جست‌وجوی استاد و پژوهشگر

Bradley J. Bakerمشاهده پروفایل

استادیار

• Consumer Behavior
• Digital Sport Management
• Brand Management
• +۶ مورد دیگر

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
• +۳ مورد دیگر

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 Darrallمشاهده پروفایل

دانشیار

• Computational Mechanics
• Applied Mechanics
• Finite Element Methods
• +۵ مورد دیگر

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
• +۹ مورد دیگر

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
• +۴ مورد دیگر

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 MARINمشاهده پروفایل

استاد

• Optimal Control
• Thermoelasticity
• Microstructure Analysis
• +۳ مورد دیگر

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 Potirnicheمشاهده پروفایل

استاد

• Solid mechanics
• Computational methods
• Fracture and fatigue
• +۳ مورد دیگر

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
• +۷ مورد دیگر

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 Alehosseinمشاهده پروفایل

مدرس ارشد

• Mining Engineering
• Rock Mechanics
• Cavitation Technology
• +۲ مورد دیگر

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 Huangمشاهده پروفایل

دانشیار

• Physical Oceanography
• Coastal and Estuarine Circulation
• Numerical Modeling
• +۴ مورد دیگر

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 Temizerمشاهده پروفایل

استاد

• Computational Mechanics
• Multiscale Modeling
• Multiphysics Problems
• +۲ مورد دیگر

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
• +۶ مورد دیگر

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.

Fatemeh Pourahmadianمشاهده پروفایل

استادیار

• Geomechanics
• Engineering Mechanics
• Wave Propagation
• +۳ مورد دیگر

Fatemeh Pourahmadian is an Assistant Professor in the Department of Civil, Environmental and Architectural Engineering at the University of Colorado Boulder. Her research focuses on wave motion and inverse problems in engineering mechanics, particularly in complex materials such as metamaterials and porous media. Education: PhD in Civil Engineering, University of Minnesota (2016) MS in Geo-Engineering, University of Minnesota (2015) MS in Mechanical Engineering, Iran University of Science and Technology (2010) Research Interests: Wave propagation in heterogeneous materials Inverse scattering techniques Elastic-wave cloaking and nonlinear dynamics Development of computational methods for material characterization Key Themes in Publications: Recent work emphasizes machine learning-based approaches for ultrasonic imaging, fast Fourier transform solvers for micropolar composites, and inverse elastic scattering in time/frequency domains. Her studies bridge theoretical frameworks with experimental validations in geomechanics and material science. Professional Affiliations: Society of Engineering Science (SES) Engineering Mechanics Institute (EMI) American Society of Mechanical Engineers (ASME) American Rock Mechanics Association (ARMA)

• Complex Systems
• Network Analysis
• Mobility Data
• +۳ مورد دیگر

Vsevolod Salnikov is a Researcher affiliated with the Namur Institute for Complex Systems at the University of Namur. His work focuses on complex systems, network analysis, and mobility data applications. He has led research projects such as OpenNext (2017-2019) and MyOpenNext (2020-2021), which developed predictive models and mobile applications for transportation analysis. Salnikov holds a Doctor of Sciences degree in Human Mobility from the University of Namur (2015), focusing on data acquisition and algorithmic solutions for mobility challenges. His research interests span topology, dynamics of networks, transportation economics, and higher-order network analysis. Notable contributions include studies on emergent grammar dynamics, simplicial complex applications in mathematics, and taxi price comparison mobile apps. Salnikov actively participates in conferences, presenting work on topics like non-Markovian dynamics and community detection in temporal networks. Key projects include leveraging mobility data for public interest and developing tools for urban transport optimization. His datasets include contributions to studies on emergent grammar dynamics, reflecting interdisciplinary collaborations in linguistics and network science.

• Reservoir Engineering
• Carbon Capture and Storage
• Pore-Scale Flow Modeling
• +۳ مورد دیگر

Rumbidzai Nhunduru is a Research Associate at the Institute of Mechanical, Process & Energy Engineering within Heriot-Watt University's School of Engineering & Physical Sciences, United Kingdom. Her research focuses on experimental and computational analysis of fluid flow in porous media with direct applications to carbon sequestration and enhanced oil recovery operations. Her core expertise spans pore-scale modeling of multiphase flow systems, specializing in fluid displacement mechanisms, wettability effects, and residual trapping phenomena. She employs advanced micromodel experiments and direct numerical simulations to investigate ganglion dynamics, surface roughness impacts, and transport kinetics in geological formations relevant to subsurface energy storage. Analysis of her publications reveals a cohesive research trajectory bridging microscopic flow observations with macroscopic reservoir behavior. Key contributions include quantifying wettability's role in dynamic fluid connectivity and elucidating surface roughness effects on residual trapping—critical insights for optimizing CO2 storage in saline aquifers and improving reservoir simulation accuracy. Dr. Nhunduru collaborates extensively with an international team led by Professor Mercedes Maroto-Valer, contributing to laser-manufactured microfluidic device development and pore-scale visualization techniques. Her work supports next-generation modeling for subsurface energy applications, with recent datasets on wettability impacts and fluid displacement kinetics publicly archived through Heriot-Watt University.