Dr. Muhammad Azmi UMER is a Lecturer at DHA Suffa University and a Ph.D. Scholar at Karachi Institute of Economics and Technology, Pakistan. His research focuses on Machine Learning applications in Cyber Physical Systems (CPS), particularly intrusion detection in industrial control systems like the SWaT testbed. He holds a Master’s in Computer Science from Karachi Institute of Economics and Technology and a Bachelor’s from the University of Karachi. His academic work emphasizes cybersecurity challenges in smart grids, IoT healthcare systems, and adversarial machine learning techniques. Key contributions include developing decision tree-based intrusion detection frameworks and adversarial attack simulations for industrial systems. He collaborates with researchers like Dr. Jit BISWAS and Dr. Eyasu G. CHEKOLE within interdisciplinary teams. Publications span machine learning applications in smart cities, CPS security protocols, and IoT conceptual frameworks. His research bridges theoretical models with practical implementations in critical infrastructure security and urban technology systems.
Dr. Matt Bonney is a Lecturer in Space Engineering at Swansea University, affiliated with the School of Aerospace, Civil, Electrical and Mechanical Engineering. He holds a position in the Department of Aerospace Engineering and is actively involved in postgraduate supervision. His research focuses on digital twin technology, nonlinear structural dynamics, mechanical joint modeling, seismic reliability, and uncertainty quantification, with recent emphasis on digital twin security and thermo-mechanical coupling in assembled structures. Dr. Bonney's expertise spans multi-physics joint modeling and multi-disciplinary development of digital twins, with international collaborations. He teaches modules such as 'Advanced Space Systems' (EG-M334) and 'Aerospace Systems' (EGA220), emphasizing space system design, orbital mechanics, and cyber-physical security. His research highlights include the development of a Python Flask-based digital twin operational platform, contextualization of information in digital twin processes, and experimental studies on frictional interfaces. His work on uncertainty quantification and seismic reliability has applications in nuclear reactor systems and civil engineering structures. Dr. Bonney currently supervises a PhD student focusing on nonlinearities in thermal-mechanical joints. His research outputs include over 30 peer-reviewed publications, with contributions to journals like Mechanical Systems and Signal Processing and Data-Centric Engineering .
Philippe Moireau is a Full Professor in the Department of Applied Mathematics at École Polytechnique, where he is also affiliated with the Center for Applied Mathematics (CMAP). He serves as the head of the Inria Project-Team MΞDISIM (Mathematical and Mechanical Modeling with Data Interaction for Simulation in Medicine) and holds the distinguished position of Ingénieur Général of The Corps des Mines. His primary research focuses on inverse problems and data assimilation for partial differential equation models, with particular emphasis on: Observer-based methods from optimal control perspectives Stabilization approaches for evolution equations Numerical analysis of time-dependent control problems Digital twin applications in cardiovascular medicine Professor Moireau's publication portfolio demonstrates consistent focus on mathematical methods for physical systems, with recurring themes in: Data assimilation techniques for PDE-based models Numerical stabilization and discretization methods Cardiovascular biomechanics and hemodynamics Stochastic modeling of biological systems Epidemiological forecasting and control He leads the ANANKΞ project-team at Inria focused on Analysis And Numerics of physical-Knowledge-based Estimation. His educational contributions include lectures on data assimilation theory at CEMRACS and courses on mathematical modeling in cardiac biomechanics at Institut Polytechnique de Paris.
Joost Batenburg is a Professor at Leiden Institute of Advanced Computer Science (LIACS) , with a chair in Imaging and Visualization . He is affiliated with the Centrum Wiskunde & Informatica (CWI) and serves as Program Director for the interdisciplinary Society, Artificial Intelligence and Life Sciences (SAILS) initiative. His research focuses on tomographic image processing and reconstruction , where he has published over 80 journal articles and 60 conference papers. Current projects include Universal Three-dimensiOnal Passport for process Individualization in Agriculture (UTOPIA) and Center for Optimal, Real-Time Machine Studies of the Explosive Universe (CORTEX) , both funded by NWO grants. He leads the FleX-Ray Lab , a custom CT system integrated with advanced data processing algorithms. His research spans discrete tomography , real-time imaging pipelines , and AI-enhanced reconstruction methods , with applications in industrial inspection, agricultural analysis, and cultural heritage conservation. Recent articles demonstrate novel approaches to: Single-shot dynamic object tomography using level-set methods and motion modeling X-ray scattering quantification for defect detection in real-time systems Cross-modal image registration between CT scans and physical photographs Auto-differentiation in CT workflows combining classical and machine learning algorithms Scientific Awards: Dutch Award for ICT Research (2018) C.J. Kok Prize (2007) Philips Mathematics Prize (2006) He has supervised numerous PhD candidates including Mary Go, Eani Lachmansingh, and Zhichao Zhong, while maintaining editorial roles at IEEE Transactions on Computational Imaging and Journal of Mathematical Imaging and Vision . His work bridges theoretical mathematics with practical applications in agriculture, industry, and art conservation.
Riikka Puurunen is an Associate Professor at Aalto University's Department of Chemical and Metallurgical Engineering, leading the Catalysis group since 2017. Her work focuses on developing solid heterogeneous catalysts using atomic layer deposition (ALD), microreactors, and in situ testing methods to advance sustainable biomass-based solutions.
Dr. Paolo Bergamo is a Senior Researcher at the Swiss Seismological Service (SED), ETH Zurich, since April 2016. He specializes in engineering seismology, focusing on earthquake site response models, ground-motion modeling, and seismic risk assessment. His work includes projects such as the Earthquake Risk Model Switzerland (ERM-CH23) and the SERA Horizon2020 initiative. He holds a PhD in Earth Sciences from Politecnico di Torino (2012) and advanced degrees in Environmental Engineering. Key research areas include soil amplification analysis, geophysical surveys, and the integration of empirical and computational methods for seismic hazard mitigation. His contributions span microzonation studies, site characterization using borehole and ambient vibration data, and the development of design-compatible waveforms for Swiss building codes. Education: PhD in Water and Territory Management Engineering, Politecnico di Torino (2012) MSc and BSc in Environmental Engineering, Politecnico di Torino (2008, 2005) Research Interests: Dr. Bergamo’s work emphasizes the collation of empirical ground-motion data with building codes, spatial modeling of soil amplification, and geophysical site characterization. He employs advanced techniques like surface-wave analysis, machine learning, and canonical correlation for seismic hazard assessment. Projects & Grants: ERM-CH23: Site response implementation and national seismic risk modeling SERA Project (Horizon2020): Site characterization indicators Swiss Federal Office for Environment-funded studies on microzonation and geophysical monitoring Labs & Teams: Active contributor to the Engineering Seismology group at SED, leading efforts in alpine valley seismic modeling and offshore site characterization in Lake Lucerne.
Rainald Loehner is a Distinguished Professor of Fluid Dynamics at George Mason University's Center for Computational Fluid Dynamics. Since 2003, he has led the Center for Computational Fluid Dynamics at George Mason University. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS) for 2023, hosted by Professors Kai-Uwe Bletzinger and Roland Wüchner in the 'Adjoint-Based System Identification of Large-Scale Structures' Focus Group. Loehner received his Diplom Ingenieur (Maschinenbau) degree from the Technical University of Braunschweig, and his PhD and a DSc in civil engineering from the University College of Swansea, Wales. After teaching at Swansea for a year, he worked at the Naval Research Laboratory in Washington, DC, followed by a research professorship at George Washington University. He joined George Mason University as an associate professor and was promoted to full professor in 1995 and distinguished professor in 2004. With over 35 years of experience, Professor Loehner's research spans the complete pipeline of numerical solvers and simulation tools. His expertise includes pre-processing, grid generation, numerical methods, field solvers, parallel computing, adaptive mesh refinement, fluid-structure interaction, shape optimization, system identification, and computational crowd dynamics. His current work focuses on developing advanced field solvers for compressible and incompressible flows, acoustics, electromagnetic wave propagation, heat and mass transfer, structural mechanics, and fluid-structure interaction. Key application areas include blast mitigation, ship hydrodynamics, blood flow, contaminant transport, and pedestrian safety. Loehner's recent research output (2020-2024) shows a strong trend toward digital twin technology and adjoint-based methods for structural analysis and optimization. His publications focus on high-fidelity digital twins for detecting structural weaknesses, risk assessment in engineering systems, and optimization of sensor placement. His work bridges computational mechanics with machine learning approaches, particularly in system identification and inverse problems, demonstrating how computational methods can solve complex real-world engineering challenges. 2020: Ranked #15119 in the Stanford List of Most Influential Scientists of the World; #8 in Aerospace and Aeronautics 2010: Distinguished International Career Award, Argentine Association of Computational Mechanics 2008: Fellow, International Association for Computational Mechanics 2006: Associate Fellow, AIAA 2005: Honorary Professor, University of Wales Swansea 2005: Advisory Professor, Shanghai Jiao Tong University 2004: Distinguished Professor of Fluid Dynamics, George Mason University 1999: Computational Mechanics Achievements Award, Japan Society of Mechanical Engineering 1993: Doctor of Science in Civil Engineering, University College of Swansea 1979-1983: Studienstiftung des Deutschen Volkes (Top 1% of German Students) Professor Loehner has mentored numerous students through his work at George Mason University and has supervised research in computational fluid dynamics, structural mechanics, and related fields. His research has been supported by various grants from government agencies and industry partners, enabling the development of advanced simulation tools applied in aerodynamics, hydrodynamics, shock-structure interaction, and medical applications. His codes and methods have been widely adopted in industry and academia for applications ranging from aircraft and ship design to medical simulations and urban pathogen transmission modeling. Loehner leads the Center for Computational Fluid Dynamics at George Mason University, which focuses on developing cutting-edge computational methods for fluid dynamics and related multiphysics problems. The center works on strategic application areas including blast mitigation, ship hydrodynamics, blood flow simulation, and pedestrian movement modeling. As a TUM-IAS Fellow, he collaborates with the Chair of Computational Modeling and Simulation at TUM on adjoint-based system identification of large-scale structures, bringing together expertise in computational mechanics and digital twin technology to address complex engineering challenges.
Rowena Hill is a Professor of Psychology at Nottingham Trent University's School of Social Sciences, specializing in disaster psychology and emergency response systems. She holds key roles including ESRC Policy Fellow for Climate Change, Honorary Research Lead for the Fire Fighters Charity, and Chair of the National Fire Chiefs Council's Academic Collaboration Group. Her work bridges academic research with policy, focusing on resilience strategies for emergency responders, community risk management, and mental health support systems. Education: Not explicitly stated in provided texts Her research emphasizes psychological health in emergency contexts, including pandemic response, climate adaptation, and familial impacts of frontline work. She has led over 60 evidence-based reports for UK pandemic policy during her 2020–21 secondment to the C19 National Foresight Group. Key interests include humanitarian assistance frameworks, public risk communication, and organizational resilience in critical sectors. Recent publications analyze firefighter wellbeing, police resilience training, and extreme weather preparedness. Notable achievements include establishing evaluation frameworks for fire service interventions and advising national security inquiries. Awards: Fellow of the British Psychological Society, Fellow of the Higher Education Academy Dr. Hill collaborates with governmental bodies and emergency services, contributing to policy development through evidence synthesis. Her work addresses systemic challenges in emergency service collaboration, climate change adaptation, and psychological support structures for responders and affected communities. She leads the NTU Emergency Services Research Unit, focusing on operational learning and health strategies for emergency personnel. Current projects explore long-term resilience in post-pandemic recovery and climate-related disaster preparedness.
Mohsen Heidari is an Assistant Professor in the Department of Computer Science at Indiana University, Bloomington. He is affiliated with the IU Quantum Science and Engineering Center (QSEc) and the NSF Center for Science of Information (CSoI). He previously held positions as a Visiting Assistant Professor at Purdue University and as a Postdoctoral Research Associate at CSoI. Ph.D. in Electrical Engineering (2019) and M.Sc. in Applied Mathematics (2017) from the University of Michigan His research focuses span quantum computing, theoretical machine learning, and information theory. Key themes include: Quantum algorithm design and sample complexity Fourier-based learning frameworks Quantum-classical duality in learning problems Information-theoretic approaches to biological systems Article trends show a strong emphasis on quantum-classical learning intersections (6/15 papers), Fourier analysis applications (5/15), and information-theoretic foundations (12/15). Notable venues include NeurIPS, IEEE Transactions, and ISIT. He directs research involving: Quantum Neural Network development Quantum measurement simulation Quantum data compression techniques Quantum algorithm implementation constraints
Dr. Andrzej Ożadowicz is a University Professor at the Department of Power Electronics and Automation of Energy Conversion Systems within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His office is located in room 510, building C-1, with contact details including phone +48 12 617 50 11 and email ozadow@agh.edu.pl. He holds PhD, DSc, and Engineering degrees, reflecting his dual expertise in academic research and practical engineering applications. His research spans Power Electronics, Building Automation, Smart Grids, and IoT-driven energy systems. Key interests include energy efficiency optimization through digital twins and BIM, distributed energy resource integration , and AI-enhanced demand management . Notably, he pioneers applications of deep reinforcement learning in home energy systems and develops frameworks for Smart Readiness Indicator implementation. His work bridges theoretical innovation with practical case studies in building thermal modeling and dynamic façade systems. Recent publications (2021-2025) reveal three dominant trends: (1) Convergence of digital twin technology with building automation for real-time energy management; (2) Critical analysis of IoT security and interoperability in smart infrastructure; (3) Pedagogical innovations in engineering education through blended learning methodologies post-COVID-19. His scholarly output demonstrates consistent focus on energy transition challenges and smart grid evolution. Professor Ożadowicz actively contributes to the Discipline Council for Automation, Electronics, Electrical Engineering and Space Technologies at AGH. He is instrumental in the AutBudNet initiative —a network of certified laboratories for energy efficiency assessment that implements "learning by doing" principles in building automation education. His work with this consortium emphasizes practical validation of smart grid technologies and demand response systems.
Prof Scott Crowe is a leading academic and clinical researcher in radiation oncology medical physics, affiliated with the Royal Brisbane and Women’s Hospital and the Hudson Institute of Medical Research (HBI) Cancer Care Services. His work bridges clinical practice and advanced research in radiotherapy technologies. Clinical Role: Clinical Lead for Cancer Care Services at HBI, overseeing radiation oncology medical physics. Education: Post-doctoral fellowship at Queensland University of Technology (QUT). Research Interests focus on: 3D Printing: Developing patient-specific phantoms and devices for radiotherapy applications (e.g., lung, vaginal, and oral molds). Dosimetry: Advancing measurement techniques (ionization chambers, Monte Carlo simulations) and addressing challenges like small field dose corrections, skin dose enhancement, and secondary cancer risk assessment. Adaptive Radiotherapy: Real-time motion adaptation systems, including Radixact Synchrony and TomoTherapy, to improve treatment accuracy. Quality Assurance: Statistical process control for beam energy variations, gamma evaluation methods, and machine performance checks. Publication Trends highlight his expertise in integrating 3D printing with dosimetry, optimizing adaptive radiotherapy workflows, and improving quality assurance protocols. His work spans Monte Carlo simulations , proton therapy , and image-guided radiotherapy . Supervision: Mentors higher degree research students in radiation oncology physics. Conferences: Regular presenter at international scientific meetings. Labs & Collaborations: Manages the radiation oncology medical physics research portfolio at Royal Brisbane and Women’s Hospital, collaborating with Hudson Institute on clinical translation projects.
Prof. Ady Arie is a Professor of Electrical Engineering at Tel Aviv University, where he serves as the Head of the Tel Aviv University Center for Light-Matter Interaction and holds the Marko and Lucie Chaoul Chair in Nano-Photonics. He has been a faculty member at the Iby and Aladar Fleischman Faculty of Engineering since 1993, previously serving as Head of the School of Electrical Engineering (2013-2017) and Vice Dean of Research (2011-2013). His educational background includes: B.Sc. in Mathematics and Physics from Hebrew University of Jerusalem (1983) M.Sc. in Physics from Tel-Aviv University (1986) Ph.D. in Engineering from Tel-Aviv University (1992) Prof. Arie's research spans multiple frontiers of optics and photonics. His work in nonlinear optics focuses on advanced frequency conversion techniques and shaping of light parameters using nonlinear photonic crystals. In quantum optics , he develops quantum light sources based on spontaneous parametric down conversion and explores applications in quantum sensing and communication. His plasmonics research investigates manipulation of surface plasmon polaritons on metal surfaces. In electron optics , he studies electron-matter-light interactions and techniques for sculpting electron wave functions. His lab also explores hydrodynamics through quantum simulations with water waves, creating analogies to quantum mechanical phenomena. Analysis of Prof. Arie's recent publications (2023-2025) reveals a strong focus on quantum technologies, particularly in quantum light generation, quantum sensing, and quantum information processing. His work increasingly integrates concepts from nonlinear optics, electron microscopy, and quantum physics, with growing emphasis on practical applications in quantum communication and computation. The research shows sophisticated manipulation of light-matter interactions across multiple platforms including nonlinear photonic crystals, plasmonic structures, and electron beams. Prof. Arie has received significant recognition for his work: Kadar Foundation Award for Excellence in Research (2016) Fellow of the Optical Society of America Editorial roles including Topical Editor of Optics Letters (2008-2014) and Associate Editor of Optica (since 2018) Prof. Arie leads the Nonlinear Optics and Wave Propagation Laboratory at Tel Aviv University, where his team investigates diverse wave phenomena from light frequency conversion to electron beam manipulation. He has served as chair of the national steering committee of the Israeli Planning and Budgeting Committee on Quantum Science and Technology. His research has been supported by various grants enabling the development of novel optical technologies and quantum systems. While specific grant details aren't provided in the text, his extensive publication record and leadership positions suggest substantial research funding. Prof. Arie's laboratory focuses on the intersection of classical and quantum wave phenomena. The lab investigates light manipulation through nonlinear optical processes, plasmonic structures, and electron microscopy techniques. Current research directions include quantum light generation, electron-photon interactions, and hydrodynamic analogs to quantum systems. The lab appears well-equipped for advanced optical experimentation with capabilities spanning visible to infrared wavelengths, nonlinear crystal engineering, and electron beam characterization.
Wei Gao is an Associate Professor at the Swanson School of Engineering, University of Pittsburgh. His research focuses on the design, deployment, analysis and measurement of on-device AI architectures and algorithms on mobile, embedded and networked systems. He has strong interests in unveiling analytical principles underneath practical AI deployment problems, and designing systems based on these principles. The developed AI and system solutions are widely applied to various application scenarios, including Internet of Things, edge computing and smart health. Dr. Gao received his PhD from Pennsylvania State University in 2012 and his B.E. from the University of Science and Technology of China in 2005. Dr. Gao's research spans across Cyber-Physical Systems , Infrastructure Security , High Performance Computing , and the Distributed Governance of Information . His work particularly emphasizes on-device AI architectures and algorithms for mobile and embedded systems. He explores how to deploy AI efficiently on resource-constrained devices, with applications in Internet of Things, edge computing, and smart health. His research aims to bridge theoretical principles with practical system implementations, focusing on creating efficient, secure, and reliable AI solutions for real-world deployment scenarios. His recent work has increasingly focused on bringing Large Language Models to edge devices while maintaining performance and security. Analysis of Dr. Gao's recent publications (2021-2025) reveals a strong focus on on-device AI, particularly around Large Language Models for resource-constrained environments. His work addresses critical challenges including model personalization, security against illegal adaptation, sparse activation techniques, and physics-grounded generation. Much of his research targets making AI more efficient, secure, and practical for deployment on edge devices with limited computational resources, while also exploring applications in health monitoring and power systems. Dr. Gao has received significant recognition for his research, including: NSF Faculty Early Career Development (CAREER) Award (2016) Dr. Gao mentors numerous graduate students who contribute to his research in mobile computing, embedded systems, and on-device AI. His research has been supported by various grants, most notably the NSF CAREER award, enabling his team to explore innovative approaches to mobile and embedded AI systems. His lab investigates how to optimize AI for resource-constrained environments while maintaining performance and security, with particular focus on balancing computational efficiency with model accuracy. Dr. Gao leads a research group focused on mobile and embedded AI systems, with particular emphasis on making AI practical for deployment on everyday devices. His team explores novel techniques for model compression, efficient inference, and secure deployment of AI models on edge devices, with applications ranging from health monitoring to smart infrastructure.
Susanne Narciss is a Professor at the Psychology of Learning and Instruction department of Technische Universität Dresden, leading the Center of Tactile Internet with Human in the Loop (CeTI). Her research focuses on error processing in educational contexts, with 15 recent publications analyzing error climates, feedback strategies, and motivational frameworks. Key Research Areas : Learning from errors/failure, instructional feedback design, affective-motivational responses, error-related metacognition. Methodological Scope : Combines longitudinal studies, experimental designs, and qualitative analyses across K-12, university, and informal learning settings (museums, home contexts). Her work emphasizes context-specific interventions for educators, parents, and students, including error-competency training programs and metacognitive scaffolding tools. Current projects examine vibrotactile feedback systems for motor learning and cultural responsiveness in psychology education. Collaborative Networks : Works with international teams on the International Competences for Undergraduate Psychology model and cyber-physical system pedagogy. Recent Trends : 2025 articles focus on collaborative error processing, scenario-based human-machine interaction, and generative learning tasks in digital environments.
Friedl De Groote is a Senior Lecturer at KU Leuven's Faculty of Human Movement and Rehabilitation Sciences, Department of Human Movement Sciences, specializing in the biomechanics of human movement. She leads the Biomechanics of Human Movement Research Group and is a member of the iSi Health - KU Leuven Institute for Physics-based Modeling for In Silico Health. Her research focuses on understanding neuromusculoskeletal control of human movement, particularly through computational modeling approaches. She investigates gait disorders in children with cerebral palsy and Duchenne muscular dystrophy, examining how muscle impairments, contractures, and neural control mechanisms contribute to altered movement patterns. Her work combines experimental biomechanics with predictive computer simulations to uncover the underlying mechanisms of movement disorders and develop new rehabilitation approaches. She also studies fundamental aspects of human locomotion, balance control, and energy expenditure during walking. Her recent publications demonstrate a strong emphasis on predictive simulations to understand the relationship between neuromusculoskeletal impairments and movement pathology. Her research spans multiple domains including cerebral palsy, Duchenne muscular dystrophy, gait analysis, balance control, and musculoskeletal modeling. She frequently collaborates with clinical researchers to bridge the gap between computational models and clinical applications. Dr. De Groote is actively involved in various academic councils including the Faculty Council FaBeR, POC Rehabilitation Sciences and Physiotherapy, POC Physical Education and Movement Sciences, and multiple departmental councils within Human Movement Sciences. Her ORCID identifier is 0000-0002-4255-8673 . She currently leads or co-leads numerous research projects funded through KU Leuven and external grants, with a focus on understanding walking control mechanisms, energy expenditure during locomotion, and developing computational models to inform rehabilitation strategies for children with movement disorders. Her research portfolio demonstrates a commitment to translating biomechanical insights into clinically relevant applications.
