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Valeria CardelliniView profile

Associate Professor

• Distributed Computing Systems
• Cloud Computing
• Edge Computing
• +3 more

Valeria Cardellini is an Associate Professor in Computer Engineering at the University of Rome Tor Vergata, with a PhD in Computer Science and Automation Engineering (2001). Her research focuses on distributed computing systems, particularly cloud-based and edge-based systems, with emphasis on resource provisioning, self-adaptation, and QoS-driven optimization. She has published over 90 peer-reviewed papers, including award-winning works at IEEE SOSE and ACM DEBS conferences, and has led collaborations in EU projects like EoCoE (2015-2018) and the Cloud for Europe tender (2016-2017). She has organized major events such as UCC 2018 and contributed to workshops like Auto-DaSP. Teaching includes distributed systems, cloud computing, and computer architectures at both undergraduate and graduate levels. Education: PhD in Computer Science and Automation Engineering (2001), University of Rome Tor Vergata Previous academic qualifications not explicitly listed Research Interests: Her work spans geo-distributed systems, serverless computing, edge-cloud continuum optimizations, and reinforcement learning for adaptive systems. Key areas include energy-efficient scheduling, fault-tolerant architectures, and QoS-aware service composition. Recent projects address scalability challenges in data stream processing and cybersecurity for distributed environments. Awards & Recognition: Best Paper Awards at IEEE SOSE 2011, ACM DEBS 2015, and ACM DEBS 2016 Keynote Speaker at IEEE MASCOTS 2016 Grants & Projects: Led EU H2020 projects (EoCoE), contributed to the European Cloud tender, and managed COST Action IC1304 (ACROSS). Current research involves cloud-edge continuum frameworks like Serverledge and FIGARO. Labs & Teams: Active in developing experimental platforms such as MOSES (for self-adaptation testing) and OpenCoarrays (coarray Fortran frameworks). Collaborates with interdisciplinary teams on HPC, distributed systems, and cybersecurity.

• Integrated Nonlinear Optics
• Microwave Photonics
• Neuromorphic Computing
• +3 more

Susan Sun is a Research Fellow at Swinburne University of Technology's School of Science, Computing and Emerging Technologies. Her research focuses on integrated nonlinear optics, microwave photonics, and neuromorphic computing, with a PhD from Swinburne (2020–present) and prior degrees from Beijing Institute of Technology (Bachelor, 2012–2016) and Beijing University of Posts and Telecommunications (Master, 2016–2019). She has authored 13 SCI journal papers, including high-impact works in Advances in Optics and Photonics and IEEE Journal of Selected Topics in Quantum Electronics , with over 600 citations and an H-index of 9. Her work emphasizes optical microcomb applications in high-speed signal processing, feedback control systems, and neuromorphic computing architectures. Recent contributions include microcomb-based microwave photonic transversal filters, graphene oxide-enhanced nonlinear optics, and ultra-high bandwidth optical neural networks. She is a member of Optica and IEEE, and serves as a reviewer for Optics Continuum . Key research themes include optimizing microcomb-based systems for precision and scalability, exploring novel materials (e.g., graphene oxide) for integrated photonics, and advancing optical solutions for real-time data processing and AI applications. Her lab integrates theoretical modeling with experimental validation, targeting innovations in both discrete and integrated photonic platforms.

• Pilot Wave Dynamics
• Fluid Dynamics
• Hydrodynamics
• +3 more

Anand Oza is an Associate Professor in the Department of Mathematical Sciences at the New Jersey Institute of Technology (NJIT) , within the College of Science and Liberal Arts . His research focuses on pilot-wave dynamics , fluid dynamics , and nonlinear systems , with particular emphasis on hydrodynamic quantum analogs, collective dynamics in fluid flows, and wave-particle interactions. He has secured significant grants, including awards from the Simons Foundation (2018–present) and the National Science Foundation (NSF) for projects like Modeling and Simulation of Interacting Wings (2021–2025). Key research areas include: Walking droplet behavior in pilot-wave systems Hydrodynamic interactions in flapping swimmers Microgravity studies of colloidal mixtures Biophysics of bird lung fluid dynamics Notable contributions include theoretical and experimental studies on capillary surfers, exponential mixing in pilot-wave systems, and vortex sheet simulations of flapping plates. His work has been featured in Physical Review Fluids , Nonlinearity , and Communications on Pure and Applied Mathematics . Media highlights include collaborations on bird respiratory systems with Prof. C. Frederick, explaining unidirectional breathing mechanisms in birds. Current projects explore collective dynamics in inertial fluid flows and phase transitions in microgravity environments. He teaches advanced applied mathematics courses at NJIT, including Partial Differential Equations and Linear Algebra .

Bradley J. BakerView profile

Assistant Professor

• Consumer Behavior
• Digital Sport Management
• Brand Management
• +6 more

Dr. Bradley J. Baker is an Assistant Professor in the School of Sport, Tourism and Hospitality Management (STHM) at Temple University since 2021. Prior to this, he held a similar position at the University of Massachusetts for two years. His research focuses on consumer behavior and digital sport management, particularly brand building and identity formation in mediated spaces such as social media and esports. Education : Bachelor of Science in Computer Science and Mathematical Sciences, Johns Hopkins University Master of Science in Sport and Recreation Management, Temple University Ph.D. in Business Administration, Temple University Dr. Baker’s work bridges sport management with advanced computational methods, including neural networks, artificial intelligence, and deep learning. He has published in journals like Journal of Sport Management , Psychological Science , and Proceedings of the National Academy of Sciences . His innovations in AI have led to over a dozen patents, and he co-founded D5ai, a start-up focused on algorithmic advances. Recent Trends in Research : His recent articles emphasize the intersection of esports, social media, and AI, with a focus on brand recognition, fan communities, and neural network optimization. Key sub-fields include NIL sponsorship, continual learning, and preregistration in social sciences. Scientific Awards : Research Fellow, NASSM (2022) STHM/Fox Three-Minute Research Competition (2024) Emerald Literati Award (2022) NASSM Reviewer of the Year (2021, 2022) Dr. Baker is on the editorial boards of Sport Management Review , European Sport Management Quarterly , and Journal of Electronic Gaming and Esports . He contributes to labs such as the Athletic Innovation, Research, and Education Lab at STHM.

• Augmented Reality
• Conceptual Modeling
• Enterprise Modeling
• +3 more

Dr. Fabian Muff is a Senior Researcher at the Department of Informatics, University of Fribourg, within the Interfaculty Informatics unit. He holds a position as Oberassistent (Senior Research Assistant) specializing in Digitalization and Information Systems. His research focuses on the intersection of conceptual modeling with augmented reality (AR) and virtual reality (VR), emphasizing workflow modeling languages, AR application frameworks, and AI integration in modeling tools. He teaches courses on Requirements Engineering, Information Systems Modeling, and Project Development. Dr. Muff's work addresses challenges in hybrid modeling environments, contextual AR applications, and the limitations of large language models in conceptual modeling. His recent projects include developing the M2AR web-based modeling tool and exploring the use of ChatGPT in schema generation. He actively collaborates with academic and industry partners, publishing widely in venues like ER Conference and AAAI workshops. His contributions span technical innovations in AR modeling languages (e.g., ARWFML) and methodological advancements in combining conceptual modeling with emerging technologies. Current research explores AI-augmented visualization, context-aware AR systems, and the ethical implications of digital-physical integration in enterprise systems.

Brad DarrallView profile

Associate Professor

• Computational Mechanics
• Applied Mechanics
• Finite Element Methods
• +5 more

Brad Darrall is an Associate Professor of Teaching in the Department of Mechanical and Aerospace Engineering at the University at Buffalo. He serves as the Faculty Advising Coordinator within the School of Engineering and Applied Sciences. His research focuses on computational and applied mechanics, including finite element methods, variational approaches, multphysic continuum dynamics, and size-dependent continuum theories. Education: PhD in Mechanical Engineering, University at Buffalo (2016) MS in Mechanical Engineering, University at Buffalo (2015) BS in Mechanical Engineering, University at Buffalo (2011) Research interests include advanced finite element formulations for anisotropic materials, coupled thermomechanical systems, and electromechanical phenomena in piezoelectric materials. His work emphasizes variational principles and their integration with numerical methods for solving complex engineering problems. Key awards include the SEAS Teaching Faculty of the Year (2022), Vanderhoef Faculty Award for Excellence in Mentorship (2021), and Tau Beta Pi Professor of the Year (2017). His NSF Graduate Research Fellowship (2011–2015) supported foundational work in computational mechanics. Advising and grants: While no specific grants are listed, his mentorship is highlighted through awards. He advises students through his teaching and advising roles but no formal advisees are named in the text.

• Hydrology
• Environmental Engineering
• Hydrological Modeling
• +9 more

Riccardo Rigon is a Full Professor at the University of Trento, affiliated with the Department of Civil, Environmental and Mechanical Engineering and the Center Agriculture Food Environment (C3A). He serves on the University Council and leads the C3A Director role. His expertise spans hydrology, environmental engineering, and software design, with a focus on process-based modeling and digital twin systems. He teaches courses on hydrology, environmental meteorology, and modeling, emphasizing practical applications in water resource management and climate change adaptation. Research interests include snow modeling, debris flow dynamics, slope stability, and the integration of remote sensing data into hydrological models. He has pioneered the GEOframe system, an open-source platform for hydrological modeling, and contributed to initiatives like the Participatory Digital Earth Twin Hydrology (DARTHs) framework. His work bridges theoretical hydrology with practical solutions for flood prevention, water scarcity, and sustainable engineering. Key projects include hydrological modeling of the Po River Basin and Adige River Basin, snow water equivalent datasets, and climate impact assessments. While no specific awards are listed, his extensive publication record highlights contributions to hydrological science and open-source tools. His advising and grants focus on advancing participatory modeling and critical zone observatories through networks like WATZON. Labs/Teams: Active in the GEOframe development team, CLIMAWARE project (climate impact studies), and WATZON ecohydrology network. His research integrates field data, computational models, and stakeholder engagement to address complex environmental challenges.

• Mathematical Biology
• Partial Differential Equations
• Tumor Growth Modeling
• +4 more

Dr. Abramo Agosti is a Researcher in the Mathematical Analysis and Applications group at the University of Pavia. His work develops mathematical frameworks for biological and physical systems, specializing in tumor growth modeling, biomechanics, and computational neuroscience applications. He creates phase-field models using Cahn-Hilliard equations to simulate tumor evolution under mechanical constraints and microenvironmental interactions. His recent research establishes energy-variational solutions for viscoelastic fluids and analyzes large deformations in elastic materials. He co-develops the Deep Anatomical Federated Network (Dafne) for collaborative medical image segmentation. His glioblastoma models integrate radiological data to predict spatial progression patterns. Dr. Agosti's computational approaches bridge mathematical analysis with clinical applications, particularly in neuro-oncology where his models incorporate patient-specific imaging data to simulate brain tumor mechanics and growth dynamics.

Marin MARINView profile

Professor

• Optimal Control
• Thermoelasticity
• Microstructure Analysis
• +3 more

Marin MARIN is a Professor at the Department of Mathematics and Computer Science , part of the Faculty of Mathematics and Computer Science at the Technical University of Banat. His research focuses on advanced mathematical modeling in thermoelasticity, optimal control, and microstructure analysis, with a particular emphasis on fractional calculus and nonlocal effects in materials. He has contributed extensively to topics such as Cosserat bodies, porous media, and viscoelastic systems. Key research interests include the development of novel mathematical frameworks for analyzing thermal and mechanical interactions in complex materials, including functionally graded materials, nanobeams, and porous structures. His work often integrates advanced numerical methods and fractional-order models to address challenges in heat transfer, wave propagation, and structural stability. Marin’s publications span high-impact journals such as Continuum Mechanics and Thermodynamics and Acta Mechanica , with a focus on boundary value problems, variational principles, and the application of multi-phase-lag models. His research bridges theoretical mechanics with practical engineering applications, particularly in semiconductor materials and composite systems. Despite his prolific output, no specific academic awards or student advisement details are explicitly mentioned in the provided text. His work continues to explore interdisciplinary challenges at the intersection of mathematics, materials science, and mechanical engineering.

Gabriel PotirnicheView profile

Professor

• Solid mechanics
• Computational methods
• Fracture and fatigue
• +3 more

Gabriel Potirniche is a Professor and Associate Dean at the University of Idaho's College of Engineering, with affiliation to the Department of Mechanical Engineering. He holds a Ph.D. in Mechanical Engineering from Mississippi State University (2003), dual M.S. degrees from Polytechnic University of Bucharest (Mechanics, 1998; Transportation, 1999), a B.B.A. from Academy of Economic Studies (1998), and a B.S. in Transportation from Polytechnic University of Bucharest (1995). His research focuses on computational solid mechanics, fracture/fatigue behavior of metals, thermoelectric material performance, and high-temperature material deformation modeling. Key areas include: Finite Element Method applications Creep-fatigue interaction mechanisms Nanostructured thermoelectrics Plasticity-induced crack closure Recent publications demonstrate strong focus on fatigue crack growth modeling in high-temperature alloys (2019-2024), with applications in nuclear reactor components and energy systems. His work combines experimental validation with advanced computational simulations. Awards include: University Mid-Career Faculty Award (2015) Orr Early Career Award, ASME (2007) Award for Excellence in Multiscale Modeling (2005) Research has been funded by Department of Energy, National Science Foundation, and Department of Defense. Current projects involve creep-fatigue characterization in nuclear reactor alloys and thermoelectric device development.

• Optimization
• Convex Optimization
• Nonconvex Optimization
• +7 more

Wotao Yin is a Professor of Mathematics at the University of California, Los Angeles, with a distinguished research career spanning over two decades in optimization theory and its applications. His work bridges theoretical mathematics with practical applications in machine learning, image processing, and signal analysis. As a leading researcher in optimization algorithms, he has made significant contributions to the development of methods like ADMM (Alternating Direction Method of Multipliers), proximal algorithms, and decentralized optimization techniques. Department: Department of Mathematics School: College of Letters and Science University: University of California, Los Angeles Yin's research focuses on developing efficient algorithms for large-scale optimization problems, with particular expertise in convex and nonconvex optimization, distributed and decentralized optimization, and mathematical foundations of machine learning. His work has profound implications for image reconstruction, signal processing, and modern machine learning systems. He has pioneered methods for handling sparse data, non-smooth objectives, and constrained optimization problems that arise in real-world applications. An analysis of his recent publications reveals a strong trend toward addressing optimization challenges in machine learning, particularly in federated learning, attention mechanisms, and nonconvex problem structures. His work demonstrates a consistent pattern of bridging theoretical optimization with practical machine learning applications, developing algorithms that balance computational efficiency with theoretical guarantees. Recent papers show increasing focus on heterogeneous data settings, large language model optimization, and fundamental limitations of optimization methods in complex learning scenarios. Throughout his career, Professor Yin has mentored numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. His collaborative network spans multiple institutions worldwide, with particularly strong connections to researchers in China and across the United States. His work has been supported by various funding agencies recognizing the fundamental importance of optimization theory for advancing computational science. Professor Yin leads a vibrant research group focused on mathematical optimization and its applications, where students and collaborators work on cutting-edge problems at the intersection of mathematics, computer science, and engineering. The group maintains strong connections with both theoretical and applied research communities, participating in major conferences across optimization, machine learning, and computational mathematics.

Habib AlehosseinView profile

Senior Lecturer

• Mining Engineering
• Rock Mechanics
• Cavitation Technology
• +2 more

Habib Alehossein is a Senior Lecturer in Mining Engineering at the University of Southern Queensland's School of Engineering. His research spans mining engineering, rock mechanics, cavitation technology, and geotechnical stability. Supervised students include Sahar Ardehali and other graduate researchers in soil stability and mining vehicle safety frameworks. Research focuses on rock cutting mechanics, backfill grouting for subsidence prevention, ultrasonic cavitation, and advanced material testing methodologies. Recent publications emphasize computational modeling of rock masses and optimization of mining productivity and safety. Article analysis shows consistent themes in geomechanical modeling, experimental validation of material properties, and mining safety innovations. Technical contributions include modified testing procedures for brittle materials and theoretical advances in rock fragmentation. No awards or lab affiliations are documented.

Haosheng HuangView profile

Associate Professor

• Physical Oceanography
• Coastal and Estuarine Circulation
• Numerical Modeling
• +4 more

Haosheng Huang is an Associate Professor in the Department of Oceanography & Coastal Sciences at Louisiana State University's College of the Coast & Environment. His research focuses on coastal ocean dynamics and numerical modeling, with significant contributions to understanding Gulf of Mexico ecosystems and Mississippi River Delta processes. His academic credentials include: B.S. from Fudan University, Shanghai (1990) M.S. from First Institute of Oceanography (1993) Ph.D. from Florida State University (2003) Huang's research spans physical oceanography, specializing in coastal and estuarine circulation dynamics, unstructured grid numerical modeling, data assimilation, storm surge simulation, and bio-physical ecosystem modeling. He develops advanced computational approaches for studying continental shelf processes and employs the Finite Volume Coastal Ocean Model (FVCOM) extensively for simulating complex coastal phenomena including sediment transport, hypoxia, and wetland degradation. Analysis of his 15 most recent publications reveals a dominant focus on Gulf of Mexico and Mississippi Delta systems, with recurring themes of sea-level rise impacts, hypoxia dynamics, sediment transport, and storm surge modeling. His work increasingly integrates multi-scale processes across river-estuary-shelf-ocean continua and emphasizes climate change impacts on coastal resilience. His scientific recognition includes: LSU Tiger Athletic Foundation Undergraduate Teaching Award (2012) Huang teaches foundational courses including Introduction to Oceanography and advanced numerical methods, while his research program addresses critical coastal management challenges through sophisticated modeling of circulation dynamics, ecosystem responses, and climate vulnerability. His work demonstrates strong interdisciplinary connections between physical oceanography, biogeochemistry, and coastal engineering.

İlker TemizerView profile

Professor

• Computational Mechanics
• Multiscale Modeling
• Multiphysics Problems
• +2 more

İlker Temizer is a Professor and Chair of Mechanical Engineering at Bilkent University, where he leads the Computational Multiscale Mechanics Laboratory (CMML). His research focuses on computational mechanics, multiscale-multiphysics modeling of heterogeneous materials, and interface behaviors. Research interests include computational homogenization techniques for materials and interfaces, thermomechanical contact problems, and isogeometric analysis. His work bridges theoretical mechanics with applied engineering solutions. Recent publications emphasize multiscale modeling in density functional theory, hydrodynamic lubrication texture optimization, and smart material design, demonstrating consistent innovation in computational mechanics methodologies.

• Bayesian uncertainty quantification
• Computational mathematics
• Scientific computing
• +6 more

Nicholas J. Zabaras is a Professor in the College of Engineering at the University of Notre Dame and serves as director of the Warwick Centre for Predictive Modelling at the University of Warwick. He holds a Hans Fischer Senior Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) since 2014. His academic journey began with a diploma in Mechanical Engineering from the National Technical University of Athens (1982), followed by an M.Sc in Material Science and Engineering from the University of Rochester (1983), and a PhD in Theoretical and Applied Mechanics from Cornell University (1987). His research spans computational mathematics, computational statistics, and scientific computing with focus on predictive modeling of complex multiscale and multiphysics materials systems. Key research themes include Bayesian uncertainty quantification, high-dimensional problem modeling, information-theoretic coarse graining, stochastic model reduction, and optimization under uncertainty. His work has significant applications in materials science, particularly in uncertainty propagation from ab initio to continuum simulations and modeling of random microstructures. His recent publications demonstrate strong activity in Bayesian coarse-graining techniques, deep Gaussian processes, and uncertainty quantification for multiscale materials systems. The research shows consistent focus on developing computationally efficient methods for high-dimensional problems with applications across materials science and engineering disciplines. Major Awards and Recognitions: Royal Society Wolfson Research Merit Award (2014) Research Fellow, Isaac Newton School of Mathematical Sciences, University of Cambridge (2011) Michael Tien'72 College of Engineering Teaching Award, Cornell University (2009) Fellow, American Society of Mechanical Engineers (2006) Presidential Young Investigator Award (1991) Zabaras leads the Scientific Computing and Artificial Intelligence (SCAI) Laboratory and the Computational Science and Engineering (CSE) Laboratory at Notre Dame, where his team develops innovative mathematical and statistical approaches addressing unique challenges in predictive modeling. His research integrates computational mathematics, machine learning, and multiscale/multiphysics modeling to address problems in materials physics, geological sciences, and climate modeling.