جستجوی استاد راهنمای دانشگاهی

Nori Francoمشاهده پروفایل

استاد

• Condensed Matter Physics
• Quantum Information
• Quantum Optics
• +۶ مورد دیگر

Nori Franco serves as Professor in the Department of Physics at the University of Michigan and Chief Scientist at RIKEN's Theoretical Quantum Physics Laboratory in Japan. His dual appointments reflect his significant contributions to both American and Japanese academic communities, with continuous service at Michigan since 1990 and at RIKEN since 2002. His research spans quantum information, condensed matter physics, and quantum optics, with particular focus on light-matter interactions, superconducting qubits, optomechanics, and quantum open systems. Franco's work bridges theoretical foundations with experimental implementations, especially in circuit quantum electrodynamics and quantum computing applications. Analysis of his recent publications reveals a strong emphasis on non-Hermitian quantum systems, quantum control techniques, and applications of quantum information science to fundamental physics problems. His research group consistently produces highly cited work, with publications appearing in top journals across quantum physics and condensed matter disciplines. Scientific Awards: Charles Hard Townes Medal (2024) - sole recipient for fundamental contributions to quantum optics and quantum information processing Research Doctorate Honoris Causa from University of Messina (2024) Highly Cited Researcher for eight consecutive years (2017-2024) Member of Academia Europaea (2023) Willis E. Lamb Medal (2023) for quantum electronics research Throughout his career, Franco has secured significant research funding and mentored numerous students and postdoctoral researchers. His work has received international recognition through invitations to deliver prestigious lectures including the Stanislav Ulam Lecture and Sir Nevill Mott Lecture in 2024. His research group maintains strong collaborations across multiple continents, reflecting his global impact on quantum physics. At RIKEN, Franco leads the Quantum Information Physics Theory Research Team within the Quantum Computing Center, directing cutting-edge theoretical work that complements experimental efforts in quantum computing hardware development.

• Probability Theory
• Statistical Mechanics
• Random Walks
• +۳ مورد دیگر

Dr. Quirin Thomas Simon Vogel is a Senior Lecturer at the Department of Statistics, University of Klagenfurt. He previously held postdoctoral positions at the Technical University of Munich, New York University Shanghai, and served as an Interim Professor at Ludwig-Maximilians University of Munich. His research bridges probability theory with statistical mechanics and algorithmic applications. Current role: Senior Lecturer (2025) Previous roles: Postdoc (TUM, NYU Shanghai), Interim Professor (LMU Munich) His research focuses on: Random walks and their geometric/stochastic properties Randomized algorithms with applications in statistical models Quantum-inspired probabilistic systems (e.g. interacting bosonic loop soups) Large deviation theory for complex systems Percolation and phase transitions in particle models The articles reflect trends in probability theory, mathematical physics, and algorithmic applications. Key topics include high-dimensional percolation, Bose gas models, neural network theory, and stochastic geometry. The work combines rigorous mathematical analysis with interdisciplinary applications in physics and computer science. Scientific awards and functions cannot be determined from the provided data, as they describe other researchers. The department's research activities include projects on statistical learning, quantum models, and algorithmic probability, though Vogel's direct involvement in these specific funded projects isn't explicitly stated.

• Neurosymbolic AI
• Answer Set Programming
• Visual Question Answering
• +۳ مورد دیگر

Nelson Nicolas Higuera Ruiz is a PreDoc Researcher at the Vienna University of Technology, affiliated with the Faculty of Informatics' Knowledge-Based Systems research group. His work bridges logic programming and deep learning for explainable AI. Research Focus: Neurosymbolic AI, Visual Question Answering (VQA), Answer Set Programming (ASP), and hybrid reasoning systems Projects: Leads optimization research in the LCS (2017–2025) project, developing neurosymbolic approaches for intelligent systems Key Contributions: Pioneering adaptive large-neighbourhood search algorithms for ASP optimization, modular neurosymbolic architectures, and contrastive explainability frameworks for VQA Collaborations: Active in international workshops and conferences including IJCAI, AAAI, and CLeaR, frequently collaborating with researchers like Thomas Eiter and Johannes Oetsch Publications: Focus on neurosymbolic integration, optimization algorithms, and explainability across AI, logic programming, and computer vision domains

• Medical Biotechnology
• Genetic Engineering
• Antibody Engineering
• +۴ مورد دیگر

Gordana Wozniak-Knopp is a researcher at the University of Natural Resources and Life Sciences, Vienna (BOKU), affiliated with the Institute of Molecular Biotechnology under the Department of Biotechnology and Food Science. Her work focuses on antibody engineering, molecular biotechnology, and protein stability optimization. Key research areas include Development of multispecific antibodies using controlled Fab-arm exchange and SEED technology Engineering antigen-binding sites in Fc regions Stabilization of antibody fragments via disulfide bonds and domain exchange Extracellular vesicle functionalization for targeted drug delivery Design of diagnostic antibody microarrays and SARS-CoV-2 antigen platforms Between 2020–2024, she led an FWF-funded project on therapeutic antibodies for birch pollinosis and a 2016–2023 CD Laboratory for Innovative Immunotherapeutics. Her recent publications (2022–2023) emphasize trispecific antibody design, SARS-CoV-2 diagnostics, and CD81-based delivery systems. Scientific contributions include peer-review activities for journals like Protein Science , Nature Communications , and Scientific Reports , along with organizing events such as the Lange Nacht Der Forschung 2024 . She actively participates in international conferences and project evaluations for organizations like Poland’s National Science Centre.

Klemens Fellnerمشاهده پروفایل

استاد

• Partial Differential Equations
• Reaction-Diffusion Systems
• Kinetic Equations
• +۶ مورد دیگر

Klemens Fellner is a Professor of Mathematics/Computational Sciences and Group Leader of the Applied Analysis Group at the Institute of Mathematics and Scientific Computing, University of Graz. His research focuses on the analysis of partial differential equations and mathematical modeling across physics, chemistry, and biology. Research Interests: Prof. Fellner's work spans theoretical analysis of nonlinear PDEs (reaction-diffusion, kinetic, and non-local equations) using entropy/duality methods, with applications to: Biological systems (lipolysis, protein localization, stem-cell division) Physical processes (organic photovoltaics, semiconductor modeling) Collective behavior (swarming micro-organisms, aggregation dynamics) Interdisciplinary Mathematics-Arts collaborations Publication Trends: His recent articles (2018-2021) demonstrate strong focus on: Global existence and regularity for reaction-diffusion systems Convergence to equilibrium via entropy methods Drift-diffusion models in semiconductor physics Mathematical biology applications (prion dynamics, lipolysis) Novel approaches for non-local aggregation and hysteresis phenomena Research Leadership: Currently leads the Applied Analysis Group with members including postdocs and PhD students. Key projects: Doctoral School IGDK (International Graduate School) SFB Lipid Hydrolysis (Special Research Program) Mathematics and Arts collaborations Colibri research platform Supervises PhD candidate Reymart Lagunero studying generalized reaction-diffusion systems.

Sophie N. Parraghمشاهده پروفایل

استاد

• Vehicle Routing
• Passenger Transportation
• Green Logistics
• +۷ مورد دیگر

Sophie N. Parragh is Professor and Head of the Institute of Production and Logistics Management at Johannes Kepler University Linz, where she also serves as program director of the master's degree program in Economic and Business Analytics. She received her PhD from the University of Vienna in 2009 and completed her habilitation in 2016, following postdoctoral research at the IBM Center for Advanced Studies in Porto and a visiting professorship at the Vienna University of Economics and Business. Her research focuses on developing exact and heuristic optimization algorithms for complex logistics and transportation problems. Key areas include vehicle routing, green logistics, disaster relief distribution planning, scheduling, and multi-objective optimization. She has particular expertise in branch-and-bound, branch-and-price, column generation, and metaheuristics approaches to solve challenging combinatorial optimization problems. Dr. Parragh's publication record shows a consistent trend toward increasingly complex multi-objective problems, with recent work focusing on electric vehicle routing, multi-echelon production planning under uncertainty, and bi-objective facility location problems with applications in disaster relief. Her research bridges theoretical optimization methods with practical applications in logistics and transportation. Scientific Awards: ÖGOR (Austrian Society for Operations Research) dissertation prize doc.award from the University of Vienna Hertha Firnberg Postdoc fellowship from the Austrian Science Fund (FWF) Dr. Parragh has served as department editor for OR Spectrum and as associate editor for Transportation Science, Transportation Research Part B: Methodological, INFORMS Journal on Computing, and Networks. She has led and participated in numerous third-party funded research projects in operations research, including work in healthcare logistics, field staff routing, production planning, and electric vehicle routing. In 2021-2022, she co-organized the monthly VeRoLog webinar series, demonstrating her active engagement with the international operations research community. She maintains strong research collaborations across Europe, evidenced by her co-authored publications with researchers from institutions in Austria, France, Portugal, Denmark, and beyond. Her work consistently addresses both theoretical challenges in optimization and practical applications in industry and public service contexts.

Adel Aazamiمشاهده پروفایل

استادیار

• Operations Research
• Optimization
• Supply Chain Management
• +۸ مورد دیگر

Dr. Adel Aazami is an Assistant Professor at the Institute of Transport Economics and Logistics at Vienna University of Economics and Business (WU Vienna) since 2023. His academic journey began with a B.Sc. in Industrial Engineering from University of Tehran (2010-2014), followed by an M.Sc. (2014-2016) and Ph.D. (2016-2021) from Iran University of Science and Technology (IUST), Tehran. Prior to his current position, he worked as a Postdoctoral Researcher at Sharif University of Technology (2021-2022) and was a Visiting Researcher at the University of Toronto (2020). His educational background includes: Ph.D. in Industrial Engineering (2016-2021) - Iran University of Science and Technology (IUST), Tehran, Iran M.Sc. in Industrial Engineering (2014-2016) - Iran University of Science and Technology (IUST), Tehran, Iran B.Sc. in Industrial Engineering (2010-2014) - University of Tehran, Tehran, Iran Dr. Aazami's research spans multiple interconnected domains within operations research and supply chain management. His primary focus areas include Operations Research and Optimization, Supply Chain and Logistics, Production and Distribution/Transportation Planning, Competition and Game Theory, Stochastic Programming, and Decomposition Algorithms. His work demonstrates a strong emphasis on developing mathematical models and optimization algorithms for complex supply chain problems, particularly those involving perishable goods, competitive environments, and sustainability considerations. He has made significant contributions to integrating environmental factors into traditional logistics problems and developing robust optimization approaches for supply chain networks. Analysis of Dr. Aazami's publication record reveals a consistent trajectory of increasingly sophisticated research in supply chain optimization. His work shows a clear progression from foundational mathematical optimization techniques to increasingly complex integrated problems involving multiple stakeholders, uncertainty, and environmental considerations. A notable trend is his focus on perishable products within supply chains, developing models that account for limited product lifetimes while optimizing across multiple echelons of the supply chain. More recently, his research has expanded to incorporate green logistics considerations, developing algorithms that balance economic and environmental objectives in transportation and distribution problems. His notable scientific achievements include: Winner of the 'Best Student' award among nationwide students evaluated by the Iranian Ministry of Science (2020) Winner of the Iranian Nobel Prize (known as the Alborz National Foundation Prize) (2019) Winner of the Best Student Award at IUST (2018) Winner of the Top Researcher Award at IUST (2018) Annual Awards of the National Elites Foundation Iran (2015-2020) Dr. Aazami has extensive teaching experience across multiple Iranian universities including Tehran University, Amirkabir Technical University, Isfahan University, Yazd University, Zanjan University, Damghan University, Abrar University and Iran Technical University. His peer review activities include reviewing for prestigious journals such as Soft Computing, Expert Systems with Applications, and Annals of Operations Research. While specific grant information isn't detailed in the provided text, his research output suggests active engagement with complex optimization problems relevant to transportation and logistics industries. At WU Vienna, Dr. Aazami is part of the research team at the Institute of Transport Economics and Logistics, working alongside other faculty members including Prof. Kummer and Prof. Wakolbinger. His research integrates theoretical optimization methods with practical applications in transportation and logistics, contributing to the institute's focus on sustainable and efficient supply chain solutions.

• Cybersecurity
• Game Theory
• Quantum Cryptography
• +۵ مورد دیگر

Stefan Rass is a Professor at the Institute of Networks and Security within the Faculty of Engineering & Natural Sciences at Johannes Kepler University Linz (JKU), where he leads the LIT Secure and Correct Systems Lab. As Principal Investigator for FFG-funded projects including reSilienz (digital supply chain resilience, 2023–2025) and ITPUK (AI signature verification, 2022–2024), he bridges theoretical game theory with practical cybersecurity solutions for critical infrastructures and robotics systems. His research spans game-theoretic security models (patrolling games, defense-in-depth strategies), quantum cryptography (QKD network architectures), and cyber deception frameworks like Honeyquest for measuring honeypot effectiveness. Recent work addresses robotics security benchmarking (RobotPerf), cryptographic instruction chaining for control flow protection, and risk assessment methodologies for interdependent infrastructures. His mathematical decision-making approach integrates bounded rationality and stochastic modeling to solve real-world security challenges. Professor Rass actively shapes the field through program committee roles (ARES 2023), peer reviews, and invited talks on security transparency. His current projects focus on cost-benefit-aware monitoring for cyber-physical systems and quantum key distribution standardization, reflecting Austria’s strategic priorities in digital resilience. The LIT Secure and Correct Systems Lab under his direction develops foundational theories while deploying tools for industrial applications, particularly in critical infrastructure protection and secure robotics workflows.

Franz Wotawaمشاهده پروفایل

استاد

• Model-Based Reasoning
• Qualitative Reasoning
• Theorem Proving
• +۶ مورد دیگر

Franz Wotawa is a Professor of Software Engineering at Graz University of Technology. He holds a M.Sc. (1994) and PhD (1996) from Vienna University of Technology. He has served as head of the Institute for Software Technology from 2003–2009 and since 2020. His research focuses on model-based reasoning, software testing, autonomous systems, and diagnosis, with over 390 peer-reviewed publications. He founded Softnet Austria (2006) to bridge research and industry. He leads the Christian Doppler Laboratory for Quality Assurance Methodologies for Autonomous Cyber-Physical Systems since 2017 and has supervised 90+ master and 36+ PhD students. His awards include the 2016 Lifetime Achievement Award from the International Diagnosis Community. He is a member of Academia Europaea, IEEE, and AAAI. **Education**: M.Sc. in Computer Science, Vienna University of Technology, 1994 PhD, Vienna University of Technology, 1996 **Research Interests**: Model-based reasoning, qualitative reasoning, theorem proving, mobile robotics, verification/validation, software testing/debugging, AI, and autonomous systems. **Notable Projects**: A-IQ Ready (2022–2026): Quantum sensing for autonomous systems. ALFA (2024–2027): AI for smart diagnosis in building automation. Bilateral AI (2024–2029): Combining symbolic and sub-symbolic AI. VARCOS (2025–2028): Vehicle-road cooperative systems for autonomous driving. **Awards & Memberships**: Lifetime Achievement Award (2016, International Diagnosis Community) Senior Member, AAAI Member of Academia Europaea, IEEE, ACM, and Austrian Computer Society **Labs/Teams**: Christian Doppler Laboratory for Quality Assurance Methodologies (since 2017). Active in Cluster of Excellence “Bilateral AI” at TU Graz.

• Algorithms
• Data Structures
• Dynamic Graph Algorithms
• +۴ مورد دیگر

Monika Henzinger is a Professor at the Institute of Science and Technology Austria (ISTA) since 2023 and Vice President for Technology Transfer at ISTA since 2024. Her research focuses on efficient algorithms and data structures , particularly in dynamic settings where input data changes continuously. She also investigates privacy-preserving algorithms and the practical implementation of theoretically optimal algorithms. Education: PhD in Computer Science (1993, Princeton University, USA) Research Interests: Her work spans dynamic graph algorithms, differential privacy, and transforming theoretical algorithms into practical solutions. Key areas include minimizing computational resources, proving limits of efficiency, and ensuring data privacy through noise addition. Article Trends: Recent publications emphasize dynamic algorithms for graph problems , differential privacy , and clustering techniques . Topics such as minimum cut, bipartite matching, and edge coloring in dynamic environments are prominent, alongside privacy-preserving data analysis. Scientific Awards: 2024 Best Paper Award (SODA) 2021 Wittgenstein Award and ERC Advanced Grant 2019 Carus Medal (Leopoldina Academy) 2016 ACM Fellow 2014 ERC Advanced Grant 2004 European Young Investigator Award Advising & Grants: She advises PhD students and leads projects funded by grants such as the ERC Advanced Grant, Wittgenstein Award, and Austrian Science Fund initiatives. Her team includes postdocs and PhD candidates working on dynamic data structures and privacy. Labs & Teams: Leads the Henzinger_Monika Group at ISTA, focusing on algorithms, data structures, and dynamic graph problems. Collaborates with institutions globally on projects related to network optimization and privacy.

Arnold Neumaierمشاهده پروفایل

استاد

• Global Optimization
• Heuristic Optimization
• Nonlinear Optimization
• +۶ مورد دیگر

Arnold Neumaier is a Full Professor (Chair for Computational Mathematics) at the Faculty of Mathematics, University of Vienna, Austria. His research focuses on global optimization, heuristic optimization, nonlinear optimization, and optimization under uncertainty. He leads a research group developing state-of-the-art software for optimization, numerical analysis, and statistics. Collaborations include work on global optimization with Immanuel Bomze and Vladimir Kolmogorov, heuristic optimization with Nysret Musliu and Günther Raidl, and optimization for data analysis with Dan Alistarh and Radu Boț. Neumaier holds a Ph.D. from the Free University of Berlin (1977) and has held academic positions at the University of Freiburg (1987–1993), AT&T Bell Laboratories (1993–1994), and visiting roles at Princeton University, the University of Nice, and RWTH Aachen. His work spans computational mathematics, quantum physics, and mathematical software development. He maintains extensive web resources on optimization and numerical analysis. Research interests include algorithmic software development, verified computing, mathematical modeling languages, uncertainty modeling, and quantum physics. His group comprises 1 full professor, 1 associate professor, 1 university assistant, 3 postdocs, and multiple PhD students. Key contributions include the 'Global Optimization' WWW site and foundational work in causal perturbation theory for quantum field theory.

Leoni Brethمشاهده پروفایل

پژوهشگر

• Condensed Matter Physics
• Materials Science
• Micromagnetics
• +۳ مورد دیگر

Dr. Leoni Breth is a Researcher at the University for Continuing Education Krems, affiliated with the Department of Integrated Sensor Systems and the Center for Modelling and Simulation. She holds a PhD in Technical Physics from the Vienna University of Technology, specializing in Condensed Matter Physics and micromagnetic sensor research. Her work integrates theoretical modeling, experimental validation, and AI-driven approaches to advance materials science. Education: PhD in Technical Physics, Vienna University of Technology (focus: magnetoresistive sensors and thermal fluctuations) Undergraduate Studies in Technical Physics at Vienna University of Technology Research Interests: Dr. Breth's research focuses on micromagnetic simulations, magnetoresistive sensors, and the application of machine learning to analyze First-Order-Reversal Curves (FORCs) in materials science. Her work bridges fundamental physics and industrial applications, particularly in optimizing magnetic materials for advanced technologies like permanent magnets and cemented carbides. Key areas include coercivity enhancement, domain nucleation dynamics, and AI-based predictive modeling. Projects & Grants: FFG-funded project (2020-2023): AI-driven FORC analysis in carbide production FWF-funded project (2023-2026): Combinatorial synthesis and micromagnetic graph networks for magnet design Key Contributions: Her publications span topics like FORC diagram interpretation, skyrmion modeling in bulk materials, and machine learning for mechanical property prediction. She has also contributed to international conferences, including presentations at IEEE Magnetics Society events and the Joint European Magnetics Symposia.

• Algorithmic Social Choice
• Fair Division
• Multi-Winner Elections
• +۴ مورد دیگر

Jiehua Chen is an Associate Professor in the Department of Algorithms and Complexity at TU Wien (Vienna University of Technology), part of the School of Informatics. Her research focuses on algorithmic social choice, fair division, and computational complexity with applications to multi-agent systems, stable matchings, and parameterized algorithms. She leads the project Structural and Algorithmic Aspects of Preference-based Problems in Social Choice (2019–2027), funded by the Vienna Science and Technology Fund (WWTF). Her work spans theoretical contributions to voting systems, fair allocation mechanisms, and graph-based problems, with notable publications in venues like AAAI, IJCAI, and ACM Transactions on Economics and Computation. She teaches courses such as Algorithmic Social Choice and Quantum Computing and Complexity Theory , and has supervised research like E. Ceylan's diploma thesis on optimal seat arrangement algorithms. Chen’s research emphasizes practical algorithm design for social choice challenges, including refugee resettlement, participatory budgeting, and hedonic games. She frequently presents at international conferences and collaborates on interdisciplinary projects involving computational complexity and graph theory.

• Software Engineering
• Machine Learning
• Static Code Analysis
• +۳ مورد دیگر

Markus Böck is a PreDoc Researcher at the Software Engineering department of Technische Universität Wien (TU Wien), Austria. His academic work focuses on the intersection of software engineering and machine learning , particularly in performance prediction and probabilistic program analysis . He contributes to automated code analysis and intelligent software systems research. Role: PreDoc Researcher Department: Software Engineering, TU Wien Contact: markus.h.boeck@tuwien.ac.at His research explores: Language-agnostic static analysis of probabilistic programs Context-dependent performance prediction from source code Interactive machine learning systems for software engineering Distributional reinforcement learning applications Code-based performance modeling Program analysis for probabilistic systems Key publication trends include: Advancing probabilistic program analysis (2024-2025) Performance prediction techniques (2022-2023) Reinforcement learning innovations (2020-2022) Application of machine learning to software engineering challenges Game theory algorithm optimization (2025)

• Algorithms
• Data Structures
• Dynamic Graph Algorithms
• +۳ مورد دیگر

Monika Henzinger is a Professor at the Institute of Science and Technology Austria (ISTA), where she has been serving since 2023, and additionally holds the position of Vice President for Technology Transfer since 2024. Her academic journey includes professorships at the University of Vienna (2009-2023) and EPFL in Switzerland (2005-2009), as well as industry experience at Google (1999-2005) and Digital Equipment Corporation (1996-1999). She earned her PhD from Princeton University in 1993 and served as an Assistant Professor at Cornell University from 1993-1996. Dr. Henzinger's research focuses on the design and analysis of efficient algorithms and data structures, with particular emphasis on dynamic settings where inputs change repeatedly, privacy-preserving algorithms, and translating theoretical algorithms into practical implementations. Her work bridges theoretical computer science with practical applications, addressing fundamental questions about computational efficiency in evolving data environments. Her recent publications (2024-2025) demonstrate a consistent focus on dynamic graph algorithms, differential privacy, and optimization problems. These works explore cutting-edge approaches to maintaining graph structures under continuous updates, developing privacy-preserving mechanisms for streaming data, and creating efficient approximation algorithms for fundamental graph problems. The research shows strong connections between theoretical guarantees and practical implementations, with many papers addressing both theoretical bounds and experimental validation. Dr. Henzinger has received numerous prestigious awards throughout her career, including: The 2024 Best Paper Award at the Symposium on Discrete Algorithms The 2021 Wittgenstein Award Two ERC Advanced Grants (2014, 2021) Fellow of the Association of Computing Machinery (2016) Member of the Austrian Academy of Sciences (2017) The Carus medal of the German Academy of Sciences Leopoldina (2019) She currently leads an active research group comprising PhD students and postdoctoral researchers, and her team is supported by multiple significant grants including an ERC Advanced Grant for 'The design and evaluation of modern fully dynamic data structures,' a Wittgenstein Award from the Austrian Science Fund, and several other projects focused on dynamic graph algorithms and data structures. Her collaborative work spans theoretical computer science, algorithm design, and practical implementations. Dr. Henzinger's research group operates within a vibrant ecosystem of algorithmic research at ISTA, focusing on transforming theoretical insights about computational efficiency into practical tools for handling dynamic data. The group maintains strong connections with both theoretical and applied research communities, bridging the gap between abstract algorithm design and real-world implementation challenges.