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.
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.
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.
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 .
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.
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.
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.
Gemma de les Coves is an ICREA Research Professor at the Departament d'Enginyeria of Universitat Pompeu Fabra (Barcelona) and holds an Associate Professorship at the University of Innsbruck (Austria). Her research bridges quantum physics, mathematical theory, and philosophy, focusing on universality, undecidability, and interdisciplinary frameworks. She leads the Mathematical Quantum Physics research group in Innsbruck and has received prestigious awards including the START Prize (2020) and the ICREA professorship (2024). Education & Academic Path: PhD in Theoretical Physics (University of Innsbruck, 2011) Postdoc at Max Planck Institute for Quantum Optics (2011–2016) Assistant Professor (University of Innsbruck, 2018–2023) ICREA Research Professor (2024–present) Research Interests: Universality in physical and computational systems Undecidability in quantum models and formal languages Mathematical foundations of quantum theory Interdisciplinary connections between physics, philosophy, and culture Awards & Recognition: START Prize (FWF, 2020) Elise Richter Fellowship (2016–2018) Emmy Noether Visiting Fellowship (Perimeter Institute, 2016) Advisees & Grants: Supervised PhD students include Tobias Reinhart, Andreas Klingler, and Mirte van der Eyden Recipient of the START Prize grant (FWF) Labs & Outreach: Runs the Mathematical Quantum Physics group at Innsbruck Active in science communication via YouTube, podcasts, and public lectures
Elias Weiss is a researcher at the University of Vienna's Institute of Political Science within the Faculty of Social Sciences. He holds a BA and MA in Political Science from the University of Vienna, focusing on interpretive qualitative and exploratory research methods. His bachelor's thesis examined the duration of electoral periods, specifically the extension of Austria's legislative period from four to five years. His master's work shifted to solidarity research, particularly in labor market, economic, and health policies, analyzing the GameStop incident's political-solidarity dimensions. Research Interests: Solidarity studies, labor market dynamics, health policy, universal basic income, election period analysis, and policy discourse analysis. His work bridges political theory with empirical policy evaluation, emphasizing interdisciplinary approaches to contemporary societal challenges. Articles Trends: His publications span cognitive science, health policy, and education, reflecting a focus on mental health interventions, workplace environments, and neurocognitive adaptations. Recent work includes studies on pandemic impacts, light therapy efficacy, and stroke rehabilitation. These themes underscore a commitment to applying empirical research to real-world challenges in academia, healthcare, and public policy. Advising & Grants: No specific advising roles or grants are mentioned. His contributions are primarily through research and academic publications. Labs/Teams: Associated with the CESCoS (Contemporary Solidarity Studies) research group at the Institute of Political Science, focusing on socio-political solidarity dynamics in modern contexts.
Manfred Dorninger serves as Associate Professor in the Department of Meteorology and Geophysics at the University of Vienna's Faculty of Earth Sciences, Geography and Astronomy. His research spans meteorological modeling, renewable energy systems, and atmospheric observation techniques. His primary research interests focus on weather forecasting in complex terrain , cloud physics and classification , lightning phenomena , and renewable energy integration . Recent work leverages machine learning for ground-based cloud observations and improves photovoltaic efficiency under variable cloud conditions. His fingerprint analysis reveals significant contributions to Complex Terrain (100%), Lightning (72%), and Weather Forecast (42%) research domains. Analysis of his 111 publications shows increasing emphasis on AI-driven meteorological analysis (2024-2025), with recent papers developing neural network ensembles for cloud classification and novel metrics for ensemble forecast verification. His work bridges fundamental atmospheric science with practical applications in renewable energy and weather impact assessment. He has led significant research projects including NWP-Modellverifikation (2008-2011) and actively participates in the MesoVICT (Mesoscale Verification in Complex Terrain) initiative. His 120 recorded activities include 78 scientific talks and 38 poster presentations, demonstrating extensive knowledge dissemination. Dorninger maintains strong collaborative networks with researchers like Markus Rosenberger and Martin Weißmann, focusing on computational meteorology and atmospheric observation systems. His laboratory work centers on ground-based optical radar systems and neural network applications for weather parameter analysis.
Lars Mehnen serves as a Senior Lecturer and Researcher at the University of Applied Sciences Technikum Wien (UAS Technikum Wien) in Austria, where he has been teaching since 2019 in Computer Science and previously from 2003-2019 in Biomedical Engineering. His academic career includes significant roles at Vienna University of Technology as University Assistant (2004-2007) and Research Assistant (1998-2002), along with leadership positions in major international space initiatives including QB50 (2010-2013), GENSO (2006-2010), and SSETI (1999-2006). TU-Wien: Dipl.Ing. in Computer Engineering and Medical Informatics Dr. Techn. from Institute for Fundamentals and Theory of Electrical Engineering and Institute of Technical Mathematics Mehnen's research spans multiple domains with particular expertise in Artificial Intelligence, Evolutionary Algorithms, and Sensor Technology. His work in explainable and non-parametric evolutionary algorithms represents cutting-edge contributions to AI methodology. In biomedical engineering, he has developed innovative magnetoelastic skin curvature sensors for cardiovascular monitoring and other medical applications. His teaching portfolio is exceptionally diverse, covering Statistics (parametric and non-parametric), Artificial Intelligence, Aerodynamics (particularly for sports equipment), Physics for game engineering, and extensive programming topics including Java, C/C++, Python, and functional programming languages. His publication record from 2001-2024 shows a clear evolution from early work on magnetostrictive bilayer sensors toward more recent applications of machine learning in biological classification, medical diagnostics, and educational technology. The 2024 publication on ChatGPT's diagnostic accuracy for medical conditions demonstrates his engagement with the latest AI developments. His research consistently bridges theoretical computer science with practical applications in healthcare, sports engineering, and space technology. Mehnen has been deeply involved in European collaborative projects throughout his career, including QB50 (EC F7), GENSO (ESA initiative), and B-Sens (EC-Project in 5th framework). These projects involved coordination with numerous universities and institutions across Europe, demonstrating his ability to lead international research collaborations. His work on the SSETI Express satellite project resulted in the successful launch of a micro-satellite at the end of 2005. At UAS Technikum Wien, Mehnen contributes to curriculum development across multiple domains including statistics, AI, programming, and physics. His research on paraglide control systems demonstrates application of his expertise to sports safety technology, while his recent work in teaching analytics shows commitment to improving educational outcomes through data-driven approaches.
Martin Geroldinger is a researcher at the Research Program of Biomedical Data Science at Paracelsus Medical University. His work focuses on statistical methodologies for clinical trials in rare diseases, particularly Epidermolysis Bullosa, and biomedical data science applications. Key roles: Co-author in clinical trials, contributor to AI-based diagnostic pathways, organizer of biomedical data science colloquia Research interests: He specializes in optimizing clinical trial designs for rare genetic disorders, analyzing count and binary data in cross-over studies, and leveraging machine learning for medical research. His work addresses challenges in patient burden reduction and outcome measurement. Projects: Active in AI-driven medical knowledge extraction, 'long COVID' diagnostic pathways, and statistical approaches for rare epilepsies. Collaborates with Prof. Zimmermann and Dr. Thiel on rare disease trials. Activities: Organized the 2nd Biomedical Data Science Colloquium (2024), presented on statistical inference for rare disease trials (2022)
Mirjam Ernestus is a Professor of Psycholinguistics at the Centre for Language Studies within Radboud University's Faculty of Arts. She serves as Chair of the Editorial Board of Radboud University Press and Scientific Director of the Centre for Language Studies since 2017. Her career spans 20+ years in psycholinguistics and phonetics research. Member of Royal Netherlands Academy of Arts and Sciences Recipient of ERC Starting Grant and NWO VICI grant Specializes in speech comprehension and conversational speech Research Focus: Her work integrates psycholinguistics and phonetics to explore auditory word recognition, morphological processing, and cross-linguistic speech phenomena. She develops computational models like DIANA for speech comprehension analysis. Publication Trends: Recent studies examine exemplar-based processing differences between native/non-native speakers, phonetic-morphological interactions, speech rate dynamics, and multimodal language learning approaches. Her work frequently applies machine learning and experimental paradigms. Awards: ERC Starting Grant (2011) NWO VICI grant (2011) KNAW membership (2015) EURYI Award (2006) Leadership: She has directed major research initiatives including the gravitation project Language in Interaction and the Research Unit Spoken Morphology. Her management roles include overseeing 160+ researchers at the Centre for Language Studies.
Mark Steedman is a Professor of Cognitive Science at the School of Informatics , University of Edinburgh, and an Adjunct Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research bridges Artificial Intelligence , Cognitive Science , and Computational Linguistics , with a focus on Combinatory Categorial Grammar (CCG) , Prosody and Intonation , and Temporal Semantics . He has led the Institute for Language, Cognition, and Computation and contributed to interdisciplinary research at the Human Communications Research Center and Centre for Speech Technology Research . Research Interests : Steedman's work explores the intersection of formal grammar, computational models, and cognitive processes. He investigates how CCG parsing can enhance semantic inference, how prosodic features improve speech processing, and the role of temporal semantics in language understanding. His projects often integrate language models with entailment graphs for question answering and dialogue systems. Scientific Awards : Fellow of the American Association of Artificial Intelligence (1993) Fellow of the Royal Society of Edinburgh (2002) Fellow of the British Academy (2002) Member of Academia Europaea (2006) Best Paper Awards at ACL 2023 and AACL/IJCNLP 2023 Influential Paper Award (IFAAMAS 2017) Recent Trends in Publications : His recent work emphasizes language models for semantic inference , entailment graphs in multilingual settings, and incremental parsing for brain-language interfaces. Papers address challenges in hallucination , cross-lingual transfer , and prosody-text alignment .
Kathryn Lilley is a Professor in the Department of Biochemistry at the University of Cambridge, where she has held academic positions since 2004. She is Director of the Cambridge Centre for Proteomics and a Fellow of Jesus College, Cambridge. She also serves as a Member of the Milner Therapeutics Institute and previously led the Mass Spectrometry theme at the Rosalind Franklin Institute. Her research focuses on developing innovative technologies for spatial proteomics and transcriptomics, with particular emphasis on understanding the dynamic organization of proteins and RNA within cells. Her group creates robust open-source informatics pipelines that enable end-to-end analysis of proteomic data, making their methods widely accessible to the research community. Recent work has expanded into investigating the subcellular spatial transcriptome and its interactions with the proteome, generating significant interest in RNA biology. Lilley's publications reveal a strong focus on advancing spatial proteomics methodologies, with recent work emphasizing computational approaches, neural networks for data analysis, and novel techniques for studying RNA-protein interactions. Her research bridges biochemistry, computational biology, and cell biology, with applications in understanding cellular perturbation responses and disease mechanisms. 2020 Elected as a member of EMBO 2018 Human Proteomics Organisation, Distinguished Achievement in Proteomic Sciences Award 2017 European Proteomics Association Juan Pablo Albar Proteomics Pioneer award 2012 Fellow of the Royal Society of Biologists Lilley receives considerable funding from the Pharmaceutical Industry and collaborates widely both nationally and internationally. She heads a large research group that develops technologies for interrogating spatial distributions of the proteome and its reorganization upon cellular perturbation. Her work is highly regarded in the field, as evidenced by numerous plenary and keynote invitations at international conferences.
