD studies since 2021 as part of the "Building Like Nature" program at BOKU University. His work focuses on applying advanced laser scanning technologies to forest resource management and inventory. Dr. Tockner's educational background includes a Diplom-Ingenieur (Dipl.-Ing.) and Bachelor of Science (B.Sc.) degrees. His PhD studies at BOKU University began in 2021 as part of the "Building Like Nature" program. He is actively developing expertise in software development for instance segmentation and feature extraction of 3D point clouds. His research interests center around forest resource management with a strong emphasis on ground-based laser scanning technologies, particularly mobile LiDAR systems. He specializes in software development for instance segmentation and feature extraction of 3D point clouds, which has significant applications in modern forest inventory and monitoring. His work bridges the gap between advanced geospatial technologies and practical forestry applications, enabling more precise and efficient forest management practices. He has developed expertise in analyzing forest structures through 3D point cloud data, with particular focus on tree species classification, forest regeneration monitoring, and timber measurement at individual log levels. Dr. Tockner's publication record reveals a clear progression toward increasingly sophisticated applications of laser scanning technology in forestry. His work has evolved from basic measurement techniques to complex analysis of forest ecosystems, including species identification, wood quality prediction, and even long-term forest projections using digital twin technology. His interdisciplinary approach combines forestry, computer science, and data analytics to solve practical challenges in forest management. Advancements in personal laser scanning for forest inventory Methods for tree species classification using intensity patterns Techniques for quantifying forest regeneration Digital twin applications for forest modeling and future projections Dr. Tockner has supervised two Master's theses in 2025: "Evaluierung boden-, luftgestützter und hybrider Methoden zur Forstinventur im Naturpark Sparbach" by Elias Kimmel and "Assessing the Potential of Personal Laser Scanning to Quantify Tropical Tree Structures" by Luca Stephan Seiler. His research is supported by multiple projects including "Lidar based forest monitoring and harvesting planning" (2023-2026) funded by Federal Ministries and "Forest Inventory with Personal Laserscanners" (2022-2025) funded by the Austrian Research Promotion Agency (FFG). He is actively involved in developing practical applications of laser scanning technology for forest management, with a particular focus on making these technologies accessible for field operations. His work on using Apple iPad Pro with integrated LiDAR technology demonstrates his commitment to practical, field-deployable solutions that can transform traditional forest inventory practices.
Researcher
Paul Griesberger is a Senior Researcher at the Institute of Wildlife Biology and Game Management under the University of Natural Resources and Life Sciences, Vienna , where he has contributed to both basic and applied research since 2017. He completed his Master’s in Wildlife Ecology and Wildlife Management (2014–2017) and Bachelor’s in Biology (Zoology) (2010–2014). Key research areas: Wildlife Management, Wildlife Ecology, Wild Ungulates, Spatio-Temporal Modeling, GPS Telemetry His recent work integrates decision-support tools for sustainable ungulate management, mathematical approaches like Benford’s Law for telemetry data validation, and climate adaptation strategies for forest protection. Collaborations span EU institutions and national parks. His 14 publications (2018–2025) focus on wild ungulate behavior, interspecies competition, telemetry analytics, and canid morphology, with methodologies bridging ecology, statistics, and conservation policy. He has advised 4 Master’s students and contributed to practical guidelines for red deer and chamois management. Scientific awards: Granser-United Global Academy Forschungspreis für eine Nachhaltige Jagd (2022) YO Research Award by CIC (2020) Nationalpark Hohe Tauern stipend (2021)
Researcher
Salvatore Romano is a researcher affiliated with the Faculty of Physics , specializing in computational and soft matter physics. His work combines machine learning techniques with molecular dynamics simulations to study complex physical systems. Computational Physics Soft Matter Physics Machine Learning Neural Networks Rare Event Sampling Surface Science Romano's research focuses on the structure and dynamics of material interfaces, particularly using neural network potentials for rare event sampling and machine learning-based investigation of phase transitions. His recent publications emphasize computational methods for studying magnetite-water and ice-water interfaces. His 2022-2025 publications demonstrate continuous research activity in computational physics with increasing application of machine learning tools. Collaborations include interdisciplinary work with computer scientists and material engineers. He has presented at scientific conferences through oral presentations and poster sessions, including the 2024 international conference on computational physics research.
