جست‌وجوی استاد و پژوهشگر

Robert L. Carrawayمشاهده پروفایل

استاد

• Data Analytics
• Decision Sciences
• Optimization
• +۴ مورد دیگر

Robert L. Carraway is a Professor at the University of Virginia's Darden School of Business, where he has been a faculty member since 1984. He currently serves as the Quantitative Analysis (QA) area head and previously held the position of Senior Associate Dean for MBA Programs from 2006-2011. Carraway teaches Data Analytics and Decision Sciences courses in Darden's MBA program, including First Year Decision Analysis, Managerial Quantitative Analysis, Optimization Models, and Darden Business Projects: Venturing. His global executive education work spans Europe, Asia, and Latin America. Education: B.S., East Carolina University M.B.A., East Carolina University Ph.D., Purdue University Carraway's research spans multiple areas of quantitative business analysis. His original work focused on Optimization, but he has expanded his focus to the broader role of analytics in decision making. He examines the relationship between intuition and rational analysis, exploring how these approaches complement one another in business contexts. His work investigates how analytical methods enhance innovation processes, making them more effective and efficient. A significant portion of his recent research addresses 'Big Data' applications in business. He has authored articles such as 'Big Data, Small Bets' and 'Meeting the Big Data Challenge: DON'T Be Objective,' which explore practical approaches to leveraging large datasets for competitive advantage. His work emphasizes combining analytical rigor with business judgment rather than relying solely on data-driven approaches. Carraway's publication record demonstrates a consistent focus on quantitative methods for business decision-making. His earlier work centered on optimization models and mathematical programming, while more recent publications address analytics, probability distributions, and risk analysis. A notable trend is his exploration of how decision-makers integrate quantitative analysis with intuitive judgment in complex, uncertain environments. Award Recognition: Multiple teaching honors at Darden School of Business Carraway has significantly influenced business education through his case method teaching, with numerous cases used in Darden classrooms worldwide. His global executive education work extends his impact to practicing managers across continents. While specific doctoral students aren't listed, his long tenure suggests substantial mentoring experience. As QA area head, he oversees faculty specializing in quantitative business analysis and collaborates with colleagues like Sam Bodily, Phillip Pfeifer, and Sherwood Frey on research teams focused on decision sciences.

Wolfgang Gaißmaierمشاهده پروفایل

استاد

• Judgment and decision making
• Individual differences in decision making
• Risk perception and communication
• +۴ مورد دیگر

Prof. Dr. Wolfgang Gaißmaier is a full-time faculty member at the Department of Psychology, University of Konstanz, specializing in Social Psychology and Decision Sciences. His research explores judgment and decision-making processes under uncertainty, with particular focus on individual differences in decision strategies, risk perception and communication, medical decision-making, ecological rationality, and heuristic modeling. Current research projects include "Political Elites and Inequality: Information, Heuristics and Policy" Co-developer of the riskyrApp and FFTrees R packages for risk literacy and decision modeling Key contributor to the Collective Behaviour Cluster of Excellence at University of Konstanz His recent publications examine cognitive biases in statistical information processing, the effectiveness of visual risk communication tools, and the psychological foundations of collective behavior. He collaborates extensively with researchers across psychology, economics, and computer science, particularly with Dr. Hansjörg Neth and Dr. Helge Giese. Major contributions include: Developing computational models of decision heuristics Investigating social learning mechanisms in risk perception Advancing tools for transparent risk communication Exploring the narrative bias in health decision-making Studying group dynamics in knowledge diffusion

• Cryptography
• Mathematics
• Computer Science
• +۲ مورد دیگر

Sorina Ionica is a Lecturer at the University of Picardie Jules Verne, specializing in Cryptography , Mathematics , and Artificial Intelligence . Her research focuses on the application of optimization techniques and algebraic geometry to cryptographic protocols, particularly in the context of genus 3 curves and isogeny-based cryptography. Research Interests: Cryptography, isogeny graphs, complex multiplication, index calculus, and AI-driven optimization algorithms. Projects: Involved in BforSAT (application of SAT solvers to cryptographic problems) and POSTCRYPTUM (post-quantum cryptography research). Recent Articles: Her work spans mathematical cryptography (e.g., CM curves and Weil descent attacks), algorithm optimization (Crossbred algorithm), and AI integration in cryptanalysis (tree search and SAT solvers). Labs: Sorina is affiliated with the OCIA Lab (Optimization and Cryptography, AI).

• Behavioral Finance
• Sustainable Finance
• Nudging Theory
• +۵ مورد دیگر

Cristiana Cerqueira Leal serves as Assistant Professor in the Management Department at the University of Minho, Portugal, holding this position continuously since 2000. Her academic profile centers on behavioral finance and sustainable investment practices within management science. Her educational foundation includes a Ph.D. in Business Administration (Finance) from the University of Minho. Research Focus: Dr. Leal investigates psychological influences on financial decisions through three interconnected domains: (1) Behavioral Finance examining cognitive biases in individual investor trading patterns, particularly disposition effects and repurchasing behaviors in Portuguese markets; (2) Sustainable Finance analyzing ESG integration and green investment strategies; and (3) Nudging Applications developing choice architecture interventions for sustainable financial behavior using AI-driven approaches. Her recent work emphasizes media effects on crowdfunding success and sustainable transitions in higher education. Publication Trajectory: Her scholarly output shows increasing specialization in sustainable finance since 2020, with 7 of 13 publications (2020-2024) addressing ESG, nudging, and sustainability. The research demonstrates methodological diversity spanning journal articles, book chapters, and Portuguese-language academic works, with strong emphasis on practical applications for financial markets and policy design. Research Funding: Dissociando a solidão do envelhecimento (2023-2024): Fundação para a Ciência e a Tecnologia (FCT) grant PTDC/2022.07029.PTDC examining multimodal approaches to aging and loneliness Individual investors' buying and selling behaviour (2007-2011): FCT grant PTDC/GES/73012/2006 analyzing Portuguese stock market trading patterns

Wolfgang Echelmeyerمشاهده پروفایل

استاد

• Logistics and Supply Chain Management
• Material Handling
• Operations Research
• +۳ مورد دیگر

Professor Wolfgang Echelmeyer is a faculty member at Reutlingen University’s Faculty of Sustainability and Technology, specializing in Technical Logistics. His research focuses on logistics automation, human-robot interaction, and sustainable supply chain systems. Position: Professor Department: Technical Logistics Email: wolfgang.echelmeyer@reutlingen-university.de Research interests include robotic container unloading, lean logistics, and ergonomics in automated systems. Recent projects involve EU-funded initiatives like RobLog (EU# 270350) for cognitive robotics in freight handling. His work spans applications in city logistics, milk-run transportation systems, and decision support tools for automation investments. Key article trends explore robotics in logistics, sustainability in freight systems, and ergonomic resource planning. Publications from 2018–2024 emphasize innovative solutions for intermodal terminals, deformable cargo handling, and smart transportation networks. Administrative roles include Dean of Studies for the BSc in Sustainable Production and Business and Pro-Dean for Teaching at the Faculty of Sustainability and Technology. Collaborators include Marco Bonini, Tuan Nguyen, and Augusto Urru. Labs and teams: Active in projects like LogFabr (Logistics Fabrication) and partnerships with industry leaders such as Merck AG, Proctor & Gamble, and STIHL. His work addresses real-world challenges in logistics planning and automation deployment.

Rubén Alcarazمشاهده پروفایل

دانشیار

• Digital Humanities
• Web Accessibility
• Human-Computer Interaction
• +۳ مورد دیگر

Rubén Alcaraz is an Associate Professor at the University of Barcelona's Department of Library and Information Science, with teaching roles at Pompeu Fabra University (since 2013) and EINA Barcelona (since 2014). He holds a PhD in Engineering and Information Technology from University of Lleida (cum laude), Master's in Digital Content Management (UB/UPF), and Library Science Diploma (UB).

Rizos Sakellariouمشاهده پروفایل

استاد

• Parallel Computing
• Distributed Systems
• Large-Scale Software Systems
• +۵ مورد دیگر

Rizos Sakellariou is a Professor in the Information Management department at the University of Manchester. His research focuses on Parallel, Distributed and Large-Scale Software Systems , with significant contributions to Cloud Computing and Social Network Analysis. He is actively accepting PhD students for projects related to his expertise. Research Interests Parallel and Distributed Computing Cloud and Fog Computing Scientific Workflow Optimization Quality of Service in Distributed Systems Application of Computational Methods to UN Sustainable Development Goals Recent Research Trends Recent publications demonstrate his ongoing work in: Algorithm Design for Social Network Analysis Resource Optimization in Fog/Edge Computing Virtual Machine Migration in Cloud Environments Uncertainty Modeling with Fuzzy Logic Network Dynamics and Ethical AI Considerations

• Data Science
• Machine Learning
• Safety Machine Learning
• +۹ مورد دیگر

Tareq Al Jaber is an Assistant Professor in Computer Science at the School of Computer Science & DAIM (Data Science, AI and Modelling Centre), University of Hull. With over 13 years of teaching experience, he has instructed more than 2,000 hours at the MSc level and over 1,000 hours at the BSc level in Computer Science, Data Science, and Artificial Intelligence. His educational background includes: BSc in Computer Science from University of Hull MSc in .NET Distributed Systems Development from University of Hull PhD in Computer Science (An evaluation framework and selection tool for education apps usability, with a case study from health education apps) from University of Hull Dr. Al Jaber's research focuses on the intersection of Data Science, Machine Learning, and Healthcare applications. His work particularly emphasizes Mobile Health (mHealth) applications, usability evaluation, and the application of AI in educational contexts. He has published extensively on topics including machine learning for health tracking, usability of mobile health apps, AI ethics in education, and the application of data science in healthcare. His recent publications demonstrate a strong trend toward applying machine learning techniques to health monitoring and mobile applications, with particular attention to usability factors and ethical considerations in AI systems. The research spans multiple disciplines including computer science, healthcare informatics, and educational technology. Professional recognition includes: Fellow of the Higher Education Academy (FHEA) Guest Editor for Plos One journal Editor for Engineering Applications of Artificial Intelligence journal Member of the British Computer Society Dr. Al Jaber has supervised over 300 MSc and 200 BSc dissertation projects in Computer Science, Data Science, and Artificial Intelligence. He teaches modules across all levels, from undergraduate to postgraduate, including "Fundamentals of Data Science," "Big Data and Data Mining," "Programming for Artificial Intelligence," and other AI and Data Science courses. His teaching and research activities are closely aligned with the Data Science, AI and Modelling Centre at the University of Hull, where he contributes to advancing knowledge in machine learning applications for health and education.

Florian Pommereningمشاهده پروفایل

استاد

• Classical Planning
• Heuristic Search
• Linear Programming
• +۳ مورد دیگر

Dr. Florian Pommerening is a Professor at the Department of Mathematics and Computer Science within the Faculty of Science at the University of Basel, Switzerland. He holds an email address of florian.pommerening@unibas.ch and can be reached via +41 61 207 05 37. His academic career is deeply rooted in classical domain-independent planning, with a focus on optimal planning through heuristic search and the application of linear and mixed integer programming in planning domains. University of Basel, Switzerland Faculty of Science Department of Mathematics and Computer Science Professor Rank Florian completed his PhD at the University of Basel in 2017 under the supervision of Malte Helmert. He also holds a Master's degree in Computer Science from the University of Freiburg (2012) and a Master's in Information Technology from Monash University (2010). His research spans topics such as classical planning, heuristic search, linear programming, mixed integer programming, and automated planning systems. His publication record from 2012 to 2025 demonstrates sustained contributions to planning algorithms, including cost partitioning heuristics, Lagrangian decomposition, and transition landmark generation. His work has been recognized with multiple awards, including the ICAPS 2024 Influential Paper Award, ECAI 2023 Quality Champion PC award, and the Dux of the Master of Information Technology at Monash University. ICAPS 2024 Influential Paper Award Quality Champion PC award (ECAI 2023) Runner Up, Best Student Paper Award (ICAPS 2023) CoRe Challenge 2022 Awards Best Student Paper Runner-Up (ICAPS 2022) Best System Demonstration (ICAPS 2022) Outstanding Senior Program Committee Award (AAAI 2021) ICAPS 2019 Best Paper Award Faculty Award 2018 (University of Basel) EurAI Artificial Intelligence Dissertation Award 2017 ICAPS 2018 Best Dissertation Award Outstanding PC Member Award (SoCS 2016) Winner, Unsolvability IPC 2016 AAAI 2015 Outstanding Paper Award ICAPS 2014 Outstanding Paper Award Dux of the Master of Information Technology 2010 (Monash University) Florian actively contributes to the planning community through tutorials at AAAI 2019 and ICAPS 2015, and through software development such as the Fast Downward planner and Planutils framework. His notable side project includes creating a quine in the Shakespeare Programming Language (SPL) with Thomas Mayer, showcasing technical creativity beyond core research.

• Modeling and Optimization
• Supply Chain Management
• Operations Research
• +۳ مورد دیگر

Assoc. Prof. Dr. Barış Keçeci is an Associate Professor in the Industrial Engineering program at Başkent University. He earned his PhD in Industrial Engineering from Gazi University (2014), Master's Degree from Başkent University (2008), and BSc from Gazi University (2002). Research interests include Modeling and Optimization, Supply Chain Management, Operations Research, Decision Science, and Multi-Criteria Decision Making, with a focus on logistics and transportation systems. His work often integrates advanced algorithms like fuzzy logic and neural networks for solving complex optimization problems. Recent publications emphasize variants of the Traveling Salesman Problem (e.g., with hotel selection, clustered trips), vehicle routing with simultaneous pickup/delivery, and applications of hesitant fuzzy models. These studies span sectors like defense industry, tourism, and shared mobility systems. Advising includes Master's theses on topics such as pandemic-era educational quality, intelligent transportation systems, and ERP software selection. Notable advisees include Neslişah Albayrak Melis Boran Cemal Aykut Gencel Nelişah Albayrak İpek Damla Akpinar Pelin Akyıl Kurt

Marina Indriمشاهده پروفایل

دانشیار

• Industrial Robotics
• Mobile Robotics
• Collision Detection and Avoidance
• +۷ مورد دیگر

Marina Indri is a Tenured Associate Professor in the Department of Electronics and Telecommunications at Politecnico di Torino, Italy. She serves as a member of the Interdepartmental Center PIC4SeR for Service Robotics and chairs the Robotics Laboratory (1999–present). With over 100 publications, her work bridges industrial robotics , mobile robotics , and Industry 4.0 . Education: Laurea (Electronic Engineering, cum laude , 1991) Ph.D. (Systems Engineering, 1995) Research Interests: Specializing in Collision detection and avoidance for industrial manipulators Friction modeling and compensation Path planning and autonomous navigation Smart manufacturing systems Human-robot collaboration Awards: James C. Hung Best Paper Award (ETFA 2013, 2024) euRobotics Technology Transfer Prize (2014, 2017) Leadership: As IEEE Senior Member , she chairs the IEEE Industrial Electronics Society’s Factory Automation Technical Committee and serves on editorial boards for IEEE Transactions on Industrial Informatics and IEEE/ASME Transactions on Mechatronics . She leads projects like SpaceItUp! (space robotics) and PNRR FAIR (AI-driven manufacturing).

Marko Đukanovićمشاهده پروفایل

استادیار

• Combinatorial Optimization
• Graph Theory
• Bioinformatics
• +۴ مورد دیگر

Marko Đukanović serves as an Assistant Professor at the Department of Computer and Information Sciences within the Faculty of Natural Sciences and Mathematics at the University of Banja Luka. His academic career spans combinatorial optimization, graph theory, and bioinformatics applications, with significant contributions to algorithm design for complex computational problems. His research focuses on combinatorial optimization , particularly in graph domination problems and longest common subsequence variants . His work bridges theoretical computer science with practical applications in bioinformatics, social network analysis, and computational biology. Recent publications demonstrate expertise in developing advanced algorithms including biased random-key genetic algorithms, variable neighborhood search, and neural network-guided optimization techniques. Key publication trends show his specialization in solving constrained optimization problems on graphs, with growing emphasis on integrating machine learning components into traditional combinatorial algorithms. His 2024 publications in IEEE Transactions on Evolutionary Computation and Applied Mathematics and Computation represent significant contributions to the field. Best Paper Award at EvoStar Conference 2024 for 'A Neural Network Based Guidance for a BRKGA: An Application to the Longest Common Square Subsequence Problem' Dr. Đukanović actively participates in international academic collaboration, including recent panel discussions on knowledge transfer between Bosnia and Herzegovina and global expert communities. His work demonstrates strong international collaboration, particularly with researchers from TU Wien and institutions across Europe.

Monika Lanzenbergerمشاهده پروفایل

استادیار

• Semantic Web
• Ontology Alignment
• Information Visualization
• +۴ مورد دیگر

Monika Lanzenberger is an Assistant Professor at the Institute of Software Technology and Interactive Systems, Faculty of Informatics, Vienna University of Technology. Her research focuses on the intersection of Semantic Web technologies, ontology alignment, and information visualization. She has established herself as a notable researcher in visual ontology alignment through her development of the AlViz tool and related publications. Dr. Lanzenberger received her PhD from the Vienna University of Technology, Faculty of Informatics in 2003 and her MSc in Computer Science from the same institution in 2001. She completed a Post-Doc Fellowship (ERCIM) with the NTNU, Department of Computer and Information Science, Trondheim, Norway (February - October 2005) and with the Centre for IT Innovation (CITI) at the CRP Henri Tudor, Luxembourg (November 2005 - July 2006). Her primary research interests center around Semantic Web and Ontology-based Data, with specific focus on ontology alignment and visualization techniques. She has made significant contributions to the field through her work on visual ontology alignment tools, particularly the AlViz system. Her research also extends to Human Computer Interaction, Information Visualization techniques, feminist perspectives on computer science, and online learning and didactics. She has published extensively in top conferences and journals in these areas, with a particular emphasis on making ontology alignment more accessible through visualization. Dr. Lanzenberger has received recognition for her work, including an Outstanding Paper Award in 2002 for her research on the evaluation of navigation in hypertext learning systems. She is an active member of the scientific community, serving as a reviewer for international conferences and co-organizer of workshops in her field. As an educator, Dr. Lanzenberger has taught numerous courses including Information Visualization, Women in Science and Technology, Fundamentals of Methodical Working, and Semi-Automatic Information and Knowledge Systems. She has supervised multiple student projects, Bachelor's theses, and Master's theses, particularly in areas related to information visualization and semantic technologies. She is a member of several professional organizations including IEEE, ÖGAI, OCG, and GI, demonstrating her active engagement with the broader computer science community.

Konstantin Pavlikovمشاهده پروفایل

دانشیار

• Operations Research
• Stochastic Programming
• Integer Programming
• +۳ مورد دیگر

Konstantin Pavlikov is an Associate Professor at the Department of Business & Management (DBM) under Strategic Organization Design (SOD) at the University of Southern Denmark. His research focuses on Operations Research, Integer Programming, and Stochastic Programming , with particular emphasis on vehicle routing optimization and network flow modeling. Education : PhD in Operations Research (University of Florida, 2014), MSc in Applied Mathematics (Moscow State University, 2007) His work spans combinatorial optimization and network interdiction problems , developing exact and approximate solution algorithms for complex logistics challenges. Recent publications analyze heterogeneous vehicle routing capacity inequalities and two-commodity flow formulations for routing problems. Scientific contributions have been recognized with the Best Reviewer Award (2019). He actively reviews for journals like Computational Management Science and supervises academic works through examination roles.

Martin Kouteckýمشاهده پروفایل

دانشیار

• Parameterized Complexity
• Integer Programming
• Combinatorial Optimization
• +۱ مورد دیگر

Martin Koutecký is an Associate Professor at the Institute of Informatics, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic. His research primarily focuses on parameterized complexity of integer programming, combinatorial optimization, and computational social choice. He completed his PhD under Petr Kolman at Charles University and conducted postdoctoral research at Technion (Haifa, Israel) under Asaf Levin and Shmuel Onn. Research Interests: Koutecký's work bridges theoretical computer science and optimization, with emphasis on: Designing efficient algorithms for integer programming under structural constraints Applying optimization techniques to computational social choice problems Developing parameterized approaches for combinatorial optimization Exploring geometric perspectives in election modeling and bribery problems Publication Focus: His recent articles (2020-2025) demonstrate consistent work in algorithm design for optimization problems, particularly in integer programming variants and computational social choice. Key themes include block-structured IP, parameterized complexity, election modeling, and convex optimization. Methodological innovations frequently involve polyhedral theory, approximation algorithms, and complexity analysis.