جست‌وجوی استاد و پژوهشگر

Michael Havbro Faberمشاهده پروفایل

استاد

• Structural Integrity Management
• Structural Health Monitoring
• Offshore Wind Energy Systems
• +۴ مورد دیگر

Michael Havbro Faber is a Professor at the Department of the Built Environment within the Faculty of Engineering and Science at Aalborg University. His affiliations include the Risk, Resilience, Safety, and Sustainability of Systems Research Group and the Danish Centre for Risk and Safety Management . He holds leadership roles in multiple research projects and serves on the board of the Independent Research Fund Denmark | Technology and Production sciences . Education: Not explicitly detailed in the text. His research focuses on structural integrity management , risk-based inspection planning , and sustainability in infrastructure . Notable contributions include advancements in offshore wind energy systems , structural health monitoring , and decision analysis methodologies . Recent work emphasizes resilience optimization in energy networks and probabilistic safety frameworks. Key awards include the 2019 C. Allin Cornell CERRA Award and the 2012 Hojjat Adeli Award . Over 400 publications and 20+ projects highlight his prolific output, with recent articles addressing offshore wind turbine integrity, subsurface well reliability, and philosophical aspects of probability in engineering safety. Grants & Projects: STROWIN (2022–2024): Reliability-Based Structural Health Monitoring in Offshore Wind VALID (2020–2023): Wave Energy Converter Survivability CO2 Efficiency & Sustainability in Built Environments (2022–2022) Labs/Teams: Danish Centre for Risk and Safety Management, Risk-Based Integrity Management Group.

Jannie Sønderkær Nielsenمشاهده پروفایل

دانشیار

• Risk Management
• Offshore Wind Energy
• Structural Health Monitoring
• +۳ مورد دیگر

Jannie Sønderkær Nielsen is an Associate Professor at the Department of the Built Environment within the Faculty of Engineering and Science at Aalborg University. Her research focuses on risk-based decision making for offshore wind energy infrastructure, structural health monitoring, and probabilistic design methodologies. She leads the Risk, Resilience, Safety, and Sustainability of Systems Research Group and collaborates with the AI for the People initiative and Danish Centre for Risk and Safety Management. Educated at Aalborg University, she earned her PhD in 2013 with a thesis on Risikobaseret drift og vedligehold for havvindmøller (Risk-based operation and maintenance of offshore wind turbines). Her work integrates Bayesian networks, Markov decision processes, and advanced statistical methods to optimize inspection, maintenance, and operational strategies for offshore energy systems. Key research interests include probabilistic load analysis, life extension of wind turbine components, and quantifying the value of structural health monitoring. Her projects address challenges in submarine landslide hazards for offshore energy infrastructure and resilience assessment frameworks. Notable awards include Teacher of the Year at Aalborg University (2024) and multiple faculty-level teaching awards. She supervises PhD research in offshore engineering and has authored over 55 peer-reviewed publications across journals like Structural Safety and Energies . Her research activities span 14 funded projects including the PRO-SLIDE initiative for probabilistic decision frameworks addressing submarine landslide risks. She actively collaborates with industry partners on O&M optimization and participates in international initiatives like IEA Wind Task 42.

• Artificial Intelligence
• Automated Planning
• Machine Learning
• +۷ مورد دیگر

Dimitris Vrakas is an Assistant Professor at the Department of Informatics, School of Informatics, Aristotle University of Thessaloniki (AUTh). He holds a PhD in Intelligent Planning Systems and has conducted post-doctoral research focusing on Planning for the Semantic Web. His professional experience includes roles as a Lecturer (per Greek law 407/80), Informatics Instructor in multiple educational institutions, and IT consultant for medium-sized enterprises through the Go-online Project. Education: Bachelor of Science (1995–1999): Informatics, Aristotle University of Thessaloniki PhD (2000–2004): Intelligent Planning Systems, Dept. of Informatics, AUTh Postdoctoral Research (2005–2007): Planning for the Semantic Web, AUTh Research Interests: Automated Planning, Heuristic Functions, Intelligent Autonomous Systems, Machine Learning Applications (Energy Management, NILM), Semantic Web Services, and Smart Environment Technologies. His work bridges theoretical AI advancements with practical implementations in energy systems and emergency response. Publications: Over 45 papers (including 15 journal articles) and 5 book chapters, with a focus on energy disaggregation, evacuation algorithms, and semantic web composition. His work demonstrates expertise in applying advanced techniques like GANs for data generation and reinforcement learning for adaptive planning. Awards: 2025 ACM SIGMOD Test-of-Time Award for contributions to time-series clustering Advising & Grants: Active in guiding PhD candidates (as per recent calls) and coordinating projects like the IRIS RISK PARADIGM for industrial safety. His work involves developing smart university platforms for energy monitoring and collaborating on EU-funded initiatives. Labs & Teams: Member of the Intelligent Systems Lab at AUTh, contributing to projects like the BOnSAI ontology for smart buildings and the IRISPortal for risk management. He also coordinates the Hellenic Artificial Intelligence Society and chairs conferences like SETN 2016.

• Structural Dynamics
• Nonlinear Mechanical Joints
• Frictional Contact Interface Modeling
• +۵ مورد دیگر

Hassan Jalali is an Assistant Professor at Northumbria University's Mechanical and Construction Engineering Department. He specializes in structural dynamics, focusing on modeling nonlinear mechanical joints and frictional contact interfaces. His research aims to develop simplified yet accurate mathematical models to enhance dynamic analysis precision while reducing computational demands. Education: PhD in Mechanical Engineering (Awarded: 31 Aug 2007) Research Interests: Jalali's work revolves around structural dynamics, inverse vibration problems, finite element model improvement using experimental data, nonlinear joint modeling, uncertainty quantification, and rotating machinery dynamics. He emphasizes practical applications, such as optimizing bolted joint models to address real-world variability and computational inefficiencies. Publications: His recent work includes studies on harmonic-balance parameter estimation, equivalent models for nonlinear bolted joints, stochastic contact interface modeling, and surface roughness effects on joints. These contributions highlight advancements in nonlinear dynamics and model simplification. Awards/Grants: No specific awards or grants are listed in the provided text. Advising: Accepting PhD students but no listed advisees. Labs/Teams: No specific lab affiliations mentioned, but collaborations involve global research networks in structural dynamics and vibration analysis.

• Structural Health Monitoring
• Machine Learning
• Earthquake Engineering
• +۳ مورد دیگر

Dr. Rigoberto Burgueño is a Professor and Chair of the Department of Civil Engineering at Stony Brook University since 2018. Previously, he served as a Professor at Michigan State University's Department of Civil and Environmental Engineering. He earned a Ph.D. in Structural Engineering from the University of California, San Diego, and his work focuses on leveraging elastic instabilities for smart structures, AI-driven structural health monitoring (SHM), and energy dissipation systems. Research Highlights: Adaptive materials/structures, self-powered SHM using machine learning, earthquake engineering, and composite material characterization. Funding: National Science Foundation (NSF), Federal Highway Administration (FHWA), Michigan Department of Transportation (MDOT), Precast/Prestressed Concrete Institute (PCI). Leadership: Chair of Civil Engineering at Stony Brook, active member of the American Concrete Institute (ACI), editorial board member of the Journal of Composites for Construction . His publications focus on seismic performance, post-buckling behavior of cylindrical shells, self-powered sensors, and machine learning frameworks for damage identification. Collaborative efforts include developing pendulum shear walls and energy-efficient SHM systems for bridges and aircraft structures.

Ramesh B. Mallaمشاهده پروفایل

استاد

• Structural Mechanics
• Structural Dynamics
• Finite Element Analysis
• +۵ مورد دیگر

Ramesh B. Malla is a Professor in the School of Civil and Environmental Engineering at the University of Connecticut (UConn). He holds a Ph.D. from the University of Massachusetts Amherst (1986). His research focuses on theoretical and applied structural mechanics, with specialties in structural dynamics, finite element analysis, lunar and orbital structures, and planetary exploration systems. His work extends to railroad and highway bridge engineering, geomechanics, and renewable energy harvesting. Key research areas include: Lunar habitat design under extreme thermal and impact conditions Dynamic analysis of truss and frame structures Health monitoring of civil infrastructure using sensor systems Planetary drilling mechanisms for extraterrestrial exploration Railroad bridge vibration and failure analysis He leads projects funded by NASA and federal agencies, with a focus on resilient space habitats and advanced sensor technologies. His lab develops innovative solutions for extreme environment engineering challenges, including meteoroid impact shielding and regolith utilization. Recent work highlights include: Development of AI-driven wireless sensing systems for bridge integrity assessment Finite element modeling of lunar surface structures Experimental studies on granular material behavior for life-support systems Collaborative efforts involve academic-industry partnerships for space technology and sustainable infrastructure. His research has been published in top journals and presented at international conferences.

Sakdirat Kaewunruenمشاهده پروفایل

استاد

• Railway Engineering
• Structural Dynamics
• Infrastructure Resilience
• +۵ مورد دیگر

Dr. Sakdirat Kaewunruen is a Professor and Reader in Railway and Civil Engineering at the University of Birmingham, UK. He leads the Birmingham Centre for Railway Research and Education (BCRRE) and convenes the BEng/MEng Railway Engineering and MSc Structural Engineering programs. His roles include Chief Editor of Frontiers in Transportation and Transit Systems and member of EU-funded research initiatives like RISEN and S-CODE. Education: PhD in Civil Engineering (Structures/Railway Infrastructure), University of Wollongong, Australia MBA (Executive), Australian Graduate School of Management BEng (Hons) in Civil Engineering, Suranaree University of Technology Research Interests: Focuses on resilient transport infrastructure, train-track interaction dynamics, smart materials, risk management, and sustainability. His work integrates advanced modeling, sensor technologies, and circular economy principles to enhance railway systems' reliability and environmental impact. Awards: NSW Young Tall Poppy Science Award (Australia) Japan Society for the Promotion of Sciences Long-term Fellowship World Top 100 Most Cited Scientist in Civil Engineering (Stanford University) Grants & Projects: Secured over £15M in research funding, including EU H2020 and UK DfT projects. Leads initiatives like RISEN (Euro1.2M) and collaborates with MIT, University of Illinois, and Southwest Jiaotong University. Labs & Teams: Coordinates interdisciplinary teams in railway resilience, smart infrastructure, and sustainable urban systems. Active in ISO/BSI standards for railway sleepers and recycling of rolling stocks.

Mauro Corradoمشاهده پروفایل

استاد

• Civil and Structural Engineering
• Composite Material Design
• Computational Mechanics
• +۱۲ مورد دیگر

Mauro Corrado is a Full Professor at the Department of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, Italy. He is a member of the Interdepartmental Center SISCON – Safety of Infrastructures and Constructions and leads the GLASS T&AM research group focused on glass structures and advanced materials. His academic career includes a Marie Curie Intra-European Fellowship at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland (2014–2016). University: Politecnico di Torino Department: Department of Structural, Geotechnical and Building Engineering (DISEG) Academic Rank: Professor Email: mauro.corrado@polito.it His research interests include civil and structural engineering, composite material design and testing, computational mechanics, fracture mechanics, reinforced concrete, structural glass, and structural health monitoring. He applies multi-scale and multi-physics modeling to study failure mechanisms in concrete, composites, and photovoltaic modules. His work integrates experimental validation with advanced numerical simulations using cohesive zone models, phase field methods, and XFEM. The recent publications reflect a strong trend in structural glass, dynamic fracture, acoustic monitoring of concrete structures, and multi-scale modeling of material degradation. Key themes include the development of functional coatings for glass, computational modeling of fracture in heterogeneous materials, and structural health monitoring using acoustic event detection in prestressed concrete bridges. Scientific awards include: Marie Curie Intra-European Fellowship for Career Development (FP7-PEOPLE-2013-IEF) Fellow at École Polytechnique Fédérale de Lausanne (2014–2016) He has supervised several PhD students including Vida Bagherpour Aliabadi, Diogo Manuel Goncalves Dias, and Arturo Chao Correas. He has led research projects funded by PNRR, corporate partners, and competitive grants. His teaching includes Structural Glass Technology, Advanced Structural Mechanics, and Computational Mechanics. He is also involved in PhD programs at Politecnico di Torino and Università degli Studi di Catania. He is a member of the editorial boards of the International Journal of Structural Glass and Advanced Materials Research and Fracture and Structural Integrity, and actively collaborates with institutions such as TU Delft, EPFL, and IMT Lucca.

Andrea Rapisardaمشاهده پروفایل

استاد

• Complex Systems
• Statistical Mechanics
• Deterministic Chaos
• +۷ مورد دیگر

Andrea Rapisarda is a Full Professor of Theoretical Physics at the University of Catania, Italy, affiliated with the Department of Physics and Astronomy "Ettore Majorana" and INFN Catania. He is the coordinator of the PhD program in Complex Systems for Physical, Socio-economic and Life Sciences and serves as co-director of the International School on Complexity at the Ettore Majorana Foundation in Erice. Additionally, he is an External Faculty member at the Complexity Science Hub Vienna, reflecting his international academic engagement. His research spans Complex Systems, Statistical Mechanics, Deterministic Chaos, Nonlinear Dynamics, Complex Networks, and Agent-Based Models , with a strong emphasis on applications to socio-economic systems. He investigates the role of randomness, luck, and inefficiencies in hierarchical organizations, drawing from interdisciplinary methods in physics and computational modeling. His work has demonstrated how random strategies can outperform merit-based ones in organizational efficiency and democratic design. The 15 most recent publications highlight a consistent focus on modeling randomness in success, inequality, and decision-making across domains such as sports, economics, politics, and public health. His work integrates agent-based simulations, network analysis, and statistical physics to understand complex phenomena, often with policy-relevant implications. Ig Nobel Prize for Management (2010) Ig Nobel Prize for Economics (2022) Rapisarda is an active advisor and researcher, though specific student names are not listed in the provided texts. He has contributed to numerous editorial boards, including Physica A, Entropy, Frontiers in Physics (Social Physics) , and Complexity . He has also been involved in public outreach, policy discussions on democratic reform via sortition, and science communication through media interviews and public lectures. He leads research projects on improving organizational and societal systems using insights from complexity science.

• Artificial Intelligence
• Machine Learning
• Constraint Programming
• +۴ مورد دیگر

Siegfried Nijssen is a Professor at the Department of Computer Science within the Faculty of Engineering Science at KU Leuven. He is a core member of the Declarative Languages and Artificial Intelligence (DTAI) research group at the Arenberg campus and affiliated with Leuven.AI, the university-wide Institute for Artificial Intelligence. His academic position is designated as 'professor BOF', reflecting a specialized research-focused appointment. Nijssen's research centers on the integration of declarative programming paradigms with machine learning, particularly through constraint programming frameworks. Key focus areas include interpretable rule learning, neural-symbolic integration, and constraint-based optimization for combinatorial problems. His work bridges theoretical computer science with practical applications in bioacoustics, pandemic response modeling, and network analysis, emphasizing transparency and reliability in AI systems. Analysis of his 2021-2024 publications reveals a strong trajectory toward interpretable AI, with significant contributions like RL-Net (combining neural networks with rule-based reasoning) and novel approaches to NP-hard optimization using structured perceptrons. His research consistently targets the intersection of symbolic reasoning and statistical learning, addressing critical challenges in constraint imposition, model explainability, and stochastic optimization. Nijssen currently leads two major research initiatives: 'Declarative Languages for Imposing Constraints on Machine Learning Models' (2025-2027) and the long-term 'Declarative Programming for Machine Learning (DeclaLearn)' project (2025-2035), demonstrating sustained leadership and funding in his specialized domain. These projects extend his foundational work on constraint-based machine learning frameworks. Based at the DTAI research group, Nijssen contributes to KU Leuven's AI ecosystem through collaborative research in logic programming, constraint solving, and data mining. His work with Leuven.AI positions him at the forefront of institutional efforts to advance trustworthy and constraint-aware artificial intelligence systems.

• Structural Health Monitoring (SHM)
• Machine Learning
• Seismic Engineering
• +۷ مورد دیگر

Eloi Figueiredo is a Full Professor at the Faculty of Engineering of Lusófona University in Lisbon. His expertise lies in Structural Health Monitoring (SHM) , Machine Learning , and Bridge Management . He leads the Civil Research Group focused on sustainable infrastructure and serves as Associated Editor for the Structural Health Monitoring: An International Journal . Research Interests : SHM algorithms, climate change adaptation of bridges, damage identification via machine learning, finite element modeling. Projects : ClimaBridge Project (EEA Grant), Buildingadapt Project on climate resilience. Collaborations : European Union, United States, Brazil, UK, Norway institutions. Awards : EEA Grant for climate-bridge research. His 15 most recent publications address transfer learning , Bayesian calibration , and climate impact on structural integrity , with 100+ total SHM publications and 70+ opinion pieces promoting science. He has delivered international keynote speeches and developed tools like SHMTools and smartphone-based monitoring applications.

Wei Gaoمشاهده پروفایل

استاد

• Structural Engineering
• Mechanical Engineering
• Stochastic Analysis and Modelling
• +۶ مورد دیگر

Professor Wei Gao is a faculty member at the School of Civil and Environmental Engineering, University of New South Wales. His research focuses on integrating AI and machine learning with structural engineering, stochastic mechanics, composite materials, and infrastructure safety against natural hazards. He leads national research grants including ARC-funded projects on generative design of nondeterministic impacts and data-driven structural safety assessment. Research Interests: AI-driven structural analysis/design Computational uncertainty mechanics Composite materials and metamaterials Infrastructure safety under fire/natural hazards Engineered cementitious composites Grants & Projects: ARC Discovery Projects (e.g., DP240102559, DP210101353) ARC Linkage Projects (e.g., IH210100048, IH200100010) Focus areas: wildfire safety, composite material optimization, circular economy in construction Editorial Roles: Editor-in-Chief of Modelling: An International Journal Associate Editor of Engineering Structures Subject Editor of Applied Mathematical Modelling His work spans computational tools for structural assessment, smart material design, and probabilistic safety frameworks. Current projects address AI-assisted design, sustainable materials, and fire-resilient infrastructure systems.

Chris Wigginsمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Computational Biology
• +۱۲ مورد دیگر

Chris Wiggins is an Associate Professor of Applied Mathematics and Systems Biology at Columbia University, with affiliations in the Department of Statistics. He is based in the School of Engineering and Applied Science and conducts interdisciplinary research at the intersection of data science, machine learning, and biological systems. His work bridges computational modeling with applications in genomics, single-molecule biophysics, digital health, and disinformation studies. His research interests include: Data Science and its societal implications Machine learning for biological networks and gene regulation Bayesian and statistical inference in single-molecule experiments Privacy-aware data analysis and algorithmic ethics Modeling in digital health, including menstrual cycle prediction and cancer drug screening Information-theoretic approaches to network structure and dynamics The trends in his recent publications reveal a growing focus on the ethical and societal dimensions of data science, as seen in works like How Data Happened and An Agenda for Disinformation Research , while maintaining strong technical contributions in biomedical modeling, particularly in cancer and women’s health. His work combines rigorous statistical methods with real-world data challenges. Notable scientific contributions include foundational work on ARACNE for gene regulatory network inference, Bayesian modeling of single-molecule dynamics, and methodological advances in dose-response modeling. His publications span top journals in bioinformatics, computational biology, and data science. He has advised doctoral students in applied mathematics and computational biology, though specific names are not listed in the provided text. His research has been supported by interdisciplinary grants, particularly in systems biology and data science initiatives. He is actively involved in shaping the philosophy and pedagogy of data science as a field. Chris Wiggins is associated with research centers at Columbia that focus on applied statistics, probability, and data science. He contributes to seminars and educational programs that promote quantitative reasoning across disciplines, particularly in the context of modern data-driven challenges.

Pietro Borghesaniمشاهده پروفایل

دانشیار

• Asset Health Monitoring
• Vibration Sensing
• Signal Processing
• +۶ مورد دیگر

Associate Professor Pietro Borghesani is affiliated with the School of Engineering and the Department of Mechanical and Manufacturing Engineering at the University of New South Wales (UNSW). He holds an Associate Professor rank and focuses on asset health monitoring, vibration sensing, signal processing, and machine learning applications in mechanical systems. His research spans aerospace and rail industries, emphasizing condition-based maintenance optimization and fault diagnostics. Education: PhD in Mechanical Engineering (2013, Politecnico di Milano), postdoctoral work at Politecnico di Milano, and prior experience at McKinsey & Co. as a business analyst consultant. Research interests include: Health monitoring of rotating machinery Signal processing for diagnostics Machine learning in condition assessment Wear mechanisms in gears and bearings Integration of physics-based models with AI Publications highlight advancements in neural network explainability, vibration-based diagnostics, and cepstral analysis. His work bridges signal processing with tribology and mechanical systems. Grants include projects on railway track geometry monitoring (LP200100382), sugar industry maintenance (LP150100545), and aero-engine bearing health (LP180101161). He is an Editorial Board Member for Mechanical Systems and Signal Processing and a member of the International Society for Engineering Asset Management (ISEAM). Labs/Teams: Collaborations with Australian/international companies in aerospace and rail sectors; involvement in experimental setups for vibration and acoustic emission studies.

Jean-Marc Schwartzمشاهده پروفایل

مدرس ارشد

• Systems Biology
• Metabolic Networks
• Constraint-based Analysis
• +۵ مورد دیگر

Jean-Marc Schwartz is a Senior Lecturer in the Division of Evolution, Infection and Genomics at the Faculty of Biology, Medicine and Health, University of Manchester. He has held this position since 2016, having previously served as Lecturer (2011-2016) and Physical/Life Sciences Interface Independent Research Fellow (2006-2011) at the University of Manchester. He also serves as an External Examiner at the University of Glasgow (2020-2027) and is affiliated with multiple research institutes including the Digital Futures Institute for Data Science and AI, Lydia Becker Institute, and Christabel Pankhurst Institute. His educational background includes: PhD in Computer Engineering from Laval University (Canada), 1999-2003 Research Associate at Max-Planck Institute for Biochemistry (Germany), 1995-1999 Engineering degree from École Centrale Paris (France), 1991-1994 Dr. Schwartz specializes in Systems Biology, with research focusing on constraint-based analysis of metabolic networks, integrative and dynamical modeling of biological systems, and systems pharmacology. His work bridges computational approaches with experimental biology to understand complex cellular processes through chemical and physical models. He develops innovative methodologies for data integration, network analysis, model construction and simulation to advance global understanding of biological functions beyond traditional reductionist approaches. His recent publications demonstrate strong trends in metabolic modeling, biomanufacturing optimization, and computational approaches to cellular dynamics. His work increasingly focuses on applying systems biology to pharmaceutical development and bioproduction challenges, particularly with Chinese Hamster Ovary (CHO) cells for therapeutic protein production, showing significant contributions to biomanufacturing processes and metabolic engineering. Dr. Schwartz is actively involved in research funding and collaboration: Co-Investigator on "Optimisation of CHO for Biotherapeutic Manufacture" (2021-2026) Member of the Digital Futures Institute for Data Science and AI Member of the Lydia Becker Institute Member of the Christabel Pankhurst Institute With 37 supervised students and 124 research outputs including 92 articles, 11 conference contributions, and 6 review articles, Dr. Schwartz maintains an active research program that bridges computational modeling with experimental biology. His work contributes to UN Sustainable Development Goals, particularly in health and well-being areas through systems pharmacology and drug metabolism research.