Find Academic Supervisors

• Structural Engineering
• Structural Mechanics
• Progressive Collapse
• +16 more

Valerio De Biagi is an Associate Professor in the Department of Structural, Geotechnical and Building Engineering (DISEG) at the Polytechnic University of Turin, Italy. He is a member of the Interdepartmental Center SISCON (Safety of Infrastructures and Constructions) and serves as Vice Coordinator of the PhD program in Civil and Environmental Engineering. He also acts as the Partnership Agreement Coordinator with STRADA DEI PARCHI, reflecting his strong engagement with industry and infrastructure safety. Research Interests: Structural robustness and progressive collapse Natural hazards (rockfalls, avalanches, debris flows) Structural health monitoring and damage modeling Reliability and risk assessment of civil infrastructure Mechanics of granular materials (snow) His recent research output shows a strong trend toward probabilistic risk assessment, experimental and numerical modeling of impact events, and the development of resilient structural systems. He frequently applies advanced computational methods, including finite element analysis and generative AI, to simulate extreme loading scenarios and infrastructure response. Scientific Roles: Effective Member, SISCO - Italian Society of Building Science (2019–present) Guest Editor, Applied Sciences (2020–present) Evaluator for Swiss National Science Foundation (MINT 2023, COST 2022) Advising and Grants: He actively supervises multiple PhD students across several cycles and leads numerous funded research projects, including competitive national grants (PRIN), PNRR initiatives, and commercial research contracts with public infrastructure agencies. His projects focus on structural safety assessment, development of monitoring protocols, and risk mitigation strategies for bridges, tunnels, and rockfall-prone areas. Labs and Teams: He leads research teams focused on structural complexity, natural hazard interaction, and granular material mechanics. His work involves experimental campaigns, field surveys, and back-analyses of real-world structural failures.

Giancarlo ManziView profile

Associate Professor

• Machine Learning
• Multivariate Statistics
• Bayesian Statistics
• +5 more

Giancarlo Manzi is an Associate Professor of Statistics at the Department of Methods and Models for Economics, Territory, and Finance, University of Rome La Sapienza. He holds a PhD from the University of Milan-Bicocca and conducted thesis research at the University of Toronto. His career includes roles at the Medical Research Council Biostatistics Unit in Cambridge and the University of Milan. University of Rome La Sapienza (Current) Medical Research Council Biostatistics Unit (Former Researcher) University of Milan (Former Researcher and Associate Professor) University of Milan-Bicocca (PhD) University of Toronto (Thesis collaboration) His research spans Machine Learning , Bayesian Statistics , and Smart Mobility , with a focus on Covid-19 analytics , data visualization , and epidemiological modeling . He integrates Multivariate Statistics with Public Health to address complex challenges in health systems and urban environments. The 15 most recent publications highlight expertise in quantile regression , Bayesian networks , time-series analysis , and smart mobility optimization . His methodological contributions include wavelet analysis, cross-correlation models, and SIRD modeling frameworks applied to pandemic dynamics and bike-sharing systems. Scientific awards and honors are not explicitly mentioned in the provided texts. Giancarlo Manzi has taught at the University of Verona, Catholic University of the Sacred Heart, and Universidad Carlos III in Madrid, maintaining strong ties with Italy's academic institutions.

Colin G. WalshView profile

Associate Professor

• Predictive Analytics
• Clinical Informatics
• Machine Learning
• +7 more

Dr. Colin G. Walsh is an Associate Professor in the Department of Biomedical Informatics, Medicine, and Psychiatry at Vanderbilt University School of Medicine. He is a practicing internist and clinical informatician with a research focus on predictive analytics, machine learning, and natural language processing applied to mental health, suicide risk, and value-based care. He leads the Walsh Lab, mentoring trainees from diverse backgrounds. Education: Undergraduate: Mechanical Engineering, Princeton University Medical Degree: University of Chicago Residency & Chief Residency: Internal Medicine, Columbia University Medical Center Fellowship: Biomedical Informatics, Columbia University (NLM-funded) Dr. Walsh's research centers on developing clinically grounded predictive models using structured and unstructured clinical data. His work includes machine learning for mental health, utilization optimization, and value-based healthcare analytics. He focuses on translating data science into actionable clinical tools for suicide screening, risk prediction, and quality improvement. His recent publications demonstrate a strong trend in applying NLP and machine learning to EHR data for suicide risk prediction, bipolar disorder modeling, and healthcare utilization. His work spans high-impact journals in psychiatry, informatics, and genetics, often involving multi-site collaborations and real-world validation. Scientific Awards and Honors: Fellow, American College of Medical Informatics (FACMI) Fellow, American Medical Informatics Association (FAMIA) Fellow, International Academy of Health Sciences Informatics (FIAHSI) Dr. Walsh actively mentors trainees and leads projects involving NLP, predictive modeling, and clinical decision support. His lab focuses on ethical AI, algorithmic bias, and stakeholder engagement in model development. He has no indication of part-time status, retirement, or former staff designation.

Giuseppe QuarantaView profile

Associate Professor

• Structural Monitoring and Control
• Sensing systems for structural monitoring
• Dynamic identification
• +9 more

Giuseppe Quaranta is an Associate Professor in the Department of Structural and Geotechnical Engineering at Sapienza University of Rome, Faculty of Civil and Industrial Engineering. He is actively engaged in teaching Construction Techniques and Construction Process Management, and maintains regular office hours for students. His research focuses on structural monitoring, dynamic identification, seismic protection systems, and AI applications in civil engineering. Department: Department of Structural and Geotechnical Engineering Faculty: Faculty of Civil and Industrial Engineering Institution: Sapienza University of Rome Email: giuseppe.quaranta@uniroma1.it Dr. Quaranta's research interests span structural monitoring and control, sensing systems, dynamic identification, diagnostics of civil structures, passive vibration control devices, structural concrete, and the analysis and design of reinforced concrete and composite steel-concrete structures. His work integrates computational modeling, experimental validation, and data-driven approaches, particularly using artificial intelligence and machine learning for structural health monitoring and seismic assessment. He investigates energy harvesting using piezoelectric materials for powering wireless sensors in smart infrastructure. The recent publications of Dr. Quaranta demonstrate a strong trend toward energy-based seismic engineering, machine learning for structural performance prediction, nonlinear dynamics of isolation systems, and advanced modal identification techniques. His work combines theoretical modeling, numerical simulation, and experimental testing, with applications to bridges, buildings, and historical structures like the Leaning Tower of Pisa and the Colosseum. Dr. Quaranta is involved in the research project titled "Smart technologies and decision support tools for the assessment of deteriorating reinforced concrete infrastructures in seismic areas at territorial scale," which aligns with his expertise in corrosion hazard mapping, seismic resilience, and smart monitoring systems. While student advising is implied through research collaboration, specific names of advisees are not listed in the provided materials. Smart technologies and decision support tools for the assessment of deteriorating reinforced concrete infrastructures in seismic areas at territorial scale His research team conducts experimental and computational studies on structural systems, including dynamic monitoring of iconic structures such as the Leaning Tower of Pisa and the Colosseum. The lab focuses on developing and applying advanced signal processing techniques, nonlinear system identification methods, and AI-driven tools for structural health monitoring and seismic risk assessment.

• Earthquake Engineering
• Seismic Hazard Analysis
• Structural Engineering
• +2 more

Dr. Ebru Harmandar is an Associate Professor at the Department of Civil Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University. She holds a Doctorate in Earthquake Engineering from Boğaziçi University (2009) and has contributed extensively to earthquake hazard assessment, structural response analysis, and ground motion modeling. Her research focuses on seismic resilience, spatial coherency of ground motions, and infrastructure risk mitigation. Key research areas: Earthquake Engineering, Seismic Hazard Analysis, Structural Engineering Major projects: EMME ground-motion logic tree, Istanbul Rapid Response Network Notable students: Soukaina Mellouk (2021), Hüssam-Almukdad (2022) Her recent work includes improving seismic resilience indices for school buildings (2024) and analyzing multi-point earthquake effects on bridges. She has served as an academic editor for journals like Soil Dynamics and Earthquake Engineering and received the 2016 Yollar Türk Milli Komitesi award.

• Data management and data science
• Artificial intelligence
• Distributed computing and systems software
• +6 more

Thomas C Henderson is a tenured Professor at the School of Computing within the College of Engineering at the University of Utah. His research focuses on autonomous systems , computational models , and intelligent machine systems , with specific interests in biosystem simulation, distributed systems, and adaptive algorithms. He has contributed to Bayesian sensor networks , UAS traffic management , and probabilistic logic frameworks for intelligent agents. Current academic appointments since 1989 Adjunct roles in Biomedical Engineering Active in IEEE committees (2024-2025) Research trends in recent publications emphasize autonomous aircraft coordination , probabilistic reasoning , and multi-sensor integration . Key subfields include lane-based airspace modeling, reinforcement learning for UAS, and pseudogradient navigation techniques. His work has received recognition through a Best Paper Award (IEEE 2008) and the US Air Force Summer Faculty Fellowship . Strategic deconfliction protocols Dynamic data-driven applications Structural health monitoring systems He actively mentors undergraduate research through courses like Deep Learning Capstone and Senior Capstone Design . Current grants include "Deep Learning in AI and Robotics" (2024-2029) and "Robust Reasoning using Geometric SAT/PSAT" (2022-2023).

• Numerical and computational mathematics
• Data management and data science
• Artificial intelligence
• +2 more

Musa Mammadov is a Senior Lecturer in Data Science at Deakin University's School of Information Technology, part of the Faculty of Science Engineering and Built Environment. His research focuses on data science, machine learning, and computational mathematics with applications in environmental modeling, healthcare analytics, and financial systems. Education: Doctor of Philosophy from University of Ballarat Research Interests: Specializing in numerical and computational mathematics, Mammadov develops advanced machine learning techniques for complex classification problems while exploring optimization methods in mathematical economics. His work spans environmental modeling applications in Sri Lanka's Kalu River Basin, healthcare fraud detection algorithms, and financial market analysis. Scientific Contributions: The recent publications highlight his work in hydrological forecasting using deep learning architectures, anomaly detection in medical billing systems, and probabilistic modeling of financial indices. His methodological contributions include improving Bayesian network classifiers and developing novel dependency estimation techniques. Academic Roles: Mammadov serves as editorial board member for Optimization Letters and Annals of Data Science . He supervises doctoral students in data science projects including health provider billing analysis and satellite downlink scheduling optimization.

• Civil Engineering
• Materials Science
• Concrete Durability
• +6 more

Rachid Cherif is a Teacher-Researcher at the University of Limoges, France, affiliated with the E3-TDVM team. His work bridges civil engineering, materials science, and musicology, with a focus on chloride ion transport modeling in cementitious materials and musical discourse analysis in Tunisian traditional and modern music. Key research areas: Concrete durability, multi-ion transport, recycled aggregates, AI applications in construction, ethnomusicology, and music pedagogy. Email: rachid.cherif@univ-lr.fr His publications (2010–2025) span 15+ articles on topics like: Chloride migration models with thermodynamic equilibria Predictive AI frameworks for recycled concrete properties Non-invasive corrosion diagnostics Ethnomusicological studies of Tunisian popular music Hygrothermal behavior of bio-based concretes Collaborations include researchers like Abdelkarim Aït-Mokhtar, Carmen Andrade, and Emilio Bastidas-Arteaga. No scientific awards or students are explicitly mentioned. His work also explores music education in Tunisian primary schools and interplay between authenticity and modernity in Tunisian music.

Karen EilbeckView profile

Professor

• Bioinformatics
• Genomics
• Precision Medicine
• +5 more

Karen Eilbeck is Professor of Biomedical Informatics and Adjunct Associate Professor of Human Genetics at the University of Utah , where she directs a bioinformatics research lab focused on precision medicine and genomic data management. Education B.S., University of Salford, United Kingdom Ph.D., University of Manchester, United Kingdom Research Interests The Eilbeck lab leverages computer science and ontology engineering to address contemporary questions in genomics and molecular biology. Central themes include: Development of ontologies such as the Sequence Ontology (SO) and Non-Coding RNA Ontology (NCRO) to standardize and structure biological data. Design of ontology-enabled software tools that enhance data sharing, variant interpretation, and precision medicine workflows. Metagenomic pathogen detection, clinical genome interpretation, and integration of multi-omics datasets to advance personalized healthcare. Publication Trends Recent publications (2023-2025) emphasize AI-driven clinical decision support, standardized genomic terminology, and large-scale infectious-disease risk modeling. Earlier works (2005-2018) established foundational ontologies and data formats (GVF, VCF) now widely adopted by the genomics community. Contact & Resources Email: keilbeck@genetics.utah.edu Lab website: available via University of Utah Department of Biomedical Informatics Full publication list: PubMed search | Google Scholar

Nidhal JamiaView profile

Lecturer

• Linear and Nonlinear Structural Dynamics
• Turbomachinery Blade Vibrations
• Experimental Modal Analysis
• +5 more

Dr. Nidhal Jamia is a Lecturer in Aerospace Engineering at Swansea University, affiliated with the School of Aerospace, Civil, Electrical and Mechanical Engineering. He holds a PhD in Mechanical Engineering and has experience as a Research Officer and Research Assistant at Swansea's Faculty of Science and Engineering (FSE). His expertise lies in Linear and Nonlinear Structural Dynamics, Turbomachinery Blade Vibrations, and Experimental Modal Analysis, with a focus on jointed structures and bolted interfaces. Dr. Jamia's research emphasizes predicting dynamic responses in complex systems, such as bolted joints and turbomachinery blades, with practical applications in vibration control and structural integrity. He has contributed to advancements in blade tip timing techniques, eddy current sensor modeling, and nonlinear system identification. His work bridges theoretical models with experimental validation, addressing challenges in aerospace and mechanical engineering. Selected research highlights include studies on mistuned bladed disks using wavelet transforms, sensor characteristics in blade tip timing, and the development of equivalent models for nonlinear joints. His recent efforts focus on stochastic updating of nonlinear systems, digital twin platforms, and the TRC benchmark system's experimental analysis. Education: PhD in Mechanical Engineering, Swansea University (2019–2022) MSc in Computational Mechanics, Polytechnical School of Tunisia (2015–2019) BEng in Civil Engineering, Engineering School of Gabes, Tunisia (2010–2014) Dr. Jamia is actively involved in teaching modules such as Engineering Mathematics, Experimental Studies for Mechanical and Aerospace Engineers, and Design and Laboratory Classes. He is available for postgraduate supervision and collaborates with industry on research projects involving structural dynamics and vibration analysis.

Lance ManuelView profile

Professor

• Uncertainty Quantification
• Wind Energy
• Offshore Structures
• +5 more

Lance Manuel is a Professor of Engineering at The University of Texas at Austin. His research focuses on uncertainty quantification in engineered systems, particularly wind energy and offshore structures. He has led projects related to wind turbine fatigue analysis, hurricane risk assessment, and climate change adaptation. Ph.D., Civil Engineering (Stanford University) M.S., Civil Engineering and Applied Mechanics (University of Virginia) B.Tech., Civil Engineering (Indian Institute of Technology, Bombay) His work bridges civil infrastructure with climate resilience, emphasizing probabilistic methods for structural reliability. Key areas include extreme climate modeling , floating offshore wind turbines , and fatigue damage prediction under non-stationary conditions. Recent publications highlight interdisciplinary trends in renewable energy systems and climate hazard quantification . He advises graduate students like Taemin Heo and Ding Peng Liu, whose work spans stochastic processes and offshore structural reuse.

• Structural Engineering
• Concrete Structures
• Structural Reliability
• +7 more

Max Hendriks is Professor in the Department of Structural Engineering at the Norwegian University of Science and Technology (NTNU), specializing in advanced structural analysis and assessment methodologies for concrete infrastructure. His work integrates computational modeling with experimental validation to address critical challenges in structural safety and durability. His research focuses on: Structural reliability assessment using proof load testing and monitoring data Acoustic emission techniques for concrete damage detection Nonlinear finite element analysis of cracking and shear behavior Durability mechanisms including alkali-silica reaction and corrosion Numerical modeling of structural response to extreme events Analysis of his 2021-2025 publications reveals a methodological evolution toward integrated experimental-computational frameworks, particularly in acoustic emission waveform modeling for fracture characterization and reliability-based assessment of aging infrastructure. His work consistently bridges theoretical advances with practical engineering applications, emphasizing probabilistic approaches for structural safety evaluation under complex degradation scenarios.

Zexun ChenView profile

Lecturer

• Probabilistic Machine Modelling
• Non-parametric Bayesian Methods
• Gaussian Process Modelling
• +5 more

Zexun Chen is a Lecturer in Predictive Analytics at the University of Edinburgh Business School , specializing in probabilistic machine modeling and non-parametric Bayesian predictive methods. He is affiliated with the Edinburgh Centre for Financial Innovations and holds memberships in the Royal Statistical Society (Fellow), Institute of Mathematics and its Applications (MIMA), London Mathematical Society (LMS), and International Statistical Institute.

• Multi-Objective Optimisation
• Nature Inspired Optimisation Algorithms
• Multidisciplinary Design Optimisation Under Uncertainties
• +7 more

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.

Pietro D'AntuonoView profile

Researcher

• Offshore Wind Turbines
• Structural Health Monitoring
• Fatigue Analysis
• +5 more

Pietro D'Antuono is a postdoctoral researcher affiliated with the Applied Mechanics Acoustics & Vibration Research Group. His research focuses on fatigue analysis, structural health monitoring, and offshore wind turbine systems. He has contributed to advancing methodologies for data-driven lifetime assessment of support structures and foundations, integrating physics-informed machine learning and probabilistic modeling. His work bridges mechanical engineering, civil engineering, and renewable energy systems. Research Interests: Offshore wind turbine structural dynamics Fatigue and damage mechanics Data-driven predictive models Structural health monitoring Materials degradation analysis Key Contributions: Developed the py-Fatigue open-source toolbox for fatigue assessment. Authored 24+ peer-reviewed publications and datasets on structural reliability and offshore energy systems. Awards: Best Paper Award (2nd place, ex-aequo) at an international conference (2022). Professional Activities: Presenter at conferences on topics like fatigue modeling, data-driven methodologies, and structural health monitoring. Contributed to international research collaborations in Europe and beyond.