Find Academic Supervisors

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.

Fatemeh PourahmadianView profile

Assistant Professor

• Geomechanics
• Engineering Mechanics
• Wave Propagation
• +3 more

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
• +3 more

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
• +3 more

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.

Megan OlsenView profile

Professor

• Complex Systems
• Self-organizing Systems
• Computational Biology
• +3 more

Megan Olsen is a Professor of Computer Science at Loyola University Maryland and serves as Director of the Hyman Science Scholars Program and Camp BaltiCode. She holds a Ph.D., M.S., and B.S. in Computer Science from the University of Massachusetts Amherst and Virginia Polytechnic Institute. Education: Ph.D., Computer Science, University of Massachusetts Amherst, 2011 M.S., Computer Science, University of Massachusetts Amherst, 2009 B.S., Computer Science, Virginia Polytechnic Institute & State University, 2005 Research Interests: Her work focuses on complex systems, computational biology, and simulation validation. Recent efforts include improving agent-based model validation, parameter exploration, and tumor angiogenesis modeling. She develops methods to enhance simulation credibility through metamorphic testing and combinatorial approaches. Advising & Leadership: PhD Committee Member for Youssaf Menacer (University of Houston, 2024) Former Department Chair (2020–2023) Member of the Data Science Advisory Board (2020–present) Labs & Teams: Leads the Validation Lab, advancing simulation validation techniques. Collaborates on projects like the Hyman Scholars Program and Camp BaltiCode to promote STEM education.

Thomas RauschView profile

Lecturer

• Edge Computing
• Cloud Engineering
• Artificial Intelligence Systems
• +3 more

Thomas Rausch is an External Researcher and former University Assistant at TU Wien's Distributed Systems Group (Faculty of Informatics). He holds a PhD in Computer Science (2021) and master's degree in Software Engineering from TU Wien (2016). His work focuses on edge computing, cloud engineering, and AI systems, with notable contributions to frameworks like LocalStack and CognitiveXR. Education: M.Sc. in Software Engineering & Internet Computing (2016), Ph.D. in Computer Science (2021 – TU Wien). Research Interests: Edge computing systems, distributed interactive systems, cognitive augmentation, and cloud-edge AI integration. He has pioneered privacy-preserving edge systems and serverless platforms for data-intensive workloads. Publications: Over 20 peer-reviewed articles on edge computing frameworks, privacy-aware systems, and large-scale AI operations. Recent work includes faas-sim (2023) and Mobility-Aware Serverless Adaptions (2022). Awards: SEC 2019 Best Demo Award, 2020 Netidee Grant, Finalist TU Wien Teaching Awards. Grants: Netidee Grant (2020), CPS/IoT Ecosystem collaboration (2019–present). Teams: Founded CognitiveXR (edge-AI platform) and co-founded LocalStack (serverless testing).

Henderik ProperView profile

Professor

• Enterprise Architecture
• Business Process Modeling
• Conceptual Modeling
• +5 more

Henderik Proper is a Full Professor of Business Informatics at TU Wien's Faculty of Informatics, leading the Research Unit in Business Informatics. He holds roles as Head of Research Unit, Faculty Council Substitute Member, and Curriculum Commission Principal Member. His expertise spans enterprise architecture, digital transformation, and conceptual modeling. Education: Holds a PhD and has held academic positions at TU Wien and previously at the University of Nijmegen. Research focuses on enterprise modeling, AI-driven systems, and governance frameworks. He leads projects like INTEND (2024–2026) and MFP 4.2 (2022–2023), emphasizing digital twin integration and low-code applications. Publications: Over 50 peer-reviewed articles in top venues like Springer and IEEE, focusing on enterprise engineering, process mining, and AI applications. His work bridges theory and practice, addressing challenges in digital transformation and model-driven architectures. Grants: Active in EU-funded projects (INTEND) and national initiatives (MFP 4.2). Advises on doctoral theses and teaches courses like 'Enterprise & Process Engineering' and 'Ontology-Driven Conceptual Modeling'. Labs/Teams: Leads research groups on enterprise architecture and digital systems within TU Wien's Informatics department, collaborating internationally on standards like ArchiMate extensions.

Alireza FurutanpeyView profile

Researcher

• Edge Computing
• Edge Intelligence
• Distributed Inference
• +3 more

Alireza Furutanpey is a University Assistant (PreDoc Researcher) at TU Wien's Distributed Systems Group within the Institute of Information Systems Engineering, holding a Master's degree with distinction. His academic role combines research in distributed systems with teaching responsibilities for core computer science courses. His educational background includes: Bachelor of Science (BSc) Master of Science (Dipl.-Ing.) with distinction Furutanpey's research centers on Edge Computing and Edge Intelligence, specializing in Distributed Inference, Neural Data Compression, and AI-Systems integration. He pioneers techniques for neural feature compression in satellite/edge environments and develops frameworks for federated learning orchestration under communication constraints. His work bridges theoretical AI with practical system implementation, focusing on resource-constrained scenarios where bandwidth and computational efficiency are critical. Analysis of his 15+ publications reveals dominant trends in neural compression for distributed systems (60%), federated learning optimization (25%), and serverless edge frameworks (15%). Key contributions include solving satellite downlink bottlenecks through feature compression and enabling adaptive inference in heterogeneous edge networks, with methodologies increasingly incorporating generative modeling and robustness against adversarial attacks. Scientific Awards: None documented in available sources. He actively supervises master's theses, guiding students on adversarial machine learning, neural compression, and image retrieval systems. His research is supported through major projects: AloTwin (2023-2025) focusing on edge intelligence, INTEND (2024-2026) on industrial IoT, and TEADAL (2022-2025) on federated learning. He serves as a reviewer for 15+ IEEE/ACM venues including IEEE Transactions on Mobile Computing and ICDCS. Furutanpey operates within TU Wien's Distributed Systems Group, which specializes in edge-cloud continuum research. The team develops tools like faas-sim for serverless edge simulation and explores quantum-classical hybrid architectures, maintaining strong industry collaborations in industrial IoT and satellite communications.

• Human-Technology Teaming
• Motor Control
• Cognitive Function and Performance
• +3 more

Heather Neyedli, PhD, is an Associate Professor in the School of Health and Human Performance at Dalhousie University. Her research focuses on human-technology interaction in military and sports contexts, motor learning, and decision-making processes. She leads the Cognitive and Motor Performance Lab and coaches the Dalhousie Women’s Rugby Team. Education: Postdoctoral Fellowship: Nuffield Department of Clinical Neurosciences, University of Oxford PhD: Kinesiology and Physical Education, University of Toronto M.A.Sc: Mechanical and Industrial Engineering, University of Toronto BSc (Honours): Kinesiology, Dalhousie University Research Interests: Dr. Neyedli explores how humans interact with AI, automated systems, and technology. Key areas include: Trust in automation (e.g., military systems, sports technology) Cognitive and motor performance adaptation Error correction and skill acquisition Human decision-making under uncertainty Recent Publications: Her work spans VR training efficacy, trust calibration in automation, and team performance in complex missions. Recent studies examine navigation performance with haptic feedback and the impact of automated planning aids on situation awareness. Labs/Teams: Directs the Cognitive and Motor Performance Lab, collaborating on projects involving neurorehabilitation, sports biomechanics, and human-technology interfaces.

• Software Engineering
• Web Engineering
• Edge Computing
• +7 more

Kari Systä is a Professor of Software Engineering at Tampere University's Faculty of Information Technology and Communication Sciences. His career spans academia and industry, including 16 years at Nokia Corporation and current leadership in software engineering research. He teaches courses like Continuous Development and Deployment - DevOps and supervises Master's theses. Research Focus: Data-driven software engineering, web/cloud platforms, IoT architectures, energy systems software, and edge computing. Current Projects: LiquidAI (2023-2025) for IoT-Edge-Cloud ML systems; 6GSoft (2023-2026) for 6G software engineering; Microblock (2021-2023) on blockchain micro-credentials; and DELI (2023-2025) for energy research infrastructure. Research Unit: Leads work at TASE (Tampere Software Engineering Research Group). His expertise bridges software engineering theory and practice, with collaborations across Finnish universities and industry. He advocates for integrating AI/ML into software development workflows and optimizing energy systems through programmable edge-cloud solutions.