جست‌وجوی استاد و پژوهشگر

Jebraeel Gholinezhadمشاهده پروفایل

مدرس ارشد

• Machine Learning in Energy Engineering
• Carbon Capture and Storage (CCS)
• Energy Storage Materials
• +۳ مورد دیگر

Dr. Jebraeel Gholinezhad is a Senior Lecturer in Energy Engineering and Course Leader for the MSc programmes in Petroleum and Gas Engineering and Energy and Power Systems Management at the University of Portsmouth. He is a chartered engineer (CEng) registered with the UK Engineering Council. His research focuses on machine learning applications in thermofluid characterization, carbon capture and storage (CCS), and energy storage materials. He has supervised multiple PhD students and holds expertise in reservoir engineering, well performance analysis, and flow assurance. Education: PhD in Energy Engineering from Heriot-Watt University; MSc in Reservoir Engineering from IFP School (with industry placement at TOTAL). Prior to academia, he worked at the Research Institute of Petroleum Industry (RIPI) for six years, focusing on reservoir fluid studies and flow assurance. He has presented industry workshops on thermofluids, gas hydrates, and production optimization. Research interests include AI-driven reservoir modeling, CO2 storage simulation, and unconventional reservoir characterization. His teaching includes Engineering Management, Carbon Capture and Storage, and Well Engineering. He is affiliated with the Centre for Environmental and Renewable Energy Solutions.

• Mechanical Engineering
• Artificial Intelligence
• Tribology
• +۳ مورد دیگر

Hassan Shirvani is a Professor of Engineering Design and Simulation at the School of Engineering and the Built Environment, Anglia Ruskin University. He serves as Director of the Engineering Analysis Simulation and Tribology (EAST) Research Group, focusing on industry collaborations to solve engineering challenges. PhD in Mechanical Engineering, University of Bath MSc in Mechanical Engineering, University of Birmingham Member, Institute of Mechanical Engineers (IMechE) His research spans mechanical engineering, artificial intelligence, and biomedical applications, including: Thermal system optimization Machine learning in clinical decision-making Composite metal foil manufacturing Virtual reality medical training systems Flow dynamics in heat exchangers and nozzles AI-assisted diagnostics Hybrid manufacturing processes Hassan's publications reflect expertise in computational modeling, multi-physics simulations, and industrial applications. Notable areas include deep learning for suicide prediction, thermodynamic analysis of sustainable energy systems, and tribology in mechanical components.

• Machine Learning
• Automated Machine Learning (AutoML)
• Hyperparameter Optimization
• +۳ مورد دیگر

Janek Thomas is a researcher actively contributing to the fields of Machine Learning and Automated Machine Learning (AutoML). His work focuses on hyperparameter optimization, multi-objective algorithms, and improving the interpretability of machine learning models. Collaborating with institutions like TU Munich and LMU Munich, he has authored over 35 publications since 2016. Key contributions include the AMLB benchmark suite for AutoML systems and foundational research on multi-objective hyperparameter tuning. His research bridges theoretical advancements with industrial applications, emphasizing robust model verification and scalable optimization techniques.

• Antenna Engineering
• 6G/5G Communications
• Metamaterials
• +۶ مورد دیگر

Ming Shen is an Associate Professor at the Department of Electronic Systems, part of The Technical Faculty of IT and Design at Aalborg University. His research focuses on antennas, millimeter-wave systems, and AI-driven RF sensors with applications in 5G/6G communications, biomedical engineering, and smart systems. His research interests span antenna design (including phased arrays, metamaterials, and compact structures), AI integration in electromagnetic systems, and medical sensor technologies. Recent projects include drone-based electromagnetic signature analysis, vibration energy harvesting for pacemakers, and smart healthcare systems for posture recognition and surgical site infection monitoring. Key projects include DRONES: Drone-Obtained Electromagnetic Signatures (2024–2028), Sensor Intelligence for Healthcare and Sports (2022–2027), and DeepBone (2021–2022), which explored deep learning for surgical infection detection. His work also bridges machine learning and electromagnetic design, with breakthroughs in surrogate modeling and automated antenna optimization. Ming Shen has supervised 6 PhD students and published over 160 peer-reviewed articles. Notable contributions include AI-assisted NLOS sensing, ultra-wideband antenna innovations, and medical applications such as electrical impedance-based bone healing assessment.

• Multi-Objective Optimisation
• Nature Inspired Optimisation Algorithms
• Multidisciplinary Design Optimisation Under Uncertainties
• +۷ مورد دیگر

Dr. Edmondo Minisci is a Senior Lecturer in Mechanical and Aerospace Engineering at the University of Strathclyde, part of the Faculty of Engineering. He leads the Intelligent Computational Engineering Laboratory (ICE-Lab) within the Aerospace Centre of Excellence. With over 25 years of experience, his expertise spans model-based analysis and design optimization of complex mechanical/aerospace systems, including aeronautical vehicles, wind turbines, and sustainable energy systems. He actively supervises PhD students in areas like multi-fidelity methods, intelligent control, and sustainable agriculture engineering. Dr. Minisci has received prestigious awards, including the NATO STO “Panel Excellence” Award (2024) and the Sir Arthur C Clarke Award (2015). His research focuses on multidisciplinary design optimization under uncertainties, machine learning applications, and aerospace technologies for sustainable development. Key projects include optimizing high-lift devices, wind turbine performance, and precision agriculture systems. He has contributed to numerous conferences, including organizing the International Conference on Bioinspired Optimisation Methods and serving as an examiner for PhD theses. His work bridges computational intelligence with practical engineering challenges, emphasizing resilience and innovation in aerospace and energy sectors. Research Interests: Multi-Objective Optimization, Nature-Inspired Algorithms, Uncertainty Treatment, Machine Learning, Intelligent Control. Recent Projects: Symbolic Computation for Differential Equation Systems, EPSRC-funded Uncertainty Quantification in Aeroelastic Systems, and Advanced Natural Language Models for Satellite Communications.

Kalyanmoy Debمشاهده پروفایل

استاد

• Multi-Objective Optimization
• Evolutionary Algorithms
• Genetic Algorithms
• +۵ مورد دیگر

Kalyanmoy Deb is a Professor at the Department of Electrical and Computer Engineering, Michigan State University. Previously affiliated with Indian Institute of Technology Kanpur and Indian Institute of Technology Madras, he is a leading researcher in evolutionary algorithms and multi-objective optimization. Research Focus : Multi-Objective Optimization, Genetic Algorithms, Constraint Handling, Machine Learning-assisted Optimization Key Contributions : Development of NSGA (Non-dominated Sorting Genetic Algorithm) series, hybridization of machine learning with evolutionary algorithms, fidelity evaluation techniques Scientific Accomplishments : With over 347 publications and 19,618 citations, his work on NSGA algorithms has become foundational in evolutionary computation. His recent research explores mixed-fidelity evaluations, innovized knowledge extraction, and neural architecture search.

Jose Tomas Palma Mendezمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Knowledge Engineering
• Machine Learning
• +۴ مورد دیگر

Jose Tomas Palma Mendez is an Associate Professor at the University of Murcia's Faculty of Informatics, affiliated with the Department of Information and Communication Engineering. He holds a PhD in Computer Science from the same institution (1999), specializing in knowledge engineering for real-time systems. His research focuses on artificial intelligence, knowledge engineering, machine learning, and their applications in healthcare and environmental monitoring. He leads the AIKE research group (Artificial Intelligence and Knowledge Engineering), previously contributing to the 'Inteligencia Artificial e Ingenieria del Conocimiento' group. Key research themes include explainable AI, feature selection in high-dimensional datasets, time series forecasting (e.g., air quality, water consumption), and computational methods for medical prognosis (e.g., cancer outcomes, long-COVID prediction). His work integrates evolutionary algorithms, deep learning, and spatio-temporal modeling. Notable contributions span AI-driven healthcare systems, ambient intelligence for elderly care, and ontology-based decision support tools. His methodologies emphasize multi-objective optimization, surrogate modeling, and interpretable machine learning frameworks. Education: PhD in Computer Science, University of Murcia (1999) Key Projects: Development of SAVIA (agricultural pest management tool), T-CARE (temporal case retrieval), and InSCo-Gen (model-driven web applications) Labs/Teams: AIKE Research Group (University of Murcia) Publications highlight advancements in imbalanced learning frameworks, multi-criteria environmental forecasting, and medical diagnostic support systems. His work bridges theoretical AI innovations with real-world applications in healthcare, environmental science, and smart infrastructure systems.

Danial Faghihiمشاهده پروفایل

استادیار

• Multiscale Computational Modeling
• Data-Driven Predictive Modeling
• Uncertainty Quantification
• +۴ مورد دیگر

Danial Faghihi is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University at Buffalo. His research focuses on multiscale computational modeling, data-driven predictive methods, and uncertainty quantification with applications in advanced manufacturing, thermal insulation materials, and biomedical systems. He holds a PhD from Louisiana State University and completed a postdoctoral fellowship at the University of Texas at Austin. Education: Postdoc, Computational Engineering, University of Texas at Austin (2015) PhD, Civil Engineering, Louisiana State University (2012) MS, Civil Engineering, Sharif University of Technology (2008) BS, Civil Engineering, K. N. Toosi University of Technology (2008) His research interests integrate advanced computational frameworks with experimental data to address challenges in materials science and biomedical engineering. Notable areas include predictive modeling of silica aerogels, stochastic surrogate models for uncertainty quantification, and tumor growth simulations using Bayesian methods. He has received prestigious awards such as the NSF CAREER Award (2022) and the SES Travel Award (2013). Recent work emphasizes dynamic data-driven approaches for real-time material damage prediction and scalable algorithms for multi-material design under uncertainty. His contributions span interdisciplinary collaborations in thermal management systems, carbon sequestration materials, and personalized oncology through computational oncology models. Key Honors: NSF CAREER Award (2022) SES Travel Award (2013) NSF Summer Institute Fellowship (2013) His research narrative includes development of novel surrogate models for high-dimensional systems and validation frameworks that bridge atomistic to continuum scales. While specific grants or lab affiliations are not detailed in the provided text, his work reflects sustained innovation in computational science and engineering applications.

• Artificial Intelligence
• Advanced Analytics
• Operations Research
• +۵ مورد دیگر

Dr. Su Nguyen is a Senior Lecturer in AI and Analytics at RMIT University's Department of Accounting, Information Systems & Supply Chain, located at the City Campus in Australia. His research focuses on integrating artificial intelligence, analytics, and operations research to address challenges in sustainability, safety, and critical domains like healthcare and logistics. Key areas include transparent AI systems, simulation models for dynamic environments, and optimization algorithms for energy and transportation. His work emphasizes making AI systems accountable to mitigate risks such as discrimination and ensures fairness in autonomous decision-making. Collaborations span Australia, New Zealand, and the Asia Pacific region, promoting AI applications in industry. Supervised projects include topics like personalized care plans, workforce rostering optimization, and AI-driven solutions for supply chains and cyber-risk evaluation. Dr. Nguyen's research bridges theoretical advancements with practical industry engagement, aiming to enhance decision-making through advanced analytics and business optimization frameworks.

• Artificial Intelligence
• Machine Learning
• Data-Driven Science
• +۷ مورد دیگر

Janardhan Rao (Jana) Doppa is an Associate Professor in the School of Electrical Engineering and Computer Science (EECS) at Washington State University (WSU). He holds the Huie-Rogers Endowed Chair in Computer Science and the Berry Distinguished Professorship in Engineering. His research focuses on artificial intelligence, machine learning, and data-driven science, with applications in structured prediction, Bayesian optimization, and sustainable computing. He chairs the EECS Graduate Studies program and has received awards such as the NSF CAREER Award (2019) and the Voiland College Anjan Bose Outstanding Researcher Award (2024). Education: Ph.D., Computer Science, Oregon State University (2014) M.Tech., Computer Science, Indian Institute of Technology Kanpur (2006) Research Interests: Dr. Doppa's work spans AI-driven adaptive experiment design, sequential decision-making under uncertainty, robust machine learning, and applications in agriculture, health informatics, and electronic design automation. He leads the NSF-USDA AI Institute for Agricultural Decision Support and explores AI methods for nanoporous materials design and sustainable computing. Awards: NSF CAREER Award (2019) Voiland College Anjan Bose Outstanding Researcher Award (2024) Best Paper Awards at multiple conferences (e.g., ACM/IEEE Embedded Systems Week 2023) Outstanding Junior Faculty in Research (2020) and Reid Miller Teaching Excellence (2018) Advising & Grants: He advises a dynamic group of PhD and MS students, many of whom have won awards. His grants include NSF-CAREER and USDA AI Institute funding. He teaches courses like Machine Learning and Structured Prediction at WSU. Labs/Teams: His research group collaborates with industry and academia, advancing AI for engineering and scientific domains through interdisciplinary projects.

William A. Crossleyمشاهده پروفایل

استاد

• Optimization and design methods for aerospace systems
• Systems of systems
• Design under uncertainty
• +۲ مورد دیگر

William A. Crossley is the Uhrig & Vournas Head of the School of Aeronautics and Astronautics and a Professor at Purdue University. He holds a B.S.E. from the University of Michigan (1990), and M.S. and Ph.D. from Arizona State University (1992 and 1995, respectively). His research focuses on optimization methods for aerospace systems , systems of systems , design under uncertainty , and the environmental impact of aviation . Awards and Recognition: Purdue Book of Great Teachers (2013) Multiple AAE Elmer Bruhn Teaching Awards (2012, 2009, 2006, 2003) Purdue University College of Engineering A.A. Potter Award (2007) NASA recognition for outstanding scientific contributions (2005) Fellow of the American Institute of Aeronautics and Astronautics (AIAA) Leadership and Affiliations: Director, FAA’s Partnership to Enhance General Aviation Safety, Accessibility and Sustainability (PEGASAS) Member of the International Society for Structural and Multidisciplinary Optimization (ISSMO) and the International Council on Systems Engineering (INCOSE) Research Trends: Crossley’s work emphasizes multidisciplinary design optimization , environmental impact analysis , and robust system design . His recent publications explore aircraft sizing, morphing structures, and fleet-level environmental metrics. Over 15 articles since 2010 reflect a focus on sustainable aviation technologies and systems integration.

Ursula Eickerمشاهده پروفایل

استاد

• Urban energy systems
• 3D City modeling
• Urban data and simulation platform development
• +۷ مورد دیگر

Prof. Ursula Eicker is the Canada Excellence Research Chair in Smart, Sustainable and Resilient Cities and Communities at Concordia University , leading cutting-edge research in urban energy systems. Her work integrates 3D city modeling, renewable energy systems, and sustainable transport to develop zero-carbon city strategies. PhD in Solid State Physics (Heriot-Watt University) Habilitation in Renewable Energy Systems (Technische Universitat Berlin) Research Interests focus on urban simulation platforms, district energy networks, and climate-resilient infrastructure. The INSEL4Cities platform enables holistic urban modeling for building demands, transportation, and greenery. Her Residential Densification studies demonstrate 65% energy reduction through retrofits and solar integration. Recent publications explore urban solar shading , transactive energy systems , green infrastructure equity , and decentralized hydrogen production . Awards include the German-African Innovation Incentive and recognition for photovoltaic research in Egypt. Over 50 graduate students in her lab examine zero-carbon pathways. Teaches ENCS 691 on urban energy systems. Secured 10M CAD for the CERC chair and multiple grants. Co-Director of Concordia's Next Generation Cities Institute .

Johan Perssonمشاهده پروفایل

دانشیار

• Computer Aided Product Development
• Surrogate Models
• Finite Element Analysis (FEM)
• +۴ مورد دیگر

Johan Persson is an Associate Professor and Head of Unit at Linköping University's Department of Management and Engineering (IEI). He leads the Product Realisation (PROD) division, focusing on advancing Computer Aided Product Development (CAD) and optimization techniques. His research emphasizes computationally efficient surrogate models to enhance FEM/CFD analyses and streamline product development processes. Key areas of interest include design automation frameworks, additive manufacturing integration, and multidisciplinary optimization strategies. He teaches courses such as Machine Elements (TMKT39), Design Optimization (TMKT48), and Collaborative Multidisciplinary Optimization (TMKT79). His work bridges academic research and industry applications, with notable contributions to frameworks supporting Additive Manufacturing (AM) and OpenMDAO-based optimization. Research trends in his publications highlight advancements in AM applications, design automation tools, and collaborative optimization methods. Notable projects include a framework for AM design integration and studies on hydraulic pump design using additive techniques. Johan supervises PhD students including Erik Gustafsson and Javier Villena Toro. His research is supported by collaborations with colleagues like Anton Wiberg and Johan Ölvander. The Automation Lab, a key platform for his work, pioneers innovations in CAD automation and digital design processes.

Neil W. Bressloffمشاهده پروفایل

استاد

• Biomedical Engineering
• Cardiovascular Engineering
• Computational Fluid Dynamics
• +۱۱ مورد دیگر

Professor Neil W. Bressloff is a leading academic in the School of Mechanical Engineering at the University of Leeds, where he holds the position of Professor of Biomedical Engineering & Design. He serves as Head of School and has a distinguished research profile spanning aerospace and biomedical engineering. His work bridges computational design, haemodynamics, and clinical innovation. Education: PhD in Mechanical Engineering, University of Cranfield MSc in Advanced Mechanical Engineering, Imperial College London PGCE in Mathematics and PE, University of Leeds BA Hons in Engineering Sciences and Economics, St John’s College, University of Oxford Prof Bressloff's research focuses on applying advanced computational and optimization methods to solve clinical problems in cardiovascular engineering. His work includes haemodynamic modeling , design of coronary stents and bioresorbable scaffolds , and innovative heart valve development . He pioneered the design of the ArterioSorb™ coronary scaffold via Arterius Ltd, a UK-based medical device company. His methodology, originally developed for aerospace design, enables systematic exploration of device performance under anatomical variation. The recent publications reflect a strong trend in computational biomechanics , medical device optimization , and surrogate-based design . These works integrate CFD , finite element analysis , and multi-objective optimization to improve clinical outcomes. Keywords across articles include Biomedical Engineering, Haemodynamics, and Computational Design, with subfields ranging from stent deployment mechanics to transcatheter valve performance. Scientific Awards: Leading Opinion Paper, Biomaterials (2011) Prof Bressloff has secured over £1.5 million in research funding from UKRI and industry partners over the past five years. He actively supervises postgraduate researchers, including Taskin Md Siham Sayeed, and fosters strong collaborations with clinicians and industrial partners. His leadership in the Rolls-Royce and BAE Systems University Technology Partnership advanced aerodynamic design methodologies before transitioning to impactful biomedical applications. He leads a research group focused on computational engineering and design , with emphasis on translating aerospace-inspired optimization into clinical solutions. The team works on next-generation cardiovascular devices, leveraging digital design and patient-specific modeling to address unmet needs in coronary and valvular disease treatment.

• Discrete Element Method
• Granular Materials
• Transport Engineering
• +۵ مورد دیگر

D.L. Schott serves as a Professor in the Department of Mechanical Engineering, Transport Engineering and Logistics at Delft University of Technology, specializing in computational modeling of granular systems and transport infrastructure. His research integrates Discrete Element Method (DEM) simulations with emerging AI techniques to solve complex engineering challenges in offshore energy, material handling, and maritime operations. His primary research focuses on particle-scale mechanics in industrial and marine environments, with significant contributions to scour protection systems for offshore wind foundations, hopper discharge dynamics, and metamaterial design. Recent work demonstrates a strategic shift toward AI-enhanced simulation frameworks, particularly using transfer learning for multi-component segregation modeling and dual-regime approaches for time consolidation phenomena. This reflects growing industry demand for computationally efficient solutions in bulk material processing and offshore construction. Professor Schott maintains active industry collaborations, evidenced by 19 datasets on hopper discharge mechanics and press coverage of innovative grab unloader designs with Nemag. His 12 supervised student projects and 26 recent activities—including 22 conference presentations in 2025—highlight sustained engagement in geotechnical and transport engineering communities. Current research trajectories emphasize resilient maritime systems and AI-driven optimization of industrial material flows.