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.
Walter Binder is a Full Professor in the Faculty of Informatics at the Università della Svizzera italiana (USI). He holds a MSc, PhD, and venia docendi from Vienna University of Technology. Previously, he was a senior researcher at the Artificial Intelligence Laboratory, EPFL. His research focuses on program transformations, virtual execution environments, aspect-oriented programming, profiling, and resource management. Education: MSc from Vienna University of Technology PhD from Vienna University of Technology Venia docendi (Habilitation) from Vienna University of Technology Research Interests: His work spans JVM optimization, parallel computing, and dynamic program analysis. Notable contributions include tools like Renaissance (a JVM benchmark suite), S2S (SQL-to-Stream translator), and Akkaprof (profiler for actor-based systems). He explores topics such as thread management, vectorization, and performance profiling in distributed systems. Articles Trends: Recent work emphasizes JVM performance (e.g., Java Vector API, Native Image optimizations), parallel execution policies (NAS benchmarks), and adaptive runtime systems (e.g., MPR framework). He also investigates big data systems (Spark, Node.js) and runtime verification tools. Grants & Labs: Leads projects on JVM profiling, polyglot runtimes, and large-scale program analysis. His lab develops tools like NodeMOP for Node.js and AccStream for stream processing systems.
Geoffrey Mainland is an Associate Professor in the Department of Computer Science at the College of Computing and Informatics, Drexel University. His research bridges programming languages and systems, with a focus on enabling high-level, efficient programming of specialized hardware such as GPUs, FPGAs, and software-defined radios. He has developed domain-specific languages and runtime systems to simplify and optimize non-general-purpose computation. PhD in Computer Science, Harvard University, 2011 AB in Physics, Harvard University, Magna Cum Laude, 2000 His research interests include high-level programming languages, runtime support for specialized devices, functional programming (particularly in Haskell), stream fusion, SIMD vectorization, and domain-specific languages for systems programming. He investigates how to make it easier to exploit the performance of GPUs, sensor networks, and FPGAs without sacrificing abstraction or safety. The recent publications highlight a consistent theme: enhancing performance in functional languages through language design, compiler optimizations, and runtime systems. Key areas include SIMD support in Haskell, GPU programming via Nikola and MetaHaskell, staged programming for sensor networks (Flask), and domain-specific languages like Ziria for software-defined radio. These works demonstrate a strong focus on bridging the gap between high-level abstractions and low-level efficiency. Geoffrey Mainland has not been mentioned as receiving any scientific awards in the provided material. He advises current PhD student Dresden Feitzinger and has previously advised Mahshid Shahmohammadian, now at Intel. His research has been supported through academic and possibly federal funding, though specific grants are not detailed. His work is implemented in open-source projects hosted on GitHub, including contributions to the Haskell vector library and the development of Ziria and DragonRadio. He leads a research group focused on programming systems, with active projects in software-defined radio (Ziria, DragonRadio), high-performance numerics in Haskell (HSpiral), and language support for heterogeneous computing. His team develops tools that enable researchers and developers to write efficient, correct code for specialized hardware using high-level functional abstractions.
Adria Armejach Sanosa is a Senior Lecturer in the Department of Computer Architecture at the Faculty of Computer Science of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC). He is also affiliated with the Barcelona Supercomputing Center (BSC-CNS), a leading institution in high-performance computing in Europe. His research spans computer architecture, high-performance computing, memory systems, and hardware acceleration for genomics and machine learning. PhD from UPC His research interests focus on optimizing computer systems for performance and efficiency, particularly in the areas of hardware transactional memory, cache optimization, RISC-V architectures, and acceleration of bioinformatics workloads. He investigates how to improve data movement, prefetching, and parallelism in large-scale heterogeneous systems. His work combines architectural innovations with practical implementations on real-world HPC platforms. The most recent articles reflect a strong trend towards high-performance computing for genomics, sparse data handling, and efficient hardware/software co-design. Topics include genomics benchmarking on ARM processors, tensor marshaling, RTL simulation scalability, and low-precision training for deep neural networks. These works demonstrate a consistent focus on bridging architectural research with real-world applications in science and AI. HiPEAC Paper Award 2024 HiPEAC Paper Award Armejach has advised several doctoral students, including J. Pavón, G. López, and J. Osorio. He has been involved in numerous competitive R&D+i projects such as Digital Autonomy for RISC-V in Europe, Laboratorio Zettaescala de Barcelona, and Genome Analysis Acceleration on HPC Architectures. These projects are often funded by national and European programs, indicating strong recognition and support for his research. He collaborates extensively within the CAP (High-Performance Computing) research group and with key figures like Miquel Moreto, Mateo Valero, and Osman Unsal. He is a member of the CAP research group and contributes to initiatives like the Laboratory for Open Computer Architecture and systems (RISC-V Chip Development) and the Barcelona Zettascale Lab. These labs focus on open hardware, European technology sovereignty, and next-generation supercomputing. His work on Metro-MPI for RTL simulation and hardware accelerators for databases highlights his contributions to both design automation and data-intensive computing.
Angela Pohl serves as a Professor in the Department of Computer Science and Media at Brandenburg University of Technology. Her academic office is located in Building C, Room C.2.18 at Magdeburger Straße 50, 14770 Brandenburg an der Havel, Germany, with contact available via telephone (+49 3381 355 - 459) and email (angela.pohl@th-brandenburg.de). Her research program centers on high-performance computing and compiler optimization , with critical contributions in: Vector length agnostic programming models for modern SIMD architectures Cost modeling and performance prediction for auto-vectorizers Architecture-specific optimizations for ARM NEON, Intel AVX, and SVE Application of vectorization to multimedia (VVC decoder) and scientific computing (RICH particle detector) Analysis of her 2015-2020 publications reveals a progression from foundational SIMD model evaluation to advanced portable cost modeling. Her work demonstrates consistent focus on bridging compiler technology with hardware capabilities, particularly emphasizing real-world applications in video coding and high-energy physics where vectorization delivers substantial performance gains.
Sylvain JUBERTIE serves as a Lecturer at the University of Orleans, affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) research laboratory. His academic career spans over two decades with continuous contributions to high-performance computing, particularly in vectorization techniques and seismic simulation methodologies. His research interests demonstrate deep specialization in: Hardware-specific vectorization (ARM NEON/SVE, SIMD) for seismic kernels GPU acceleration and code portability across architectures High-order spectral finite element methods (EFISPEC3D) Energy efficiency optimization in parallel computing Algorithmic skeleton libraries (OSL, NSIMD) for parallel programming Memory layout reorganization for numerical kernels Analysis of his 15 most recent publications (2013-2025) reveals a consistent research trajectory focused on seismic wave propagation simulation. His work systematically addresses vectorization challenges across ARM architectures, GPU offloading, and memory access patterns, with EFISPEC3D serving as the primary application framework. Notable trends include the development of the NSIMD abstraction layer and rigorous energy-performance trade-off studies on embedded platforms like Jetson boards. Dr. Jubertie maintains active collaboration with key researchers including Fabrice DUPROS, Florent DE MARTIN, and Guillaume QUINTIN through the LIFO laboratory, contributing to France's geophysics research infrastructure while advancing compiler-level optimizations for emerging processor architectures.
Scott Mahlke is a Professor and Associate Chair in the Electrical Engineering and Computer Science Department at the University of Michigan. He is affiliated with both the Advanced Computer Architecture Laboratory and the Software Systems Laboratory. He joined the University of Michigan in 2001 after receiving his Ph.D. from the University of Illinois and working at HP Laboratories for six years. Dr. Mahlke received his Ph.D. in Electrical Engineering from the University of Illinois at Urbana-Champaign in January 1997, with Wen-mei Hwu as his advisor. His thesis title was "Exploiting Instruction-level Parallelism in the Presence of Conditional Branches." He also holds an M.S. (1992) and B.S. (1988) in Electrical and Computer Engineering from the University of Illinois. Professor Mahlke's research focuses on compilers, computer architecture, and high-level synthesis. His work centers on designing next-generation computer systems that overcome challenges in performance, power consumption, and reliability. His research group, Compilers Creating Custom Processors (CCCP), designs customized processors and accelerators by customizing hardware to the software that will run on the system. His publications demonstrate significant contributions to compiler technology, instruction-level parallelism, predicated execution, and custom processor design, spanning from fundamental compiler optimizations to practical applications in embedded systems and high-performance computing. Professor Mahlke has received numerous awards including: National Science Foundation CAREER Award (2003) Morris Wellman Faculty Development Assistant Professorship (2004) ISCA Most Influential Paper Award (2006) 2007 Young Alumni Award from the ECE Department at the University of Illinois Multiple best paper awards at MICRO, SAMOS, and CASES conferences He has advised several PhD students to completion and his research group receives funding from the National Science Foundation, Gigascale Systems Research Center, ARM Ltd., Motorola, and Samsung Advanced Institute of Technology. Professor Mahlke has been actively involved in the computer architecture and compiler research communities, serving on program committees for major conferences including ISCA, MICRO, HPCA, PLDI, and CGO, and holding editorial positions for ACM Transactions on Architecture and Code Optimization and the Journal of Instruction Level Parallelism.
Muaaz Gul Awan is a Computer Systems Engineer 4 at the National Energy Research Scientific Computing Center (NERSC) under the Science Engagement & Workflows Dept. His work focuses on high-performance computing (HPC) with specialization in bioinformatics software development, GPU porting, and performance optimization for large-scale scientific applications.
Daniel Lemire is a full professor of computer science at the Université du Québec (TELUQ), recognized as one of the top 2% most cited scientists globally according to Stanford University's 2024 rankings. He ranks among the 0.0006% most followed programmers on GitHub, with his work adopted by major technology companies including Google, Facebook, Intel, and Shopify. Education: Ph.D. in Engineering Mathematics (University of Montreal and Polytechnique Montréal), Master's in Mathematics (University of Toronto), Bachelor's in Mathematics with High Distinction (University of Toronto) Current Role: Editor of Software: Practice and Experience journal since 2020 Professional Recognition: Co-chair of NSERC's Computer Science Discovery Grants Committee (2020-2021) Professor Lemire's research focuses on software performance optimization and data indexing techniques. His work bridges theoretical computer science with practical applications, particularly in areas where performance bottlenecks exist in real-world systems. He specializes in leveraging hardware capabilities through vectorization (SIMD instructions) to dramatically improve processing speeds for fundamental operations that have remained inefficient for decades. His approach combines deep theoretical understanding with practical implementation, resulting in algorithms that are both mathematically sound and immediately applicable in production systems. Lemire's research portfolio demonstrates a consistent pattern of identifying critical performance bottlenecks in widely used software operations and developing innovative solutions that achieve order-of-magnitude improvements. His work spans multiple domains including JSON parsing, Unicode string processing, URL parsing, base64 encoding, and bitmap indexing. A common thread through his publications is the application of hardware-specific optimizations, particularly SIMD instructions, to accelerate operations that were previously considered near-optimal. His research has evolved from foundational algorithm development to influencing major software ecosystems, with his libraries becoming integral components of industry-standard tools. Among the 2% most cited scientists globally (Stanford University, 2024) Université du Québec's Prix d'excellence 2020 for research success Most read articles at Software: Practice and Experience (2024, 2025) Best voted talk at QCon San Francisco 2019 Editor of Software: Practice and Experience journal since 2020 Numerous citations in patents held by Microsoft, LinkedIn, Oracle, and Fujitsu Professor Lemire maintains an active research group that has graduated numerous PhD students, many of whom now hold key positions at leading technology companies. He offers automatic scholarships for all students making progress on M.Sc. theses and Ph.D. programs in his lab, with tuition waivers for international Ph.D. students. His laboratory is equipped with a diverse server farm featuring multiple processor architectures (Intel Xeon, Core i7, Xeon Phi, POWER9, ARMv8) specifically designed for software performance experiments. The lab also explores virtual reality applications in data science. Lemire actively recruits students who are passionate about high-performance programming and open-source development, with special programs for Canadian undergraduate and graduate students through NSERC funding mechanisms.
Dr. Arsène Pérard-Gayot is a Research Fellow in the Computer Graphics Lab at Saarland University, Germany. His research develops high-performance rendering systems through compiler innovations and hardware optimization techniques. Pérard-Gayot's work creates frameworks for automatic generation of optimized renderers using partial evaluation, eliminating manual compiler development. His FLOWER compiler enables efficient dataflow programming for FPGAs, while Rodent generates specialized renderers for CPUs/GPUs. Additional contributions include ray tracing acceleration structures for irregular grids and BVH traversal abstractions. Publications demonstrate consistent focus on bridging high-level programming models with hardware efficiency. Collaborative projects include molecular dynamics simulation mapping to heterogeneous architectures and distributed ray tracing for massive scenes. Research outcomes advance real-time rendering capabilities and domain-specific compiler technologies for scientific computing.
Paulo Flores holds the position of Associate Professor at the Department of Electrical and Computer Engineering within the Instituto Superior Técnico of the University of Lisbon. His academic career is deeply rooted in Electronics and Digital Systems, with a focus on VLSI design, FPGA optimization, and hardware acceleration for bioinformatics applications. He has contributed extensively to research in multiplierless constant multiplication algorithms, low-power circuit design, and quaternary logic implementations. Notable achievements include the SAT Competition 2014 and 2013 Bronze Medals for innovative contributions in formal methods and algorithmic efficiency. His teaching responsibilities include advanced courses in digital systems design, electronic engineering projects, and laboratory guidance in topics like operational amplifiers and FIR filters. Flores actively collaborates with research units like INESC-ID and has supervised multiple academic projects in electronic engineering and digital signal processing. Research interests span across circuit testing, embedded systems optimization, and multi-valued logic architectures. His work frequently explores trade-offs between computational efficiency and energy consumption in hardware implementations.
Matteo Basso is a researcher at Università della Svizzera italiana (USI), Switzerland, specializing in compiler design, virtual machines, and WebAssembly technologies. His work bridges high-level programming languages with efficient execution on modern hardware platforms, with a strong focus on practical systems that improve performance and efficiency. His research interests include: WebAssembly and its applications in web and server environments Compiler optimization techniques for virtual machines Memory management and binary serialization formats Performance analysis of programming language runtimes Java Virtual Machine enhancements and thread management Matteo's publication record shows consistent contributions to top-tier conferences including SPLASH, OOPSLA, and CGO, with recent work emphasizing WebAssembly technologies and Java performance optimization. His research on heap management, native-image startup performance, and compiler-level profiling demonstrates practical impact in language implementation. Notable contributions include: Heap snapshot matching using context-augmented heap-path representations Improvements to native-image startup performance in GraalVM Optimization-aware compiler-level event profiling techniques Analysis of Java Vector API performance characteristics As an active open-source contributor, Matteo maintains several popular repositories including asm-dom (a WebAssembly virtual DOM implementation with 2.8k stars) and awesome-wasm (a curated WebAssembly resource list with 9.4k stars).
Andrea Rosà is a researcher at the University of Lugano (USI), Switzerland, with a focus on performance optimization, concurrency, and profiling in the Java Virtual Machine (JVM) ecosystem. His work spans tool development, benchmarking, and parallel computing, often targeting metrics related to task granularity and synchronization. He has contributed to major conferences such as SPLASH, APLAS, and GPCE, and is actively involved in program committees and artifact evaluations. His research includes designing profiling tools like P3 and Renaissance, which analyze parallel applications, lock-free operations, and dynamic program behavior. These tools have been instrumental in benchmarking modern JVM performance and identifying variability in workloads. Andrea has authored numerous papers on profiling Java Streams, vector APIs, and multi-language dynamic analysis. His work often addresses challenges in automated benchmarking, runtime transitions, and task granularity optimization.
Martin Kruliš is an associate professor in the Department of Distributed and Dependable Systems at the Faculty of Mathematics and Physics, Charles University , Prague, Czech Republic. His primary roles include research, teaching, advising, and leading projects that bridge high-performance computing, GPU programming, and self-adaptive systems. Education: While exact details of his own degrees are not provided, Dr. Kruliš’s extensive publication record and faculty position at Charles University indicate advanced training in computer science with specialization in parallel and distributed computing. Research Interests: High-Performance GPU Computing: Deep investigation into CUDA kernel optimization, memory bandwidth utilization, and workload dispatching for massively parallel accelerators. Self-Adaptive & Self-Optimizing Systems: Designing ensemble-based abstractions that integrate machine-learning estimators to enable runtime adaptation in component architectures. Parallel Algorithms & Data Structures: Development of cache-friendly, SIMD-aware, and GPU-accelerated algorithms for clustering, dimensionality reduction, and similarity search. Software Engineering for Parallelism: Creation of C++ libraries, DSLs (e.g., Bobolang), and educational tools (ReCodEx) that simplify parallel programming and automated evaluation. Publication Trends: Across 2011–2025, Kruliš’s articles reveal a clear trajectory from foundational GPU-accelerated indexing and multimedia retrieval toward sophisticated self-adaptive systems that leverage machine learning. Recent works (2023–2025) increasingly focus on integrating LLMs and neural networks into compiler and runtime optimization loops, reflecting a convergence of AI and systems research. Scientific Awards & Recognition: No specific awards or fellowships are mentioned in the provided text; however, sustained publication in top-tier venues (JPDC, IPDPS, Euro-Par, SEAMS) and active involvement in program committees and tool development indicate strong peer recognition. Teaching & Student Supervision: Teaches Programming in Parallel Environment (NPRG042) , Advanced Programming in Parallel Environment (NPRG058) , Computer Systems (NSWI170) , and Software Projects . Supervises numerous bachelor’s and master’s theses; exact student names are not listed in the text. Labs, Projects & Tools: ReCodEx: A widely used platform for semi-automated evaluation of programming assignments at Charles University. Simdex: A modular simulator of the ReCodEx backend that enables realistic experimentation with self-adaptive job dispatching and machine-learning controllers. Active contributor to open-source repositories on GitHub, focusing on GPU kernels, benchmarking frameworks, and educational tooling.
Milind Kulkarni is a Professor and the Michael and Katherine Birck Head at the Elmore Family School of Electrical and Computer Engineering at Purdue University . He earned degrees from North Carolina State University (B.S. 2002) and Cornell University (M.S. 2005, Ph.D. 2008) under advisor Keshav Pingali . His research focuses on compiler and programming language design for privacy-preserving computation , GPU acceleration , and parallel execution of irregular algorithms . Recent publications emphasize Vectorization in FHE (ASPLOS 2023, PPoPP 2025) Sparse Tensor Optimization (ICS 2022, ICS 2024) Ray Tracing Hardware Utilization (ICS 2023, ICS 2024) His research group has received numerous accolades including 4 IEEE TCSE Distinguished Paper Awards , 3 Purdue Seed for Success Awards , and 2 Presidential Early Career Awards . Current advisees include Raghav Malik and Vani Nagarajan , while former students have joined institutions like Amazon Web Services and Virginia Tech . He leads the PurPL programming languages research group and collaborates with the Center for Resilient Infrastructures (CRISP).
