جست‌وجوی استاد و پژوهشگر

• Bayesian Probability Theory
• Uncertainty Quantification
• Machine Learning
• +۶ مورد دیگر

Sascha Ranftl is an Erwin Schrödinger Fellow at the Courant Institute of Mathematical Sciences, New York University. Their research spans interdisciplinary domains including Bayesian probability theory, uncertainty quantification, computational engineering, and biomedical applications. Affiliation: Courant Institute, NYU Former Affiliation: Graz University of Technology (Institute of Theoretical Physics & Computational Physics, Graz Center of Computational Engineering) Research Interests: Ranftl's work focuses on integrating Bayesian inference with machine learning and physics-informed modeling to address uncertainty quantification in biomedical simulations, particularly for aortic dissection and impedance cardiography . Their approach harmonizes statistical rigor with engineering pragmatism, emphasizing surrogate models and computational fluid dynamics. Recent Publications: Analyze geometric uncertainties in patient-specific blood vessels, develop physics-consistent neural networks, and apply Bayesian frameworks to biomedical diagnostics and elastoplastic material modeling. Scientific Awards: Erwin Schrödinger Fellowship Contact: ranftl@tugraz.at sranf@duck.com

• Low intensity psychological interventions
• Primary Mental Health Services
• Remote delivery of interventions
• +۷ مورد دیگر

Karina Lovell is a Professor of Mental Health at the University of Manchester , affiliated with the School of Health Sciences. She has held leadership roles, including Director of Research, and has over 20 years of academic and clinical experience in mental health nursing and psychological interventions. University of Manchester (2005–present) Bethlem and Maudsley NHS Trust, London (1996–1997) Institute of Psychiatry, London (1992–1996) Karina Lovell specializes in: Developing and evaluating low-intensity psychological interventions for common mental health conditions Remote delivery of interventions (telephone, computerised therapies) for chronic widespread pain Involving service users and families in mental health care planning Conducting randomised controlled trials (RCTs) and public/patient involvement research Her recent research focuses on accessible mental health care models and global health initiatives, particularly in Indonesia and Sub-Saharan Africa. Key projects include: NIHR-funded global health research on stillbirth prevention Capacity-building workshops for early-career researchers Development of culturally adapted mental health programs Scientific Awards: NIHR Senior Investigator (2012, 2016) MHRN prize for outstanding carer involvement (2014) CRN award for public engagement (2015) McPin/NIHR Service User and Care Involvement Research Award (2018) Her work has resulted in over 200 peer-reviewed publications and a Google Scholar h-index of 52. She has secured over £4.5m as Chief Investigator and £35m as co-investigator from major funders like NIHR, MRC, and GCRF. She has delivered keynote lectures across six countries and pioneered telephone-based CBT programs now implemented nationwide in the UK.

Xu Nuمشاهده پروفایل

استاد مدعو

• Heavy-ion Phenomenology
• Nuclear Physics
• High-Energy Nuclear Collisions
• +۳ مورد دیگر

Nu Xu is a distinguished academic and researcher in nuclear and particle physics. She currently serves as Dean of the College of Physical Science and Technology at Central China Normal University (since 2010) and holds positions as Senior Staff Scientist at Lawrence Berkeley National Laboratory (since 2001) and Adjunct Professorships at multiple institutions including the University of Science and Technology of China (since 2002), National Institute of Science Education and Research (India, since 2015), and Shanghai Institute of Applied Physics (since 2008). Her leadership roles include President of the International Organization of Chinese Physicists and Astronomers (since 2017) and Spokesperson for the STAR Experiment at RHIC (2008–2014). Education: B.S. in Physics from University of Science and Technology of China (1982), Ph.D. in Physics from Stony Brook University (1990). Postdoctoral and fellowship experiences include Los Alamos National Laboratory (1994–1997) and Lawrence Berkeley National Laboratory (1997–2001). Research interests focus on heavy-ion collisions, QCD phase diagram, critical point exploration, and collective phenomena in high-energy nuclear physics. Key contributions include studies of elliptic flow, strangeness production, and dielectron spectra in RHIC experiments. Awards include the 1000-Talent Plan of China (2010), American Physical Society Fellowship (2009), and Fellowship from the Institute of Physics, UK (2004). Service work includes editorial roles in journals like Nuclear Physics A and Science China , advisory roles in international collaborations such as the Helmholtz International Center for FAIR (Germany), and leadership in Quark Matter Conferences.

Thomas Stöcklمشاهده پروفایل

استاد

• Experimental Finance
• Asset Markets
• Behavioral Economics
• +۴ مورد دیگر

Thomas Stöckl is a Professor and Deputy Head of the Department & Study Programs in Business & Management (BA) at MCI | Die Unternehmerische Hochschule® since 2018. Previously, he held roles including Assistant Professor at the University of Innsbruck (2010-2018) and visiting positions at CalTech and Aarhus University. He holds a Prof. FH title from MCI (2017) and a Priv.Doz. (postdoctoral qualification) from the University of Innsbruck (2015). His research focuses on experimental finance, asset markets, behavioral economics, and financial regulation. Key contributions include studies on insider trading dynamics, market efficiency, and regulatory impacts. He has received notable awards including the Innsbruck City Research Prize (2018) and Modigliani Research Grant (2013). Active in professional networks like the Society for Experimental Finance (SEF), he advises numerous theses and publishes widely in top journals like Journal of Banking and Finance and Experimental Economics . His work bridges theoretical models with real-world market behaviors, emphasizing experimental methods to understand trader decisions and policy effects.

Anita Onayمشاهده پروفایل

استاد

• Production Management
• Supply Chain Management
• Operations Research
• +۵ مورد دیگر

Anita Onay is a Professor of Production Management at MCI Management Center Innsbruck. She holds a PhD in Production Management from the University of Innsbruck (2012–2016) and a Mag. rer. soc. oec. in Business Administration with a focus on Production and Logistics (2004–2010). Her academic career includes roles as Senior Lecturer (2021–2023), Lecturer (2018–2021), and Research Assistant (2016–2018) at MCI. She is actively involved in professional associations including the EURO Working Group on Behavioral OR (Coordination Board Member since 2022) and the Industry 5.0 Community of Practice (EU member since 2023). Her research focuses on integrating behavioral aspects into production and supply chain systems, with emphasis on simulation-based decision support, stochastic processes, and human factors in operations. Key areas include hierarchical production planning, lead time management, and behavioral workload control. She has led projects such as the Tyrolean Innovation Funding initiative on digital traceability in timber trade (2022–2024) and a TWF-funded study on lean production in SMEs (2017). Teaching responsibilities span Industrial Engineering and Mechatronics programs at MCI, covering courses like Production Planning & Control, Simulation & Optimization, and Lean Practice Lab. She advises over 40 students in bachelor and master theses, focusing on topics like production simulation, lean manufacturing, and Industry 4.0 implementation. Notable achievements include the 2023 Innsbruck Prize for Research and Innovation for her doctoral work on behavioral operations. She also received a PhD Program Support Grant from the University of Innsbruck (2014). Her professional experience includes roles as a project manager in quality management (GE Jenbacher, 2008–2009) and IT project leadership (Auviso Ltd., 2010–2014).

Friedrich Slivovskyمشاهده پروفایل

پژوهشگر

• Quantified Boolean Formulas
• Propositional Model Counting
• Knowledge Compilation
• +۵ مورد دیگر

Friedrich Slivovsky is a researcher at the Institute of Logic and Computation within the Faculty of Informatics at Technische Universität Wien (Vienna University of Technology). His work focuses on theoretical and practical aspects of computational logic, with particular expertise in Quantified Boolean Formulas (QBFs), Propositional Model Counting (#SAT), and Knowledge Compilation. His research interests span the theoretical foundations and practical applications of computational logic. Slivovsky investigates the complexity of logical reasoning problems, develops efficient algorithms for solving them, and creates practical tools that implement these theoretical advances. His work bridges the gap between theoretical computer science and practical applications in areas like hardware verification, artificial intelligence, and electronic design automation. Analysis of his publication trends reveals a consistent focus on QBF solving techniques, with increasing emphasis on circuit minimization, proof complexity, and practical solver engineering. His recent work (2023-2024) shows a strong focus on circuit minimization techniques, combining QBF and SAT approaches to solve complex optimization problems in hardware design. Earlier work (2019-2021) emphasized dependency schemes, certification methods, and theoretical foundations of QBF solving. Slivovsky leads several significant software projects that have become important tools in the computational logic community: Qute : A dependency learning QBF solver with GitHub repository showing active development (latest commit December 2024) Unique : A preprocessor for (D)QBF that computes unique Skolem and Herbrand functions Pedant : A certifying DQBF solver These projects demonstrate his commitment to translating theoretical advances into practical tools that benefit the broader research community.

Alexey Ignatievمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Formal Methods
• Satisfiability Modulo Theories (SMT)
• +۳ مورد دیگر

Alexey Ignatiev is an Associate Professor in the Optimisation research group at Monash University's Faculty of Information Technology. Previously, he was a postdoctoral researcher and researcher at the University of Lisbon's Faculty of Sciences, focusing on SAT/SMT-based decision procedures. He holds a Ph.D. from the Matrosov Institute for System Dynamics and Control Theory (Russian Academy of Sciences), where his thesis explored parallel CDCL-BDD integration. His research emphasizes formal methods in AI, including explainable AI (XAI), SAT-based reasoning, and optimization for applications like software upgradability, model-based diagnosis, and fault localization. His work spans over 100 publications, with notable contributions to MaxSAT solving (RC2 solver), neuro-symbolic frameworks (NEUSIS), and rigorous explanations for machine learning models. He has collaborated extensively with institutions like the University of Lisbon and Monash University, contributing to advancements in formal verification and interpretable machine learning.

• Formal Methods
• Model-Driven Engineering
• SAT/QBF Solving
• +۴ مورد دیگر

Martina Seidl is a researcher and principle investigator at TU Wien's Institut für Softwaretechnik und Interaktive Systeme (E188). Her main affiliation is within the Faculty of Informatics. She leads the FAME Project (Formalizing and Managing Evolution in Model-Driven Engineering), focusing on advancing formal methods in software engineering. Her research interests include formal verification techniques, SAT/QBF solving algorithms, model-driven engineering, and automated reasoning. She has contributed to advancements in quantified Boolean formula (QBF) solving, parallel computing methodologies for logical problems, and formal methods in software model analysis. Her work spans theoretical computer science and practical applications in automated theorem proving and model checking. Notable contributions include expansion-based QBF solving approaches, parallel solving frameworks, and feature-based classifications of formal verification techniques. Seidl co-organized the QBF Gallery initiative, which curates benchmark suites for quantified Boolean formula competitions. She has also published extensively on clause redundancy optimization, blocked clause analysis, and the integration of formal methods in educational contexts like UML@Classroom.

• Artificial Intelligence
• Scheduling Algorithms
• Metaheuristics
• +۴ مورد دیگر

Prof. Nysret Musliu is an Associate Professor at TU Wien's Department of Databases and Artificial Intelligence within the Faculty of Informatics. His primary affiliation is with the E192-02 research area, focusing on optimization, scheduling, and AI applications. He leads projects like 'Artificial Intelligence in Employee Scheduling' and 'Predictive Analytics for Emergency Call Infrastructure,' demonstrating expertise in combinatorial optimization and real-world problem-solving. Academic Rank: Associate Professor Institution: TU Wien Key Projects: AI-driven scheduling, constraint programming, metaheuristics Research interests include scheduling algorithms, constraint programming, and hybrid optimization methods. His work bridges theoretical advancements with industrial applications, addressing challenges in manufacturing, healthcare, and transportation. Recent publications emphasize hyper-heuristics, large neighborhood search, and AI integration for complex scheduling problems. Notable contributions include systematizing test laboratory scheduling and developing exact methods for oven scheduling. His research group collaborates on projects involving personnel scheduling, production leveling, and parallel machine optimization.

Thomas Eiterمشاهده پروفایل

استاد

• Answer Set Programming
• Knowledge Representation
• Logic Programming
• +۷ مورد دیگر

Thomas Eiter is a Professor at TU Wien's Institute of Logic and Computation. His research focuses on declarative programming paradigms, knowledge representation, and artificial intelligence. He leads projects in neurosymbolic systems, answer set programming (ASP), and stream reasoning, with applications in visual question answering, scheduling optimization, and semantic scene generation. Eiter has contributed to foundational work in ASP semantics, computational complexity, and hybrid reasoning frameworks. His work bridges logical formalisms with practical AI challenges, emphasizing explainability and scalability. Projects like ALASPO and neurosymbolic integration showcase his focus on advancing both theoretical and applied aspects of AI. Projects: HumanE AI Network, WASP, REWERSE Research Themes: Neurosymbolic AI, Answer Set Programming, Stream Reasoning Notable achievements include pioneering work on semiring-based reasoning frameworks and developing efficient ASP solvers like Alpha. His contributions span over 471 publications, emphasizing interdisciplinary applications in computer vision, robotics, and automated planning.

Johannes Oetschمشاهده پروفایل

پژوهشگر

• Answer Set Programming
• Neural-Symbolic Computation
• Visual Question Answering
• +۳ مورد دیگر

Johannes Oetsch is a researcher at TU Wien's Forschungsbereich Knowledge Based Systems within the Faculty of Informatics. His work focuses on Answer Set Programming (ASP) , neuro-symbolic computing , and visual question answering systems . He holds a Diplom-Ingenieur (Dipl.-Ing.) and a Doctor of Technical Sciences (Dr.techn.) in informatics. Key research areas include: Integration of large language models with symbolic reasoning frameworks Optimization techniques in ASP for scheduling problems Explainability mechanisms for neuro-symbolic systems Recent work emphasizes visual question answering using graph-based representations and contrastive explanation methods. He has contributed to the development of ALASPO , an adaptive optimization framework for ASP solvers. His research also explores applications in manufacturing scheduling and automated testing of logic programs. Notable contributions include: Neuro-symbolic pipelines combining ASP with vision-language models Lexicographical makespan optimization in parallel machine scheduling Large-neighbourhood search strategies for ASP-based optimization

Matthias Stefanمشاهده پروفایل

پژوهشگر

• Behavioral Economics
• Behavioral Finance
• Experimental Economics
• +۳ مورد دیگر

Matthias Stefan is a Researcher at the University of Innsbruck , affiliated with the Department of Banking and Finance . His work bridges Behavioral Economics and Finance , utilizing experimental methods to explore decision-making complexity. As a coordinator for the Innsbruck Decision Sciences research center, he emphasizes collaborative innovation and methodological transparency. Research Focus: Behavioral Economics & Finance Decision-Making Biases Risk Preferences Choice Architecture Delegation Dynamics Market Ethics Methodological Expertise: Specializes in laboratory and online experiments , with recent work addressing reproducibility in management science and the interplay between monetary/social incentives. His publications reveal a pattern of investigating decision-making frameworks across finance, portfolio allocation, and supply chain contexts. Collaborative Vision: Expresses enthusiasm for cross-disciplinary cooperation, particularly through crowdsourcing hypothesis testing and transparent experimental design. His research trajectory suggests growing emphasis on methodological rigor and scientific integrity in behavioral finance.

• Logistics
• Supply Chain Management
• Production Planning
• +۵ مورد دیگر

Prof. Margaretha Gansterer is a full professor and Dean of the Faculty of Economics and Law at Alpen-Adria-Universität Klagenfurt. She leads the Institute for Production, Energy and Environmental Management and the Department of Production Management and Logistics. Her work focuses on production economics, logistics optimization, and collaborative transportation systems. Key research areas include supply chain resilience, vehicle routing problems, and disaster response logistics. Her research addresses challenges such as decentralized production planning, automated parcel locker networks, and emergency resource allocation during crises. Notable contributions include methodologies for demand uncertainty mitigation, horizontal collaboration in transportation, and optimization of multi-agent systems. Prof. Gansterer's recent publications (2023–2025) emphasize pandemic-related logistics, such as SARS-CoV-2 testing strategies in healthcare settings and contingency planning for driver absenteeism. She also explores innovative applications of combinatorial auctions and artificial intelligence in logistics decision-making. Research Priorities: Business Administration, Logistics, Production Management Leadership Roles: Senate Dean, Institute Head, Department Head Key Facilities: Production Management and Logistics Department (Campus South Wing East) Her work integrates simulation-based optimization with real-world supply chain modeling, contributing to both academic and practical advancements in operational efficiency and sustainability.

• Protostars
• Protoplanetary Disk
• Star Formation
• +۵ مورد دیگر

Eduard Vorobiev is a researcher affiliated with the Faculty of Earth Sciences, Geography and Astronomy, specializing in astrophysics. His work focuses on star formation, protoplanetary disks, and computational methods for high-performance astrophysical simulations. Research interests include: Protostars and accretion bursts Protoplanetary disk dynamics Gravitational instability in stellar systems Young stellar objects and disk winds Computational astrophysics with hybrid parallelization ALMA observational constraints Recent publications highlight his contributions to modeling high-mass star formation and optimizing Fortran-based simulations. He participates in academic events like the Graz Vienna Exoplanet Science Meetings and leads research projects on star formation and galaxy evolution. His work involves collaborations with institutions using ALMA surveys and gravitational instability analysis.

• Combinatorial Optimization
• Polynomially Solvable Special Cases
• Assignment Problems
• +۶ مورد دیگر

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.