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Renan SilvaView profile

Research Fellow

• Coastal Engineering
• Coastal Protection
• Hydrodynamics
• +3 more

Renan Silva is a Research Fellow at the UWA Oceans Institute and a Lecturer in the School of Earth and Oceans at The University of Western Australia. He holds a PhD in Coastal Engineering from UWA (2022), MSc degrees in Coastal and Marine Engineering from TU Delft (2017), NTNU (2017), and the University of Southampton (2017), and a BSc in Environmental Engineering from UFRJ (2010). His research focuses on coastal processes, particularly the interaction between waves and artificial reefs to optimize coastal protection design guidelines. Education: PhD: Coastal Engineering, UWA (2018–2022) MSc: Coastal Engineering (CoMEM Track), TU Delft (2015–2017) MSc: Coastal Engineering (CoMEM Track), NTNU (2015–2017) MSc: Coastal Engineering (CoMEM Track), University of Southampton (2015–2017) BSc: Environmental Engineering, UFRJ (2005–2010) Research interests include coastal hydrodynamics, wave-energy dissipation mechanisms, and the application of computational models (e.g., SPH, CFD) to study nearshore processes. Recent work emphasizes submerged coastal structures' influence on wave dynamics and reef protection strategies. Key projects include a CSIRO sub-contract for software development (2021) and collaborative studies on seagrass meadow energy dissipation (2025). His work contributes to UN Sustainable Development Goals related to climate action and ocean sustainability. Grants/Projects: Software development sub-contract to CSIRO for NGLP (2021) Labs/Teams: Active in the UWA Oceans Institute and collaborates with global institutions on coastal modeling and infrastructure resilience.

• Fluid Dynamics
• Combustion Engineering
• Gas Turbine Technology
• +3 more

Dr. Jialin Su is a Lecturer in Fluid Dynamics at Loughborough University, UK. He holds a PhD from Loughborough University (2012-2016) and has prior research experience at Rolls-Royce University Technology Centre, Imperial College London, and NUMECA International. His work focuses on computational fluid dynamics (CFD), gas turbine combustion systems, and acoustic-thermo interactions. Dr. Su's career began in R&D engineering roles at Hewlett-Packard and Seagate Technology in Singapore. Education: BEng and MEng, Nanyang Technological University, Singapore MSc in Aeronautics from Technical University of Munich MSc in Aeronautics from Cranfield University PhD in Gas Turbine Aerodynamics from Loughborough University Research interests include high-order CFD methods for combustion simulation, acoustic forcing effects on fuel injectors, and thermo-acoustic instability mitigation. His work bridges fundamental fluid dynamics principles with industrial applications in aerospace propulsion systems. Key Achievements: Recipient of Rolls-Royce Howes, Ruffles and Parker Award (2016) for doctoral research Co-developed methods to model helical modes in aero-engine fuel injectors Advances in acoustic impedance prediction for orifice systems Currently supervising PhD research on high-order CFD for combustion simulations. Previously advised projects at Rolls-Royce UTC and Imperial College's thermo-acoustic research group.

Ardiansyah FauziView profile

Researcher

• Civil Engineering
• Disaster Risk Management
• Coastal Engineering
• +3 more

Ardiansyah Fauzi is affiliated with the Faculty of Engineering and Environment at Northumbria University, Newcastle. His research focuses on disaster risk reduction, coastal engineering, and environmental sustainability. He specializes in tsunami forecasting, flood management, and the application of machine learning in geohazard analysis. His work integrates computational models, hydrological studies, and community-based approaches to address challenges in disaster mitigation and infrastructure resilience. Key research areas include eco-friendly construction materials for coastal protection, urban flood risk modeling, and hydropower potential assessment in Indonesian regions. Recent contributions involve developing predictive systems for tsunamis and floods using deep learning, as well as participatory research to enhance community disaster literacy. His interdisciplinary approach bridges engineering, environmental science, and social science to create actionable solutions for vulnerable regions.

• Quantum Chemistry
• Density Functional Theory
• Reduced Density Matrix
• +1 more

Dr. Klaas Giesbertz is an Assistant Professor in Chemistry and Pharmaceutical Sciences at Vrije Universiteit Amsterdam, with joint appointment at AIMMS (Amsterdam Institute for Molecular and Life Sciences). His research focuses on theoretical quantum chemistry and density functional theory. Research Focus: Specializes in reduced density-matrix functional theory, electronic structure calculations, and quantum many-body systems. Current work develops advanced computational methods for electronic excitations and strongly correlated systems. Honors: Journal of Chemical Physics Editor's Choice Award (2013, 2012) Publications: Research demonstrates consistent innovation in density matrix methodologies, with recent emphasis on machine learning applications, exact solutions to quantum representability problems, and relativistic effects in electron correlation.

Monique HeadView profile

Professor

• Structural Engineering
• Bridge Engineering
• Earthquake Engineering
• +3 more

Monique Head is a tenured Professor in the Department of Civil and Environmental Engineering at the University of Delaware. She is affiliated with the Center for Innovative Bridge Engineering (CIBrE), Center for Composite Materials (CCM), and Delaware Center for Transportation (DCT). With over 20 years of experience, she has managed over $5M in grants from state and federal agencies. Education: PhD in Civil (Structural) Engineering from Georgia Institute of Technology; MS and BS in Civil Engineering from the University of Delaware. PhD: Georgia Institute of Technology MS: University of Delaware BS: University of Delaware Research focuses on bridge evaluation using vision-based measurements, earthquake engineering, and flood/salinity impacts on infrastructure. She chairs the Transportation Research Board’s Seismic Design and Performance of Bridges Committee (AKB50). Her work spans structural monitoring, material innovation, and disaster resilience. Recent articles explore hurricane-induced loadings, corrosion-resistant materials, and multi-robot inspection systems. Labs/Teams: CIBrE, CCM, DCT Grants: Over $5M in federal/state funding

Daniel HagenView profile

Associate Professor

• Robotics
• Modeling and Simulation
• Actuation Systems
• +4 more

Daniel Hagen is an Associate Professor at the Department of Engineering Sciences, University of Agder (UiA), Norway. He holds a Ph.D. in Engineering Sciences (2020), M.Sc. and B.Sc. in Mechatronics (2014 and 2012), and a Trade Certificate as Automation Mechanic (2009). His academic career is complemented by extensive industrial experience in roles such as Chief Scientist at Twilligent and Senior Technical Advisor at MotionTech, focusing on control systems, robotics, and energy-efficient mechatronics. Dr. Hagen's research interests include robotics, actuation systems, fluid power, and control systems. He leads projects in digital twins, autonomous robotics (e.g., UiAbot), and energy-efficient electro-hydraulic systems. His work emphasizes practical applications in offshore engineering and industrial automation. He teaches courses like Robotics and Instrumentation, Programming for Intelligent Robotics, and Mechatronic System Design. He is affiliated with research groups such as CAIR (Artificial Intelligence Research) and the Intelligent Mechatronics (iTron) group. Recent publications focus on real-time control systems, payload motion prediction, and digital twin frameworks for mechatronics. His research bridges academic innovation with industrial challenges, particularly in energy efficiency and autonomous systems.

Yangkang ChenView profile

Academic

• Seismic Signal Analysis
• Seismic Modeling and Inversion
• Deep Learning
• +4 more

Yangkang Chen is a Research Assistant Professor at the Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin. He leads AI/ML research at the Texas Seismological Network (TexNet). His expertise includes seismic signal analysis, deep learning, and high-performance computing. Chen holds a Ph.D. in geophysics from UT Austin (2015) and was a postdoc at Oak Ridge National Laboratory (2016-2018). Research Focus: AI-driven earthquake monitoring, seismic modeling, and reproducible research. Key Contributions: Developed EQCCT, TXED datasets, and open-source tools like pyseistr and DRR. Leadership: Editor of JGR: Machine Learning and Computation, and AGU fellowships. Education: Ph.D. in Geophysics, UT Austin, 2015 Postdoctoral Research at Oak Ridge National Lab (2016-2018) Research Interests span AI applications in seismology, seismic inversion, and reproducible methodologies. His work bridges computational methods with real-world seismic challenges, such as induced seismicity detection and earthquake cataloging. Awards include First Prize in GeoHackathon 2024, Clarivate Highly Cited Researcher (2020), and multiple IEEE recognitions. He advises over 20 graduate students/postdocs, focusing on ML-driven geoscience problems. Chen's lab develops tools for seismic data analysis, DAS processing, and machine learning frameworks.

Tim BrownView profile

Senior Lecturer

• Antenna Design
• Radio Frequency Propagation
• Wireless Communication Systems
• +7 more

Dr. Tim Brown is a Senior Lecturer in RF Antennas and Propagation at the University of Surrey's Institute for Communication Systems (ICS), part of the School of Computer Science and Electronic Engineering. He leads postgraduate studies and oversees academic programs in electronic engineering. His expertise spans antenna design, radio frequency propagation, and wireless communication systems, with a focus on optimizing spectrum use and reducing electromagnetic field (EMF) exposure in user devices. Education: Completed a PhD in diversity antennas for mobile terminals (2004) and postdoctoral research at Aalborg University, Denmark, focusing on MIMO channel characterization and antenna designs. Research Interests: Includes MIMO systems, UWB, RFID/NFC technologies, THz communication, and SAR reduction techniques. His work bridges theoretical research with practical applications, such as CPD courses for industry professionals and collaborations with EU frameworks and UK research councils. Publications: Over 50 peer-reviewed articles in IEEE journals and conferences, emphasizing THz channel modeling, antenna innovation, and EMF mitigation. Recent work addresses sub-THz scattering, LAMM multiplexing, and SAR reduction in smartphone terminals. Awards: None explicitly listed, though recognized for editorial roles and research impact (e.g., leading a £8M initiative for AI-era network training). Advising & Grants: Coordinates MSc dissertations and leads industry partnerships. His lab focuses on future wireless systems, including 6G and THz technologies. Labs/Teams: Directs the ICS's antenna and propagation research group, contributing to Surrey's reputation for vocation-oriented education and professional skills development.

• 5G & Beyond Networks
• Green Communication
• mmWave Communication
• +3 more

Dr. Haris Pervaiz is a Research Fellow at the University of Surrey's 5G Innovation Centre (5GIC), working on Huawei and EPSRC-funded projects focused on energy-efficient 5G networks and beyond. He holds a PhD in Communication Systems from Lancaster University (2016), an MPhil in Electrical and Electronic Engineering from Queen Mary University of London (2011), and an MSc in Information Security from Royal Holloway University of London (2005). His research focuses on 5G & Beyond Networks, Green Communication, mmWave Communication, Energy Efficiency in HetNets, and IoT Data Analytics. He has contributed over 35 publications in top-tier journals/conferences and has mentored numerous students. Key projects include optimizing energy proportional eNodeBs for LTE-Advanced and developing distributed emergency management systems (DARE). Collaborations include institutions like the University of Glasgow, Essex, and Huazhong University of Science & Technology.

• Indoor Positioning Systems
• Human Activity Recognition
• Environmental Monitoring
• +3 more

Yonglei Fan is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on interdisciplinary applications of sensor technologies, machine learning, and spatial analytics. Key areas include indoor positioning systems, human activity recognition, environmental monitoring, and public health analysis. He has contributed to studies on smart city infrastructure, pollution impact assessment, and energy resource forecasting. Research interests span technical innovations in sensor fusion (IMU/UWB/FMCW radar), Bayesian-based navigation algorithms, and environmental health studies using big data. His work integrates IoT and behavioral data to address challenges in urban sustainability, healthcare monitoring, and energy systems. Notable projects include analyzing dockless bike-sharing systems for urban mobility insights, developing contactless health monitoring systems, and evaluating indoor positioning technologies through competitive benchmarks. His research often combines experimental methods with advanced computational models to solve real-world problems in smart cities and environmental science.

Donald LeeView profile

Associate Professor

• Data Science
• Survival Analysis
• Longitudinal Analysis
• +3 more

Donald Lee is an Associate Professor in the Department of Biostatistics and Bioinformatics at Emory University. His research focuses on integrating machine learning and statistical methods to address challenges in healthcare analytics, survival analysis, and operations research. He specializes in developing predictive models for real-time clinical monitoring, healthcare resource optimization, and understanding cyclic patterns in customer or patient data. Key research themes include: Dynamic survival analysis using boosted nonparametric hazards Machine learning applications in ICU mortality prediction and invasive ventilation risk assessment Time-series analysis of clinical and customer data streams Cyclical arrival rate modeling and distributional forecasting His work bridges methodological advancements with practical healthcare systems optimization, particularly in emergency department operations and nursing productivity evaluation. He has developed open-source tools like BoXHED for scalable survival analysis, demonstrating commitment to reproducible research. Current projects involve refining real-time prediction systems using language models and exploring distributional forecasts beyond point estimates. While no specific awards are listed, his contributions to healthcare analytics demonstrate impactful interdisciplinary research.

Lan Boon LeongView profile

Associate Professor

• Biomedical Engineering
• Quantum Physics
• Nonlinear and Statistical Physics
• +3 more

Assoc. Prof. Lan Boon Leong is an Associate Professor at the Malaysia School of Engineering, Monash University. His research focuses on Biomedical Engineering, Quantum Physics, and Nonlinear Dynamics. He has received prestigious awards including the TWAS-UNESCO Associateship and teaching accolades such as the Vice-Chancellor’s Distinguished Teaching Award. His work addresses critical transitions in disease states (e.g., cardiac arrhythmia), quantum system approximations, and noise-enhanced balance mechanisms. Education: PhD in Physics (Georgia Institute of Technology, 1990), Master’s in Physics (Georgia Tech, 1989), BA in Physics and Mathematics (Hanover College, 1986). Research interests include biomedical engineering applications like lung cancer imaging and prostate cancer detection, alongside quantum dynamics and complex systems analysis. Key projects include investigations into voltage control in renewable energy grids and UAV swarm communication reliability. His teaching commitment includes the PHS1002 Physics for Engineering course. Recent publications span topics like UAV communication reliability, seizure classification via EEG, and cardiac alternans control. Media coverage includes features in NST, Phys.org, and The Star regarding cardiac arrhythmia research.

Mohd-Zulhilmi Paiz IsmadiView profile

Senior Lecturer

• Imaging Techniques
• Stratified Flow Behavior
• Experimental Fluid Mechanics
• +4 more

Dr. Mohd-Zulhilmi Paiz Ismadi is a Senior Lecturer at Monash University's School of Engineering, specializing in Mechanical Engineering. He holds a PhD in Fluid Mechanics of Bioreactors from Monash University (2013) and a BEng (Hons) in Mechanical Engineering (2009). His research bridges academia and industry, focusing on imaging techniques, stratified flow behavior, and non-destructive assessment. He has secured over RM2 million in grants and collaborates with multinational organizations. Dr. Ismadi teaches courses such as MEC2402 Engineering Design I and MEC4407 Design Project. His industrial expertise includes engineering consultancy for government/private sectors and development of machine learning-driven data interpretation systems. He serves as Head of Panel at Malaysia's Engineering Accreditation Council (EAC) and contributes to youth development via the Ministry of Youth and Sports. Research highlights include wind turbine performance optimization, crack segmentation using deep learning, and acoustic-enhanced water treatment. He has published 24+ articles and received the 2019 PVC Excellence in Education Award. Current projects involve Bayesian Monte Carlo methods for pipe damage assessment and single-pixel imaging via generative adversarial networks. Dr. Ismadi's work aligns with UN SDGs related to affordable energy and sustainable cities. He actively peer-reviews for journals like Measurement and IEEE Sensors Journal , and chairs accreditation panels for engineering programs.

Seyed Mehdi ZahraiView profile

Adjunct Professor

• Seismic Control
• Structural Engineering
• Steel Structures
• +5 more

Seyed Mehdi Zahrai is an Adjunct Professor in the Department of Civil Engineering at the University of Ottawa. He holds a PhD in Structural Engineering from the University of Ottawa (1997), an MSc from the University of Tehran (Iran), and a BSc from Amirkabir University of Technology (Iran). His primary roles include academic research, professional engineering consulting, and leadership in educational administration. Dr. Zahrai has been a faculty member at the University of Tehran since 2014, serving as a Professor there and Deputy Director for Educational Affairs (2008–2014). Education: PhD (U Ottawa, 1997), MSc (U Tehran, 199?), BSc (Amirkabir U, 199?) His research focuses on seismic control systems, structural health monitoring, steel and concrete materials science, and sustainable construction. Key areas include damper systems (viscoelastic, magnetorheological), vibration mitigation, and retrofitting techniques for buildings and bridges. He has pioneered methods for shifting plastic hinges in connections and integrating AI into structural diagnostics. Research Themes: Passive/Active Control, Seismic Retrofits, Steel Connections, Concrete Materials With over 450 publications and 10 books, his work bridges theoretical and applied engineering. Recent studies explore time-delay compensation in fuzzy control systems and additive manufacturing for structural reinforcement. Key Projects: NSERC Postdoctoral Fellowship (NRC Canada, 1997–1999), Startup Firm Consulting, Multi-University Collaborations He has received the Governor General Gold Medal nomination and led international editorial boards. His advisory roles include directing large-scale construction projects in Iran and Canada, emphasizing practical seismic resilience strategies. Awards: NSERC Postdoc Fellowship, 2012 & 2018 Sabbaticals at Canadian Universities As an educator, he has supervised 180+ graduate students and developed curricula for civil engineering programs. His startup firms focus on applying academic research to real-world infrastructure challenges.

• Data Assimilation
• Meteorology
• Numerical Weather Prediction
• +4 more

Javier Amezcua is a researcher specializing in data assimilation, numerical weather prediction, and atmospheric dynamics, affiliated with the University of Maryland. He holds a PhD in Atmospheric Sciences from the University of Maryland (2012), focusing on sequential data assimilation and numerical weather forecasting. His work bridges advanced statistical methods with meteorological modeling, particularly in ensemble Kalman filters, model error estimation, and tropical climate dynamics. Research interests include: Development of ensemble-based data assimilation techniques for improving weather and climate models Integration of observational data (e.g., infrasound, satellite) to enhance atmospheric wind field estimation Study of model error dynamics and their impact on forecast accuracy Applications in renewable energy, such as wind resource prediction and statistical-dynamical downscaling Notable contributions include advancements in ensemble transform filters, weak-constraint 4D ensemble variational methods, and the implicit equal-weights particle filter. Collaborations span institutions like the University of Reading, the Norwegian Meteorological Institute, and the European Centre for Medium-Range Weather Forecasts. His thesis explored sequential data assimilation methodologies, including the Ensemble Transform Kalman-Bucy Filter and the effects of time-stepping schemes in atmospheric models. Ongoing work emphasizes interdisciplinary applications of data assimilation in epidemiology and hydrology.