جست‌وجوی استاد و پژوهشگر

Gia-Wei Chernمشاهده پروفایل

دانشیار

• Theoretical Condensed Matter Physics
• Magnetism
• Strongly Correlated Electron Systems
• +۷ مورد دیگر

Dr. Gia-Wei Chern is an Associate Professor in the Department of Physics at the University of Virginia, specializing in theoretical condensed matter physics with a focus on magnetism and strongly correlated electron systems. He applies machine learning and data science methods to study multi-scale dynamical modeling of complex systems and functional materials, particularly their nonequilibrium behavior. His research extends to artificial spin ice and photonic lattices, with recent work on orbital ordering kinetics and phase separation dynamics. He is a member of the EXCLAIM collaboration, applying ML/AI to nuclear physics. Awards include the Shannon Fellowship and 3Cavaliers Awards for his contributions to discovery and inquiry. Scientific awards include Shannon Fellowship 3Cavaliers Awards (as lead researcher) His work spans fundamental theoretical physics and computational materials science, with notable publications in PNAS , Nature , and Physical Review Letters .

• Computational Mathematics
• Inverse Scattering
• Wave Propagation
• +۲ مورد دیگر

Alexander Mamonov is an Associate Professor in the Department of Mathematics at the University of Houston's College of Natural Sciences and Mathematics. He holds a Ph.D. in Computational and Applied Mathematics from Rice University and a Diploma with Distinction from Lomonosov Moscow State University. His research focuses on computational mathematics, inverse problems, wave propagation, and model order reduction. His research interests span computational mathematics, inverse scattering, wave propagation, model order reduction, and numerical linear algebra, with applications in geophysical imaging and scientific computing. Mamonov's publications demonstrate sustained focus on inverse problems and model reduction techniques, with recent work advancing tensor-based methods for parametric systems and data-driven approaches for waveform inversion. His articles consistently integrate mathematical theory with computational applications in wave propagation and imaging. Mamonov has received the ICERM Research Fellowship and maintains active research collaborations, including visiting positions at the University of Coimbra and ICERM. He coordinates the Seminar on Computational Mathematics in Oil & Gas Exploration and Imaging.

Rahul Mouryaمشاهده پروفایل

مدرس

• Machine Learning
• Computer Vision
• Inverse Problems
• +۶ مورد دیگر

Rahul Mourya is a Lecturer in Computer Science at the Faculty of Science and Engineering , University of Wolverhampton, UK. He joined in October 2023 and is affiliated with the Digital Innovations and Solution Centre (DISC) , which focuses on fundamental and applied research with societal and economic impact. PhD & MSc in Computer Science (Signal/Image/Vision) from Université Jean Monnet Saint-Etienne, France BEng in Electronics & Telecommunications from University of Pune, India His research develops foundational tools for machine learning, computer vision, inverse problems , and signal/image processing , with applications in autonomous systems, robotics, and sensor networks . Teaching includes computational mathematics, robotics engineering, and deep learning modules. Collaborations span institutions like Heriot-Watt University, Telecom ParisTech, and interdisciplinary projects in underwater acoustics and astronomical imaging. Current research explores measurement-consistent neural networks and optimization algorithms for inverse problems.

Corentin Caudronمشاهده پروفایل

دانشیار

• Volcanology
• Geophysics
• Volcanic Hydrothermal Systems
• +۵ مورد دیگر

Corentin Caudron is an Associate Professor & Principal Investigator at the WEL Research Institute, Université Libre de Bruxelles (ULB). His research focuses on volcano monitoring through geophysical approaches, with expertise in hydrothermal systems, magma dynamics, seismic noise analysis, volcanic lakes, infrasound, and hydroacoustics. He collaborates internationally with institutions in Indonesia, Iceland, New Zealand, the Philippines, and Hawaii. PhD in Science (2013) from ULB and Royal Observatory of Belgium MSc in Earth Sciences (2009) from ULB His three primary research routes include: Understanding (volcano-)hydrothermal systems using seismic and lake-monitoring approaches, with a focus on precursors to gas-driven eruptions Developing seismic noise-based methodologies (seismic interferometry, amplitude ratios, tremor analysis) for volcanic system monitoring Investigating low-frequency seismic and acoustic signals for underwater volcano degassing quantification Recent publications highlight trends in fiber optic volcano monitoring, machine learning for eruption forecasting, infrasound-seismic integration, and seasonal influences on volcanic activity. His team employs interdisciplinary techniques combining geochemistry and geophysics. Scientific contributions include: FNRS postdoctoral Fellow (2016-2017) Postdoctoral Fellow at University of Cambridge (2015-2016) Postdoctoral Fellow at Earth Observatory of Singapore (2013-2015) He actively supervises 8 PhD students and has co-supervised 2 former PhDs, with projects funded by FWO, FNRS, Wel-T, Fondation Wiener Anspach, and Catalyst. His lab maintains equipment like DAS/FBG interrogators, seismic arrays, and hydrophones. He serves as Co-Leader of the IAVCEI Commission on Volcanic Lakes and Editor for multiple journals.

Hannah Worthingtonمشاهده پروفایل

مدرس

• Statistical Ecology
• Animal Conservation
• Population Management
• +۷ مورد دیگر

Hannah Worthington is a Lecturer in Statistics at the University of St Andrews, affiliated with the Statistics Centre for Research into Ecological & Environmental Modelling. She holds a PhD in Statistical Development of Integrated Multi-State Stopover Models from the University of St Andrews (2016). Her research focuses on developing and applying statistical ecology methods to animal conservation and population management. Key interests include capture-recapture techniques, spatial capture-recapture, passive acoustic/visual monitoring, and workflow pipelines for new technologies. She also investigates public health issues such as antibiotic misuse in East Africa through mixed-methods approaches, linking multidimensional poverty to healthcare behaviors. Her work contributes to UN Sustainable Development Goals related to environmental protection and global health. Recent publications highlight her contributions to citizen science data analysis, spatial modeling bias reduction, and antibiotic resistance studies in East Africa. Her earlier work includes foundational research on hidden Markov models for birth cohort recruitment and therapy termination in clinical psychology. Scientific Awards: Conference Award (2012), Conference Award (2013) Summer Studentship (2009, 2010) Travel Bursary (2014) She actively participates in academic activities such as conferences (e.g., International Statistical Ecology Conference 2020) and public engagement (e.g., EXPLORATHON @ The Shops in 2023). Her research collaborations span institutions in East Africa (Kenya, Tanzania, Uganda) and Mongolia. She supervises postgraduate research students, offering projects on topics like movement ecology and statistical inference integration with machine learning. Hannah leads the development of statistical tools for ecological modeling, including a publicly available R package for seal population analysis (Zenodo, 2020). She collaborates with the HATUA Consortium on antimicrobial resistance research and maintains expertise in Hidden Markov Models and multidisciplinary data integration.

Narada Dilp Warakagodaمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Autonomous Systems
• Computer Vision
• +۳ مورد دیگر

Narada Dilp Warakagoda is an Associate Professor at the University of Oslo, affiliated with the Section for Autonomous Systems and Sensor Technologies. His research focuses on Artificial Intelligence, Autonomous Systems, Computer Vision, Robotics, Machine Learning, and Deep Learning, with notable contributions to underwater acoustics, renewable energy forecasting, and unmanned vehicle control systems. He collaborates extensively with researchers across disciplines to advance applications in environmental monitoring, energy systems, and autonomous technologies. His work integrates advanced machine learning techniques with domain-specific challenges, such as synthetic aperture sonar analysis, spatio-temporal wind forecasting, and reinforcement learning for autonomous navigation. He has published in leading journals and conferences, addressing topics like probabilistic solar irradiance prediction, graph-based wind energy modeling, and deep learning for seabed munitions detection. Warakagoda’s research bridges theoretical advancements with practical implementations, emphasizing applications in robotics, environmental science, and defense technologies. His projects often involve interdisciplinary teams, reflecting his commitment to solving real-world problems through innovative AI-driven solutions.

Olivier Lartillotمشاهده پروفایل

پژوهشگر

• Computational Music Analysis
• Artificial Intelligence
• Music Information Retrieval
• +۶ مورد دیگر

Olivier Lartillot is a Researcher at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion at the University of Oslo. His work focuses on computational music and sound analysis, artificial intelligence, and music information retrieval. He developed the MIRtoolbox and MiningSuite frameworks, tools widely used in music analysis. Previously, he held roles including Academy of Finland Research Fellow (2009–2014) and postdoctoral positions at the Finnish Centre of Excellence in Interdisciplinary Music Research. Education: PhD in Music and Acoustics from IRCAM/UPMC Paris (2000–2004); Master’s in Musicology from Sorbonne University (1996–1999). Research Projects: MIRAGE (AI-based music analysis), SoundTracer (Norwegian folk music transcription), HIGH-M (music and health interaction). His research interests span computational models of music structure, cross-cultural music analysis, and applications in music therapy and digital heritage. He has authored over 80 articles (3,000+ citations) and contributed to initiatives like the Norwegian Folk Music Archive digitization. He is a member of the editorial board for Transactions of the International Society for Music Information Retrieval and an expert evaluator for EU Horizon 2020 programs. His work bridges technical innovation with musicological theory, addressing challenges in automated transcription, segmentation, and expressive performance analysis.

• Materials Science
• Mechanical Engineering
• Biomaterials
• +۳ مورد دیگر

Prof. Nicola Pugno is a Professor at the School of Engineering and Materials Science, Queen Mary University of London, affiliated with the Centre for Sustainable Engineering. His research focuses on bioinspired materials, metamaterials, and mechanical systems, with contributions to spider silk mechanics, structural engineering, and soft robotics. Research interests span biomaterials, mechanobiology, and advanced composites, emphasizing applications in energy, sustainability, and biomedical devices. His work integrates experimental, computational, and theoretical approaches to explore material properties and design novel structures. Publications highlight interdisciplinary innovations, such as bioinspired surfaces, hierarchical metamaterials, and soft robotics. Collaborations bridge engineering, biology, and physics to address challenges in material performance and environmental sustainability.

Caspar A.S. Pouwمشاهده پروفایل

پژوهشگر ارشد

• Pedestrian Dynamics
• Crowd Flow Modeling
• Fluid Dynamics
• +۳ مورد دیگر

Caspar A.S. Pouw is a Research Fellow in the Department of Applied Physics and Science Education at Eindhoven University of Technology (TU/e). He holds a dual role as a Postdoc researcher and Data Scientist at ProRail. His work focuses on advancing human crowd flow monitoring, modeling, and nudging technologies, particularly within the HTCrowd project. Pouw’s research integrates fluid dynamics principles to analyze pedestrian behavior in crowded environments, aiming to enhance safety and efficiency in urban spaces. Educated at TU/e, he earned his Master’s in Applied Physics (specializing in Fluids and Flows) and a Bachelor’s in Combustion Science. He has taught courses on sociophysics, covering crowd dynamics analysis, modeling, and nudging strategies. His contributions align with UN Sustainable Development Goals related to safe cities and resilient infrastructure. Recent research emphasizes data-driven modeling of pedestrian dynamics, psychological influences on train boarding efficiency, and real-time monitoring systems. His work bridges physics, computer science, and urban planning, with applications in transportation and public safety. Collaborations include ProRail and interdisciplinary teams at TU/e. Pouw’s datasets and software tools, such as those for pedestrian trajectory analysis, are openly available. His media coverage highlights innovations in crowd management post-COVID-19. Future work involves expanding predictive models for crowd behavior and optimizing transport infrastructure design.

Andrea Spotoمشاهده پروفایل

استاد

• Psychology
• Cognitive Science
• Moral Judgment
• +۵ مورد دیگر

Andrea Spoto is a Full Professor at the University of Padua, Italy. His research focuses on psychological assessment methodologies, cognitive diagnostic models, moral judgment dynamics, and the application of Bayesian methods in healthcare and education. He has pioneered work on technology acceptance models (e.g., AV-TAM for automated vehicles) and developed innovative tools like the PENguIN mental health application. His interdisciplinary approach bridges psychology, statistics, and technology, addressing topics such as automated vehicles’ ethical dilemmas, clinical decision-making frameworks, and the psychometric validation of scales for anxiety, worry, and countertransference. Research interests span moral reasoning in self-sacrificial dilemmas, the role of decision thresholds in professional judgments (e.g., nursing triage, sports refereeing), and the network structure of psychopathological responses to crises. He has contributed to understanding nonverbal behavior in schizophrenia, coping mechanisms in neurofibromatosis patients, and the psychological impact of the COVID-19 pandemic across multiple countries. His work integrates formal concept analysis and knowledge space theory to enhance adaptive assessment systems, particularly in educational and clinical contexts. Notable contributions include refining item response theory models and advancing methodologies for measuring emotional intelligence, scientifical-medical satisfaction, and social odor awareness.

Yeong Shiong Chiewمشاهده پروفایل

مدرس ارشد

• Biomedical Engineering
• Mechanical Ventilation Protocols
• Artificial Intelligence in Healthcare
• +۵ مورد دیگر

Dr. Yeong Shiong Chiew is a Senior Lecturer at Monash University Malaysia's Malaysia School of Engineering. He holds a PhD in Mechanical Engineering from the University of Canterbury (New Zealand), specializing in model-based mechanical ventilation for respiratory failure patients. His research focuses on biomedical engineering, physiological modeling, and clinical trial design, with a strong emphasis on AI applications in healthcare and mechanical system control. He also serves as an Adjunct Senior Fellow at the University of Canterbury's Mechanical Engineering Department and holds leadership roles in global engineering organizations like the International Federation of Automatic Control (IFAC). Education: PhD in Mechanical Engineering, University of Canterbury (2013) MEng in Mechanical Engineering, Universiti Teknologi Malaysia (2010) BEng in Mechanical-Automotive Engineering, Universiti Teknologi Malaysia (2007) Research Interests: Model-based mechanical ventilation protocols AI-driven healthcare solutions Respiratory mechanics modeling Clinical decision support systems Medical device development Virtual reality in healthcare Recent Projects: Clinical Application of Respiratory Models (CARE Study) – evaluating ventilator-patient interaction AI-enhanced breast cancer risk modeling Real-time palm fruit ripeness detection via UAV High-resolution single-pixel imaging using GANs Awards: RINENG Young Investigator Award (2023) MUPA Supervisor of the Year (2020) PVC Excellence in Education Award (2019 Team) ITEX Gold Medal (2021) Grants & Supervision: Accepting PhD students in mechanical ventilation modeling, AI healthcare, and physiological systems Principal investigator on multiple interdisciplinary grants totaling over MYR 5M Supervised projects in ICU monitoring systems and medical device prototyping Labs & Teams: Leading the Model-Based Clinical Ventilation Research Group at Monash Malaysia Collaborator with the University of Liege's Biomedical Engineering Lab Part of the IFAC Technical Committee on Biological & Medical Systems

Hossein Masoumiمشاهده پروفایل

دانشیار

• Rock Mechanics
• Geotechnical Engineering
• Constitutive Modeling of Intact Rock
• +۵ مورد دیگر

Hossein Masoumi is an Associate Professor in Rock Mechanics and Rock Engineering at Monash University's Department of Civil and Environmental Engineering. He previously held roles at UNSW Australia, including Senior Lecturer (2018–2022) and Lecturer (2016–2018). His academic qualifications include a PhD in Geotechnical Engineering from UNSW (2013), MEng in Mine Management (2009), and B.Sc. in Mining Engineering from Tehran University (2007). Research focuses on rock mechanics, particularly size/scale effects on intact rock, constitutive modeling, and jointed rock behavior. He leads the Resources Engineering group at Monash and holds editorial roles in top journals like International Journal of Rock Mechanics and Mining Sciences and Rock Mechanics and Rock Engineering . Major grants include the $5M Hub for Infrastructure Net Zero (2024–2029) and ACARP-funded projects on coal mine stability and RFID-based monitoring. Supervised over 15 PhD students, with ongoing advisees in rock mechanics and AI applications. Teaches courses like RSE1010 and MNE3040. Contributions span 98+ publications, including books on size effects in rock. Active in industry partnerships, advising firms like Geo-Engineering Solutions and New Century Resources on advanced testing and numerical modeling.

Weiqiang Zhuمشاهده پروفایل

استادیار

• Seismology
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Weiqiang Zhu is an Assistant Professor at the University of California, Berkeley, specializing in earthquake physics and geophysical signal analysis. His research integrates machine learning, deep learning, and cloud computing to analyze large seismic datasets, focusing on understanding complex earthquake sequences, fault zone structures, and subsurface processes like fluid migration and volcanic unrest. He develops advanced computational methods such as neural operators and physics-informed models to improve seismic inversion, earthquake simulation, and early warning systems. Key contributions include the development of tools like PhaseNet for seismic phase picking, QuakeFlow for scalable earthquake monitoring, and DeepShake for shaking intensity prediction. His work bridges artificial intelligence with geophysical applications, emphasizing high-resolution earthquake cataloging and fault zone mechanics. Collaborative projects include imaging fault zones using fiber optics and studying magmatic systems beneath Hawaii. Zhu’s research also explores the interplay between fluid dynamics and seismic activity, particularly in simulating earthquake sequences with inertia, viscoelasticity, and fault zone fluid migration. He advocates for open-source tools, contributing datasets like the STanford EArthquake Dataset (STEAD) to advance AI-driven seismology.

• Autonomous Systems
• Unmanned Aerial Vehicles (UAVs)
• Artificial Intelligence
• +۵ مورد دیگر

Professor Jon Platts is the Dean of Cardiff School of Technologies and a Professor of Autonomous Systems at Cardiff Metropolitan University. He holds a BEng in Electrical and Electronic Engineering from the University of Bradford, an MSc in Aerosystems Engineering, and a PhD in Self-Organising Fuzzy Logic from Loughborough University. With a career spanning the UK Royal Air Force and industry roles at QinetiQ, he specializes in autonomous systems, UAVs, and cybersecurity. He is a Chartered Engineer and Fellow of the Institution of Engineering and Technology, with affiliations to the Institution of Measurement and Control and the International Council on Systems Engineering. His research focuses on advancing autonomous systems, IoT applications, cybersecurity in multinational operations, and ethical AI. Recent work includes smart water management, metaverse security, and sustainable hospitality technologies. He collaborates with organizations like Dstl, BAE Systems, and Thales UK, emphasizing cross-disciplinary and industry-academia partnerships. Key achievements include leading QinetiQ’s autonomy division, shaping defense research programs, and publishing widely on UAV control, AI ethics, and IoT. Awards include his IET Fellowship and Chartered Engineer status. His leadership drives innovation in Cardiff School of Technologies’ research centers like the Centre for Industry 4.0 and Blockchain Research.

Annegret Falknerمشاهده پروفایل

استادیار

• Neuroscience
• Behavioral Neuroscience
• Social Behavior
• +۳ مورد دیگر

Annegret Falkner is an Assistant Professor at the Princeton Neuroscience Institute (Princeton University). Her research focuses on the neural mechanisms underlying social behavior, social affective states, and hormone-dependent neuroplasticity in adults. She holds a BA in Biology and Biochemistry from Oberlin College (2002), a PhD in Neuroscience from Columbia University (2012), and completed postdoctoral training at NYU. Her work integrates experimental and computational approaches to study aggression circuits, social learning, and stress responses. Education: B.A. Biology & Biochemistry, Oberlin College (2002) PhD in Neuroscience, Columbia University (2012) Postdoctoral Fellowship, NYU (2012–2016) Research Interests: Dr. Falkner’s lab investigates how neural circuits mediate social interactions, aggression, and hormonal influences on behavior. Key themes include: Neuroendocrine regulation of social motivation Aggression circuits and behavioral persistence Neural substrates of social learning Stress and resilience mechanisms Her recent studies explore dopamine’s role in resilience, oxytocin’s role in maternal behavior, and computational tools for multi-animal tracking. Recent Research Trends: Her articles highlight advancements in aggression circuit dissection, neuroendocrine systems analysis, and behavioral tracking technologies. Work on the lateral habenula and dopaminergic signatures underscores her focus on stress and resilience pathways. Lab & Collaborations: Her lab employs rodent models, electrophysiology, and machine learning (e.g., SLEAP system). Collaborations emphasize interdisciplinary approaches to neurobehavioral problems.