جست‌وجوی استاد و پژوهشگر

Stephan Leitnerمشاهده پروفایل

دانشیار

• Agent-Based Modeling and Simulation
• Organizational Dynamics
• Complex Systems
• +۵ مورد دیگر

Associate Professor at the University of Klagenfurt , affiliated with the Department of Management Control and Strategic Management under the Faculty of Economics and Law . Research focuses on agent-based modeling applied to organizational dynamics , complex systems , and managerial economics . Holds a doctoral degree in Social Sciences and Economics (2012) and venia docendi in Business Economics (2018) . Core faculty member in the Self-Organizing Systems research cluster Academic editor for PLoS ONE and editorial board member for multiple journals Recipient of the 2021 Advancement Award (Humanities/Social Sciences) from Carinthian government Research integrates computational simulation with organizational theory , examining phenomena like decentralized task allocation , incentive mechanisms , and reproducibility in social sciences . Teaching portfolio includes business analytics , management control , and scientific modeling at undergraduate and graduate levels. Recent publications explore organizational resilience , team coordination dynamics , and financial modeling using agent-based simulation techniques. Active participant in international conferences like Social Simulation Conference and European Conference on Operational Research .

Efstathia Buraمشاهده پروفایل

استاد

• Dimension Reduction in Regression
• High-Dimensional Statistics
• Multivariate Analysis
• +۵ مورد دیگر

Efstathia Bura is a Professor heading the Applied Statistics Research Unit (ASTAT) within the Institute of Statistics and Mathematical Methods in Economics at TU Wien's Faculty of Mathematics and Geoinformation. Her research focuses on dimension reduction techniques in regression and classification, high-dimensional statistics, and their applications in biostatistics, econometrics, and legal statistics. She leads projects like ProbInG (WWTF-funded) and the SecInt Doctoral College on statistical verification of cyber-physical systems. Her work integrates advanced statistical methodologies with interdisciplinary applications, emphasizing practical solutions for complex data challenges. Current projects explore probabilistic program analysis, security properties in cyber-physical systems, and dynamic econometric modeling. She collaborates internationally, with notable contributions to statistical theory and applications in law, healthcare, and telecommunications. Key research themes include time-varying regression models, sufficient dimension reduction for mixed predictors, and fusion of statistical methods with machine learning. Her publications bridge theoretical advancements and real-world problem-solving, reflecting her role as a leading academic in modern applied statistics. Her team includes postdocs and assistants working on WWTF and SecInt grants, focusing on probabilistic systems and statistical verification. While no formal student advisees are listed, her collaborative projects engage junior researchers in cutting-edge statistical research.

Adriana Rathnerمشاهده پروفایل

پژوهشگر

• Protein-NMR Spectroscopy
• Host-Pathogen Interactions
• Molecular Recognition
• +۴ مورد دیگر

Dr. Adriana Rathner serves as a University Assistant at the Institute of Biochemistry, Faculty of Engineering and Natural Sciences, Johannes Kepler University Linz. Her research integrates biological chemistry and structural biology to investigate protein behavior in the presence of interaction partners and ligands, with particular focus on understanding molecular mechanisms in vivo. Her research interests span multiple interdisciplinary domains including protein-NMR spectroscopy, host-pathogen interactions, molecular recognition, and structural biology. Dr. Rathner specializes in investigating how proteins interact with ligands at atomic resolution, with significant contributions to understanding Borrelia pathogenesis mechanisms and ribosomal protein functions. Her publication portfolio demonstrates strong trends in applying NMR spectroscopy to solve biomedical problems, particularly in host-pathogen interactions and ribosomal protein research. The work shows increasing emphasis on translational applications, moving from fundamental protein characterization to therapeutic development for conditions like Epidermolysis bullosa and sensor development for chemical threat detection. Dr. Rathner actively mentors students across multiple projects, supervising both undergraduate and graduate researchers. Her grant portfolio includes significant international collaborations with institutions in the Czech Republic, Taiwan, Poland, and the United States, with current funding supporting the LiGAlert project (2024-2027) and other research initiatives. She maintains strong laboratory collaborations through the Protein-NMR.org research group, working closely with theoretical physics researchers on molecular dynamics simulations and with international partners on specialized biochemical assays and glycosaminoglycan synthesis.

• Optimization
• Genetic Programming
• Dynamic Environments
• +۴ مورد دیگر

Philipp Fleck is a researcher and PhD student at the University of Applied Sciences Upper Austria's Research Center Hagenberg, affiliated with the Center of Excellence for Smart Production and HEAL research group. His work focuses on digital transformation within ICT and production systems, leveraging optimization techniques for industrial applications. His academic background includes: PhD in Computer Science (ongoing since 2021) Master of Science in Software Engineering (2013-2015) Bachelor of Science in Software Engineering (2010-2013) IT-HTL diploma (2005-2010) Fleck specializes in dynamic optimization environments, genetic programming, and machine learning applications for production systems. His research develops adaptive algorithms for real-time decision-making in logistics and manufacturing, with emphasis on tree search methods, storage optimization, and evolutionary computation. Current work integrates machine learning with heuristic optimization for dynamic symbolic regression and transport-lot assignment problems. His publication trends show concentrated focus on evolutionary computation applied to industrial challenges, particularly dynamic environments requiring real-time adaptation. Recent work bridges genetic programming with production logistics and machine learning update strategies, demonstrating strong interdisciplinary connections between computer science and operations research. No scientific awards are documented in available sources. Fleck has secured significant grant funding through Austrian research programs, including the Josef Ressel Center for Adaptive Optimization in Dynamic Environments (2019-2024), Fakultätsübergreifendes Institut für intelligente Produktion (2014-2019), and Josef Ressel Center for Heuristic Optimization (2008-2013). These projects address priority rules, distributed intelligence, and rapid prototyping in production systems. He operates within the HEAL research ecosystem at Hagenberg's Center of Excellence for Smart Production, collaborating with interdisciplinary teams on digital transformation initiatives. His work integrates with the university's broader Strength Area ICT framework, focusing on practical implementations of optimization algorithms in production environments.

• Operations Research
• Optimization Algorithms
• Combinatorial Optimization
• +۴ مورد دیگر

Adam Letchford is a Professor in the Department of Management Science at Lancaster University, specializing in Operations Research and Optimization. His research focuses on combinatorial optimization, integer programming, and algorithm design, with particular contributions to vehicle routing, network design, and polyhedral theory. He has authored numerous peer-reviewed articles, including works on clique partitioning polytopes, knapsack problems, and matheuristics. Letchford is actively involved in academic conferences and editorial roles, contributing to the advancement of operational research methodologies and applications in logistics and decision sciences. His work bridges theoretical foundations with practical implementations, addressing challenges in both academic and real-world contexts.

Regino Criadoمشاهده پروفایل

استاد

• Complex Networks
• Discrete Mathematics
• Cybersecurity
• +۴ مورد دیگر

Dr. Regino Criado is Full Professor of Applied Mathematics at Rey Juan Carlos University (URJC), where he has held academic positions since June 2000. He currently serves as Academic Director of the Data, Complex Networks and Cybersecurity Sciences Institute (since March 2017) and was elected to the Academia Europaea in May 2023. His career spans European research projects (ESPRIT II/III) and interdisciplinary collaborations at the intersection of mathematics, computer science, and cybersecurity. His research focuses on Complex Networks , including hyper-networks, multiplex networks, and mesoscale structures, with applications to cybersecurity, intentional risk management, and synchronization phenomena. Key innovations include: A game theory-based intentional risk model integrating accessibility, anonymity, and value metrics Hybrid network mining algorithms for credit card fraud detection PageRank extensions to multiplex networks Framework for discrete resilience analysis in technological systems His work on the 2014 Physics Reports monograph The structure and dynamics of multilayer networks (3,500+ citations) established foundational concepts in multilayer network theory. Current research explores linguistic pattern analysis through multilayer hypergraphs for automatic text summarization. Member of Academia Europaea (2023) Chaos Journal Best Paper Award (2011) Academic Entrepeneurs Prize (2002) EUROPA-1992 Prize (1992) As director of the DCNC Institute (2017-present) and BBVA-URJC Chair (2017-2018), he has led major academic-industry collaborations. His 180+ publications (6,500+ citations) demonstrate sustained impact across network science, applied mathematics, and cybersecurity.

Stephan Holzerمشاهده پروفایل

پژوهشگر

• Distributed algorithms
• Graph theory
• Wireless networks
• +۳ مورد دیگر

Stephan Holzer is a computer scientist and mathematician currently affiliated with the Massachusetts Institute of Technology , where he works within the Computer Science and Artificial Intelligence Laboratory (CSAIL) . He has held postdoctoral positions at MIT (2013–2017) and visiting roles at Harvard and ETH Zurich. His expertise lies in the theory of distributed systems, with a focus on efficient algorithms for networks. Education: Stephan earned his Diplom (M.Sc.) and B.Sc. from TU Munich within Germany’s TopMath excellence program (2006–2008), followed by a Ph.D. in Computer Science from ETH Zurich (2009–2013) under the supervision of Prof. Roger Wattenhofer. Research Interests: His work spans distributed algorithms , graph theory , wireless communication , communication complexity , and network optimization . He particularly explores how devices in large-scale networks can compute distances, disseminate information, and coordinate despite interference and limited resources. Publications & Impact: Stephan has published extensively in premier venues such as PODC, DISC, SODA, and TCS. His recent work addresses security of crypto-currencies, leader election in wireless models, and approximation algorithms for shortest-path problems, reflecting a blend of theoretical rigor and practical relevance. Scientific Awards & Honors: Best Paper Award, 24th SIROCCO 2017 Member, Young Academy | Mainz 2016 Nominated delegate, Global Young Scientist Summit 2016 Participant, Heidelberg Laureate Forum 2013 Teaching & Mentoring: Stephan co-created and lectures the MIT graduate course 6.S899 Distributed Graph Algorithms with Profs. Mohsen Ghaffari and Nancy Lynch. He has mentored numerous undergraduate and master’s students through UROP, UAP, and thesis projects at MIT and ETH Zurich. Service: He has served on the program committees of SPAA, ICDCN, SSS, SOFSEM, and ALGOSENSORS, and regularly reviews for top-tier journals and conferences in distributed computing and algorithms.

• Personnel Scheduling
• Workforce Optimization
• Hyper-heuristics
• +۳ مورد دیگر

Lucas Kletzander is a PostDoc Researcher at the Institute of Logic and Computation within the Faculty of Informatics at Vienna University of Technology. He teaches courses in theoretical computer science and project-based programming while leading research in scheduling optimization through the CD Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling (ARTIS project). Education: Bachelor of Science (BSc) Diplom-Ingenieur (Dipl.-Ing.) in Engineering Doctor of Technical Sciences (Dr.techn.) His research centers on solving complex personnel scheduling problems using artificial intelligence and operations research. Kletzander specializes in hyper-heuristics for cross-domain optimization, multi-objective workforce scheduling, and real-world applications like bus driver rostering and physician shift planning during pandemics. His work bridges theoretical algorithm development with industrial implementation, particularly in transportation and healthcare sectors. Recent publications reveal a strong trajectory toward reinforcement learning for hyper-heuristics and multi-objective optimization in scheduling. He pioneers instance space analysis to characterize problem difficulty and develops hybrid methods combining column generation with large neighborhood search. His research consistently addresses practical constraints like complex break rules in transportation scheduling. Advising and Grants: Supervised master's theses on explainability in rotating workforce scheduling and deep reinforcement learning for hyper-heuristics Principal investigator in the Christian Doppler Laboratory ARTIS (2017-2025) funded by the CDG Collaborates with industry partners on bus scheduling and aircraft maintenance routing systems Kletzander actively contributes to the Databases and Artificial Intelligence research group, where he develops decision support systems for automated scheduling and investigates the integration of constraint programming with metaheuristics for real-world optimization challenges.

Leslie Ann Goldbergمشاهده پروفایل

استاد

• Algorithms
• Complexity Theory
• Randomized Algorithms
• +۶ مورد دیگر

Leslie Ann Goldberg is Professor of Computer Science at the University of Oxford and Senior Research Fellow at St Edmund Hall. She has held academic positions at the University of Liverpool (2006-2013) and the University of Warwick (1995-2006), and previously worked at Sandia National Labs (1992-1995). Currently serving as Head of Department (sabbatical planned for 2025-26), she leads research in theoretical computer science with focus on algorithms and complexity theory. Her research interests span algorithms for approximately counting and randomly sampling combinatorial structures , analysis of Markov-chain based sampling algorithms , complexity of counting , and stochastic processes in computer science . She specializes in randomized algorithms, particularly their mathematical analysis regarding runtime and accuracy. Current projects include contentions resolution via backoff protocols, random sampling and approximate counting algorithms, counting complexity theory, and analyzing processes on networks like the Moran process. Professor Goldberg has published over 100 papers in top theoretical computer science venues, with recent work focusing on spin systems, graph homomorphisms, subgraph counting, and network processes. Her publications show consistent output in premier venues including ICALP (where she has won three Best Paper Prizes), SODA, STOC, and RANDOM. ERC Advanced Grant 'Mapping the Complexity of Counting' (2014-2019) Best Paper Prize at ICALP 2012, 2010, and 2006 UK Distinguished Dissertations in Computer Science (1991-1992) She actively supervises PhD students and collaborates with researchers including Andreas Galanis, Marc Roth, John Lapinskas, and Paulina Smolarova. Her work bridges theoretical computer science with statistical physics, combinatorics, and network theory, with applications to database systems, evolutionary dynamics, and communication protocols.

• Operations Research
• Optimization Models
• E-Mobility Infrastructure
• +۳ مورد دیگر

Rostislav Staněk is a Professor and Chair of Applied Mathematics, specializing in optimization models, operations research, and interdisciplinary studies in e-mobility and electoral behavior. His work bridges theoretical mathematics with practical applications in infrastructure planning and social sciences. Research focuses on advanced optimization techniques, Tabu Search algorithms, and sustainable transportation systems. Conducts empirical studies on electoral competence and student engagement in political processes. Publications span peer-reviewed journals and international conference proceedings, including contributions to the Central European Journal of Operations Research and the Information – Wissenschaft & Praxis . Has supervised four academic works, emphasizing hands-on mentorship in mathematical modeling and algorithm development.

• Heuristic Optimization
• Metaheuristics
• Simulation Optimization
• +۳ مورد دیگر

Erik Pitzer is a Professor at FH Hagenberg, part of the Research Center Hagenberg. He leads the Josef Ressel Center for Heuristic Optimization and has contributed to projects like SMART UNLOAD (2021-2022) focusing on unloading bay optimization and BIOBOOST (2012-2014) addressing biofuel production via biomass. His work spans heuristic algorithms, simulation-based optimization, and fitness landscape analysis. Research interests include developing metaheuristics for complex optimization problems, distributed modeling frameworks, and dynamic problem adaptation. Notable contributions include Composable Evolutionary Computation (2025) and middleware for distributed systems (2024). Key Projects : SMART UNLOAD (Operational Excellence), BIOBOOST (Bioenergy Production), Josef Ressel Center (Heuristic Optimization) Advising : Supervised 4 works (names withheld) Labs : Josef Ressel-Zentrum für Heuristische Optimierung

Nysret Musliuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Optimization
• +۶ مورد دیگر

Nysret Musliu is an Associate Professor and Head of the Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling at TU Wien (Vienna University of Technology). He holds a PhD in Computer Science from TU Wien (2001) and a Habilitation (2007). His research focuses on AI techniques, optimization, scheduling, and timetabling, with applications in workforce scheduling, combinatorial optimization, and constraint satisfaction. Education: PhD in Computer Science, TU Wien, 2001 Habilitation in Applied Computer Sciences, TU Wien, 2007 MSc (Dipl.-Ing) in Computer Science, University of Prishtina, 1996 Research Interests: His work addresses AI-driven solutions for scheduling problems, including staff scheduling, timetabling (e.g., high school timetabling), and combinatorial optimization. He has developed algorithms for tree/hypertree decompositions and hybrid methods combining constraint programming with metaheuristics. Grants & Leadership: Project leader of the Christian Doppler Laboratory (2017–2024) Funded projects: ARTE (employee scheduling), Softnet Austria, Hypertree Decompositions Professional Activities: Conference chairs for CPAIOR 2021/2020 and PATAT 2018 Editorial roles in Constraints Journal and Journal of Mathematical Modelling and Algorithms Program committee member for IJCAI, AAAI, and GECCO Teaching: He teaches courses on artificial intelligence, problem-solving techniques, and machine learning at TU Wien. Lab & Teams: Leads the Christian Doppler Laboratory and collaborates with industry partners like Bosch and Ximes Corp.

• Computational Social Choice
• Algorithmic Game Theory
• Formal Methods
• +۶ مورد دیگر

Jie He is a PreDoc Researcher at the Department of Cyber-Physical Systems, Technische Universität Wien. His research spans computational social choice, algorithmic game theory, and formal methods in robotics and IoT systems. He works on multidisciplinary problems involving complexity analysis, fair division, and preference modeling. Current projects: EdgeAI (2022–2025), TAIGER (2023–2027), ADEX (2020–2024) Key collaborations: Research with R. Grosu, E. Bartocci, D. Nickovic Research interests focus on computational aspects of collective decision-making , including fair division, matching problems, and preference modeling. He works on both theoretical foundations (e.g., parameterized complexity) and practical applications (e.g., robotic-IoT systems). His publication history reveals deep expertise in computational complexity of social choice problems, with recent work on 3D stable roommates , fair division in graph-structured settings , and preference modeling through Euclidean and Manhattan geometries. As an advisor, he supervised diploma theses on: Optimization strategies for 5G transceivers Dynamic object detection in multi-agent systems His work appears in top conferences like ACM/IEEE DAC, ICSE, and various computational social choice venues.

• 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.

• 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.