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Stuart GibsonView profile

Senior Lecturer

• Digital Image Processing
• Machine Learning
• Forensic Science
• +5 more

Dr. Stuart Gibson is a Senior Lecturer in Physics and Astronomy at the School of Physics and Astronomy, University of Kent. He is the co-inventor of the EFIT-V facial composite system, widely adopted by UK police constabularies and international agencies. His academic contributions span interdisciplinary research bridging forensic science, computational methods, and machine learning. Research Interests: Forensic applications of digital image processing Machine learning in natural sciences Facial composites for criminal investigations Medical image analysis Computer vision with security applications Teaching: Stuart teaches numerical and computational methods, mathematical techniques for physical sciences, and digital forensics. His pedagogical focus integrates theoretical frameworks with practical forensic and computational tools. Publications & Collaborations: Over his career, Dr. Gibson has published extensively in journals such as Pattern Recognition Letters , ACS Nano , and Utilities Policy . His work includes innovations in evolutionary algorithms, facial composite systems, and applications of machine learning to muon spectroscopy and Raman spectroscopy.

Gabor KolohView profile

Assistant Professor

• Demographic History
• Social History
• Economic History
• +3 more

Dr. Gábor Koloh is a contemporary Assistant Professor and Research Fellow at the Eötvös Loránd University (Department of Economic and Social History) and the HUN–REN RCH Institute of History . His scholarly work focuses on demographic history , social history , and rural economic structures , with particular emphasis on fertility transitions , household organization , and modernization processes in 19th-20th century Hungary. Education : Law (ELTE), Aesthetics (BA), MA and PhD in Economic and Social History (supervised by György Kövér) Research Themes : Demographic change in interwar Hungary Ethnic agricultural practices in Baranya Temporal/spatial patterns in peasant communities Social mobility of Protestant clergy Methodology : Micro-historical analysis, family reconstitution, manuscript source interpretation Current Projects : Interdisciplinary studies on pronatalist discourse, cooperative territorial development, and aristocratic demographic patterns

Elisabeth FevangView profile

Research Fellow

• Labour Market Economics
• Health Economics
• Welfare System Analysis
• +3 more

Elisabeth Fevang is a Senior Research Fellow at the Frisch Centre, affiliated with the University of Oslo. She completed her PhD at the University of Bergen in 2019 with a dissertation titled "Economic Incentives, Employment and Health." Her research focuses primarily on labor market economics and health economics, with particular attention to welfare systems, disability insurance, sick leave patterns, and elderly care economics. Dr. Fevang's research interests center on the intersection of health and labor economics. She investigates how economic incentives affect work behavior, particularly regarding sick leave, disability, and care responsibilities. Her work often examines the Norwegian welfare system's impact on labor market participation, with specific focus on vulnerable groups such as elderly caregivers and healthcare workers. She frequently employs register-based data analysis to study policy impacts on labor supply decisions. Her publication record shows consistent output with multiple high-impact articles in 2024, indicating active research in current labor market issues. Her recent work has increasingly focused on healthcare sector employment patterns, particularly shift work schedules in municipal care services, and their relationship to sick leave. Earlier publications established her expertise in disability insurance systems and the "sick pay trap" phenomenon where benefit structures may inadvertently encourage prolonged absence from work. Dr. Fevang is actively involved in numerous research projects at the Frisch Centre, including those examining labor market exclusion, social insurance systems, long-term care effects on elderly workers' participation, and effects of welfare system reforms. Her collaborative work with researchers like Knut Røed, Simen Markussen, and Heidi Gautun demonstrates her integration within Norway's economic research community.

• Fluid Dynamics
• Biomedical Fluid Mechanics
• Microfluidics
• +3 more

Dario De Marinis is an Assistant Professor at the Department of Mechanics, Mathematics & Management, Politecnico di Bari, Italy. His research focuses on fluid dynamics with applications in biomedical engineering, aerospace, and computational physics. Research Interests Fluid-structure interaction modeling Microfluidics and particle transport Biomedical applications (blood flow, valve mechanics) Aerospace engineering (hypersonic flows, turbulence) Numerical methods (Lattice Boltzmann, immersed boundary) Publications Trend Dario's recent work (2015–2025) spans computational fluid dynamics, with emphasis on multiphase flows, viscoelastic material behavior, and biomedical microfluidic devices. He has contributed to aerospace applications and turbulent thermal flows.

Jamal AtifView profile

Professor

• Artificial Intelligence
• Responsible AI
• Privacy Preservation in Machine Learning
• +4 more

Jamal Atif is a Professor at Paris-Dauphine University and holds multiple significant leadership positions including Project Manager for 'Data Science and Artificial Intelligence' at the Institute of Information Sciences and their Interactions (INS2I) of the CNRS, Deputy Scientific Director of 3IA PRAIRIE, Head of the MILES team/project at LAMSADE (UMR CNRS-Université Paris-Dauphine), Co-leader of the Transverse Artificial Intelligence Program at PSL University, and Director of the Dauphine Numérique program. Professor Atif's primary research focuses on the foundations of responsible artificial intelligence, with specific expertise in privacy preservation in machine learning, robustness of deep learning algorithms to malicious attacks, causality, and explainability. His work bridges theoretical foundations with practical applications in security and reliability of AI systems. He has developed innovative approaches to address adversarial vulnerabilities in machine learning models and has made significant contributions to privacy-preserving techniques in data analysis. His publication record demonstrates a consistent focus on robust and trustworthy AI systems, with recent work exploring differential privacy in clustering, adversarial robustness, and explainable AI. The research spans theoretical foundations in logic and knowledge representation to practical applications in finance, healthcare, and computer vision. His publications appear in top-tier venues including Machine Learning journal, Neural Information Processing Systems, and International Joint Conferences on Artificial Intelligence. Scientific Awards: Recipient of two awards from the North American Society of Radiology for his thesis work Professor Atif has co-supervised or is currently supervising around fifteen doctoral students, demonstrating his commitment to mentoring the next generation of AI researchers. His leadership extends to directing major institutional programs including Dauphine Numérique and the Transverse Artificial Intelligence Program at PSL University, where he shapes strategic research directions in AI. He leads the MILES team/project at LAMSADE, which focuses on foundational aspects of machine learning and artificial intelligence. The team's research spans theoretical aspects of learning algorithms to practical applications requiring robust and reliable AI systems, with particular emphasis on security and privacy considerations in modern machine learning deployments.

Markus ZimmermannView profile

Researcher

• Medical Imaging
• Quantitative MRI
• Neuroscience
• +3 more

Dr. Markus Zimmermann is a researcher at the Institute of Neuroscience and Medicine (INM-4: Physics of Medical Imaging) at the Research Center Jülich. His work focuses on advancing quantitative MRI techniques, particularly in water content mapping, multiparametric imaging, and ultrahigh-field MRI applications. He contributes to developing methods for eddy current characterization, multi-exponential relaxometry, and rapid whole-brain protocols. His research addresses neurological and medical imaging challenges, including cerebral pathologies and neurobiological implications. Key areas of expertise include MRI parameter estimation, medical imaging algorithms, and the integration of advanced imaging techniques for clinical and neuroscience applications. His projects often involve collaborations to validate methodologies using in vivo/ex vivo experiments and super-resolution reconstruction. Dr. Zimmermann’s work aims to enhance diagnostic precision and understanding of brain physiology through innovative MRI technologies.

Fu YunView profile

Professor

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +1 more

Yun Fu is a Distinguished Professor at Northeastern University, affiliated with the College of Engineering and Khoury College of Computer Science. He holds tenure in Electrical and Computer Engineering (ECE). His roles include Professor, Senior Vice President at Shiseido Americas, founder of Giaran (acquired by Shiseido), and co-founder of TVision Insights. He earned his Ph.D. from the University of Illinois at Urbana-Champaign. His research focuses on artificial intelligence, computer vision, machine learning, and data mining. Key achievements include over 500 publications, 50+ patents, and prestigious awards like IEEE Fellow, OSA Fellow, and AAIA Fellow. He leads the SMILE Lab, exploring AI applications in vision, robotics, and healthcare. Notable entrepreneurship includes AI-driven ventures in cosmetics and media analytics. Research interests emphasize AI-driven solutions for computer vision challenges, including anomaly detection, trajectory prediction, and multimodal learning. His work bridges academia and industry, with impactful contributions to both fields.

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +3 more

Daolang Huang is a Doctoral Researcher and Student in the Department of Computer Science at the School of Science, affiliated with Professor Samuel Kaski's group. He holds a Bachelor's degree in Engineering and Technology from Jinan University (2020). His research focuses on advanced machine learning techniques, including Bayesian inference, robust statistical modeling, and simulation-based methods. Key areas include experimental design optimization, neural processes, and equivariance in deep learning. Recent work emphasizes decision-aware algorithms and cost-effective simulation frameworks. Huang has collaborated internationally, with publications in top venues like NeurIPS. Despite no listed awards, his work demonstrates significant contributions to probabilistic modeling and optimization. Education : Bachelor's degree in Engineering and Technology, Jinan University (2020) Research Interests : Bayesian methods and amortized inference Robust statistics under model misspecification Continuous control and neural process architectures Optimization algorithms with decision-theoretic foundations Recent Research Trends : His articles (2020–2025) emphasize Bayesian experimental design, preference-based optimization, and equivariant neural networks. Themes include balancing statistical rigor with computational efficiency, particularly in high-dimensional decision-making contexts. Labs/Teams : Active member of Samuel Kaski’s research group, focusing on interdisciplinary applications of machine learning.

Junlin YuanView profile

Associate Professor

• Fluid Dynamics
• Turbulence Modeling
• Computational Fluid Dynamics (CFD)
• +4 more

Dr. Junlin Yuan is an Associate Professor in the Department of Mechanical Engineering at Michigan State University, within the College of Engineering. Her research focuses on large-scale numerical simulations of complex turbulent shear flows, particularly addressing non-equilibrium turbulence, wall roughness, and fluid-structure interaction. Applications span engineering, environmental, and bio-locomotive systems. She has secured funding from NSF, ONR, and industry partners. Education: Ph.D. Mechanical Engineering, Queen's University, Canada (2015) M.S. Mechanical Engineering, Queen's University, Canada (2011) B.Eng. Aerospace Engineering, Northwestern Polytechnical University, China (2009) Research Interests: Dr. Yuan's work emphasizes understanding turbulence in complex geometries through direct numerical simulations (DNS) and developing physics-based models. Key areas include roughness sublayer dynamics, adverse pressure gradient effects, hyporheic exchange modeling, and turbulence-induced noise prediction. Her lab, the Turbulence Simulation & Modeling (TSM) Lab, bridges fundamental turbulence physics with practical engineering challenges. Grants & Advising: She secured a $400k NSF grant (CC* Compute) for high-memory computing infrastructure and a $490k NSF award on biomimetic fluid-structure interaction. Advised PhD students include Guangchen Shen, Saurabh Pargal, and Sai Mangavelli. Students have received fellowships such as the Richard H. Brown Endowed Fellowship and CFD Society of Canada scholarships. Labs & Teams: Directs the TSM Lab, which employs DNS and CFD tools to explore turbulence in environmental and engineered systems. Collaborates on projects involving sediment-water interface dynamics and rough wall aerodynamics.

Soheil SolhjooView profile

Assistant Professor

• Materials Science
• Multidisciplinary Engineering
• Mechanical Metallurgy
• +5 more

Dr. Soheil Solhjoo is an Assistant Professor at the University of Groningen (UG) within the Engineering Systems and Design (ESD) group, part of the Engineering and Technology Institute Groningen (ENTEG) at the Faculty of Science and Engineering. His research focuses on model-based engineering design, physics-based deep learning, and digital twins, with expertise in constitutive modeling, molecular dynamics simulations, contact mechanics, and physics-informed neural networks. Prior to UG, he conducted postdoctoral research in European projects like VMAP and UPSIM, contributing to multiscale material modeling and hyperelastic material development for soft biological tissues. He holds a PhD from the University of Groningen (2017), where his thesis addressed nanotribological studies, including contact area measurement in atomistic simulations and continuum mechanics applications in nanocontacts. Academic Role: Assistant Professor (since 2024) Institution: University of Groningen Department: Engineering Systems and Design (ESD) Research Interests Dr. Solhjoo's research spans materials science and mechanical engineering, emphasizing: Constitutive modeling of metallic materials Molecular dynamics and statics simulations Contact mechanics at atomic and macro scales Integration of physics-based principles into neural networks Multiscale material characterization Publications Trends His articles predominantly address material deformation mechanisms, constitutive model validation, and nanoscale contact analysis. Key themes include hot deformation behavior of metals, computational methods for material characterization (e.g., HDFT tool), and bridging atomistic simulations with continuum mechanics. Awards & Grants No specific awards listed, but contributions to collaborative EU projects (VMAP, UPSIM) highlight his grant-funded research activities. Actively involved in educational grants for mechanical and industrial engineering curriculum innovation. Labs & Teams Part of the ESD group and ENTEG, collaborating with academic and industrial partners in EU frameworks. Maintains a research portal with open-access tools like the Hot Deformation Fitting Tool (HDFT).

Gail KaiserView profile

Professor

• Software Systems
• Static and Dynamic Program Analysis
• Software Testing
• +3 more

Dr. Gail Kaiser is a Professor of Computer Science at Columbia University, where she has served for 40 years. She directs the Programming Systems Lab (PSL) and is affiliated with the Software Systems Lab (SSL). Her research focuses on software systems, program analysis, testing, security, and AI-driven software engineering. She earned her PhD from Carnegie Mellon University and a BS from MIT. Education: PhD in Computer Science, Carnegie Mellon University (1985) MS in Computer Science, Carnegie Mellon University (1980) BS in Computer Science and Engineering, MIT (1979) Research & Teaching: Dr. Kaiser teaches COMS W4156 (Advanced Software Engineering) and COMS E6156 (Topics in Software Engineering). Her work spans metamorphic testing for machine learning, secure computing, and AI in SE. Notable contributions include pioneering metamorphic testing for classifiers and secure containers research. Awards & Grants: 2025 Distinguished Journal Award (ICST) NSF grants totaling over $2M for secure computing and software assurance ACM SIGSOFT Distinguished Paper Awards (2023, 2014) Labs & Collaborations: Her labs (PSL and SSL) drive innovation in program analysis and security. Collaborations include DARPA, NIH, and industry partners like IBM and Microsoft.

• AI for Sound
• Machine Learning
• Signal Processing
• +4 more

Mark Plumbley is a Professor of Signal Processing at the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering at the University of Surrey. He holds an EPSRC Fellowship in 'AI for Sound' and has led major research initiatives, including the DCASE challenges. His work focuses on AI-driven analysis of acoustic scenes and events, with contributions to machine learning, audio source separation, and sparse representations. Previously, he was Director of the Centre for Digital Music at Queen Mary University of London and Head of the School of Computer Science at Surrey. Education: PhD in Neural Networks (1991). Academic roles include Professorships at King’s College London (1991–2002) and Queen Mary University of London (2002–2014). Research spans audio event detection, sound scene classification, and generative AI for audio synthesis. He leads projects like the EPSRC-funded 'Making Sense of Sounds' and 'Musical Audio Repurposing using Source Separation', and co-edited the Springer book on Computational Analysis of Sound Scenes and Events. Research Interests: AI for Sound: Machine learning applied to real-world audio analysis. Acoustic Scene and Event Recognition: Developing models for sound classification and localization. Generative Audio Models: Text-to-audio systems and diffusion models for sound synthesis. Healthcare Applications: Audio-based diagnostics and bioacoustic signal processing. Grants and Awards: EPSRC Fellowships, EU-funded networks (SpaRTaN, MacSeNet), and Fellowships from IET and IEEE. Notable awards include the IEEE Young Author Best Paper Award (co-authored with students) and leadership in the DCASE community. Labs and Collaborations: CVSSP at Surrey, collaborations with BBC R&D, and interdisciplinary projects on urban soundscapes and noise pollution (UK Acoustics Network Plus).

Dong S. HaView profile

Professor

• Analog IC Design
• RF ICs
• Power Management Circuits
• +7 more

Dong S. Ha is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. As Founding Director of the Multifunctional Integrated Circuits & Systems (MICS) Lab, he focuses on advanced circuit design for energy harvesting, RF systems, and high-temperature electronics. His work spans analog/RF ICs, power management circuits, and wireless IoT solutions with machine learning integration. Education: PhD (1986) and MS (1984) in Electrical and Computer Engineering from the University of Iowa; B.S. (1974) in Electrical Engineering from Seoul National University. Research interests include energy harvesting (piezoelectric, thermal, RF), high-temperature RF circuits for oil/gas/spacecraft applications, and smart IoT systems. He actively seeks students for projects in RF design, energy harvesting, and embedded systems. His lab develops cutting-edge solutions for harsh environment communication, sustainable energy systems, and smart agriculture monitoring. Awarded IEEE Fellow (2008) for contributions to VLSI design/test. Key publications span energy harvesting circuits, GaN-based high-temperature systems, and low-power IoT architectures. Current projects include self-sustaining smart farm networks using federated learning and attack-resistant sensor systems. Labs/Teams: Leads MICS Lab focusing on integrated circuits and systems. Collaborates on cross-disciplinary projects involving machine learning, embedded systems, and sustainable energy. Active in industry partnerships for aerospace and automotive applications.

• Computer Vision
• Extended Reality (XR)
• Artificial Intelligence
• +7 more

Prof. Helmut Grabner is a Professor at the Zurich University of Applied Sciences (ZHAW), leading the Visual Intelligence and Applications Group and the Entrepreneurship initiatives within the School of Engineering. His work bridges computer science, medical technology, and visual communication, with a focus on Extended Reality (XR), surgical training simulations, and AI-driven decision making. Education: PhD in Computer Science (Graz University of Technology, 2008), Master's in Computer Science (2008), and a Certificate of Advanced Studies in Higher Education (ZHAW, 2021). Prior to academia, he held roles including CTO at Logitech and co-founder of upicto, applying computer vision in industry and startups. Research spans augmented reality medical training tools, NMR spectrum analysis via deep learning, and understanding visual engagement in advertising. Awards include the prestigious Koenderink Prize (2018) for contributions to computer vision. Projects include Immersive Education frameworks, bias-mitigation in venture capital algorithms, and surgical proficiency measurement systems. Teaching includes courses on Visual Computing, Machine Learning, and Deep Learning. His work integrates academic research with practical applications in healthcare, education, and entrepreneurship.

Jean-Luc DugelayView profile

Professor

• Biometrics
• Image Forensics
• Video Surveillance
• +7 more

Professor Jean-Luc Dugelay is a faculty member at EURECOM's Digital Security Department, specializing in facial image processing, image forensics, and biometrics. He holds a PhD in Image Processing from the University of Rennes (1992) and is a Fellow of IEEE (2012) and IAPR (2018). His research focuses on security applications like deepfake detection, privacy protection, and multi-spectral imaging. He leads projects on UAV surveillance, cross-spectrum face recognition, and deepfake countermeasures. Education: PhD in Image Processing (1992, University of Rennes), HDR (2013, University of Nice). Notable projects include HEIMDALL (deepfake detection), CONVERGE (transport security), and ImVerif (image forensics). His work on biometric systems and digital watermarking has been recognized with awards like the SEE Blondel Medal (2010). Research interests span facial analysis, video surveillance, and thermal imaging. He has advised students on topics like age estimation via GANs and cross-spectrum face recognition. Key publications include work on deepfake detection (IPAS 2025), thermal-to-visible face conversion, and event-based vision systems.