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Marcus HilbrichView profile

Professor

• Distributed Systems
• Software Engineering
• Cloud Computing
• +5 more

Marcus Hilbrich is a Professor in Computer Science with extensive academic experience across multiple institutions. His primary affiliation is Chemnitz University of Technology, where he held roles including Assistant Professor, Acting Professor of Software Engineering, and Research Assistant. He also served as Scientific Coordinator at Humboldt University of Berlin (SFB 1404 FONDA) from 2022–2024. Education: He earned a Doctor of Engineering (Dr.-Ing.) from Dresden University of Technology in 2015, focusing on job/user-centric monitoring in distributed environments. His diploma thesis (2008) explored performance analysis of parallel simulations using the SUN Niagara II architecture. Research Interests: Hilbrich specializes in distributed systems, software engineering, cloud computing, and workflow management. He investigates microservices architecture, resilience engineering, and scalable monitoring techniques. His work bridges theoretical foundations (e.g., formal methods for workflows) with practical applications in HPC and data analysis. Publications: He has authored/co-authored over 15 peer-reviewed articles, including key contributions on ßMACH software management guidance, validity constraints in data workflows, and microservices design patterns. His research often addresses interdisciplinary challenges in computational systems and software lifecycle management. Projects: Active involvement in third-party funded projects like SFC (Cloud Computing for Secure Financial Transactions), ECOUSS (parallel simulations), and WisNetGrid (knowledge networks in grids). His work often integrates performance analysis and system scalability. Labs/Teams: Central to his work is the SFB 1404 FONDA collaborative research center, focusing on foundational aspects of distributed heterogeneous environments.

• Biomedical Engineering
• Semiconductor Devices
• Digital Signal Processing
• +4 more

Dr. Alok Kushwaha is a Researcher at the University of Adelaide's School of Electrical and Mechanical Engineering, affiliated with the Biomedical Engineering department. He has 24 years of experience in academia, research, and administration, with expertise in semiconductor devices, digital signal processing, and antenna design. Currently, he collaborates with Dr. Jiawen Li and Dr. Robert McLaughlin at the Institute for Photonics and Advanced Sensing (IPAS) on Optical Coherence Tomography (OCT) and Fluorescence systems. His research interests include biomedical engineering applications, advanced semiconductor technologies, and nanoelectronics. Notable projects involve developing handheld OCT probes for oral tissue imaging and analyzing terahertz communication systems. He has contributed to studies on molecular communication protocols and sensor technologies for livestock health monitoring. Research Trends : Recent work focuses on biomedical imaging innovations (e.g., 3D-printed OCT devices), semiconductor device optimization (e.g., subthreshold MOSFET performance), and high-speed antenna designs. His publications span nanotechnology, image processing, and materials science. Grants & Advising : No student advisees or grant details listed. Collaborates extensively within interdisciplinary teams at IPAS. Labs/Teams : Active member of the Institute for Photonics and Advanced Sensing (IPAS), contributing to advanced sensing and photonics research.

David ChisnallView profile

Researcher

• Cross-language interoperability
• Hardware-software co-design
• Compiler design
• +4 more

David Chisnall is a researcher affiliated with the University of Cambridge and active in systems programming, compiler design, and cross-language interoperability. He contributes to conferences like POPL, PLDI, ISMM, and SPLASH, with particular focus on secure compilation and memory management.

• High Performance Computing
• Performance Engineering
• Performance Modeling
• +7 more

Gerhard Wellein is a Professor for High Performance Computing at the Department of Computer Science of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He is the head of NHR@FAU (Erlangen National Center for High Performance Computing) and a member of the board of directors of the German NHR-Alliance. Since 2024, he has also served as a Visiting Professor for HPC at the Delft Institute of Applied Mathematics, Delft University of Technology. He holds a PhD in theoretical physics from the University of Bayreuth and has over two decades of experience in HPC education and research. Research Interests: His research focuses on performance modeling and engineering, architecture-specific code optimization, novel parallelization techniques, and the development of hardware-efficient building blocks for sparse linear algebra and stencil solvers. His work bridges computer science, applied mathematics, and computational physics, aiming to maximize efficiency on current and future HPC architectures, including exascale systems. Publication Trends: His recent publications emphasize analytical performance modeling (e.g., Roofline, oscillator models), energy efficiency, GPU optimization, and scalable linear algebra. They reflect a strong focus on both theoretical modeling and practical implementation, with applications in CFD, quantum physics, and molecular dynamics. Scientific Awards: 2011 Informatics Europe Curriculum Best Practices Award (shared with Jan Treibig and Georg Hager) for outstanding teaching contributions in HPC. Grants and Advising: He has led numerous third-party funded projects from the EU, BMBF, and DFG, including EoCoE-III, ESSEX, EXASTEEL, and ProPE. These projects focus on exascale software, performance engineering, fault tolerance, and multiscale simulation. He has mentored multiple researchers and students, contributing to the development of tools such as LIKWID, ClusterCockpit, and GEOPM. Labs and Teams: He leads the HPC research group at FAU and is deeply involved in national and international HPC initiatives. His team collaborates extensively on open-source HPC software and performance tools, fostering a strong community-driven approach to performance engineering.

• High Performance Computing
• Software Engineering for HPC
• Code Generation
• +12 more

Harald Köstler is an Associate Professor and Head of Research at the Erlangen National High Performance Computing Center (NHR@FAU) within the Department of Computer Science at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He leads the research group on HPC Software Design at the Chair of Computer Science 10 (System Simulation), focusing on software engineering for high-performance computing and data analytics. His research interests include: Software Engineering for HPC Code Generation for Numerical Solvers Performance Engineering on Hybrid Architectures Discontinuous Galerkin and Lattice Boltzmann Methods Multigrid Solvers and Parallel Algorithms Performance Portability across CPUs, GPUs, and FPGAs The recent publications highlight a strong trend in developing efficient, scalable, and portable simulation frameworks for complex physical systems. His work emphasizes code generation, performance optimization, and the integration of classical model-driven and data-driven approaches. Key application areas include computational fluid dynamics, geotechnical engineering, and climate modeling, often leveraging the waLBerla and ExaStencils frameworks. Harald Köstler has no listed scientific awards in the provided text. He advises students in the areas of high-performance computing, numerical methods, and software engineering for scientific applications. His research is supported by collaborations within the FAU HPC ecosystem and likely involves grants related to national high-performance computing initiatives. He is a key contributor to the waLBerla framework, a block-structured, high-performance software for multiphysics simulations, and is involved with the ExaStencils project, which focuses on advanced multigrid solver generation. These frameworks form the core of his research team's efforts in scalable scientific computing.

Adrián García GutiérrezView profile

Professor

• Aerospace Engineering
• Uncertainty Quantification
• Computational Fluid Dynamics
• +3 more

Adrián García Gutiérrez is a Professor in the Department of Aerospace Engineering at the University of León's College of Engineering. His research focuses on aerospace systems, uncertainty quantification in CFD, atmospheric boundary layer modeling, and airship technology. Recent publications highlight his work in parallel orbital propagation algorithms, stochastic optimization of high-altitude platforms, and neural network applications for wind profiling. Key trends include aerodynamic modeling under uncertainty and interdisciplinary approaches combining turbulence analysis with LiDAR measurements. He contributes to educational innovation in aerospace engineering through simulation-based learning tools and leads the GITA Tecnología Aeroespacial research group.

Julija ZavadlavView profile

Assistant Professor

• Multiscale Modeling
• Machine Learning
• Computational Chemistry
• +3 more

Prof. Julija Zavadlav is an Assistant Professor of Multiscale Modeling of Liquid Materials at the Technische Universität München (TUM), affiliated with the TUM School of Engineering and Design. Her research integrates physical modeling with machine learning and Bayesian techniques to develop multi-scale simulation frameworks for diverse applications in bioinformatics and engineering. Education: She earned her PhD in Physics from the University of Ljubljana (2015) and conducted postdoctoral research at ETH Zurich (2016–2019), where she received an ETH Postdoctoral Fellowship. Since 2019, she has held her current position at TUM. Research Interests: Her work focuses on advancing machine learning potentials, Bayesian uncertainty quantification, and multi-scale modeling for complex systems like ionic liquids, metal-organic frameworks, and biomolecules. Her ERC Starting Grant (2022) supports the SupraModel project, emphasizing scalable and interpretable models. Awards: Golden Teaching Award 2022 (Best Lecture), ERC Starting Grant 2022, and ETH Postdoctoral Fellowship. Her recent publications emphasize neural network potentials, transfer learning, and computational tools like JaxSGMC for Bayesian analysis. Grants and Labs: While specific lab names are not mentioned, her ERC grant underscores active funding. No formal student advisee list is provided, but her collaborative work suggests involvement in training next-generation computational scientists.

Victor J MilenkovicView profile

Professor

• Computational Geometry
• Robotics
• Computer-Aided Design (CAD)
• +3 more

Victor J. Milenkovic is a Professor and Department Chair in the Department of Computer Science at the University of Miami's College of Arts and Sciences. His research focuses on computational geometry, geometric algorithms, and their applications in robotics, computer-aided design (CAD), and high-performance computing. He specializes in developing robust algorithms for handling degenerate cases in geometric computations, free space construction for robotic motion planning, and efficient implementations of geometric operations using modern hardware like GPUs. Key contributions include: Advanced techniques for detecting degenerate predicates in computational geometry GPU-accelerated Minkowski sum computations Robust free space construction for polyhedra Geometric rounding methods for preserving mesh topology His work emphasizes practical implementations with provable correctness and efficiency, validated through applications in CAD, robotics, and HPC environments. No academic awards are explicitly listed in the provided materials. Advising and grants: No student advisees or grant details are provided in the profile. His research has been applied to problems in algorithmic robustness, geometric modeling, and parallel computing. Labs/Teams: No specific lab or team affiliations are mentioned beyond his departmental role.

• Electromagnetic Compatibility
• Electromagnetic Interference
• Signal Integrity
• +3 more

Victoria Shao is a Teaching Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign (UIUC). She specializes in electromagnetic compatibility (EMC), computational electromagnetics (CEM), and high-power microwave technology. Her work focuses on advancing numerical methods for transient electromagnetic analysis, stochastic modeling in complex enclosures, and the design of integrated electronic systems. Affiliations: Holonyak Micro and Nanotechnology Laboratory at UIUC Education: B.S. in Electrical Engineering (USTC, 2003), Ph.D. in Electromagnetics (Chinese Academy of Sciences, 2008) Prior positions: Researcher at ElectroScience Laboratory, Ohio State University (2009–2014) Research Interests: Dr. Shao’s work bridges computational methods with practical engineering challenges, emphasizing: Stochastic Green’s function approaches for statistical wave physics Multi-physics analysis of electronic systems Development of scalable algorithms for high-performance computing Nanotechnology integration for 3D RF components Her research has led to innovations in: Self-rolled-up membrane (S-RuM) nanotechnology for compact inductors Supercomputing-driven radio wave propagation models for urban environments Parallel-in-space-and-time electromagnetic simulation methods Awards: She has received multiple recognitions, including Best EMC Paper finalist awards (2022, 2023) and a Best Paper Award in IEEE Transactions (2017). Teaching and Contributions: Dr. Shao teaches core ECE courses such as ECE 110, ECE 210, and specialized EMC courses (ECE 498 YS3/YVS). She pioneers educational strategies using visualization tools and asynchronous learning to enhance STEM accessibility.

Michael KerberView profile

Professor

• Computational Topology
• Algebraic Topology
• Computational Geometry
• +4 more

Michael Kerber is a Professor at Graz University of Technology, Institute of Geometry, specializing in computational topology and geometry. His research bridges mathematical theory with applications in data analysis, focusing on persistent homology and geometric algorithms. PhD from Max Planck Institute for Informatics (2009) Postdoc positions: Max Planck Institute, Stanford University, IST Austria His work centers on designing efficient algorithms for topological data analysis, particularly: Persistent Homology 2-Parameter Persistence Geometric Filtrations Algebraic Curve Analysis High-Dimensional Sphere Packing Recent publications emphasize: Improved Delaunay bifiltration methods NP-hardness of interleaving distance computation Sparse Čech filtrations for big data Integration with graph neural networks He has co-developed key software tools: PHAT DIPHA HERA SOPHIA

Wei-keng LiaoView profile

Research Professor

• Parallel and distributed file I/O
• Storage system design
• Data mining algorithms
• +5 more

Wei-keng Liao is a Research Professor in the Department of Electrical Engineering and Computer Science at Northwestern University's McCormick School of Engineering. His research spans high-performance computing with a focus on parallel and distributed systems. Dr. Liao's research interests include parallel and distributed file I/O and storage system design, data mining algorithm design and their parallelization, data management for large-scale scientific applications, and computational model design for large-scale applications on parallel and distributed environments. He is a key contributor to the Parallel netCDF project, which provides parallel I/O capabilities for scientific applications. His recent work shows strong trends in high-performance computing infrastructure, particularly in optimizing I/O systems for scientific applications, parallel data clustering algorithms, and machine learning acceleration in distributed environments. His publications span computational science, parallel computing, and data-intensive applications across various scientific domains including materials science, astrophysics, and healthcare. Best Paper Award at IEEE International Conference on Cluster Computing (2016) for Parallel DTFE Surface Density Field Reconstruction Dr. Liao has supervised numerous research projects funded by DOE, NSF, NASA, and Argonne National Laboratory, with recent work focusing on data libraries for exascale science, machine learning-driven resilience for extreme-scale systems, and scalable data clustering for scientific computing. He leads research on the Parallel K-means Data Clustering software package and is a principal developer of Parallel netCDF. His work connects multiple research groups through the Center for Ultra-scale Computing and Information Security (CUCIS) at Northwestern University, where he collaborates with scientists across disciplines to develop scalable computing solutions for complex scientific problems.

Stephen SiegelView profile

Associate Professor

• Formal methods for software verification
• Verification of parallel and scientific software
• Concurrency in software systems
• +3 more

Stephen Siegel is an Associate Professor at the University of Delaware with a joint appointment in the Department of Computer and Information Sciences and the Department of Mathematical Sciences . Holding a PhD in Mathematics from the University of Chicago (1993), he transitioned from finite group theory research to formal methods in computer science, focusing on verification of parallel and scientific software. His research centers on the Verified Software Laboratory (VSL) and the CIVL Model Checker for HPC program verification. Recent work includes formal verification of PETSc components at CAV 2025 and collective contract frameworks for message-passing programs. Research Interests Formal methods for software verification Parallel and HPC software reliability Model checking techniques Application of mathematical logic to computing Academic Service Highlights Program Committee & Publication Chair, CAV 2025 Co-organizer, International Workshop on Verification of Scientific Software (VSS 2025) Chair, VerifyThis competition (2023) Editorial service at IEEE Transactions on Software Engineering (2015-2019) Teaching Portfolio CISC 404/604: Logic in Computer Science CISC 414/614: Formal Methods in Software Engineering CISC 372: Parallel Computing (MPI/OpenMP/CUDA instruction) Advanced Topics courses: Model Checking, Abstract Interpretation

• Marine Renewable Energy
• Techno-Economic Analysis
• Wave Energy
• +3 more

Elena Baca is a Researcher III in the Mechanical Engineering department at the National Renewable Energy Laboratory (NREL) . Her work focuses on techno-economic modeling of marine energy systems , particularly wave and tidal energy, and developing tools like the marine energy module of the System Advisor Model to estimate energy production and cost trends. She has also collaborated on offshore renewable energy feasibility studies and software development for marine energy systems. Education: Master in Advanced Energy Systems Engineering, Colorado School of Mines Bachelor in Energy Systems Engineering (Business and Entrepreneurship Minor), Oregon State University Baca’s research explores pathways to reduce energy costs, optimize marine energy systems, and enhance software for modeling and simulation. Her work spans power converter design, autonomous underwater vehicles, and data acquisition systems, emphasizing cost-effective and scalable solutions for renewable energy integration. Recent publications highlight trends in marine energy techno-economics , software development for high-performance computing , and offshore energy feasibility , reflecting her interdisciplinary expertise in engineering and economic analysis. No specific scientific awards are listed in the provided text. Baca has collaborated with multidisciplinary teams at NREL and contributed to technical reports and peer-reviewed articles. Her professional experience includes prior roles in mechanical design and energy efficiency engineering.

Dilum BandaraView profile

Academic

• Distributed Systems
• Blockchain
• Cloud Computing
• +13 more

Dilum Bandara is a Principal Research Scientist at CSIRO's Data61 in Australia and an Adjunct Senior Lecturer at the School of Computer Science and Engineering, Faculty of Engineering, University of New South Wales (UNSW). He has previously served as a Senior Lecturer at the University of Moratuwa, Sri Lanka, and has over two decades of experience in research, teaching, and consultancy in distributed systems, security, and software engineering. His academic qualifications include: PhD in Computer Science, Colorado State University, USA (2012) MS in Computer Science, Colorado State University, USA (2008) BSc Eng. (Hons) in Computer Science and Engineering, University of Moratuwa, Sri Lanka (2004) Dilum's research interests are centered on Distributed Systems (Blockchain, Cloud, P2P), Computer Security , Software Architecture , Data Engineering , Performance Engineering , and the Internet of Things (IoT) . He applies these technologies in multidisciplinary domains such as Supply Chains , Digital Finance , Environmental, Social, and Governance (ESG) , Fleet Management , and Weather Monitoring . His work emphasizes real-world impact through trusted data management in multi-party ecosystems. The analysis of his recent publications reveals a strong focus on blockchain for transparency and security, cloud-native performance engineering, IoT integration with edge and cloud, and data-driven solutions for smart cities and sustainability. His work consistently bridges theoretical innovation with practical deployment, particularly in national and international infrastructure projects. His scientific awards include: Best Paper Award at BPM 2024 Multiple CSIRO internal awards (Customer First, Engineering and Technology, Collaboration) from 2021–2023 Student Paper Award (Merit) at IEEE SOLI 2018 Award of Excellence for Outstanding Research at University of Moratuwa (2015–2018) Dilum has led and contributed to several research grants, particularly during his time at the University of Moratuwa, including projects on smart city integration, real-time data forecasting, and cloud platforms for scientific computing. He has also held leadership roles such as Director of the Engineering Research Unit and co-founder of VaticHub. He is actively involved in professional communities as a Senior Member of IEEE and a Chartered Engineer with IESL. At CSIRO, he serves as a Health and Safety Representative, reflecting his commitment to corporate citizenship. He has advised students and early-career researchers, though specific names are not listed in the provided text. His labs and research teams include the Architecture and Analytics Platforms (AAP) team at CSIRO Data61 and collaborations with academic institutions like UNSW and University of Moratuwa.

• Computer Architecture
• Programming Models
• Parallel Computing
• +4 more

Daniel Jimenez Gonzalez is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the School of Informatics (FIB). He is an active researcher in programming models and high-performance computing, contributing extensively to the field through publications, projects, and academic supervision. Research Interests: His work focuses on Computer Architecture , Programming Models , and Parallel Computing . He investigates task-based programming, runtime systems, compiler optimizations, and performance modeling for multi-core and heterogeneous architectures. His research addresses challenges in scalability, energy efficiency, and resilience in modern computing environments. His recent publications show a consistent trend in task-based programming models , runtime scheduling , and performance optimization across diverse architectures, including NUMA and GPU-accelerated systems. The work spans from theoretical modeling to practical implementation, often targeting real-world HPC applications. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: While specific students are not listed, his involvement in doctoral theses and R&D projects suggests an active role in mentoring graduate students. He has participated in both competitive and non-competitive R&D+i projects, indicating grant acquisition and project leadership experience in areas related to programming models and computer architecture. Labs and Teams: He is a member of the UPC PM - Programming Models research group, which focuses on the design, analysis, and optimization of modern programming paradigms for high-performance systems.