جست‌وجوی استاد و پژوهشگر

Banafsheh Khosraviمشاهده پروفایل

مدرس ارشد

• Operational Research
• Logistics
• Mathematical Modelling
• +۶ مورد دیگر

Dr Banafsheh Khosravi is a Senior Lecturer in Operational Research at the University of Portsmouth, affiliated with the School of Mathematics and Physics. She is a member of the Logistics, Operational Research and Analytics Research Group (LORA) and the Centre for Operational Research and Logistics (CORL). Her research focuses on mathematical modelling, combinatorial optimization, and applications in transportation and healthcare. She holds a PhD in Management Science from the University of Southampton, with earlier degrees in Industrial Engineering. Before joining Portsmouth, she worked at the University of Southampton's CORMSIS Centre and the University of Westminster's Health and Social Care Modelling Group. Dr Khosravi teaches Operational Research, Modern Computational Methods, and Supply Chain Management courses. She has supervised numerous academic projects and led industry-funded research with EPSRC, ESA, NHS, RSSB, and Arriva. Her work addresses challenges like railway scheduling, routing optimization, and healthcare capacity planning. She is a Fellow of the Higher Education Academy and contributes to professional organizations including The OR Society and IAROR. Her research trends span transportation logistics (railway rescheduling, electric vehicle routing) and healthcare systems (NHS patient activity forecasting). Recent work emphasizes risk-based surveillance and digital performance analysis in undersea infrastructure and insurance sectors. Collaborations with industry partners ensure practical impact across sectors like rail networks and space operations. Awards: Fellow of the Higher Education Academy (FHEA) Labs/Teams: LORA Group, CORL Centre, Intelligent Transport Research Cluster Grants: Funded by EPSRC, ESA, NHS, RSSB, Arriva

Tayfun Uyanıkمشاهده پروفایل

استادیار

• Marine Engineering
• Energy Efficiency
• Shipboard Microgrid
• +۳ مورد دیگر

Tayfun Uyanık is an Assistant Professor at Istanbul Technical University's Department of Marine Engineering. His research focuses on advancing energy efficiency and sustainable technologies in maritime systems, including shipboard microgrids, renewable energy integration, and data-driven operational optimization for vessels. He actively publishes on maritime risk assessment methodologies and fuel consumption modeling. Uyanık's research explores: Energy management frameworks for port electrification and cold ironing Machine learning applications for ship system optimization and safety Risk assessment in polar navigation and machinery operations Renewable energy integration in maritime infrastructure His recent articles demonstrate strong emphasis on: Data-driven approaches for ship energy systems (appearing in 9 of 15 recent publications) Maritime decarbonization strategies (6 publications) Safety and risk modeling in navigation (5 publications) He currently leads a research project: Gemi Elektrik Şebekelerinde Bataryalar için Dijital İkiz Yöntemi Araştırması (2024-2025), investigating digital twin applications for ship battery systems.

• Operations Research
• Combinatorial Optimization
• Operational Research
• +۳ مورد دیگر

Edmund Burke is a Professor and Vice Chancellor at Bangor University. His research focuses on operations research, combinatorial optimization, and algorithm development, with notable contributions to scheduling, hyper-heuristics, and airport ground movement optimization. He has published extensively on topics such as constraint propagation, routing algorithms, and multi-objective optimization. Research interests include algorithmic approaches for real-world problems, such as airport slot allocation, maintenance crew scheduling, and timetabling. His work often integrates heuristics and metaheuristics to tackle complex optimization challenges. Recent publications explore cutting-edge techniques like memetic algorithms for ground movement and hyper-heuristics for nurse rostering. Burke has contributed to interdisciplinary projects, including collaborations with Danish railways and airport logistics systems. He actively participates in university administration, organizing alumni events and engaging in outreach activities such as exhibitions and community days. His academic leadership roles underscore his commitment to both research excellence and institutional governance.

• Machine Learning
• Optimization Techniques
• Healthcare Operations Management
• +۵ مورد دیگر

Michele Samorani is an Associate Professor in the Department of Information Systems & Analytics at the Leavey School of Business, Santa Clara University, and Program Director for the Master of Science in Information Systems (MSIS). He holds a PhD in Operations and Information Management from the University of Colorado Boulder and degrees in Computer Science from the University of Bologna. His research focuses on combining machine learning and optimization to improve business processes and healthcare systems. Key areas include reducing racial disparities in medical appointment scheduling, relational data mining, and metaheuristic algorithms. Collaborations include healthcare organizations dedicated to Black women, mental health nonprofits, and pharmaceutical companies. His work has been featured in MIS Quarterly , Manufacturing & Service Operations Management , and INFORMS Journal on Computing . Award highlights include the 2021 INFORMS Minority Issues Forum Paper Competition, AACSB's 2021 Innovations That Inspire, and inclusion in a United Nations report. Media features include Forbes and WIRED . He teaches courses such as Data Science with Python, Natural Language Processing, and Business Intelligence. His research emphasizes practical impact, addressing social equity through algorithmic fairness and operational efficiency in healthcare and retail. Ongoing projects explore ethical AI applications and decision support systems for vulnerable populations.

Hasan Mansoorمشاهده پروفایل

پژوهشگر

• Optimization
• Machine Learning
• Transportation Networks
• +۳ مورد دیگر

Dr. Hasan Mansoor is a Research Officer at the School of Computing and Electronics, Bangor University. His research focuses on developing novel optimization and machine learning solutions for transportation and logistics networks, with an emphasis on enhancing efficiency to contribute to UN Sustainable Development Goals 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), and 12 (Responsible Consumption and Production). He employs a hybrid methodology combining exact methods, heuristics, matheuristics, metaheuristics, deep learning, and traditional machine learning approaches. He is actively involved in postgraduate supervision, expressing willingness to oversee PhD projects in his research domains. While no specific grants or laboratories are mentioned, his work aligns with interdisciplinary efforts in sustainable systems and intelligent transportation networks.

Mithun Gouthamمشاهده پروفایل

عضو هیئت علمی

• Combinatorial Optimization
• Cyber Physical Systems
• Operational Resilience
• +۴ مورد دیگر

Mithun Goutham is an Assistant Teaching Professor in the Department of Mechanical, Materials, and Aerospace Engineering at Illinois Institute of Technology. He holds a Ph.D. in Mechanical Engineering (expected 2025) from The Ohio State University, where he also earned an M.S. (2020) and previously obtained an M.Tech. in Design Engineering (2018) from Birla Institute of Technology & Science. His expertise spans Combinatorial Optimization, Cyber-Physical Systems, Operational Resilience, and Internal Combustion Engines. Dr. Goutham has received notable awards including the Best Student Research Award (2024) and the ENGIE-Axium Scholarship (2024). He focuses on optimizing fleet management, energy systems, and autonomous robotics. His research integrates machine learning, control systems, and operations research to address challenges in smart homes, manufacturing logistics, and renewable energy integration. His work on fleet composition and energy management has led to patents at The Ohio State University (2024) and a prior patent in vehicle exhaust design (2017). His articles explore topics like Monte Carlo-based fleet allocation, resilient intra-factory logistics, and user activity prediction in smart environments.

• Operations Research
• Logistics
• Simulation
• +۷ مورد دیگر

Wouter J.A. van Heeswijk is an Assistant Professor in the Department of Industrial Engineering & Business Information Systems at the University of Twente, Faculty of Engineering Technology. His research lies at the intersection of operations research, logistics, and artificial intelligence, with a strong focus on decision-making under uncertainty. University: University of Twente School: Faculty of Engineering Technology Department: Industrial Engineering & Business Information Systems Academic Rank: Assistant Professor His research interests include logistics, simulation, dynamic programming, reinforcement learning, urban mobility, and human-centered automation . He applies advanced computational methods to real-world challenges in transportation, disaster response, and business process optimization. His work often integrates machine learning with optimization techniques to build adaptive, data-driven decision support systems. The most recent articles reflect a strong trend toward applying reinforcement learning and simulation-based optimization in complex logistics and urban systems. Topics include traffic equilibrium prediction, post-disaster inventory allocation with UAVs, and self-organizing transportation networks. His publications span high-impact journals and conferences in operations research and computer science. Scientific Awards and Recognitions: No specific awards mentioned in the provided text. Advising and Grants: Dr. van Heeswijk supervises graduate students, including PhD candidates such as R. Bosch. While specific grants are not listed, his active publication record across multiple years and diverse topics suggests ongoing research funding. He collaborates extensively with colleagues like M.R.K. Mes and others in the logistics and operations research domain. Labs and Research Teams: He is part of a research group focused on logistics, simulation, and decision support systems within the Industrial Engineering department. His work contributes to the broader fingerprint of ‘Logistics’, ‘Simulation’, and ‘Reinforcement Learning’ at the university.

João Janelaمشاهده پروفایل

دانشیار

• Operations Research
• Computational Finance
• Fluid Dynamics
• +۳ مورد دیگر

João Janela is an Associate Professor at the Instituto Superior de Economia e Gestão (ISEG) within the Universidade de Lisboa. He holds a PhD in Mathematics from Instituto Superior Técnico (2008), following earlier degrees in Mathematics. His academic roles include coordinating the Mestrado em Matemática Financeira and serving on the CEMAPRE executive board. His research spans operations research, computational finance, and fluid dynamics, with notable contributions to waste collection optimization and blood coagulation modeling. He has supervised over 10 master’s theses, focusing on topics like numerical methods in finance and vehicle routing problems. Janela actively contributes to academic leadership through roles in curriculum development and institutional governance. Education: PhD in Mathematics, Instituto Superior Técnico - Universidade de Lisboa (2008) Master of Science in Mathematics, Instituto Superior Técnico - Universidade de Lisboa (2002) Bachelor of Science in Mathematics, Instituto Superior Técnico - Universidade de Lisboa (1997) Research Interests: Janela’s work integrates applied mathematics with real-world challenges. His research in operations research addresses logistics and waste management optimization, while his computational finance studies explore option pricing under stochastic models. In fluid dynamics, he develops numerical methods for blood flow and non-Newtonian fluid behavior. His recent projects emphasize interdisciplinary applications, such as synthetic cell-based models for biomedical systems. Advising & Grants: Supervised 9 master’s students since 2012, with topics ranging from exotic derivatives trading to decentralized exchange analysis. His work benefits from collaborations in CEMAPRE and ECCOMAS, though specific grant details are not provided in the text. Labs/Teams: Active member of CEMAPRE (Center for Mathematics and Applications) and involved in organizing conferences like ICCF2017 and MPF 2005.

• Finite Element Technology
• Computational Fluid-Structure Interaction
• Structural Finite Elements
• +۸ مورد دیگر

Professor Wulf Dettmer is affiliated with the School of Aerospace, Civil, Electrical and Mechanical Engineering at Swansea University. He specializes in Finite Element Technology , Computational Fluid-Structure Interaction (FSI) , and Morphing Aircraft research. His work spans disciplines including: Fluid-Structure Interaction Structural Dynamics Renewable Energy Systems Multi-Scale Modeling Key research trends include: Advanced time integration schemes for dynamic problems Machine learning applications in material modeling Cryogenic hydrogen energy systems Partitioned FSI solvers Flexible structures for energy harvesting Professor Dettmer has supervised numerous PhD and EngD projects in collaboration with Prof Djordje Peric and other colleagues, focusing on topics like neural network-based finite element frameworks and multi-scale composite analysis.

Yuzhe Tianمشاهده پروفایل

مدرس پاره‌وقت

• Anomaly Detection
• Industrial Internet of Things (IIoT)
• Sleep Analysis
• +۵ مورد دیگر

Yuzhe Tian is a Part-Time Lecturer at the School of Computing, Macquarie University, within the Faculty of Science and Engineering. His research focuses on interdisciplinary applications of machine learning, including anomaly detection in industrial IoT systems, sleep stage recognition via graph networks, and brain-computer interface (BCI) technologies. He holds a Doctor of Philosophy and has published extensively in top-tier conferences and journals since 2019. Research interests include real-time BCI systems using geometric learning, optimization of neural networks (e.g., model pruning and quantization), and healthcare applications like sleep analysis through graph attention networks. His work bridges computational methods with practical challenges in biomedical engineering and industrial automation. Yuzhe has collaborated on projects leveraging multi-view learning for anomaly detection and swarm intelligence algorithms (e.g., Krill Herd Algorithm). His publications span topics from lightweight BCI frameworks to cybersecurity in IIoT environments, demonstrating a strong focus on both theoretical advancements and real-world implementation.

• Structural Engineering
• Earthquake Engineering
• Seismic Retrofitting
• +۵ مورد دیگر

Santiago Londono Lopez is a Ph.D. candidate in Civil and Environmental Engineering at the Polytechnic University of Turin (2023-2026 cycle) and serves as a PartTime Lecturer at the Department of Structural, Geotechnical and Building Engineering (DISEG). He teaches in the Master of Science program for Earthquake Engineering and Urban Resilience within the Building Engineering curriculum for academic years 2024/25 and 2025/26. Education: M.Sc. in Structural Engineering B.Sc. in Civil Engineering His research specializes in sustainable structural and energy retrofitting for aging post-WWII buildings, addressing Italy's seismic risks and energy scarcity through advanced computational methods. Key focus areas include seismic protection systems, structural monitoring, and the integration of metaheuristic algorithms, machine learning, and artificial intelligence into structural design processes. His work prioritizes BIM-enabled workflows for enhanced project accuracy and seismic resilience in steel/concrete superstructures. Recent publications analyze high-damping rubber bearings for seismic isolation, emphasizing model calibration and production variability impacts. These studies reflect his cross-disciplinary approach combining structural dynamics, materials science, and computational optimization to develop resilient building envelopes. As a teaching assistant, he contributes to course development in earthquake engineering while applying his industry experience in residential/industrial structural design. His professional practice integrates regulatory standards with sustainable retrofitting techniques to address climate change challenges in the built environment.

Mieczysław Wodeckiمشاهده پروفایل

استاد

• Discrete Optimization
• Scheduling Problems
• NP-hard Problems
• +۷ مورد دیگر

Professor Mieczysław Wodecki is affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology, where he is a member of the Department of Telecommunications and Teleinformatics. He holds a DSc and PhD and is actively engaged in research and teaching. Research Interests: His work centers on discrete optimization, scheduling problems, and methods for solving NP-hard problems. He applies parallel computing and probabilistic models to challenges in project management and automation in construction. His research bridges theoretical computer science with industrial engineering applications, particularly in manufacturing and production systems. The recent publications highlight a strong focus on cyclic job shop and flow shop scheduling, with an emphasis on parallel algorithms and metaheuristics. These works contribute to the optimization of manufacturing processes, especially in cyclic and flexible production environments. Scientific Contributions: While no specific awards are listed, his extensive publication record in reputable journals such as Computers & Industrial Engineering and Bulletin of the Polish Academy of Sciences: Technical Sciences reflects sustained scholarly impact. Advising and Grants: Information on students advised or research grants received is not available in the provided text. Laboratories and Teams: He collaborates closely with researchers including W. Bożejko, J. Pempera, and A. Gnatowski, contributing to a research group focused on discrete systems and scheduling optimization. His work appears in special issues dedicated to discrete systems, indicating active participation in thematic research initiatives.

Henry Hanمشاهده پروفایل

استاد

• Data Science
• Fintech
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Henry Han is a Professor and holds the McCollum Family Chair in Data Science at Baylor University’s Hankamer School of Business, Department of Data Science. His interdisciplinary research spans data science, fintech, artificial intelligence, bioinformatics, health informatics, cybersecurity, and quantum computing. PhD, Applied Mathematical & Computational Sciences, University of Iowa (2004) MS, Computer Science, University of Iowa (2001) MS, Applied Mathematics, University of Iowa (2001) Dr. Han’s research integrates advanced machine learning, optimization, and data analytics techniques to solve complex problems in finance, healthcare, and cybersecurity. His work emphasizes manifold learning , deep neural networks , evolutionary computation , and graph-based learning for real-world applications such as high-frequency trading, automobile damage classification, and single-cell genomics. He applies these methods across domains including financial modeling, biomedical data analysis, and sports forecasting. His recent publications demonstrate a strong trend toward interdisciplinary machine learning , with applications in computational finance, bioinformatics, and intelligent systems. Many of his works utilize optimization-based deep learning and unsupervised representation learning to extract meaningful patterns from high-dimensional data. Dr. Han has received recognition through prestigious appointments: McCollum Family Chair in Data Science He actively collaborates on research projects and has secured funding for advanced data science initiatives, though specific grant details are not listed. His work supports both academic advancement and practical innovation in data-driven decision-making. He advises graduate students and contributes to the development of next-generation data science methodologies. Dr. Han is a key member of the data science research team at the Hankamer School of Business, contributing to interdisciplinary labs and research groups focused on fintech, AI, and health informatics.

Andrzej Kasprzakمشاهده پروفایل

استاد

• Machine Learning
• Computer Science
• Network Optimization
• +۳ مورد دیگر

Professor Andrzej Kasprzak is a faculty member at the Faculty of Information and Communication Technology, Wrocław University of Science and Technology, serving as Head of the Department of Systems and Computer Networks. His research spans machine learning, network optimization, and data stream processing, with applications in telecommunications, healthcare, and industrial systems. University: Wrocław University of Science and Technology School: Faculty of Information and Communication Technology Department: Department of Systems and Computer Networks Email: andrzej.kasprzak@pwr.edu.pl Kasprzak’s research focuses on integrating machine learning with network optimization, particularly in elastic optical networks and data stream environments. His work addresses challenges in multi-agent pathfinding, fake news detection, and 3D printing automation, emphasizing practical experimentation systems. Recent publications highlight trends in ensemble learning for real-time data streams, domain adaptation in AI systems, and innovative optimization algorithms for network traffic and scheduling problems. These contributions bridge theoretical advancements with industrial applications. As Head of Department, Kasprzak leads initiatives in systems and computer networks, fostering interdisciplinary collaboration in artificial intelligence and telecommunications. His career reflects a commitment to advancing computational methodologies in complex environments.

Dominik Żelaznyمشاهده پروفایل

پژوهشگر

• Scheduling
• Evolutionary Algorithms
• Multi-objective Optimization
• +۳ مورد دیگر

Dominik Żelazny is a researcher at the Faculty of Information and Communication Technology, Department of Computer Engineering, Wrocław University of Science and Technology. He focuses on scheduling algorithms and multi-objective optimization using evolutionary computation methods. Current position: Researcher Primary affiliation: Department of Computer Engineering Research interests include: Development of evolutionary algorithms for scheduling problems Multi-criteria and multi-objective optimization GPU-accelerated computing using CUDA Parallel and distributed algorithm design Industrial applications of operational research