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Richard MembarthView profile

Researcher

• High-Performance Computing
• Domain-Specific Compilers
• GPU Acceleration
• +4 more

Richard Membarth is an academic researcher at the Friedrich-Alexander-Universität Erlangen-Nürnberg, Department of Computer Science. His work focuses on high-performance computing, domain-specific compilers, and GPU acceleration. He has contributed to projects like FLOWER (dataflow compiler), Hipacc (image processing DSL), and XEngine (neural network optimization). Membarth's research bridges compiler design, parallel algorithms, and heterogeneous hardware, with applications in medical imaging, bioinformatics, and autonomous systems. Co-developer of AnyDSL framework for partial evaluation Lead in GPU acceleration for molecular dynamics (tinyMD) Specialized in compiler techniques for FPGAs and GPUs Key areas include: - Domain-specific languages (DSLs) - Parallel algorithm optimization - Medical computing pipelines - Real-time graphics rendering

• Biomedical Engineering
• Microvascular Hemodynamics
• Computational Fluid Dynamics
• +3 more

Peter Balogh is an Assistant Professor in the Department of Mechanical and Industrial Engineering at New Jersey Institute of Technology (NJIT). His research focuses on computational modeling of blood flow in microvascular systems, particularly investigating hemodynamic characteristics, red blood cell dynamics, and their implications in angiogenesis and cancer cell transport. Balogh leads the Integrated Multiphase Blood Flow Modeling and Experiments project funded by the National Science Foundation (NSF), aiming to predict microvascular growth through advanced simulations. Education: Not explicitly stated in provided texts. His research interests include microvascular hemodynamics, computational fluid dynamics (CFD), and the biomechanical interactions between blood cells and vessel walls. Key topics involve tortuous microvessel simulations, wall shear stress patterns in angiogenic networks, and the role of red blood cell stiffness in blood flow dynamics. Balogh's recent work highlights advancements in high-fidelity blood cell resolved simulations, adaptive physics refinement techniques, and their applications in understanding cancer cell trajectory tracking. His studies often bridge experimental and computational approaches to model complex biological systems. Grants & Projects: NSF Grant (2023-2026): Investigating microvascular growth through multiphase blood flow modeling and experiments. Teaching: Courses include ME 618 (High Performance Computing & BiopInspired CFD) and ME 407 (Heat Transfer). Labs/Teams: Collaborates with interdisciplinary teams on projects involving microvascular networks, cancer cell transport, and GPU-accelerated CFD simulations. Key collaborators include Prof. William Murfee (NJIT), Dr. Aaron Randles (Johns Hopkins), and others in computational biomedicine.

• Compiler Optimization
• High Performance Computing
• Computer Architecture
• +2 more

Dr. J Nelson Amaral is a Professor in the Department of Computing Science within the Faculty of Science at the University of Alberta. His research focuses on compiler optimization techniques that enhance performance in modern computing architectures through clever code transformations. Educational background includes B.Sc. in Electrical Engineering (PUCRS), M.Sc. in Electrical Engineering (ITA), and Ph.D. in Electrical and Computer Engineering (University of Texas at Austin). Research interests span compiler optimizations for resource utilization, learning technology applications in compilation processes, and feedback-directed optimization efficiency improvements. Current investigations include instruction-level parallelism exploitation, code transformations for heterogeneous architectures, and machine learning-enhanced compilation. Publication analysis reveals strong focus on compiler techniques for performance optimization, particularly for matrix operations, convolution algorithms, and memory hierarchy management. Recent work emphasizes hardware-software co-design for specialized processors and auto-vectorization methods. University of Alberta Faculty of Science Excellence in Teaching Award (2015) Distinguished Engineer, Association for Computing Machinery (2014) Distinguished Speaker, Association for Computing Machinery (2012-2014) Interdepartmental Science Students' Society Award for Excellence in Teaching (2014) IBM Center for Advanced Studies Research Faculty Fellow of the Year (2012) Research includes industry collaborations through the IBM-CAS partnership. Contributes to computing education through curriculum development and student mentoring. Leads compiler optimization research at the IBM Center for Advanced Studies, focusing on performance improvements for enterprise workloads and specialized hardware.

Elliott ForbesView profile

Assistant Professor

• Computer Architecture
• High-Performance Processors
• Energy Efficiency
• +3 more

Elliott Forbes is an Assistant Professor and Director of the Computer Engineering program at the University of Wisconsin-La Crosse's Computer Science & Computer Engineering Department. He holds a PhD in Computer Engineering from North Carolina State University (2016), with a focus on computer architecture. His research emphasizes heterogeneous multi-core processors, thread migration latency, and energy efficiency. He has developed tools like the RISC-V assembler 'dt' and the pipeline analysis tool 'dptv'. Education: BS (Michigan Tech, 2005), MS (NC State, 2008), PhD (NC State, 2016). Industry experience includes roles at Intel, Unisys, and Smiths Aerospace. Awards: IEEE Senior Member (2020), Best Paper Award at ICCD-34 (2016), MIT Lincoln Labs Fellowship (2007-2008). He teaches courses ranging from introductory computer systems to advanced topics in architecture and compiler design.

Anthony WachsView profile

Researcher

• Computational Fluid Dynamics
• Particulate Flow
• Numerical Simulation
• +5 more

Anthony Wachs is an Associate Member of the Department of Mathematics at the University of British Columbia (UBC), affiliated with the Faculty of Science. His research focuses on computational fluid dynamics (CFD), particulate flows, and multiphase systems, with emphasis on numerical methods like DEM-CFD coupling and high-fidelity simulations of complex fluid-particle interactions. He develops scalable solvers for non-spherical rigid bodies and fluid-structure interactions, contributing to fields such as granular media, viscoplastic fluids, and biological capsule dynamics. His work bridges computational approaches with real-world applications in energy, biomedical engineering, and environmental systems. Research interests include particle-resolved direct numerical simulations (DNS), fluidized beds, and the development of tools like Grains3D and PeliGRIFF for particle dynamics. His studies address challenges in flow past irregular geometries, hydrodynamic force modeling, and machine learning-enhanced fluid dynamics. Wachs collaborates across disciplines, integrating advanced numerical techniques with experimental validation to advance understanding of multiphase flow phenomena.

Melanie WilkinsonView profile

Research Fellow

• Plant Genomics
• Evolutionary Biology
• Crop Breeding
• +5 more

Dr. Melanie Wilkinson is a Research Fellow at The University of Queensland's Queensland Alliance for Agriculture and Food Innovation (QAAFI), affiliated with the Faculty of Science and the ARC Centre of Excellence for Plant Success in Nature and Agriculture. Her work bridges plant genomics, evolutionary biology, and crop breeding with a focus on adaptive mechanisms in Australian flora and horticultural crops. Education: PhD in Biological Sciences, The University of Queensland (2019) Her research integrates genomic prediction, adaptive evolution, and crop improvement, with particular expertise in mango genetics and Australian wildflower speciation. Key projects examine chromosomal inversions in mango breeding traits, gravitropic adaptation mechanisms, and ensemble-based genomic prediction models for plant breeding. Her work frequently employs GWAS, transcriptome analysis, and hybrid zone studies to unravel genetic architectures of parallel evolution. Article analysis reveals a strong focus on plant adaptation mechanisms (70% of publications), with mango genomics and Senecio speciation as dominant model systems. Recent work (2023-2025) emphasizes genomic prediction methodologies and structural variant impacts on breeding traits, showing a clear shift toward translational agricultural applications while maintaining evolutionary biology foundations. Supervision: Principal Advisor: APSIM-WGP for soybean yield stability Associate Advisor: Machine learning for gene networks, Evolution of genetic networks Completed: Polygenic architecture of Senecio adaptation (2024) Dr. Wilkinson operates within QAAFI's genomic prediction research group and collaborates extensively with the ARC Centre of Excellence for Plant Success, focusing on translating evolutionary insights into crop improvement strategies through interdisciplinary teams spanning genomics, bioinformatics, and horticulture.

Estela SuarezView profile

Professor

• High Performance Computing (HPC)
• Heterogeneous HPC Architectures
• Modular Supercomputing Architecture
• +4 more

Estela Suarez is a Professor of High Performance Computing at the University of Bonn's Computer Science Department and holds leadership roles at the Jülich Supercomputing Centre (JSC) in Forschungszentrum Jülich. Her work focuses on advancing modular supercomputing architectures, heterogeneous systems, and application optimization for exascale computing. She currently serves as Chair of the EuroHPC Joint Undertaking's Research and Innovation Advisory Group (RIAG) and leads the Next Generation Architectures and Prototypes research group at JSC. Education includes a PhD in Physics from the University of Geneva (2010) and a Master's in Physics (Astrophysics specialization) from Universidad Complutense de Madrid (2004). She has held senior research positions at JSC since 2010 and has led major EU-funded projects like DEEP, DEEP-ER, and DEEP-SEA. Research interests include HPC system design, hardware-software co-design, and energy-efficient supercomputing architectures. She was awarded the University of Bonn's 2023/2024 teaching award and actively contributes to initiatives like the European Processor Initiative (EPI) and the NUMERIQS consortium. Current projects include optimizing exascale systems for Earth system modeling (IFCES2), AI-driven data analytics (AIDAS), and modular supercomputing software (DEEP-SEA). During her 2024/2025 sabbatical, she is not accepting new students. Her work is published in journals like Geoscientific Model Development and conferences such as ISC and EuroHPC.

• Operational Research
• Management Science
• Combinatorial Optimization
• +3 more

Professor Bo Chen is a leading academic in Operational Research and Management Science at the University of Warwick's Warwick Business School (WBS). He holds a Higher Doctorate (lifetime achievement award) and is a Fellow of the Academy of Social Sciences. His research bridges ORMS, economics, and computer science, focusing on combinatorial optimization, scheduling, game theory, and AI foundations. PhD in Operations Research and Econometrics from Erasmus University Rotterdam Former Senior ESRC Management Research Fellow Visiting Professor at Stanford University Chair Professor at Tsinghua University Research interests include scheduling algorithms, mechanism design, and fairness in resource allocation. Key contributions include randomized strategyproof mechanisms and interdisciplinary auction theory integration. Teaching modules include Mathematical Programming and Statistics for BSc Accounting and Finance students. His 150+ publications span operational research methods, algorithmic game theory, and logistics optimization. Awards include the FAcSS fellowship and Warwick's Higher Doctorate.

Fatih KizilaslanView profile

Research Fellow

• Biostatistics
• Cure Models
• Frailty Modeling
• +3 more

Fatih Kizilaslan is a Research Fellow in the Department of Biostatistics at the University of Oslo, Norway, specializing in statistical models for high-dimensional and functional data. He holds a PhD in Mathematics from Gebze Technical University (2015) and has extensive academic experience, including roles as Associate Professor (2019-2023) and Assistant Professor (2016-2019) at Marmara University, Turkey. His research focuses on cure models, frailty modeling, and reliability analysis. He has conducted research at McMaster University (Canada) and the Gebze Institute of Technology (Turkey). His academic interests include survival analysis, statistical inference for extreme-value distributions, and applications of biostatistics in medical research. He is affiliated with the Statistical models for high-dimensional and functional data research group. His work emphasizes methodological advancements in stress-strength reliability, multicomponent systems, and record-based statistical models. Notable contributions include publications on cure models, frailty analysis, and reliability estimation in complex systems. His research bridges theoretical statistics with practical applications in biomedical and engineering contexts. He actively collaborates on projects involving high-dimensional covariates and predictive modeling for clinical outcomes.

Veronique ChotteauView profile

Professor

• Bioprocess Engineering
• Biopharmaceutical Production
• Perfusion Technology
• +3 more

Veronique Chotteau is a Professor and Principal Investigator of the Cell Technology group at KTH Royal Institute of Technology's Department of Industrial Biotechnology. She leads the VINNOVA Competence Centre for Advanced BioProduction (AdBIOPRO), focusing on continuous bioprocessing of mammalian cells for biopharmaceuticals, viral vectors, and therapeutic cells. Her research emphasizes perfusion technology, mathematical modeling (kinetic and metabolic flux analysis), and process optimization. Active in projects like 'Data-Limited Learning of Complex Dynamical Systems,' she explores AI-driven bioprocess control. She directs AdBIOPRO’s efforts in high-cell-density perfusion and continuous manufacturing. Her work integrates bioreactor design, process intensification, and novel separation technologies (e.g., magnetic beads). Teaching includes courses on animal cell culture, drug development, and systems analysis. Over 50 peer-reviewed publications span bioprocess modeling, metabolic engineering, and bioreactor optimization. Her lab develops tools like microfluidic cartridges and Raman-based analytics for real-time process monitoring. Key collaborations include Digital Futures (a KTH-Stockholm University-RISE initiative) and industry partners for translational bioprocess solutions. Her research addresses challenges in scalable production of complex biologics under data-limited conditions.

Ivy PengView profile

Associate Professor

• High-Performance Computing (HPC)
• Cloud Computing
• Parallel Computing
• +6 more

Ivy Peng is an Associate Professor of Computer Science at KTH Royal Institute of Technology’s School of Electrical Engineering and Computer Science (EECS), where she leads the Scalable Computing Laboratory (ScaLab). She also holds a Docent title in Parallel Computing and is affiliated with the Digital Futures Faculty and the Cooperate Working Group. Prior to Sweden, she worked at the Lawrence Livermore National Laboratory (LLNL) as a Computer Scientist and as a post-doctoral fellow at Oak Ridge National Laboratory (ORNL) in the USA. Her research focuses on large-scale parallel systems, system-level optimization through workload and architecture awareness, performance modeling and analysis, and memory subsystem optimization. Key areas include converged HPC and cloud computing, heterogeneous accelerators (e.g., GPUs, RISC-V Vector units, QPUs, DPUs), and heterogeneous memories (e.g., persistent memory, HBM). She also explores disaggregated resources to enhance computing efficiency and scalability. Ivy Peng coordinates the EU Horizon 2022 Project OpenCUBE, which develops open-source cloud-based services for EPI systems, and leads the Swedish Research Council (VR) Grant on Disaggregated Memory for Emerging Parallel Systems. She actively contributes to technical committees and review boards for journals like IEEE TPDS and conferences including SC, ICS, HPDC, and IPDPS. She advises Ph.D. students Jacob Wahlgren, Gabin Schieffer, Ruimin Shi, and Daniel Medeiros, while co-supervising Jeremy Williams and Måns Andersson. Her academic leadership extends to multiple courses at KTH, including Applied GPU Programming, Introduction to High Performance Computing, and Quantum Computing for Computer Scientists.

Jiaojiao ZhangView profile

Research Fellow

• Distributed Optimization
• Decentralized Machine Learning
• Algorithm Design
• +4 more

Jiaojiao Zhang is a Digital Futures Postdoctoral Fellow at KTH Royal Institute of Technology, focusing on decentralized machine learning algorithms with convergence guarantees. Her research addresses second-order, online, and robust methods for distributed optimization, emphasizing applications in privacy-preserving technologies and smart systems. Education: B.E. in Automation (Harbin Engineering University, 2015), M.Sc. in Control Theory (University of Science and Technology of China, 2018), Ph.D. in Operations Research (CUHK, 2022). Award: Hong Kong PhD Fellowship Scheme (2018). Supervisors: Mikael Johansson (KTH EECS) and Joakim Jaldén (KTH). Her work bridges distributed optimization and algorithm design, targeting challenges in federated learning, wireless sensor networks, and multi-core processor training. Projects like 'Decentralized Learning' aim to enhance cooperation and trust in digital futures through robust, efficient algorithms. Key areas: privacy-preserving federated learning, Byzantine-robust systems, and dynamic privacy allocation. Publications emphasize convergence analysis, communication efficiency, and adversarial resilience in decentralized settings. She actively contributes to Digital Futures, a joint initiative of KTH, Stockholm University, and RISE.

• High-Performance Computing (HPC)
• Cloud Computing
• Parallel Systems Optimization
• +5 more

Bo Peng is an Associate Professor of Computer Science at KTH Royal Institute of Technology in Stockholm, leading the Scalable Computing Laboratory (ScaLab). Previously, he held roles as a Computer Scientist at Lawrence Livermore National Laboratory (LLNL) and a postdoctoral fellow at Oak Ridge National Laboratory (ORNL). He coordinates the EU Horizon 2022 Project OpenCUBE and leads a Swedish Research Council (VR) grant on Disaggregated Memory for Emerging Parallel Systems. His research focuses on optimizing large-scale parallel systems through architecture-aware design, performance modeling, and memory subsystem innovation for heterogeneous computing environments. He teaches courses such as Applied GPU Programming, High-Performance Computing, and Operating Systems. As an academic leader, he supervises multiple PhD students and co-supervises additional researchers. His service includes roles on technical program committees for top conferences like IEEE TPDS, SC, and ICS, and journals like IEEE Transactions on Parallel and Distributed Systems. Bo Peng’s work has been recognized with prestigious awards, including the R&D 100 Award in 2024. His research bridges HPC and cloud computing, emphasizing scalable systems and resource efficiency in next-generation architectures.

Tobias LangerView profile

Researcher

• Distributed Systems
• Operating Systems
• Parallel and Distributed Processing
• +3 more

Tobias Langer is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg. His affiliation includes the Technische Fakultät and Department Informatik. He focuses on invasive computing systems, runtime support systems, and distributed architectures. Research interests include distributed systems, operating systems, parallel computing, and real-time systems. His work emphasizes runtime systems for many-core architectures, resource arbitration, and virtual shared memory solutions for MPSoCs. Recent publications explore iRTSS (invasive runtime support system) and system software for future computing architectures. His research trends address scalability, resource management, and real-time challenges in modern computing environments. No scientific awards are explicitly mentioned. He has advised multiple students on topics like OctoPOS operating system development and system monitoring units. Langer's work is affiliated with the SFB/TRR 89 Invasive Computing project and contributes to the OctoPOS kernel development. His lab is part of CS 4 at FAU, located in Room 0.041-113.

Jens SchedelView profile

Researcher

• Distributed Systems
• Operating Systems
• Invasive Computing
• +3 more

Jens Schedel is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander University Erlangen-Nuremberg. His academic activities focus on invasive computing, distributed systems, and operating systems. He is affiliated with the Faculty of Engineering and collaborates with the ergoo group (Erlangen Research Group on Distributed Objects and Operating Systems). Education: 2010: Diploma Thesis: 'Design and implementation of a system abstraction layer for the I4Copter framework (QC_OSAL)' 2009: Study Thesis: 'Design and implementation of a distributed management and control software for projectors' Research Interests: Invasive Runtime Support Systems (iRTSS), aspect-oriented operating systems (CiAO), interdisciplinary quadrocopter projects (I4Copter), resource-aware programming, and embedded systems. His work emphasizes adaptive algorithms, parallel computing, and type-safe migration in safety-critical environments. Teaching: Taught courses (SP1/SP2) across multiple semesters (2010–2015), including FITS (Formal IT Systems) and PASST (Programming and Software Systems). Labs/Teams: Member of the ergoo group and contributes to the SFB/TRR 89 Invasive Computing project.