Find Academic Supervisors

Stavros SakellariouView profile

Research Fellow

• Wildfire Management
• Drought Monitoring
• Flood Management
• +7 more

Dr. Stavros Sakellariou serves as a Research Fellow (Marie Curie) in Civil and Environmental Engineering at Brunel University London's College of Engineering, Design and Physical Sciences. His expertise centers on geospatial analysis of natural disasters, particularly wildfires and droughts, leveraging remote sensing and AI for risk management. His educational background includes: PhD in Environmental Hazards Management and Spatial Planning, University of Thessaly (2016) MSc in Geospatial Technologies (Erasmus Mundus Joint Degree), 2016 MSc in Spatial Planning & Development, University of Thessaly (2010) MEng in Spatial Planning and Regional Development, University of Thessaly (2008) Dr. Sakellariou's research integrates geographic information systems , remote sensing , and artificial intelligence to address wildfire dynamics, drought/flood modeling, and climate change impacts. His work emphasizes spatial decision support systems for disaster prevention and resilience engineering in Mediterranean ecosystems, with significant contributions to early warning frameworks and urban-wildland interface safety. Recent publications reveal a concentrated focus on wildfire risk assessment (40% of works), drought monitoring (30%), and climate adaptation modeling (20%), predominantly utilizing satellite data fusion and spatiotemporal analytics. Key innovations include optimization methods for firefighting resource deployment and vulnerability assessments of agroecosystems. His accolades include: Marie Skłodowska-Curie Postdoctoral Fellowship (FIREWISE project, 2024) 7 national/international scholarships and awards With 11.5 years of teaching experience across undergraduate and master's programs at Greek universities, he has contributed to 8 multi-partner research projects on disaster management. His service includes reviewing for 25+ journals and guest-editing special issues on natural hazards. Current work centers on the FIREWISE project's integrated framework for proactive wildfire resilience.

• Sediment Transport
• River Morphology
• Microplastic Pollution
• +2 more

Dr. BARANYA Sándor is an Associate Professor and Head of the Department of Hydraulic and Water Resources Engineering at the Budapest University of Technology and Economics, Faculty of Civil Engineering. He holds key administrative roles, including Faculty Council Representative, Chairman of the Economic Committee, and Erasmus Committee Member. His office is located in Building K, Room MF 19, and he can be contacted via email or phone. His research spans sediment transport , river morphology , microplastic pollution , and computational fluid dynamics (CFD) . He employs innovative methods such as acoustic mapping, machine learning, remote sensing (Sentinel-2, Landsat), and numerical modeling to study large river systems, particularly the Danube. His work addresses ecological impacts, including thermal pollution from power plants and microplastic distribution. Recent publications (2023–2025) focus on: Sediment-microplastic dynamics using integrated field-data and AI Historical river regulation impacts CFD-based restoration strategies Advanced monitoring techniques (e.g., acoustic velocimetry, deep learning for riverbed analysis) He teaches courses including Hydraulics , Hydromorphology , and Water Utilisation & Damage Prevention , and mentors students in TDK (Research Society) projects on sediment transport, river habitats, and microplastics. He participates in EU initiatives like DanubeSediment for transnational sediment management.

• Construction Engineering
• Structural Health Monitoring
• Concrete Structures
• +5 more

Muhammad Usman Hanif is an Assistant Professor at the Department of Technology and Innovation within the Faculty of Engineering at the University of Southern Denmark (SDU). His research focuses on structural health monitoring , concrete durability , and damage assessment in civil engineering systems. Research Highlights Advanced signal processing for bridge damage detection Acoustic Emission techniques in CFRP-concrete debonding Machine learning applications in concrete durability prediction Augmented Reality integration with bridge monitoring systems Recent Publications (2023-2025) 2025: Machine learning in concrete durability 2024: Novel ΔT mapping for debonding detection 2023: FBG sensors in CFRP retrofitted beams 2022: MEMS accelerometers for damage assessment Teaching Activities Finite Element Method (2025) Non-linear Finite Element Method (2025) Advanced Finite Element Analysis (2024)

Paulina JaramilloView profile

Professor

• Energy Systems
• Climate Change Mitigation
• Sustainable Energy Development
• +5 more

Paulina Jaramillo is a Professor in the Department of Engineering and Public Policy at Carnegie Mellon University (CMU), where she co-directs the Open Energy Outlook Initiative. She is a fellow of the Scott Institute for Energy Innovation and Research at CMU and a research affiliate of the Kigali Collaborative Research Center. Jaramillo also holds a courtesy appointment in CMU Africa. Her research focuses on the social, economic, and environmental implications of energy system transitions, with a particular emphasis on climate change mitigation, sustainable energy access in the Global South, and equitable energy planning. Ph.D., Civil and Environmental Engineering, CMU (2007) M.S., Civil and Environmental Engineering, CMU (2004) B.S., Civil and Environmental Engineering, Florida International University (2003) Her recent work explores diverse decarbonization pathways, hydrogen's role in industrial sectors, and geospatial assessments for climate resilience. Jaramillo has received prestigious awards including the Andrew Carnegie Fellowship (2020), ELATES Fellowship (2023), and leadership roles in the IPCC’s 6th Assessment Report. Andrew Carnegie Fellow (2020) Coordinating Lead Author, IPCC 6th Assessment Report (2019-2022) Member, National Academies Roundtable (2023-2024) ELATES Fellow (2023-2024) Jaramillo leads interdisciplinary research projects, including a $1.5 million NSF-funded INDABA partnership for industrial decarbonization and AfriqAir initiatives for cleaner air in Africa. She collaborates with CMU’s Kigali Collaborative Research Center and Scott Institute, emphasizing stakeholder engagement and local conditions in energy system development.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +5 more

Dr. Mubashir Ahmad is a researcher at the School of Physics, Engineering & Computer Science, University of Hertfordshire. His work focuses on applying computational methods to solve real-world challenges in healthcare, infrastructure, and environmental sustainability. Research Focus Dr. Ahmad's expertise spans: Core AI/ML Techniques : Deep learning, machine learning algorithms, computer vision systems Application Domains : Medical imaging diagnostics, pollution modeling, infrastructure inspection automation, and climate impact analysis Emerging Areas : Robotic systems integration and big data fusion for industrial applications Project Engagement Deep Learning-powered Apple Disease Detection System (ADDS) (2024-2025): Serving as Co-Investigator to develop AI tools for agricultural disease identification in collaboration with UK farmers. Publication Trends His recent publications demonstrate consistent focus on operationalizing deep learning across diverse sectors. Dominant themes include automated infrastructure inspection (sewer systems), medical image analysis (chest radiographs), and environmental forecasting (air quality/pollution). This reflects a cross-disciplinary approach bridging computer science with civil engineering, healthcare, and environmental science.

• Data science
• Machine learning
• Artificial intelligence
• +10 more

Dr. Xiaobai (Bob) Li is a Professor in the Department of Operations and Information Systems at the Manning School of Business, University of Massachusetts Lowell. His work bridges data science, machine learning, and business analytics with specialized focus on data privacy and information economics. Ph.D. in Management Science, University of South Carolina MBA, University of New Hampshire BS in Civil Engineering, Chongqing University Research Interests span data science, machine learning, business analytics, and data privacy. His work explores Privacy-preserving analytics Healthcare data anonymization Information economics Optimization techniques Big Data applications Text analytics with particular emphasis on balancing data utility and privacy protection. Scientific Awards include: Manning Research Excellence Award (2023) Best Associate Editor Award (2021) Best Paper Awards at NEAIS, WITS, and ICIS Teaching Excellence Awards Grants from NIH and NSF supported his work on Consumer Electronic Privacy Protection and New Technology to Preserve Patient Privacy . He has served as Associate Editor for journals like Information Systems Research and Decision Support Systems .

• Environmental Health Engineering
• Risk Analysis
• Resilient Systems
• +10 more

Mark Edward Borsuk is the James L. and Elizabeth M. Vincent Professor in the Department of Civil and Environmental Engineering at Duke University’s Pratt School of Engineering. He leads the Borsuk Lab, which specializes in interdisciplinary modeling of coupled social, environmental, and technical systems. His research spans climate change, ecosystem services, water resources, land use, and environmental health, using advanced methods such as Bayesian networks, agent-based modeling, game theory, and risk analysis. He co-directs the Center on Risk within Duke’s Science & Society Initiative and is an Associate of the Duke Initiative for Science & Society. B.S.E. in Civil Engineering and Operations Research, Princeton University, 1995 M.S. in Statistics and Decision Sciences, Duke University, 2001 Ph.D. in Environmental Science and Policy, Duke University, 2001 Postdoctoral Training, EAWAG (Swiss Federal Institute for Aquatic Science and Technology), Systems Analysis, Integrated Assessment, and Modelling (SIAM) Dr. Borsuk’s research focuses on integrating scientific data across disciplines to support decision-making under uncertainty. He is a leading expert in Bayesian network modeling applied to environmental and human health regulation. His work combines risk analysis, game theory, and agent-based modeling to assess climate change and environmental policy. He has developed novel frameworks for valuing ecosystem services, modeling landowner behavior, and assessing geoengineering risks. His lab emphasizes interdisciplinary collaboration, stakeholder engagement, and quantitative decision support. His recent publications reflect a strong trend toward integrating machine learning, causal inference, and spatial modeling into environmental assessment. Topics include solar radiation modification governance, land-use policy forecasting, invasive species impacts, and urban green space valuation. His work increasingly leverages big data (e.g., Zillow, remote sensing) and probabilistic programming to enhance model transparency and predictive accuracy. Chauncey Starr Distinguished Young Risk Analyst Award, Society for Risk Analysis, 2013 Early Career Research Excellence Award, International Environmental Modelling and Software Society, 2008 Earl I. Brown Outstanding Civil Engineering Faculty Award, Duke University, 2018 Best Paper, Integrated Environmental Assessment and Management Journal, 2012 Excellence in Mentoring Award, Dartmouth College Postdoctoral Association, 2010 Best Paper in Integrated Modelling, Environmental Modelling & Software Journal, 2008 Dr. Borsuk has been a principal investigator on grants from NSF, EPA, NIH, NIEHS, and USFS. He mentors a diverse group of graduate students and postdoctoral fellows, including Kim Bourne, Jon Holt, Chris Krapu, and Ryan Calder. He teaches courses such as Risk and Resilience Engineering, Engineering Economics, and Independent Study in Civil and Environmental Engineering. He is actively involved in advising and curriculum development through the Bass Connections Energy & Environment Research Team. He leads the Borsuk Lab, a dynamic research group focused on systems, risk, and decision analysis. The lab is a key contributor to the Bridge Collaborative—a partnership between Duke, The Nature Conservancy, IFPRI, and PATH—where it develops quantitative models to support cross-sectoral decision-making. The lab also investigates landowner decision-making in New England forests and the governance of solar geoengineering, using agent-based and deliberative modeling approaches.

Da ChenView profile

Research Fellow

• Composite Structures
• Graphene Reinforcement
• Mechanical Engineering
• +6 more

Dr. Da Chen is an Honorary Research Fellow at the School of Civil Engineering, The University of Queensland. His research focuses on composite structures, material mechanics, and structural analysis with applications in mechanical and civil engineering. Key Research Areas: Functionally graded porous materials, graphene reinforcement, multiscale modeling, thermal buckling, vibration analysis, and additive manufacturing. Publications: 28 journal articles, 4 book chapters, and 4 conference papers since 2014. Recent work includes machine learning applications for structural analysis and inverse design of porous systems. Email: d.chen@uq.edu.au

Hakim Al-SamouiView profile

Professor

• Concrete Technology
• Two-stage Concrete Pouring
• Concrete Durability
• +5 more

Professor Hakim Al-Samoui is a distinguished faculty member in the Department of Civil Engineering at the Faculty of Engineering, University of Tripoli. He has served at the university since 2003, obtaining the rank of Professor in 2005 and receiving a promotion to job grade 16 in 2022 (effective from January 1, 2017). His academic career spans over three decades with significant contributions to concrete technology research and education. Bachelor's degree from University of Tripoli (1988) Master's degree from University of Gdansk, Poland (1990) PhD from University of Gdansk, Poland (1996) Worked at University of Gdansk (1996-2002) Professor Al-Samoui specializes in concrete technology with particular expertise in two-stage concrete pouring, concrete durability, fabric formwork, concrete recycling, pozzolanic materials, and concrete mix design. His research bridges theoretical advancements with practical applications in construction engineering. He has published over eighty research papers in distinguished journals and presented at numerous international conferences worldwide, establishing himself as a leading expert in specialized concrete technologies. His recent publications demonstrate a clear trajectory toward sustainable construction materials, advanced concrete technologies, and computational approaches to material science. The research spans traditional concrete innovations while increasingly focusing on eco-friendly alternatives, waste material utilization, and cutting-edge technologies like 3D printing and machine learning applications in construction materials. Scientific Recognition: Fellowship from American Concrete Institute (2020) - first Libyan to receive this honor Active member of American Concrete Institute committees since approximately 1999 External evaluator for leading journals including Construction and Building Materials, American Concrete Institute publications, and American Society of Civil Engineering journals Professor Al-Samoui has supervised six Master's theses at University of Tripoli and served as advisor for graduate students from Gdansk Technical University (Poland), Tehran University (Iran), and Dublin Institute of Technology (Ireland). He has also acted as an external examiner for numerous Master's and PhD theses across multiple universities in Libya, Lebanon, Iraq, and India. His extensive international collaborations include research visits to Columbia University, University of South Carolina, Munich University, and Gdansk University of Technology. Through his active participation in international conferences across more than 20 countries and his role on scientific committees, Professor Al-Samoui maintains a robust research network that spans the global concrete technology community. His recent activities in 2023-2024 demonstrate continued research productivity and academic engagement.

• Computational Solid Mechanics
• Experimental Solid Mechanics
• Structural Optimization
• +4 more

Miguel Costas Piñó serves as Associate Professor in the Department of Structural Engineering at the Norwegian University of Science and Technology (NTNU), where he has been faculty since 2017 and was promoted to Associate Professor in 2021. His research bridges computational mechanics with practical engineering applications in structural safety and material behavior. Education PhD in Civil Engineering, University of A Coruña (2016), thesis: "Crashworthiness analysis and design optimization of hybrid impact energy absorbers" (awarded Cum Laude, International Distinctions, and Extraordinary Doctorate Award) Master of Science in Civil Engineering, University of A Coruña (2011), specialization in Structural Engineering Research Focus Dr. Costas's work centers on computational and experimental solid mechanics , with particular expertise in metal plasticity , aluminium structures , and crashworthiness under extreme loading conditions. His laboratory investigates ballistic penetration phenomena and structural optimization for energy-absorbing systems, utilizing advanced finite element methods and physical testing protocols. Current projects integrate machine learning with traditional mechanics to model complex failure modes in multi-material systems. Publication Trends His 2020-2025 publications reveal three dominant trajectories: (1) ballistic resistance of additively manufactured metals for defense applications, (2) crash safety of electric vehicle battery systems, and (3) advanced modeling of mechanical joints. These works consistently combine experimental validation with computational innovation, particularly in neural network applications for large-scale structural simulations. Awards Cum Laude Doctorate International Distinctions (PhD) Extraordinary Doctorate Award Professional Activities Dr. Costas actively collaborates with industry partners on structural safety projects and serves as principal investigator for multiple NTNU research initiatives. His teaching includes graduate courses in materials mechanics, and he maintains dual professional engagement as a classical pianist with Norway's collaborative music community. Current work focuses on AI-driven crash simulation frameworks and next-generation armor materials.

• Supply Chain Analytics
• Logistics Management
• Operations Research
• +5 more

Professor Ying Xie is a leading academic at Cranfield University's School of Management, holding the position of Professor of Supply Chain Analytics. Her work bridges engineering, data science, and business management across diverse sectors including maritime logistics, healthcare, and disaster response. Her educational background includes a BEng, MSc, and PhD in Management Science from Coventry University. Key research interests focus on supply chain digitalization and decarbonization , renewable energy supply chain configuration , and AI applications in operational resilience . Her work consistently integrates machine learning with real-world business challenges, particularly in port management and sustainability. Recent publications reveal strong trends in maritime decarbonization (25%), AI-driven supply chain optimization (30%), and healthcare systems resilience (20%). Her research increasingly addresses climate adaptation through circular economy models and clean energy transitions. Principal Fellow of the Higher Education Academy (HEA) Expert reviewer for UKRI MRC, British Academy, and EU Horizon Professor Xie has supervised numerous PhD completions and secured two dedicated scholarships for port management and digital twin research. Her £20m+ grant portfolio includes major projects with EPSRC (£7.6m Net Zero Aviation CDT), Innovate UK (£174k Sustainability Platform), and EU Horizon (£5m MEDiate disaster management). She actively collaborates with the Port of Dover, Ocado Technology, and NHS England. She leads the Co-Innovation Group in the UK National Clean Maritime Research Hub and directs academic activities for the EPSRC Centre for Doctoral Training in Net Zero Aviation, shaping national maritime strategy through the Department for Transport's 'UK Ports the Future' report.

• Computer Vision
• Pattern Recognition
• Machine Learning
• +7 more

Prof. Dr.-Ing. Joachim Denzler is a Professor leading the Chair for Digital Image Processing (Lehrstuhl für Digitale Bildverarbeitung) at Friedrich-Schiller-Universität Jena. His research spans computer vision, machine learning, medical imaging, and remote sensing applications across diverse domains including biomedical analysis, environmental monitoring, and facial recognition systems. His research interests focus on explainable AI, causal discovery, physics-informed neural networks, and bias mitigation in deep learning models. He has made significant contributions to fine-grained classification, concept activation vectors, and privacy-preserving techniques in facial recognition. His work often bridges theoretical computer vision with practical applications in medical diagnostics, environmental science, and infrastructure monitoring. His recent publications demonstrate a strong trend toward causal discovery methods, physics-informed neural networks, and addressing bias in machine learning systems. His work spans both theoretical advancements in model interpretability and practical applications in medical imaging, environmental monitoring, and infrastructure safety. The interdisciplinary nature of his research is evident from collaborations with ecologists, neuroscientists, and civil engineers. Best Paper Award at International Conference on Pattern Recognition (ICPR) 2024 Prof. Denzler actively mentors numerous PhD students and postdoctoral researchers, with frequent co-authors including Maha Shadaydeh, Niklas Penzel, Gideon Stein, and Tim Büchner appearing as first authors on significant publications. His laboratory has secured research funding across multiple domains including medical imaging, environmental monitoring, and AI safety. His work on CausalRivers represents a major contribution to benchmarking causal discovery methods with real-world time series data. The Chair for Digital Image Processing maintains strong industry and clinical partnerships, particularly in medical imaging applications for facial palsy analysis and neonatal care. Prof. Denzler's team has developed novel techniques for dam deformation monitoring using remote sensing data and created advanced methods for analyzing plant diversity effects on ecosystem functioning.

Jun YangView profile

Senior Lecturer

• Chinese linguistics
• second language acquisition
• discourse analysis
• +2 more

Jun Yang is a Senior Lecturer in Chinese Language at the Department of East Asian Languages and Civilizations, University of Chicago. He serves as Director of the Chinese Language Program and focuses on pedagogy, linguistics, and language evaluation. His work bridges theoretical and applied research in language acquisition and teaching methodologies. University: University of Chicago School: East Asian Languages and Civilizations Role: Director of Chinese Language Program His research interests span Chinese linguistics , second language acquisition , discourse analysis , and Chinese language pedagogy . These areas are reflected in his leadership and instructional strategies within the Chinese language curriculum. Jun Yang’s publication record includes 14 recent articles (2017–2022) focused on optimizing database-backed web applications. Key themes involve automated code refactoring , schema management , reinforcement learning for data pipelines , and performance bug detection . These works emphasize tools for improving software reliability and efficiency in distributed systems and IDE environments. Jun Yang holds a Ph.D. in Second Language Acquisition and Teaching, underscoring his expertise in language education. His email address is yangj@uchicago.edu , and he is based at Classics 416, 1010 E 59th St, Chicago, IL 60637.

• Digital Twin
• Augmented Reality
• Virtual Reality
• +6 more

Dr. Jiaxin Ling serves as a Research Fellow in Digital Twin and AR/VR for Heritage Buildings at The Bartlett School of Sustainable Construction, University College London. His work bridges digital innovation with sustainable construction practices, focusing on heritage preservation in complex urban environments. Academic background includes: PhD in Civil Engineering, Tongji University (2018-2024), recognized as an Outstanding Graduate of Shanghai Bachelor's in Civil Engineering, Wuhan University (2014-2018) His research spans Digital Twin for tunnels/buildings, Building Information Modeling (BIM) , and AI/ML applications in construction engineering. He pioneers AR/VR solutions for fire safety, tunnel lighting optimization, and intelligent infrastructure, directly supporting UN Sustainable Development Goals 11 (Sustainable Cities) and 9 (Industry Innovation). Analysis of recent publications reveals a cohesive trajectory in applying digital twin technology to solve critical challenges in tunnel safety, energy efficiency, and real-time construction monitoring through hybrid knowledge-data approaches. Key recognition: Outstanding Graduate of Shanghai (top doctoral honor) Dr. Ling contributes to major research initiatives funded by the European Union Horizon Programme and National Natural Science Foundation of China (NSFC) , collaborating across international teams to advance sustainable construction methodologies. His work operates within UCL's interdisciplinary research ecosystem, focusing on heritage building preservation through cutting-edge digital workflows and sensor-integrated monitoring systems.

Yuanchang XieView profile

Professor

• Transportation Engineering
• Traffic safety
• Traffic operations and simulation
• +5 more

Yuanchang Xie serves as Professor in Civil and Environmental Engineering at UMass Lowell's Francis College of Engineering, where he leads research in the Center for Smart Cyber-Physical Systems. His work integrates computational methods with transportation infrastructure analysis, focusing on safety-critical applications through federal partnerships. Dr. Xie earned his Ph.D. in Civil Engineering from Texas A&M University (2007), preceded by M.S. and B.S. degrees in Transportation Engineering from Southeast University, China (2003, 2000). His academic foundation supports interdisciplinary research bridging civil engineering and cyber-physical systems. Research centers on traffic safety, intelligent transportation systems, and logistics optimization. He pioneers AI-driven approaches for crash prediction, connected vehicle operations, and infrastructure monitoring, emphasizing real-world implementation through partnerships with USDOT and state agencies. Current work explores multimodal data fusion for safety analytics in mixed-autonomy environments. Recent publications (2024-2025) reveal accelerating focus on deep learning applications: crosswalk detection via drone imagery, trajectory prediction in mixed traffic, and real-time work zone safety monitoring. This evolution demonstrates strategic alignment with emerging transportation technologies while maintaining core safety objectives. No scientific awards were explicitly documented in source materials. Dr. Xie has secured continuous funding as Principal Investigator through NSF, USDOT, DOE, and USDA programs. Key projects include Connected Vehicles: Toward the Understanding of "Firm Science" (NSF), Center of Multi-Scale Sensing Technologies (USDOT), and nuclear evacuation modeling for rural communities (USDA). His grants consistently address infrastructure resilience through cyber-physical integration. He directs research activities within UMass Lowell's Center for Smart Cyber-Physical Systems, which develops sensor networks and computational models for transportation infrastructure monitoring. The center's work on drone-based inspection systems and emergency response logistics demonstrates practical applications of his theoretical frameworks.