جست‌وجوی استاد و پژوهشگر

• Integer Linear Programming
• Operations Research
• Scheduling
• +۶ مورد دیگر

Onur Ozturk is an Associate Professor at the Telfer School of Management, University of Ottawa. Holding a PhD in Industrial Engineering from Grenoble Institute of Technology (2012), he previously worked as a postdoctoral fellow at Ivey Business School (2012-2014) and as an assistant professor at ESIEE Paris (2014-2017). Education: Ph.D. in Industrial Engineering, Grenoble INP His research focuses on large-scale optimization applied to healthcare and transportation systems, with methodological expertise in scheduling theory, simulation modeling, and integer linear programming. Current work includes pandemic-era staff scheduling and medical sterilization process optimization. Recent publications (2018-2024) demonstrate cross-sector applications of operations research in healthcare resource allocation, pandemic logistics, batch processing systems, and urban freight transport. These works appear in journals like Omega , European Journal of Operational Research , and Production and Operations Management . NSERC Grant (2019-2026): Exact and Approximate Solution Methods for Batch Scheduling Problems ($169,000) Telfer School Internal Grants (2018-2022): Projects on home health care routing, sterilization optimization, and pandemic response He contributes to decision-making tools for urban transportation through the Efficacity Institute and teaches in areas of business analytics and operations management.

Harold Connamacherمشاهده پروفایل

دانشیار

• Random Constraint Satisfaction Problems
• Algorithms
• Artificial Intelligence
• +۴ مورد دیگر

Harold Connamacher is an Associate Professor in the Department of Computer and Data Sciences at Case School of Engineering, Case Western Reserve University . He holds the Robert J. Herbold Professor of Transformative Teaching title and serves as Associate Chair in his department. University: Case Western Reserve University School: Case School of Engineering Department: Computer and Data Sciences Academic Rank: Associate Professor Research Interests Harold's research focuses on random constraint satisfaction problems , algorithms , and artificial intelligence . He applies theoretical computer science techniques to analyze problem structures and enhance algorithm performance, particularly in combinatorial optimization and computational complexity. Teaching Interests : Programming languages, discrete mathematics, graph theory, algorithms, data structures, computer science theory, and database programming. His work in computer science education has been recognized with multiple awards, including the Carl F. Wittke Award for Excellence in Undergraduate Teaching (2019) and the Delta Upsilon Srinivasa P. Gutti Engineering Teaching Award (2017). Scientific Awards Carl F. Wittke Award for Excellence in Undergraduate Teaching 2019 Guy Savastano Outstanding Educator Award 2019 Delta Upsilon Srinivasa P. Gutti Engineering Teaching Award 2017 Tau Beta Pi Publications span topics in theoretical computer science , machine learning , and mathematical combinatorics , including works on satisfiability thresholds, spanning tree optimization, and educational methodologies in programming instruction.

Joseph Simmonsمشاهده پروفایل

استاد

• Research Methods
• Judgment and Decision Making
• Consumer Behavior
• +۲ مورد دیگر

Joseph Simmons is the Dorothy Silberberg Professor of Applied Statistics and Professor of Operations, Information, and Decisions at the Wharton School , University of Pennsylvania. His research focuses on improving research credibility through methodological innovation and combating biases in judgment and decision-making. Education: Not explicitly mentioned Research Interests include research methods , judgment and decision-making , and consumer behavior . He investigates biases in human decisions (e.g., anchoring effects, optimistic biases) and advocates for practices like pre-registration to enhance scientific rigor. Recent Articles highlight his work on confidence intervals in advice-taking, overconfidence in belief distributions, and methodological tools like specification curve analysis to address analytical bias. Scientific Awards: MBA Excellence in Teaching Award (2019, 2014, 2013, 2012) Helen Kardon Moss Anvil Award for outstanding teaching (2013) Advising: Mentors students in judgment and decision-making research through Wharton courses. Labs: Co-founded the Wharton Credibility Lab , which develops tools (e.g., AsPredicted.org, ResearchBox) to promote research transparency.

• Renewable Energy Systems
• Energy Storage Optimization
• Sustainable Building Technologies
• +۲ مورد دیگر

Andres Annuk serves as a Professor in Energy Supply at the Estonian University of Life Sciences , within the Institute of Forestry and Engineering's Chair of Energy Application Engineering. His research extensively explores renewable energy integration, sustainable construction, and energy system optimization. Research interests focus on renewable energy systems (wind-solar hybrids, geothermal), energy storage technologies (battery-hydrogen solutions, phase-change materials), and data-driven approaches for energy management (machine learning for forecasting, optimization algorithms). Applications span residential microgrids, building efficiency, and waste-to-energy conversion. Recent publications (2024–2025) demonstrate strong emphasis on: 1) Machine learning applications in energy infrastructure (soil stress prediction, solar data imputation), 2) Hybrid renewable systems (wind-hydrogen microgrids, geothermal co-generation), and 3) Sustainable construction (thermal storage materials, life-cycle assessment). Technical methodologies frequently involve finite element analysis, optimization algorithms, and stochastic modeling.

Dilek Günneç Danışمشاهده پروفایل

دانشیار

• Combinatorial optimization
• Network optimization models
• Social network analysis
• +۷ مورد دیگر

Dilek Günneç Danış is an Associate Professor in the Industrial Engineering Department at Özyeğin University, Turkey. Her academic work spans network optimization, social network analysis, and humanitarian logistics with applications in disaster management, marketing, and public health. Ph.D. in Operations Management/Management Science, University of Maryland (2012) M.Sc. in Industrial Engineering, Koç University (2007) B.Sc. in Mathematics with Minor in Operations Research, Middle East Technical University (2005) Research Interests : Combinatorial optimization and complex systems Network modeling for information propagation New product development under social influences Humanitarian logistics in refugee crises Disaster mitigation and infrastructure resilience Scientific Contributions : Developed agent-based models for refugee migration paths Created capacitated mobile facility location frameworks Advanced influence maximization algorithms for social networks Optimized disaster response logistics systems Scientific Awards : Best Poster Award (Business Analytics Workshop, 2017) Advised team: Third Place INFORMS Student Competition (2020)

Domokos Kissمشاهده پروفایل

مدرس ارشد

• Transportation Planning
• Machine Learning
• Urban Mobility
• +۴ مورد دیگر

Domokos Kiss is a Senior Lecturer at the Department of Automation and Applied Informatics, Budapest University of Technology and Economics (BME). His research focuses on transportation systems, mobility as a service (MaaS), and decision-making modeling using fuzzy logic and machine learning. University: Budapest University of Technology and Economics Department: Department of Automation and Applied Informatics Email: Kiss.Domokos@aut.bme.hu Kiss’s research spans urban mobility, public transport accessibility, and multi-criteria decision analysis, with applications in autonomous vehicles, bike-sharing systems, and sustainable transport solutions. His work often integrates advanced analytical methods like fuzzy logic, hybrid choice frameworks, and heuristic algorithms. Recent publications highlight trends in MaaS adoption, traffic safety modeling, and optimization techniques for transport planning. Key themes include generational preferences in mobility, stakeholder engagement in MaaS development, and machine learning applications for travel behavior analysis.

Angel Ruizمشاهده پروفایل

استاد

• Healthcare Operations
• Emergency Medical Services
• Mathematical Modeling and Optimization
• +۳ مورد دیگر

Angel Ruiz is a Full Professor at Laval University’s Faculty of Medicine, specializing in healthcare operations and optimization. He co-directs CIRRELT’s Laboratory on Health Service Networks and contributes to CERVO’s Clinical and Cognitive Neuroscience research axis. Laval University, Faculty of Medicine CIRRELT’s Laboratory on Health Service Networks (Co-Director) CERVO Brain Research Centre (Researcher) His research focuses on mathematical modeling, logistics optimization, and simulation frameworks to improve healthcare systems, including pandemic planning, emergency medical services, and biomedical sample transportation. He has developed systematic review tools and prioritization frameworks for rehabilitation services. Recent publications emphasize location-allocation models for vaccination facilities, stochastic programming for food bank networks, and real-time patient transport systems. While no awards or student lists are explicitly mentioned in accessible content, his work bridges healthcare management with operational research.

• Digitalization
• Energy system
• Transport modeling
• +۷ مورد دیگر

Steffen Jaap Skotvoll Bakker is an Associate Professor at the Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology (NTNU). His research bridges energy systems, digitalization, and transport logistics through interdisciplinary modeling approaches. Specializes in energy system optimization under uncertainty Focuses on automated container terminal efficiency and freight decarbonization Combines econometrics with routing/scheduling algorithms for complex systems Recent work includes collaborations with SINTEF, IFE, and UIO on strategic energy scenarios, and contributions to bioconversion technologies for circular economies. His publications span operations research journals and logistics conferences, emphasizing data-driven decision-making frameworks.

Juan Antonio Fernández del Pozoمشاهده پروفایل

دانشیار

• Decision Analysis
• Intelligent Decision Support Systems
• Probabilistic Graphical Models
• +۱۰ مورد دیگر

Juan Antonio Fernández del Pozo is an Associate Professor of Statistics and Operations Research at the School of Computer Science, Technical University of Madrid (UPM), and a member of the Computational Intelligence Group. With over 60 publications and participation in more than 30 national and international research projects, he is a leading researcher in decision analysis and intelligent systems. His educational background includes: Doctorate in Computer Science from the School of Computer Science, UPM (Thesis: "Listas KBM2L para la síntesis de conocimiento en sistemas de ayuda a la decisión") His research focuses on probabilistic graphical models (Bayesian networks/influence diagrams) for decision support, evolutionary algorithm optimization, and machine learning for data streams with concept drift. Key application domains span Social Network Analysis, Air Traffic Management, structural health monitoring, Industry 4.0, and social service quality modeling in collaboration with Spanish Foundations. His work emphasizes knowledge acquisition in large decision tables, explanation synthesis, and sensitivity analysis. Publication trends reveal two phases: foundational work (2000-2013) on Bayesian networks and medical decision systems (e.g., neonatal jaundice modeling), and recent applied research (2016-2018) addressing environmental restoration, transportation scheduling, and social innovation networks. Scientific awards: No scientific awards were mentioned in the provided text. Advising and grants: Supervised 12+ Master's Degree projects Supervised 50+ final degree projects Participated in 30+ research projects Served on 18+ doctoral thesis evaluation panels Labs and teams: Active member of the Computational Intelligence Group at UPM

Mike Wallingaمشاهده پروفایل

مدرس

• Bioinformatics
• Parallel Computing
• Database Systems
• +۲ مورد دیگر

Mike Wallinga serves as a Lecturer in the Department of Computer Science at Northwestern College while concurrently holding a quarter-time position as Director of Institutional Research. With expertise spanning computational science and statistics, he teaches core computer science courses including introductory programming, data structures, databases, and parallel computing systems. His academic credentials include: Ph.D. in computational science and statistics from the University of South Dakota M.A. in computer science from the University of South Dakota B.A. in computer science and mathematics from Northwestern College Dr. Wallinga's research bridges bioinformatics, parallel computing, and pedagogical innovation. His bioinformatics work develops novel multiple sequence alignment algorithms using morphing techniques and particle swarm optimization, while his parallel computing research explores multi-core architectures for biological data processing. In computer science education, he designs immersive learning experiences such as semester-long game projects to engage programming students. Publication analysis reveals a strong focus on computational biology (71% of recent works), particularly multiple sequence alignment methodologies that dominated his 2017 output. His research trajectory shows progression from algorithmic development (2015-2017) toward educational innovation (2019) and theological integration in computing (2021), demonstrating interdisciplinary breadth within computer science. Recognition includes: Northwestern's 2018 Faculty/Staff Inspirational Service Award As a programming competition coach, he mentored two Northwestern College teams to global top-100 rankings in the ACM International Collegiate Programming Contest. His applied research extends to institutional analytics through his directorship, where he develops data-driven decision support systems using database technologies and visualization dashboards. Professional memberships include the Association for Institutional Research and Association for Computing Machinery. He maintains active leadership in competitive programming initiatives, creating structured team environments that prepare students for high-stakes algorithmic challenges through rigorous practice regimes and strategic problem-solving frameworks.

• Operations Research
• Supply Chain Management
• Mathematical Optimization
• +۳ مورد دیگر

Gözde Önder is an Assistant Professor in the Industrial Engineering Department at Başkent University, Ankara. Her academic profile indicates active engagement in operations research with focus on complex optimization problems relevant to transportation and logistics systems. Contact information shows affiliation with the Industrial Engineering Program through university email send@baskent.edu.tr and phone extension 4016. Her research interests concentrate on Operations Research , Supply Chain Management , and Mathematical Optimization with specific emphasis on time-constrained routing problems. Current investigations prominently feature traveling repairman problems, vehicle routing with time windows, and multi-depot optimization systems. Methodologically, she employs integer programming, biogeography-based metaheuristics, and combinatorial optimization techniques to address NP-hard problems in service scheduling and transportation networks. Analysis of her publication timeline reveals consistent scholarly output from 2015 through 2025, with increasing focus on time-window constrained systems and multi-depot extensions of classical routing problems. Her work demonstrates strong collaboration with Prof. İmdat Kara and other researchers at Turkish institutions, primarily published in operations research conferences and journals including Transportation Research Procedia and Journal of the Faculty of Engineering and Architecture of Gazi University. Teaching responsibilities include core industrial engineering courses: OLASILIK VE İSTATİSTİĞE GİRİŞ (Introduction to Probability and Statistics) TEDARİK ZİNCİRİ YÖNETİMİ (Supply Chain Management) YÖNEYLEM ARAŞTIRMASI I & II (Operations Research I & II) İSTATİSTİKSEL ANALİZ (Statistical Analysis) LOJİSTİK YÖNETİMİ (Logistics Management) Professional activities include serving as a reviewer for Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi (Gazi University Journal of Engineering and Architecture) in 2023, indicating active participation in academic quality assurance processes within her field.

Sönke Hartmannمشاهده پروفایل

استاد

• Operations Research
• Logistics
• Optimization Algorithms
• +۳ مورد دیگر

Sönke Hartmann is Professor for Operations Research and Logistics at the Hamburg School of Business Administration (HSBA) since 2007. His academic work focuses on optimization algorithms, resource-constrained project scheduling, and container terminal optimization. He teaches courses in Operations Research, Statistics, Mathematics, Computer Science Foundations, AI & Data Science, and Data Analysis. Professor Hartmann completed his Computer Science studies at the University of Kiel and earned his PhD in Economics with a specialization in Operations Research. His doctoral research laid the foundation for his subsequent work in mathematical optimization and scheduling problems. His research spans multiple areas within operations research and logistics. Dr. Hartmann has made significant contributions to resource-constrained project scheduling, developing algorithms that address complex resource allocation challenges. His work on container terminal optimization has practical applications in port logistics, focusing on improving efficiency through simulation and mathematical models. He has also explored applications of mathematical programming to solve logic puzzles and smartphone applications, demonstrating the versatility of optimization techniques across different domains. Professor Hartmann's publication record shows a clear evolution from theoretical scheduling algorithms to practical applications in container terminals. His recent work (2025) provides a comprehensive review of fifty years of research on resource-constrained project scheduling, while his earlier publications established foundational work in genetic algorithms for project scheduling. The journal distribution shows strong representation in top-tier operations research journals like European Journal of Operational Research. As an active member of the academic community, Professor Hartmann serves as a reviewer for more than 30 scientific journals, contributing his expertise to maintain scholarly standards in operations research and logistics publications. His research has practical implications for container terminal operations, with applications in scheduling equipment and manpower, optimizing container movements, and improving overall terminal efficiency through simulation models. This industry-focused research demonstrates the real-world impact of his academic work.

• Quantum Computing
• Statistical Physics
• Stochastic Processes
• +۳ مورد دیگر

Sergiy Denysov serves as Professor in the Department of Information Technology at Oslo Metropolitan University's Faculty of Technology, Art and Design. He is an active member of the Mathematical Modeling research group, focusing on the intersection of computational physics and quantum information systems. His research spans: Quantum Computing and Noisy Intermediate-Scale Quantum (NISQ) devices Statistical mechanics of complex systems Stochastic processes including Lévy flights and Markovian dynamics Spectral theory of random operators Machine learning applications in physical modeling Mathematical foundations of quantum information Analysis of his 2021-2025 publications reveals strong emphasis on quantum-classical algorithm synergies, spectral properties of quantum operations, and anomalous diffusion phenomena. His work consistently bridges theoretical physics with computational implementations, particularly through Python-based tools for stochastic process modeling. Denysov actively contributes to the Mathematical Modeling research group, which develops analytical and computational frameworks for physical systems across quantum information, statistical mechanics, and complex dynamics domains.

Pandu R. Tadikamallaمشاهده پروفایل

استاد

• Simulation Methodology
• Statistical Techniques in Management
• Quality Management
• +۳ مورد دیگر

Pandu R. Tadikamalla is Professor of Business Administration at the Katz Graduate School of Business, University of Pittsburgh, where he teaches statistical techniques, decision technologies, simulation, and total quality management. With over 45 publications and editorial roles at OMEGA and the American Journal of Mathematical and Management Sciences , he bridges academic rigor with corporate problem-solving through MBA Consulting Field Projects. His educational foundation includes a PhD and MS in Industrial and Management Engineering from the University of Iowa and a BS in Mechanical Engineering from Andhra University, India. This interdisciplinary background informs his research in simulation methodology, statistical management techniques, and quality/productivity systems. Tadikamalla's research spans supply chain optimization under carbon constraints, natural disaster relief logistics, and customer risk hedging. His recent publications demonstrate methodological innovation through bilevel programming for emergency allocation, evolutionary game theory for environmental governance, and agent-based models for digital branding—consistently applying operations research to sustainability and crisis management challenges. He has earned multiple teaching accolades including five Distinguished Professor Awards across EMBA programs and the Outstanding Professor of the Year for the MBA Class of 1986. His consulting work with multinational corporations translates theoretical frameworks into optimized inventory systems and supply chain solutions. As an active community leader, Tadikamalla serves as president of the Executive Committee and chairman/treasurer of the Board of Directors for S.V. Temple, demonstrating commitment to societal impact beyond academia.

• 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.