Find Academic Supervisors

• Computational Physics
• Molecular Modelling
• Atomistic Simulations
• +3 more

Dr. Umberto Terranova serves as a Lecturer in Chemical Physics at the University of Buckingham's School of Health Sciences since 2019. His expertise combines computational physics and molecular modelling to address sustainable energy challenges, with particular focus on catalytic systems for CO 2 conversion and renewable fuel production. Academic background: BSc Physics, Università di Pisa MSc Physics (with internship at National Enterprise for nanoScience and nanoTechnology, Italy) PhD Physics, University College London (2013; research at National Institute of Materials Science, Japan) Dr. Terranova specializes in atomistic simulations using density functional theory and classical force fields to model complex systems from inorganic materials to proteins. His research centers on two interconnected themes: (1) metalloenzymes for CO 2 reduction, particularly iron-sulfur clusters in viral and biological contexts, and (2) oxide perovskites like SrTiO 3 for photocatalytic hydrogen evolution. This work bridges quantum chemistry, biochemistry, and materials science to develop sustainable catalysts for energy applications. Analysis of his 2020-2025 publications reveals consistent focus on computational catalysis, with emerging integration of machine learning methods. Key research threads include CO 2 hydrogenation, oxygen evolution reaction mechanisms, and chirality-spin interactions, demonstrating interdisciplinary impact across virology, energy storage, and space medicine. Research Funding: HPC-Europa3 programme (2019): Collaborative project with University of Barcelona on artificial photosynthesis. As a lecturer, Dr. Terranova contributes to teaching within the School of Health Sciences and likely supervises computational research projects, though specific student advisees are not documented in the provided materials. His work shows strong translational potential for carbon capture technologies and renewable energy systems.

Oleg IlievView profile

Professor

• Computational Fluid Dynamics (CFD)
• Numerical Simulation
• Porous Media
• +5 more

Prof. Dr. Oleg Iliev is a leading researcher at the Fraunhofer Institute for Industrial Mathematics ITWM in Kaiserslautern. His work focuses on computational fluid dynamics, numerical simulation of multiphase flows, and porous media processes. He specializes in developing advanced algorithms for CFD, reactive transport modeling, and multiscale methods. His research integrates computational methods with engineering applications such as filter design, battery technology, and environmental systems. Key research interests include: Porous media flow simulation CFD-DEM coupled systems Reactive transport in heterogeneous media High-performance computing and parallel algorithms His publications (2016–2022) highlight contributions to: Pore-scale modeling of filtration and catalytic processes Machine learning integration with numerical PDE solvers Uncertainty quantification in subsurface flows GPU-accelerated stochastic simulations Prof. Iliev collaborates with industry and academia on projects involving Li-ion batteries, filter efficiency optimization, and exascale computing frameworks like EXA-DUNE.

Giatrakos NikosView profile

Assistant Professor

• Big Data Management
• Real-Time Analytics
• Distributed/Decentralized Big Data Processing
• +4 more

Nikos Giatrakos is an Assistant Professor at the School of Electrical and Computer Engineering (ECE) of the Technical University of Crete. His primary research focuses on Big Data Management, Real-Time Analytics, and distributed systems. He holds a PhD in Computer Science from the University of Piraeus (2012), an MSc from Athens University of Economics and Business (2007), and a Diploma in Computer Science from the University of Piraeus (2006). His research interests include Big streaming Data & Real-Time Analytics , Distributed/Decentralized Big Data Processing , Federated Machine Learning , Edge-to-Cloud Data Management , and Complex Event Processing . He has contributed to EU projects as a key investigator and received the Best Demo Award in ACM CIKM 2020 for innovative work in streaming analytics. Notable research trends in his articles emphasize real-time resource optimization (e.g., tumor simulations over HPC), adaptive event forecasting , and distributed systems for large-scale data processing. His work bridges theoretical algorithms with practical implementations, such as the SuBiTO framework for neural learning and the FERARI system for multi-cloud event processing. Awards : Best Demo Award (ACM CIKM 2020) Key Projects : EU-funded initiatives on extreme-scale analytics and federated learning Grants : Contributions to multi-site and cross-platform analytics research He leads efforts in extreme-scale analytics-as-a-service (e.g., the INFORE approach) and has developed tools like SheerMP for scalable streaming services. His work often addresses challenges in edge computing , IoT workflows , and geospatial event detection over maritime or movement data.

Som DuttaView profile

Assistant Professor

• Computational Fluid Dynamics
• High Performance Computing
• Machine Learning for Fluid Dynamics
• +3 more

Som Dutta is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Utah State University . His research focuses on high-fidelity computational fluid dynamics (CFD) modeling with applications to environmental flows, aerospace systems, and machine learning (ML) integration. Education: PhD in Civil Engineering (2017), MS in Civil Engineering (2012) from University of Illinois at Urbana-Champaign, BS in Civil Engineering (2008) from Birla Institute of Technology, Mesra. Research Interests: Som investigates turbulent multiphase flows, environmental fluid mechanics, and ML-based surrogate models for digital twins of energy and space systems. His work spans applications in river/coastal mechanics, hybrid rocket propulsion, and bioregenerative life support systems for lunar exploration. Awards: Recognized with the Undergraduate Research Mentor of the Year (2029, 2021), Bayfield Visiting Researcher award (2019), and prestigious fellowships including ATPESC (2017) and CSE Fellowship (2011). Teaching: Courses include Fluid Mechanics, Computational Fluid Dynamics, and Numerical Methods at both undergraduate and graduate levels. Publications: His recent work includes studies on lunar life support systems, droplet fragmentation physics, and Great Salt Lake hydrodynamics. He employs GPU-accelerated CFD and DNS/LES methods for multiscale flow analysis.

Michael A. E. AndersenView profile

Professor

• Power Electronics
• Electric Power Supplies
• Switch Mode Techniques
• +3 more

Michael A. E. Andersen is a Professor and Head of Section in the Department of Electrical and Photonics Engineering at the Technical University of Denmark (DTU). His work focuses on power electronics with applications in electric power supplies, switch mode techniques, and audio amplifiers. He teaches courses at all levels in power electronics and supervises student projects including special courses, mid-term projects, thesis projects, and PhD research. Dr. Andersen's research interests center on power electronics, with specific expertise in DC-to-DC converters, boost converters, power supply design, power density optimization, and switching frequency techniques. His work contributes to UN Sustainable Development Goals related to sustainable energy and technology innovation. His research fingerprint shows strong activity in areas like DC-to-DC conversion (100%), boost converters (75%), power supply engineering (70%), and experimental power electronics (70%). His recent publications demonstrate a clear trend toward high-efficiency power conversion systems, particularly for datacenter applications, LED drivers, and wireless power transfer. The research focuses on improving power density, reducing losses, and developing novel control schemes for various converter topologies. Many papers explore partial power processing techniques to achieve efficiencies above 99% while maintaining high power density. AEG Elektronprisen (2004) 2013 IEEE ECCE Asia Downunder First Prize Paper A. R. Angelo's Grant (1990) Best Poster Prize UPEC '91 (1991) Best Presentation Award at WiPDA-Asia 2021 Multiple recognition for Power Factor Converter research Several awards related to High Efficiency Audio Power Amplifiers Professor Andersen actively supervises multiple PhD students including Andresen, J. on High-Efficiency Converters for Next-Gen HPC, Guglielmino, S. on battery cell impedance spectroscopy, and Wolstrup, A. F. on flexible and printable electronics. He serves on numerous review committees including for the Cyprus Agency of Quality Assurance and Accreditation in Higher Education, Prince Sattam Bin Abdulaziz University, and the Chilean National Commission for Scientific and Technological Research. His research contributes to sustainable energy solutions aligned with UN Sustainable Development Goals. His work involves multiple research groups focused on power electronics design, high-efficiency converters, and wireless power transfer systems. The research environment includes advanced laboratories for power converter testing, magnetic component analysis, and high-frequency circuit development. Current projects emphasize next-generation power conversion technologies for datacenters, portable devices, and industrial applications with a strong focus on efficiency and power density improvements.

Sven KarlssonView profile

Associate Professor

• Parallel Computing
• Computer Architecture
• Compilers
• +4 more

Sven Karlsson is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). He is part of the Informatics and Mathematical Modelling faculty and affiliated with the Embedded Systems Engineering Center and the Center for Quantum Technologies. His expertise lies in parallel computer design, computer architecture, compilers, operating systems, and quantum computing infrastructure. Education: Civilingeniør from Lund University (LTH), Sweden (1997), and PhD in Teleinformatics from KTH Royal Institute of Technology, Sweden (2004). Postdoctoral research at FORTH-ICS, Greece, before joining DTU in 2007. Research interests include parallel computing systems, compiler optimization, quantum computing integration, and high-performance computing (HPC). His work spans application-specific hardware design, distributed quantum infrastructures, and autonomous HPC monitoring systems. Recent publications focus on quantum computing e-infrastructure (NordIQuEst), HPC-QC integration challenges, and optimizing parallel algorithms for multigrid solvers. He leads projects such as QUEX (Quantum Excellence Centre) and NordIQuEst, addressing quantum computing applications and cross-national collaboration. Teaching responsibilities include the course “Parallelle og tidstro systemer” and supervision of MSc/PhD projects. Current advisees include PhD candidates D. Guerrero Domínguez (Quantum Transport Applications), E.J. Fangel-Lloyd (Atmospheric Physics), and M. Gammelmark (Edge Computing for Renewable Energy).

• Parallel Computing
• High Performance Computing (HPC)
• Data Race Detection
• +5 more

Joachim Jenke is a researcher at RWTH Aachen University, Germany, focusing on parallel computing and high performance computing (HPC). He has published extensively on topics related to data race detection, MPI correctness, OpenMP, and hybrid parallel programming approaches. His research includes developing tools and frameworks for runtime error detection in parallel applications, particularly for remote memory access (RMA) programs, CUDA-aware MPI applications, and hybrid MPI+OpenMP programs. He has contributed to projects like MUST, ThreadSanitizer, and DataRaceBench. Jenke's publications cover the full spectrum of parallel programming challenges, including benchmarking approaches for assessing MPI correctness tools, microbenchmark suites for evaluating race detection tools, and techniques for transparently adding metadata to MPI handles. He works closely with colleagues like Simon Schwitanski, Matthias S. Müller, Alexander Hück, and Christian H. Bischof, and his research has been presented at prestigious conferences such as SC, EuroMPI, and IWOMP.

• High Performance Computing
• Energy Efficiency
• Data Center Operations
• +3 more

Dr. Torsten Wilde is a researcher at Technical University Munich , Germany, with a focus on High Performance Computing (HPC) and Energy Efficiency in data centers. His work spans over a decade, integrating Machine Learning , Simulation and Modeling , and Operational Data Analytics to optimize HPC systems.

• Molecular Simulations
• Neurobiology
• Drug Design
• +4 more

Prof. Paolo Carloni is Director of the Computational Biomedicine group (INM-9) at the Institute of Neuroscience and Medicine (INM), Research Center Jülich. He holds a Professorship and has over 30 years of expertise in developing multiscale molecular simulation methods to study biomolecular systems. His research focuses on structure-function relationships of enzymes, neuroreceptors, and drug resistance mechanisms, leveraging high performance computing and machine learning. Research Interests: Multiscale simulation techniques (quantum to coarse-grained) Neurobiological processes and signaling cascades Drug design targeting neuroreceptors and RNA-binding proteins Metadynamics and free-energy calculations Exascale computing for biomolecular applications Recent Work: His publications emphasize computational drug discovery, ligand-receptor interactions, and understanding protein mutations linked to neurodegenerative diseases. He leads projects on exascale algorithm optimization and FAIR data principles in simulations. Awards: While no specific awards are listed, his Google Scholar h-index of 67 reflects significant academic impact. Lab & Collaborations: Active in the INM-9 lab, collaborating with Jülich Supercomputing Centre (JSC) and Helmholtz Association initiatives. His team focuses on computational tools like MiMiC and QM/MM simulations.

Felix MatuschkeView profile

Researcher

• Neuroscience
• Optical Imaging
• Computational Neuroscience
• +2 more

Felix Matuschke is a Postdoctoral Researcher at the Jülich Research Centre, affiliated with the Institute of Neurosciences and Medicine (INM) and its Structural and Functional Organisation of the Brain (INM-1) department. His work focuses on advancing 3D Polarized Light Imaging (3D-PLI) to map nerve fiber orientation in brain tissue through computational modeling and high-performance computing. His research integrates neuroscience, optical imaging, and computational methods to develop three-dimensional nerve fiber models in white matter. He has extended the simPLI simulation framework for High Performance Computing (HPC) optimization, enabling detailed analysis of brain microstructure. This work is implemented in the open-source Fiber Architecture Simulation Toolbox for 3D-PLI (fastpli), facilitating reproducible neuroimaging research. Dr. Matuschke bridges experimental neuroscience with advanced computational techniques, contributing to quantitative understanding of brain architecture. His expertise in polarized light microscopy and HPC-driven modeling supports the development of precise tools for neural pathway visualization and analysis in neuroscientific research.

Dimiter PetsevView profile

Professor

• Electrokinetic Phenomena
• Colloid Science
• Statistical Mechanics
• +7 more

Dimiter N. Petsev is a Professor in the Department of Chemical and Biological Engineering at the University of New Mexico (UNM), serving as the CBE Graduate Advisor. His research focuses on electrokinetic phenomena, complex fluids, nanomaterials, and colloidal science. He holds a Ph.D. in Chemical Physics and Engineering from the University of Sofia (1996) and has held roles at Purdue University, University of Alabama in Huntsville, and University of Houston before joining UNM in 2005. Education: Ph.D., University of Sofia, Chemical Physics and Engineering, 1996 M.S., University of Sofia, Chemical Physics, 1987 Research Interests: Dr. Petsev’s work spans electrokinetic phenomena, surface and colloid science, and fluid dynamics of complex fluids. Key areas include nanometer-sized particle interactions, emulsion stability, protein solutions, molecular dynamics, and guided self-assembly of nanostructures. He employs computational models like classical density functional theory and lattice Boltzmann methods. Research Contributions: His publications highlight studies on electric double layers, nanofluidics, porous media transport, and machine learning applications. The lab collaborates with Frank van Swol on statistical mechanics and advanced computational methods. Lab & Resources: The Colloids and Interfaces Group uses high-performance computing (HPC) resources at UNM’s Center for Advanced Research Computing (CARC) for multiscale modeling.

• Data Science
• Climate Science
• Oceanography
• +3 more

Prof. Dr. Stephan Frickenhaus is a Professor and Head of the Computing and Data Centre at the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research. His primary affiliation is with the Bremerhaven Site, where he leads the Computing Division and oversees High-Performance Computing (HPC) infrastructure. His research focuses on Cluster Analysis, Big Scientific Data Analytics, and sustainable data science infrastructures. He actively contributes to interdisciplinary projects like the MOSAiC Arctic expedition and the Digital Earth initiative, emphasizing data integration and FAIR principles. Key responsibilities include managing HPC resources, developing data management frameworks, and advancing collaborative digital ecosystems in Earth System Science. His work bridges computational methods with environmental research, addressing challenges in climate modeling, oceanography, and cryosphere dynamics. Prof. Frickenhaus is also involved in policy and steering groups such as the Steering Group Digital Earth and HIDA-Steer, promoting data-driven scientific advancements. Research interests span data science applications in polar and marine environments, with publications emphasizing Arctic ecosystem studies, HPC optimization, and AI-driven climate analysis. His contributions to projects like MOSAiC underscore his expertise in large-scale data collection and analysis, ensuring scientific data remains accessible and reusable for future research.

• High-Performance Computing
• Parallel I/O Systems
• Scientific Computing
• +3 more

Venkatram Vishwanath is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the College of Engineering. His research focuses on high-performance computing (HPC), parallel I/O systems, machine learning applications, and scientific computing. He has contributed to advancing scalable data management, performance optimization on supercomputers, and interdisciplinary projects such as the HACC cosmological simulation framework. Collaborations with institutions like Argonne National Laboratory highlight his work in bridging computational science with practical applications. Key areas of expertise include topology-aware data aggregation, GPU performance modeling, and accelerating I/O operations for large-scale simulations. His work on frameworks like vl3 and PIDX has improved parallel data handling and visualization. Awards and recognitions are not explicitly listed in the provided data, but his extensive publication record and leadership in HPC projects reflect significant contributions to the field.

Sebastian WernerView profile

Professor

• Serverless Cloud Computing
• Distributed Cloud Applications
• Sustainability in Distributed Systems
• +3 more

Dr.-Ing. Sebastian Werner is an Acting Professor leading the SWK research group at the University of Hamburg (UHH) since October 2024. He holds a PhD from TU Berlin's Information Systems Engineering group and has extensive experience in postdoctoral research and teaching. His academic focus lies in serverless computing, distributed systems, and cloud-native architectures. He teaches modules such as SE 1, SEE, and SWA at UHH, ensuring continuity in the SWK group's educational offerings. Research Interests: Sebastian’s work emphasizes sustainability, maintainability, and trustworthiness in distributed architectures. He has contributed to EU projects like TEADAL and DITAS, exploring cloud continuum data management and serverless platforms. His dissertation, 'Serverless Data Processing: Application Platform (Co-) Design,' highlights his expertise in scalable computing solutions. Key Projects: Includes the BMBF-funded SMILE project (2020), focusing on serverless migration and cloud application measurement. His research spans fog computing, blockchain-based systems, and edge performance benchmarking. Awards: Received the BMBF Software Campus - SMILE project award in 2020. Grants & Collaborations: Active in EU-funded initiatives and industry collaborations, such as the Gaia-X 4 Future Mobility project. His team at SWK includes researchers like Stephanie Schulte, Leif Bonorden, and Alireza Hakamian.

Katinka WolterView profile

Professor

• Dependable Distributed Systems
• Stochastic Models
• Performance Evaluation
• +6 more

Prof. Katinka Wolter is a Professor at Freie Universität Berlin's Department of Mathematics and Computer Science, leading the Dependable Distributed Systems group. Her research focuses on adaptive and resilient distributed computing systems, leveraging stochastic models and machine learning. She is the Erasmus Coordinator for international student exchanges and serves on the department board and exam committee. Education: PhD in Computer Science (TU Berlin, 1999), Habilitation in Computer Science (Humboldt University Berlin, 2008). Previous roles include Visiting Professorships at Imperial College London and Cochin University, and positions at Newcastle University and Humboldt University. Research Interests: Evaluating dependability, performance, and security of complex systems, stochastic modeling, fault-tolerance, blockchain technology, and phase-type distributions. Tools developed include HyperStar and HyperStarC for distribution fitting. Awards: Best Paper Awards at ICPE 2015 and QEST 2004, DFG-funded research projects, and recognition as a top reviewer for the Journal of Systems and Software. Grants: Multiple DFG-funded projects on system evaluation and service-oriented architectures. Collaborations with institutions like Imperial College London and Technical University Budapest. Labs/Teams: Head of Dependable Systems Group, managing projects on cryptocurrency analysis, distributed restart algorithms, and mobile offloading systems.