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Catherine JohnsonView profile

Associate Professor

• Shock Physics
• Blast-Induced Traumatic Brain Injury (TBI)
• Coal Dust Explosions
• +3 more

Dr. Catherine Johnson is the Robert H. Quenon Associate Professor of Mining & Explosives Engineering at Missouri University of Science and Technology. She serves as Director of the Explosives Research Facility and Associate Director of the PRECISE-TBI Education Core for the VA Interagency Resource Center (I50). Her research focuses on shock physics, blast-induced traumatic brain injury (TBI), and mining safety. Education: Ph.D. in Mining Engineering (Explosives Emphasis), University of Kentucky (2014) M.Eng. in Mining and Quarry Engineering, University of Leeds (2012) B.Eng. in Mining and Quarry Engineering, University of Leeds (2012) Her research explores the adverse effects of high explosives and blasting agents, including coal dust explosions, air overpressure from quarry blasting, and shock wave injuries such as traumatic brain and lung injuries. She develops policy, training, and protective equipment improvements to mitigate these risks. Recent publications highlight trends in blast wave propagation , TBI biomarker discovery , protective armor design , and explosive material characterization , with applications in neuroscience , materials science , and mining safety . Scientific Awards: 2018 President’s Award, International Society of Explosives Engineers 2018 Inaugural Member, University of Missouri System Presidential Engagement Fellows 2019-2021, 2023 Mining Engineering Department Research Award 2020, 2022 Department Teaching Award 2021 Dean’s Scholar, College of Engineering and Computing 2020-2021 University Research Award 2023 Missouri S&T Woman of the Year Dr. Johnson has graduated 6 PhD and 4 MS students and currently advises 10 PhD and 1 MS student. She has secured $11.7M in collaborative research funding and authored 39 peer-reviewed journal articles, 41 conference proceedings, and 3 book chapters.

Michael StinglView profile

Professor

• Topology Optimization
• Material Optimization
• Structural Optimization
• +9 more

Prof. Dr. Michael Stingl is a Professor at the Chair of Continuous Optimization, Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He is actively involved in research and teaching, leading a team focused on optimization methods with applications in engineering and materials science. His work spans topology optimization, robust design, and computational mechanics, with significant contributions to both theory and industrial applications. University: Friedrich-Alexander-Universität Erlangen-Nürnberg School: Department of Mathematics Department: Chair of Continuous Optimization Email: michael.stingl@fau.de His research interests include topology optimization, material and structural design, nonlinear and robust optimization, semidefinite programming, and applications in additive manufacturing and particulate systems. He has developed advanced methods such as the continuous stochastic gradient method and has applied optimization techniques to photonic crystals, composite materials, and fluid dynamics. His recent work emphasizes multi-scale modeling, uncertainty quantification, and the integration of simulation with optimization for real-world engineering problems. The analysis of his recent publications reveals a strong focus on gradient-based and stochastic optimization algorithms, robust design under uncertainty, and the optimization of optical and mechanical properties of advanced materials. His work frequently bridges applied mathematics with engineering disciplines, particularly in the context of additive manufacturing and nanomaterials. Prof. Stingl has no listed scientific awards in the provided text. However, his extensive publication record in top-tier journals and long-standing academic career indicate significant recognition in the optimization community. He supervises master's theses and HiWi (student assistant) positions, indicating active student mentoring. His team collaborates on interdisciplinary projects such as the CRC 1411 Design of Particulate Products, suggesting involvement in large-scale, funded research initiatives. He teaches courses in material and shape optimization and advanced algorithms in nonlinear optimization. His research is conducted within the Chair of Continuous Optimization at FAU, which is part of a vibrant academic environment involving collaborations with engineering and materials science departments. The team utilizes advanced computational methods and participates in national and international research networks focused on optimization and manufacturing technologies.

• Optical Communications
• Photonics
• Passive Optical Networks (PON)
• +4 more

Dr. Md Saifuddin Faruk is a Senior Lecturer at the School of Computing and Engineering, Bangor University. His research focuses on advanced optical communication systems, including passive optical networks (PON), fiber-wireless convergence, and machine learning applications in signal processing. He has contributed to high-speed PON solutions, data-driven models for optical amplifiers, and seamless fiber-wireless access networks. His work emphasizes practical implementations over field-deployed infrastructure, such as 200 Gb/s bidirectional coherent PON and tunable photonic mmWave generation. Dr. Faruk frequently collaborates on interdisciplinary projects, publishing in top journals like Optics Express and Journal of Lightwave Technology . He has presented invited talks at conferences like the Asia Communications and Photonics Conference (ACP), highlighting innovations in next-generation PON technologies. Education: Not explicitly stated in texts. Labs/Teams: Engaged in photonics and optical communication research groups at Bangor University. His research trends emphasize hybrid physical-data-driven models, coherent optical systems, and network convergence. Key contributions include improving SNR estimation using Gaussian processes and optimizing power budgets in high-speed PON. Grants and advising details are not specified in the provided texts.

Nadine (Igonin) UshakovView profile

Assistant Professor

• Seismology
• Machine Learning
• Geophysics
• +3 more

Nadine (Igonin) Ushakov is an Assistant Professor of Geophysics at the School of Natural Sciences and Mathematics, University of Texas at Dallas (UTD). Her research focuses on seismology and machine learning applications to study earthquakes induced by human activities such as hydraulic fracturing, as well as the impacts of climate change on near-surface geophysical processes. She leads projects on fault activation mechanisms, geothermal well monitoring, and microseismic data analysis. Education: PhD in Geophysics, University of Calgary, Canada (2021) BSc in Geophysics, University of Calgary, Canada (2016) Research Interests: Induced seismicity characterization and mitigation Machine learning for seismic data interpretation Climate change impacts via near-surface geophysics Geothermal reservoir monitoring Recent Work Trends: Her publications emphasize quantitative analysis of hydraulic fracturing-induced seismicity, real-time earthquake early warning systems, and advanced techniques for microseismic monitoring using distributed acoustic sensing (DAS). She also explores interdisciplinary applications in volcanology and archaeological geophysics. Grants & Appointments: Assistant Professor, UTD (2023–Present) SAGE Instructor, Summer of Applied Geophysical Research (2022–Present) Postdoctoral Fellow, University of Texas at Austin (2021–2023) Current research group openings are unavailable, but opportunities exist for fieldwork in Texas, New Mexico, and Finland through collaborative projects.

Bing PuView profile

Assistant Professor

• Regional Climate Change
• Dust Variability
• Climate Modeling
• +3 more

Bing Pu is an Assistant Professor in the Department of Geography & Atmospheric Science at the University of Kansas. She holds a B.S. from Nanjing Institute of Meteorology, an M.S. from Peking University, and a Ph.D. in Atmospheric Science from Cornell University (2011). Prior to joining KU in 2019, she conducted postdoctoral research at the University of Texas at Austin and served as a Research Associate at Princeton University/NOAA Geophysical Fluid Dynamics Laboratory. Her research focuses on regional climate change, dust variability, and dust-climate interactions. Key areas include dust emission modeling, climate system impacts of aerosols, vegetation-climate feedbacks, and the socio-environmental consequences of extreme dust events. Recent work examines compound heat-drought-dust events in California, record-breaking trans-Atlantic African dust plumes, and long-term aerosol trends in Asia. Publications highlight advancements in dust optical depth simulations, seasonal dust predictability in the U.S., and the role of climatic factors like the Pacific Decadal Oscillation. Her studies employ satellite data, climate models (e.g., GFDL), and observational datasets to bridge gaps between regional processes and global climate dynamics. Dr. Pu’s research group actively explores interdisciplinary approaches to address climate change challenges, leveraging climate modeling and remote sensing techniques to improve understanding of dust-driven environmental hazards.

• Fluid Dynamics
• Computational Fluid Dynamics
• Laser Ablation
• +3 more

Rob Hagmeijer is a Researcher at the University of Twente within Engineering Fluid Dynamics . With over 100 publications and an h-index of 12, his work spans computational fluid dynamics, laser ablation, and biomedical applications including nasal high-flow therapy and coronary bypass surgery optimization. Active in Medical Engineering and Fluid Dynamics research Recipient of the John Parker Medal (2017) and 3rd Prize Best Oral Presentation MST (2019) Developed algorithms for condensation experiments and multi-phase flow modeling Recent Research Trends: Focus on branched fluidic network optimization (2025), laser ablation crater analysis (2023), and computational modeling of respiratory systems (2022). His work combines theoretical analysis with clinical validation across disciplines. Scientific Awards: 3rd Prize Best Oral Presentation MST Wetenschapssymposium 2019 John Parker Medal for best clinical presentation at SCTS 2017 Collaborative Activities: Participated in presentations at conferences like SCTS 2017 , MST Wetenschapssymposium 2019 , and ECMI 2004 . Collaborated on projects involving laser-cluster interaction and high harmonic generation (2013).

• Parallel Computing
• Distributed Computing
• Computer Communications
• +6 more

Khadija Bousselmi Ep ARFAOUI is a Lecturer at Savoie Mont Blanc University and a researcher at the LISTIC laboratory. She holds a PhD in Computer Science from the University of Tunis El Manar and completed a postdoctoral fellowship at LAMSADE, Paris Dauphine. Her research focuses on optimizing data-intensive systems, including cloud-based workflow scheduling, Big Data architecture, and energy-efficient CNN design. She teaches modules like Optimization Methods, Networks, and Graph Theory at the IUT Annecy campus. PhD: Approche scalable pour l’ordonnancement des workflows scientifiques dans un environnement Cloud , 2017 Postdoc: Multi-Objective Cloud Workflow Scheduling , 2019-2020 (LAMSADE) Research Interests: Parallel computing, distributed systems, machine learning applications in data warehousing and disaster prediction, and green computing strategies. Notable contributions include frameworks like DR-SWDF for dynamic workflow deployment and decision support systems for Big Data pipelines. Recent work emphasizes ML in healthcare (speech disorder diagnosis) and environmental analytics (avalanche forecasting).

• Structural Dynamics
• Aeroelasticity
• Uncertainty Modeling
• +6 more

Professor Hamed Haddad Khodaparast is a faculty member in the School of Aerospace, Civil, Electrical and Mechanical Engineering at Swansea University, specializing in Aerospace Engineering. His roles include teaching and supervising postgraduate students in areas such as dynamics and advanced airframe structures. He has held academic positions from Lecturer to Professor since 2013, currently serving as a Professor since 2022. Research Interests: Structural Dynamics and Aeroelasticity Uncertainty Modeling and Propagation Vibration Control (Active/Passive) Composite Structures and Morphing Aircraft Reduced Order Modeling and Nonlinear Dynamics Recent Research Contributions: His work focuses on stochastic modeling, nonlinear system identification, and energy harvesting. Notable projects include gust load alleviation using hinged wingtips and nonlinear joint modeling for industrial applications. Collaborations involve aerodynamic optimization and experimental validation of piezoelectric systems. Grants and Supervision: Supervised PhD students on topics like nonlinear bolted joints and aeroelastic wing design. Active in interdisciplinary projects with industry partners, addressing challenges in morphing aircraft and structural health monitoring. Labs/Teams: Leads research in structural dynamics and aeroelasticity within the School’s Aerospace Engineering group, contributing to advanced composite materials and wind tunnel testing initiatives.

• Electromagnetic Engineering
• Magnetic Levitation
• Vibration Isolation
• +5 more

Dr. Will Robertson is a Senior Lecturer at the School of Electrical and Mechanical Engineering, University of Adelaide, within the Faculty of Sciences, Engineering and Technology. His research focuses on electromagnetics, magnetic levitation, vibration isolation, biomechanics, and sports engineering. He leads the AUMAG research group, which collaborates with industry partners on applications like wave energy conversion and maglev systems. Robertson advocates for Open Science principles to accelerate research progress. His work in biomechanics includes developing low-cost 3D scanning methods for body segment parameter estimation and musculoskeletal modeling. He teaches biomechanics and sports engineering, and supervises Honours, Masters, and PhD students in these fields. Research interests span electromagnetics (magnetic levitation systems, electromagnetic actuators) and biomechanics (human gait analysis, spinal mechanics). Recent projects include quasi-zero stiffness magnetic springs, nonlinear hydrodynamic models for wave energy devices, and gait-based person re-identification using force platforms. He has pioneered spinal load reduction techniques through braced lifting methods and developed a 3D-printed Yidaki musical instrument. His work bridges theoretical electromagnetics with practical applications in sports equipment design and healthcare. Publications reflect interdisciplinary strengths, combining electromechanical systems design with biomechanical analysis. He emphasizes reproducibility through open-source tools like the unicode-math LaTeX package and contributes to educational initiatives like Honours project assessment frameworks. Current projects explore magnetic levitation for skin friction measurement and soccer boot traction optimization.

Huw MorganView profile

Professor

• Solar Physics
• Space Weather
• Solar Wind
• +7 more

Huw Morgan is a Professor in the Department of Physics at Aberystwyth University, actively engaged in solar and space physics research. He leads multiple externally funded projects, including CorMag, MESOM, and SWEEP, and collaborates with institutions such as the UK Space Agency, STFC, and international partners. His work spans observational, theoretical, and instrumental aspects of solar physics. PhD in Physics (2005), thesis on solar wind acceleration using UV spectroscopy Principal Investigator on 11 externally funded research projects from 2015–2028 Active contributor to space missions and instrumentation development His research focuses on understanding the solar corona, solar wind origins, coronal mass ejections, and space weather prediction. He develops empirical and tomographic models to analyze solar wind properties and uses data from missions like Parker Solar Probe and IRIS. His work also extends to solar instrumentation, including high-resolution imaging spectrometers and lunar-based solar occultation missions (MESOM). He investigates magnetic field structures in active regions and quiet Sun phenomena, contributing to fundamental understanding of solar dynamics. The recent publications (2023–2025) reflect a strong trend in combining multi-instrument data with modeling to improve space weather forecasting. Topics include CME projection corrections, empirical solar wind models, coronal tomography, and high-resolution chromospheric studies. His work increasingly integrates automated analysis techniques and large statistical datasets. He supervises multiple PhD students and has mentored researchers in solar physics. His collaborative network includes experts in solar physics, space instrumentation, and data science. He is frequently cited in media for expert commentary on auroras and solar storms. Principal Investigator: CorMag (2025–2028), MESOM (2024–2025), SWEEP (2020–2023) Projects funded by STFC, Leverhulme Trust, UK Space Agency, Welsh Government Active grants support solar wind modeling, coronal imaging, and space weather forecasting He leads or co-leads several research groups and labs focused on solar system physics at Aberystwyth, including teams developing the EMPSOL model and the MESOM mission. His work bridges observational solar physics with practical space weather applications.

• Impact, damage and fracture of composite materials
• Wave propagation in acoustic metamaterials
• Mechanical metamaterials
• +3 more

Kwek-Tze Tan is an Associate Professor in the Mechanical Engineering Department at the University of Akron , specializing in composite materials and metamaterials. He joined the university in 2014 after working at the Institute of Materials Research and Engineering in Singapore. Education : B.Eng. (2004), M.Eng. (2006) from National University of Singapore; Ph.D. (2011) from Tokyo Metropolitan University; Post-Doc (2013) at Purdue University. Research Focus : Impact and fracture mechanics of composites, acoustic/elastic metamaterials, bioinspired materials, 3D printing, and Arctic temperature effects. Grants : Recipient of 2 multi-year U.S. Office of Naval Research (ONR) grants for Arctic condition composite studies. Labs : Leads research at the Metacomposites Lab (website: metacomposites.uakron.edu ). Scientific Awards include UA Faculty Research Committee Fellowship (2019), Firestone Fellowship (2018), LaunchTown Entrepreneurship Award (2016), JSPS Fellowship (2016), ICA-ASA Young Scientist Grant (2013), and Metamaterials Congress Travel Grant (2012).

Thomas PockView profile

Professor

• Computer Vision
• Image Processing
• Mathematical Optimization
• +7 more

Thomas Pock is a Professor of Computer Science at Graz University of Technology, holding the AIT Stiftungsprofessur for Mobile Computer Vision. He is affiliated with the Institute for Computer Graphics and Vision (ICG) within the Faculty of Computer Science and serves as a principal scientist at the Austrian Institute of Technology (AIT), Center for Vision, Automation & Control. He leads the Vision, Learning and Optimization (VLO) research group, which focuses on mathematical modeling and optimization in computer vision. His research interests lie at the intersection of computer vision, image processing, and mathematical optimization. Specifically, he develops mathematical models for computer vision and efficient convex and non-smooth optimization algorithms , particularly for mobile scenarios. His recent work increasingly integrates variational methods with deep learning, especially in solving inverse problems in imaging such as medical reconstruction and deblurring. The trends in his recent publications show a strong emphasis on deep learning for inverse problems , variational networks , and learned optimization . His group explores how to combine classical mathematical models with data-driven deep learning approaches to achieve stable, interpretable, and high-performance solutions in image reconstruction and processing. His scientific achievements have been recognized with several prestigious awards: START Prize, Austrian Science Fund (FWF), 2013 German Pattern Recognition Award, DAGM, 2013 ERC Starting Grant, European Research Council, 2014 Thomas Pock actively mentors students and leads a research group of 10 PhD students and 2 postdocs. He has secured significant research grants, including the ERC Starting Grant, which supports his foundational work. He is also engaged in scientific communication, giving invited talks at international venues such as SIAM and co-organizing the IMAGINE One World seminar series to foster global collaboration in imaging and inverse problems. He leads the Vision, Learning and Optimization (VLO) group at the Institute for Computer Graphics and Vision. The group develops mathematical models and efficient algorithms for computer vision and image processing, with a focus on mobile applications. The team includes multiple PhD students and postdoctoral researchers and has produced notable software and publications in top venues.

Ayesha AfzalView profile

Researcher

• High Performance Computing
• Parallel Computing
• Performance Modeling
• +5 more

Ayesha Afzal is a researcher and PhD student at the Erlangen National High Performance Computing Center (NHR@FAU), part of Friedrich-Alexander University Erlangen-Nuremberg. She holds a master's degree in computational engineering from FAU and a bachelor's degree in electrical engineering from the University of Engineering and Technology, Lahore, Pakistan. Her research focuses on high-performance computing with expertise in analytic performance models, parallel simulation frameworks, and first-principles performance modeling of distributed-memory parallel programs. She investigates multi-core and parallel architectures, parallel computing algorithms, programming models, and domain-specific languages to understand and optimize parallel application performance through advanced modeling techniques. Dr. Afzal's publication record demonstrates consistent innovation in understanding parallel program dynamics, particularly around idle waves, desynchronization phenomena, and performance bottlenecks. Her research has pioneered approaches like the physical oscillator model for supercomputing and developed frameworks such as DisCostiC for simulating MPI applications without code execution, revealing novel insights into hardware-software interactions in HPC systems. ISC PhD Forum Award (1st place, 2021) IEEE TPDS Best Paper Runner-up Award (2023) SC PMBS Best Short Paper Award (2023) SC Best Research Poster Finalist (2024) ISC Best Research Poster Award (1st place, 2025) Top 100 Future Leaders Role Model List (2022-2025) WeAreTheCity's Global Award for Achievement (2023) Dr. Afzal actively contributes to the HPC community through teaching and mentorship. She teaches courses on parallel programming of high-performance systems and supervises multiple bachelor's and master's theses on topics ranging from energy consumption analysis to MPI collective implementation. As vice chair of the IEEE Computer Society Germany Section Chapter and founder of the NHR Women in HPC chapter, she plays a significant leadership role in advancing the field and promoting diversity. She is deeply involved in major HPC initiatives including KONWHIR with the Leibniz Supercomputing Centre on performance optimization and NHR's EEC project focused on enhancing energy efficiency and managing operational costs across NHR centers, demonstrating her commitment to addressing real-world challenges in high-performance computing infrastructure.

• Statistics
• Environmental Statistics
• Time Series Analysis
• +3 more

Vincenzo Gioia is a Research Fellow at the University of Trieste's Department of Economics, Business, Mathematics and Statistics (DEAMS). She specializes in statistical methodologies for environmental and multivariate data analysis, with a focus on extreme value theory and Markov-switching models. Her work addresses climate change impacts (heat wave detection), energy demand modeling, and urban pollution analysis. Current Projects: Joint DEAMS research project analyzing public/telecom data for environmental applications Third-party transport data analytics funded by Trieste Trasporti (€33k) Research Interests: Bayesian statistics, time series modeling, spatial-temporal analysis, and algorithmic approaches to high-dimensional data. Active in environmental and energy sector applications.

Dimitris DrikakisView profile

Professor

• Fluid Dynamics
• Computational Science
• Acoustics
• +3 more

Professor Dimitris Drikakis serves as Vice President for Global Partnerships at the University of Nicosia, Cyprus, and holds a joint professorship in the School of Sciences and Engineering. His academic career spans over 24 years, including roles as Executive Dean and Head of Department at UK universities. His research focuses on fluid dynamics, acoustics, computational science, and nanotechnology, emphasizing uncertainty reduction and physics-based modeling. He has co-authored two books, published over 400 papers, and graduated 45 PhD students globally. Research interests include super-resolution fluid flow analysis, machine learning applications in turbulence simulation, and computational methods for aerodynamics and heat transfer. His work on indoor aerosol transmission and epidemiological modeling has addressed pandemic-related challenges. He serves on editorial boards for journals like Computers and Fluids and Physics of Fluids . Recent articles highlight advancements in deep learning for fluid-structure interaction, shock-wave predictions, and AI-driven thermal optimization. His contributions bridge computational science with real-world engineering and public health applications.