Jürgen Teich is a Professor at the University of Erlangen-Nuremberg, Department of Computer Science. His research focuses on computer architecture, embedded systems, and hardware-software co-design, with particular emphasis on energy-efficient and sustainable computing. He leads projects involving FPGA-based accelerators, neural networks on microcontrollers, and real-time systems optimization. His work spans topics such as approximation computing, MPSoCs (Multiprocessor Systems-on-Chip), and IoT device architectures. Key contributions include methodologies for optimizing resource allocation in heterogeneous systems and developing energy-harvesting solutions for embedded systems. Teich has authored numerous publications in top-tier conferences and journals, including DATE, FPL, and ACM Transactions. His research often collaborates with industry partners, emphasizing practical applications and open-source hardware.
Jean-Marc Jézéquel is a prominent professor and researcher at IRISA (Institut de Recherche en Informatique et Systèmes Aléatoires) in Rennes, France, a joint research unit of INRIA, CNRS, and the University of Rennes. His extensive publication record spanning over two decades demonstrates his significant contributions to software engineering, particularly in model-driven engineering and software product lines. His research expertise encompasses several critical areas in contemporary software engineering. Jézéquel has pioneered work on managing uncertainty in model-based development, developing frameworks like DataTime for temporal reasoning in models. He has made substantial contributions to deep variability management across multiple layers of software systems, investigating how compile-time and run-time configuration options interact. His recent work increasingly integrates machine learning techniques with traditional software engineering approaches, creating innovative hybrid methodologies for software development and analysis. Jézéquel's publication trends reveal a clear evolution in his research focus, moving from foundational model-driven engineering concepts toward addressing uncertainty, incorporating temporal aspects into models, and leveraging machine learning for software analysis and development. His work spans both theoretical foundations and practical industrial applications, with notable collaborations including projects with Airbus on specialized software product line engineering. The interdisciplinary nature of his research connects software engineering with artificial intelligence, data science, and systems engineering. His significant contributions to the field include foundational work on model transformation, software language engineering, and variability management. Jézéquel has helped advance model-driven engineering from what he describes as 'craft' to a more systematic engineering discipline, as evidenced in his publications spanning from early 2000s to the present. As an academic leader, Jézéquel has supervised numerous PhD students and maintained extensive international collaborations. His work demonstrates a consistent commitment to bridging the gap between academic research and industrial software development practices, with applications ranging from aerospace systems to urban transportation solutions.
Jean Pierre David is a Full Professor in the Department of Electrical Engineering at Polytechnique Montréal. He has been with the institution since January 2006, was promoted to Associate Professor in June 2013, and became a Full Professor in June 2021. His research focuses on digital systems design, reconfigurable systems, and hardware implementations of artificial intelligence applications. David received his Electrical Engineering degree (specializing in electronics) from the University of Liège (Belgium) in 1995. He completed his Ph.D. in June 2002 at the Catholic University of Louvain, with research focused on reconfigurable systems (FPGAs). Before joining Polytechnique Montréal, he was a professor at the University of Montreal from August 2002 to January 2006. Jean Pierre David's research spans several key areas in electrical engineering and computer science. His primary focus is on digital systems design, configuration, and programming, with particular expertise in reconfigurable systems such as FPGAs and microcontrollers. He has made significant contributions to Hardware Description Languages (HDL), developing methodologies for fast, safe, and simple design of digital architectures. His work extends to Hardware-in-the-Loop (HIL) simulation, Deep Packet Inspection (DPI) for high-speed communications (10GBE, 40GBE, 100GBE), and applications of digital systems in artificial intelligence, particularly neural network implementations. David's recent research has increasingly focused on energy-efficient AI hardware, RISC-V processor design for neural network acceleration, and specialized architectures for low-precision computation. His publication record shows a clear evolution from foundational work in digital system design and FPGA implementation toward increasingly sophisticated applications in artificial intelligence and neural network acceleration. The most recent publications demonstrate expertise in creating specialized hardware for efficient AI computation, with a strong emphasis on low-precision and binary neural networks that can run efficiently on resource-constrained devices. His work bridges computer architecture, electrical engineering, and artificial intelligence, creating practical hardware solutions for emerging computational challenges. David is affiliated with several important research groups and institutions including the Strategic Microsystems Group of Quebec (ReSMiQ), the Institute of Electrical and Electronics Engineers (IEEE), and the Institute for Data Valorization (IVADO). His work has been recognized through numerous publications in high-impact journals and conferences, with a total of 108 publications to his name. Professor David has supervised an impressive number of graduate students throughout his career, mentoring 9 Ph.D. students and 24 Master's students to completion. His students have worked on diverse topics including FPGA-based neural network acceleration, hardware implementations of deep learning algorithms, energy harvesting systems for IoT devices, and specialized architectures for low-precision computation. His lab appears to maintain strong connections with industry through various research projects and collaborations with researchers like Yves Savaria. His research laboratory focuses on the intersection of hardware design and artificial intelligence, with particular emphasis on creating efficient implementations of neural networks on specialized hardware platforms. The lab maintains strong connections with industry partners and collaborates extensively on projects related to network processing, AI acceleration, and energy-efficient computing systems.
Dr. Andreas Abel is a postdoctoral researcher at Saarland University working in the Real-Time and Embedded Systems Lab under Prof. Jan Reineke. His work focuses on the intersection of computer architecture, performance analysis, and security, with a particular emphasis on x86 microarchitectures. His research interests span microarchitecture analysis, performance prediction, cache systems, computer security, hardware-software interaction, and reverse engineering. Dr. Abel has made significant contributions to understanding CPU microarchitectures through tools like uops.info, which provides detailed latency, throughput, and port usage data for x86 instructions, and nanoBench, a low-overhead tool for running microbenchmarks on x86 systems. His work bridges theoretical computer science with practical performance analysis, enabling more accurate prediction of program behavior on modern processors. His publication record shows a consistent trajectory from formal methods and model checking in his early career toward increasingly sophisticated analysis of modern processor microarchitectures. Recent work focuses on accurate throughput prediction (FACILE, uiCA), security implications of microarchitectural behavior (Flushgeist), and tools for microbenchmarking (nanoBench). His research demonstrates a unique combination of formal methods expertise applied to practical problems in computer architecture. Dr. Abel has taught various courses at Saarland University including System Architecture, Design and Analysis of Real-Time Systems, and seminars on Resource Sharing in Real-Time Systems and Robustness of Hardware and Software Systems. His teaching spans both undergraduate and graduate levels, reflecting his dual expertise in theoretical computer science and practical systems engineering.
Dr. Hong Qi is a Lecturer in Mathematical Sciences at Queen Mary University of London (QMUL), leading the QMUL LIGO Scientific Collaboration (LSC) Group since October 2023. His research focuses on gravitational wave detection, multimessenger astrophysics, and quantum computing applications in data analysis. He holds a PhD in Physics from the University of Wisconsin-Milwaukee (2018) and has held postdoctoral roles at Cardiff University (2018-2022) and Louisiana State University (2022). Dr. Qi is a core member of the LSC since 2015, contributing to the first gravitational wave detection (GW150914) and over 90 subsequent observations. His current work includes accelerating gravitational wave inference algorithms, dark matter searches with LIGO, and quantum computing integration into gravitational wave astronomy. **Education**: PhD in Physics (UW-Milwaukee, 2018); Postdoctoral roles at UW-Milwaukee (2015-2018), Cardiff University (2018-2022), and LSU (2022). **Research Interests**: Gravitational wave astronomy, quantum computing for data analysis, dark matter direct detection, subsolar-mass compact objects, and multimessenger astrophysics. Recent grants include a £27k STFC Impact Accelerator Award (2023) and a £0.5M/year DRAC grant (2024-2027) for gravitational wave discovery efforts. **Awards**: Emmy Noether Fellowship (London Mathematical Society, 2023). **Advising**: Supervising PhD student Murdoc Newell (2024-). Grants include leadership in detector characterization and quantum computing initiatives. Active in the LSC, KAGRA, and GEO600 collaborations. **Labs/Teams**: Lead of QMUL LSC Group, collaborator with LIGO-Virgo-KAGRA network, and member of the Gravity Exploration Institute (Cardiff).
Dr. Anil Yildiz is a senior researcher and Deputy Director at the Chair of Methods for Model-based Development in Computational Engineering (MBD), RWTH Aachen University, Germany. He leads the research group Engineering Climate Change Response and is actively involved in computational modeling of geohazards and climate-responsive engineering systems. PhD in Civil Engineering, ETH Zurich (2018) M.Sc. in Civil Engineering, Bogazici University (2013) B.Sc. in Civil Engineering, Bogazici University (2010) Dr. Yildiz's research centers on geohazards , particularly shallow landslides , soil-plant-atmosphere interactions , and computational modeling . He is deeply committed to reproducible workflows , data-integrated modeling , executable publications , and FAIR data principles . His work integrates machine learning , uncertainty quantification , and surrogate modeling to improve the reliability and efficiency of geohazard simulations. His recent publications (2023–2025) emphasize FAIR data practices , reproducible workflows , and uncertainty-aware modeling in landslide and sea ice research. Key themes include modular frameworks , data reuse , and executable scientific publications , reflecting a strong trend toward open, transparent, and computationally rigorous geoscience. Scientific awards and recognitions include: IDEA League Fellowship (2024) Culmann Prize, ETH Zurich (2019) Outstanding Doctoral Thesis in Civil Engineering (2018) Roland Schlich Early Career Scientist's Travel Support, EGU Dr. Yildiz has been involved in significant research projects, including work on green infrastructure and ground heat exchange at the National Green Infrastructure Facility (UK), and has contributed to interdisciplinary collaborations involving geotechnical engineering , climate modeling , and infrastructure resilience . He has not formally listed advisees, but his collaborative publications suggest active mentorship and team leadership. His research group focuses on developing reliable, data-driven models for climate-related geohazards. He has been affiliated with leading institutions including ETH Zurich, Newcastle University, University of Göttingen, and RWTH Aachen University, demonstrating a strong international research trajectory.
Jeremy Bradbury is an Associate Professor in the Faculty of Science at Ontario Tech University, where he has served as Undergraduate and Graduate Program Director for Computer Science and as a member of the Board of Governors. His research focuses on software testing, analysis, and quality assurance, with a particular emphasis on human-centered approaches, empirical methodologies, and concurrency issues in software systems. He leads the Software Quality Research Lab (SQRLab) and develops tools like PIE for visualizing software design patterns and GidgetML for adaptive educational gaming. Education: PhD in Computer Science from Queen's University (2007). Research interests include software visualization, open-source software analysis, flaky test detection, and the application of machine learning to software engineering challenges. His work spans both academic contributions (e.g., combinatorial testing frameworks) and practical innovations like the Run, Llama, Run game for computational thinking education. Recent work explores AI-driven bug prediction, data augmentation bias in ML models, and cybersecurity for connected autonomous vehicles. He has organized workshops on testing configurable systems (ToCaMS) and challenges in parallel computing.
Dr. Guangli Li is a researcher affiliated with the University of New South Wales (UNSW), focusing on the intersection of programming systems and artificial intelligence. He received his PhD from the University of Chinese Academy of Sciences. Research Interests : His work centers on programming languages, compilers, and run-time systems for emerging AI applications and accelerators. He has published 30+ papers in top-tier venues like ASPLOS, CGO, TACO, and TCAD. Contact : Email: guangli.li@unsw.edu.au
Andreas Paul Eberhard Kloeckner is an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), where he has been serving since 2013 (promoted to Associate Professor in 2019). He also holds an affiliate faculty appointment in the Department of Electrical and Computer Engineering since 2016. His academic journey includes a PhD in Applied Mathematics from Brown University (2010), an MSc from Brown University (2006), and a Diplom in Applied Mathematics from Universität Karlsruhe (2005). Prior to joining UIUC, he was a Courant Instructor at the Courant Institute of Mathematical Sciences at New York University. Dr. Kloeckner's research focuses on high-order accurate integral equation methods, fast algorithms for elliptic boundary value problems, and code transformation for high-performance scientific computing. His work bridges mathematical theory with practical implementation, with particular emphasis on GPU computing and parallel architectures. He has made significant contributions to the development of open-source scientific software, most notably PyCUDA and PyOpenCL, which have become widely used tools in the scientific computing community. His publication record shows a consistent focus on advancing numerical methods for scientific computing, with recent work emphasizing code generation techniques, fast integral equation solvers, and optimization of algorithms for modern hardware architectures. The trajectory of his publications reveals a progression from foundational work on GPU-based discontinuous Galerkin methods toward increasingly sophisticated approaches for integral equation methods and automatic code generation. Among his notable recognitions is the 2017 National Science Foundation CAREER Award, which supports his work on general-purpose, high-order integral equation methods for computer simulation in engineering. His research has been published in prestigious venues including SIAM Journal on Scientific Computing and has influenced both academic research and practical applications in scientific computing. Dr. Kloeckner has advised numerous graduate students through completion of their PhD and MS degrees, with alumni moving to positions at Apple, NVIDIA, Rice University, and other leading institutions. His research group maintains an active portfolio of open-source software projects that advance the state of scientific computing infrastructure.
Dr Matthew Danish is a faculty member at the Department of Computer Science and Technology, University of Cambridge. His research spans multiple areas within computer science, with a focus on practical applications of theoretical concepts. He is based in room FE25 of the William Gates Building at 15 JJ Thomson Avenue, Cambridge CB3 0FD. Dr Danish's research interests encompass a diverse range of topics including Machine Learning and Artificial Intelligence, Programming Languages, Semantics and Verification, and Systems and Networking. His work bridges theoretical computer science with practical applications in urban computing, privacy-preserving systems, and edge computing. He has made significant contributions to the field of units-of-measure verification, developing techniques to ensure correctness in scientific computing. His recent publications (2019-2025) demonstrate a clear research trajectory focused on applying computer science to real-world problems. Danish has published extensively on units-of-measure verification, static analysis of scientific code (particularly Fortran), privacy-preserving systems for edge devices, and applications of computer vision in urban environments. His work often combines theoretical rigor with practical implementation, as evidenced by projects like DeepDish that run on off-the-shelf hardware like Raspberry Pi. Dr Danish has supervised or collaborated on numerous research projects related to smart environments, adaptive city platforms, and real-time data processing systems. His work on Cerberus demonstrates expertise in developing privacy-preserving solutions for crowd counting and localization, while his research on RACER shows capabilities in complex event recognition systems. Based in the William Gates Building, Dr Danish is part of Cambridge's vibrant computer science research community, contributing to multiple research themes within the department. His interdisciplinary approach connects computer science theory with applications in urban studies, public health, and environmental monitoring.
Alasdair MacIntyre is an Associate Lecturer at the School of Arts and Humanities, Australian Catholic University. His creative practice spans over two decades, focusing on sculptural installations, conceptual art, and exhibition curation. Key affiliations include collaborations with institutions like Hazelhurst Regional Gallery, Moreton Bay Regional Gallery, and Queensland Art Gallery. Research interests revolve around the intersection of sculpture, spatial dynamics, and cultural narratives. His work often explores themes of material transformation, environmental interaction, and historical symbolism through mixed-media installations. Notable projects include large-scale exhibitions such as Miriam Colombo: A Retrospective (2018) and The Long March (2014), which critically engage with regional Queensland contexts. Publications highlight a consistent exploration of kinetic sculptures ( Running to standstill , 2007), conceptual numerical installations ( Viginti praevius , 2007), and light-based works ( Luminous beings are we , 2006). These projects demonstrate a commitment to pushing boundaries in material, spatial, and philosophical dimensions of contemporary art.
Christian Eichler is a researcher at Friedrich-Alexander University Erlangen-Nuremberg, affiliated with the Department of Computer Science (INF) and the Chair of Computer Science 4 (System Software). His work focuses on real-time systems, embedded computing, and cyber-physical systems. His research in real-time and embedded systems includes invasive computing frameworks, deterministic I/O management, and energy-constrained system analysis. He has contributed to tools like TASKers and GenEE for benchmarking and timing analysis of real-time software. His publications from 2017-2022 span conferences like HICSS, CCS, ISORC, and workshops on benchmarking, invasive computing, and cyber-physical systems. Key co-authors include Wolfgang Schröder-Preikschat and other researchers from FAU and international institutions.
Robert Rinker, Ph.D., is an Associate Professor and Associate Chair in the Department of Computer Science at the University of Idaho, part of the College of Engineering. His office is located in Hedlund Building 202D, and he can be reached at rinker@uidaho.edu. Rinker holds a Ph.D. in Computer Science from Colorado State University, and both Master's and Bachelor's degrees in Electrical Engineering from the University of Idaho. His teaching responsibilities include courses such as Computer Science I (CS 120), Computer Organization and Architecture (CS 150), System Software (CS 270), Advanced Computer Architecture (CS 451/551), and Real-Time Operating Systems (CS 452/552). His research focuses on reconfigurable computing, embedded systems security, computational biology pipelines, and hardware compilation techniques. Specific contributions include work on FPGA-based systems, resilient multi-core architectures, and formal verification methodologies. Rinker’s research trends emphasize the intersection of hardware-software co-design and cybersecurity, with notable contributions to VANET security and computational density optimization in embedded systems. His publications span topics from workflow automation in bioinformatics to compiler-driven FPGA optimization strategies. While no formal awards or grants are listed, his extensive academic service as Associate Chair highlights his leadership in departmental operations. He advises courses but no listed students or active lab teams are mentioned in the provided materials.
Chen Ding is a Professor of Computer Science and Chair of the Department of Computer Science at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from Rice University (2000). His research focuses on program analysis, optimization, and memory management, particularly in the areas of locality theory, compilers, parallel programming, and high-performance computing. He has received prestigious awards including the DOE Early Career Principal Investigator award (2001), NSF CAREER award (2002), and IBM CAS faculty fellowship (2005-2009). Education: PhD in Computer Science, Rice University, 2000 Research Interests: Locality theory and optimization Compilers and runtime systems for parallelism Memory management strategies High-performance computing systems Key Contributions: His work on reuse distance in memory hierarchy and compiler-assisted cache management (CLAM) has been foundational in optimizing data movement and parallelism. Recent research explores data movement complexity for machine learning and generative AI-driven memory workload synthesis. Awards: IPDPS Best Paper Award (2001) DragonStar Lecturer (ICT China, 2008) Grants & Advising: As department chair, he oversees research grants and faculty development. No specific student advising details are listed here.
John Mellor-Crummey is a Professor of Computer Science and Electrical and Computer Engineering at Rice University. He leads research in high-performance parallel computing, focusing on performance analysis tools (HPCToolkit), compiler optimizations, and synchronization algorithms. His work on scalable synchronization earned the 2006 Dijkstra Prize, and he was named an ACM Fellow in 2013. Education: PhD, Computer Science, University of Rochester (1989) BSE, Electrical Engineering and Computer Science, Princeton University (1984) Research Interests: His work spans high-performance computing tools, parallel compiler technology, and performance modeling. He develops frameworks like HPCToolkit for exascale systems and explores GPU acceleration for stencil computations. His research also addresses data race detection and scalable parallel programming models. Grants & Projects: Principal investigator for the Exascale Computing Project and contributor to OpenMP standards. His HPCToolkit project is supported by the Exascale Computing Project. Awards: ACM Fellow (2013) Dijkstra Prize in Distributed Computing (2006) Teaching: Recent courses include Multi-core Computing and Parallel Programming at Rice University. Labs/Teams: HPCToolkit team, OpenMP Language Committee, and the Rice Coarray Fortran project.
