Pan Hui is Chair Professor of Computational Media and Arts at Hong Kong University of Science and Technology, where he serves as Director of the Center for Metaverse and Computational Creativity and HKUST-DT Systems and Media Laboratory. He concurrently holds the Nokia Chair in Data Science position at the University of Helsinki. His interdisciplinary research spans ubiquitous computing, mobile systems, and social networks. Research interests focus on: Data-driven systems design for mobile/cloud platforms Immersive human-data interaction through AR/VR Social network analysis using big data analytics Energy-efficient mobile computing architectures Publications show strong focus on augmented reality systems, cloud computing optimization, and human-computer interaction, with recent work advancing real-time visualization and low-latency tracking techniques. Awards and honors include: Fellowship in Royal Academy of Engineering (2020) Membership in Academia Europaea (2019) IEEE Fellow (2018) ACM Distinguished Scientist (2016) Leads multiple research initiatives including H2O (Hong Kong-Helsinki Oasis for Innovation) and supervises doctoral students working on metaverse technologies. Directs laboratories focusing on systems/media integration and computational creativity.
Richard Membarth is a Research Professor at Technische Hochschule Ingolstadt (THI) since 2021, specializing in System on a Chip (SoC) and Artificial Intelligence for Edge Computing . Previously, he worked as a Senior Researcher and Team Leader at the German Research Center for Artificial Intelligence (DFKI) in Saarbrücken (2014–2021), a Postdoc at the Intel Visual Computing Institute (IVCI) at Saarland University (2013–2014), and as a Research Employee at Friedrich-Alexander University Erlangen-Nürnberg (2008–2013), where he earned his PhD in Computer Science with a focus on medical imaging. Research Focus: Membarth's work bridges GPU computing compiler optimization domain-specific languages edge AI architectures His expertise in CUDA programming and high-performance systems informs his leadership in THI's AI Mobility Node (AImotion). Awards: HiPEAC Network Member Email: Richard.Membarth@thi.de
Sumon Biswas is a tenure-track Assistant Professor in the Department of Computer and Data Sciences at Case School of Engineering, Case Western Reserve University. Previously, he was a Postdoctoral Researcher at the Institute for Software Research (ISR) at Carnegie Mellon University, working with Dr. Eunsuk Kang. He received his Ph.D. in Computer Science from Iowa State University under the supervision of Dr. Hridesh Rajan. His research focuses on the intersection of Software Engineering and Artificial Intelligence with particular emphasis on responsible AI engineering. His work spans several key areas: Formal verification and reasoning of fairness in AI systems Designing fair and safe AI systems AI engineering and analysis of machine learning software Long-term risks in machine learning systems Analysis of technical debt in AI/ML systems Dr. Biswas has made significant contributions to understanding and addressing fairness in machine learning pipelines, verification of neural networks, causal reasoning in ML pipelines, and safety assurance of predictive systems. His recent work increasingly focuses on foundation models and large language models (LLMs), with an emphasis on safety and responsible deployment of AI agents. His lab operates the state-of-the-art AISC2 cluster with five HGX H200 servers featuring 40 NVIDIA H200 GPUs. His publications show a consistent trend toward addressing both theoretical and practical challenges in responsible AI, with increasing focus on long-term system behavior, LLMs, and practical deployment challenges. The research spans formal methods, empirical studies, and practical tool development. Dr. Biswas has received several awards including the Research Excellence Award from Iowa State University and has been invited to serve on the Board of Distinguished Reviewers for ACM Transactions on Software Engineering and Methodology (TOSEM). He serves on the program committees of major software engineering conferences including ICSE, ASE, and ESEC/FSE, and has reviewed for prestigious journals such as IEEE Transactions on Software Engineering. As an educator, he teaches courses on Responsible AI Engineering and Software Engineering, focusing on building high-quality software systems that meet responsible AI principles including fairness, robustness, explainability, and safety.
Frank Hannig is a Professor at the University of Erlangen-Nuremberg, Germany, specializing in computer architecture and high-performance computing. His research focuses on FPGA acceleration, hardware-software co-design, neural network optimization, and embedded systems. He has collaborated extensively with co-authors such as Jürgen Teich and Oliver Reiche, producing over 200 publications since 2001. His work emphasizes domain-specific languages (DSLs) for image processing (e.g., Hipacc) and compiler optimizations for FPGAs. Key contributions include techniques for quantized neural networks on microcontrollers, CGRA toolchain evaluation, and efficient mapping of CNNs onto processor arrays. Hannig also explores energy-efficient architectures and reconfigurable computing for emerging applications like edge AI and automotive systems. Publications span conferences like ASAP, FPL, and ARC, reflecting his interdisciplinary approach to bridging algorithm design and hardware implementation. His research often addresses practical challenges in deploying machine learning models on resource-constrained devices while maintaining performance and energy efficiency.
Stefano Markidis is a leading researcher in High-Performance Computing (HPC) and quantum computing. His work focuses on developing advanced simulation frameworks, such as the Neko framework for computational fluid dynamics, and optimizing algorithms for heterogeneous architectures. He collaborates extensively with institutions and researchers globally, contributing to fields like plasma physics, quantum systems, and machine learning applications. His research emphasizes scalability, performance optimization, and the integration of cutting-edge technologies like GPU acceleration and quantum computing. Key research interests include extreme-scale simulations, quantum algorithms, and in-situ data analysis techniques. He has published over 200 articles, with recent work addressing challenges in NISQ systems, tensor network simulations, and CUDA-based performance enhancements. His contributions span theoretical and applied domains, bridging computational methods with real-world applications in fusion energy, materials science, and space exploration. Notable collaborations include projects with Philipp Schlatter, Niclas Jansson, and the NISQ application development community. Markidis also explores hybrid frameworks combining classical and quantum computing, aiming to leverage emerging hardware for scientific breakthroughs.
Audrey Repetti is an Associate Professor in the Department of Actuarial Mathematics and Statistics within the School of Mathematical and Computer Sciences at Heriot-Watt University in Edinburgh, UK. She also holds a dual affiliation with the Institute of Sensors, Signals, and Systems in the School of Engineering and Physical Sciences, and is part of the Maxwell Institute for Mathematical Sciences - Edinburgh. Her research spans mathematical imaging, optimization, and computational methods with applications across astronomy, medical imaging, and optical engineering. Dr. Repetti's research focuses on developing advanced mathematical frameworks for solving imaging inverse problems. Her work centers on optimization algorithms, Bayesian uncertainty quantification, and the integration of machine learning with traditional mathematical approaches. She has made significant contributions to radio interferometric imaging, computational optical imaging with photonic lanterns, and uncertainty quantification in medical imaging. Her research bridges theoretical mathematics with practical applications in astronomy, healthcare, and engineering. Analysis of her recent publications reveals a clear trajectory toward integrating traditional mathematical imaging approaches with modern machine learning techniques. Her work increasingly focuses on 'hybrid' methodologies that combine data-driven models with optimization frameworks. Key themes include plug-and-play algorithms, uncertainty quantification in imaging, and the development of efficient computational methods for high-dimensional inverse problems. Her research demonstrates strong interdisciplinary connections between mathematics, signal processing, astronomy, and medical imaging. Dr. Repetti is actively involved in academic service, including co-organizing the 2026 ICMS Workshop on Imaging inverse problems and generating models. She has received research funding supporting her work in computational imaging and inverse problems, though specific grant details aren't listed in the provided materials. Her teaching portfolio includes advanced courses in scalable inference, deep learning, and statistics for sciences. She leads several research projects with associated software toolboxes including BUQO (Bayesian Uncertainty Quantification by Optimization), SARA-COIL (Compressive optical imaging with a photonic lantern), and CALIM (Self direction-dependent effect calibration and imaging in radio-interferometry). These projects demonstrate her commitment to developing practical computational tools that advance both theoretical understanding and real-world applications in imaging science.
Prof. Felix Fritzen is a Heisenberg Professor (W3) for Data Analytics in Engineering at the University of Stuttgart's Institute of Applied Mechanics (MIB). His work is embedded in the Cluster of Excellence Data-Integrated Simulation Science (SimTech). He leads the EMMA Emmy Noether group (2015–2020) and previously headed the KIT Young Investigator Group CAMM. His research focuses on data-driven surrogate models, uncertainty quantification, and computational mechanics of materials, with emphasis on nonlinear model reduction and multiscale simulations. Education: Ph.D. (Dr.-Ing., summa cum laude) from KIT (2011), Dipl.-Math. techn. (2007), Dipl.-Ing. (2006). Research interests include machine learning integration with mechanics, microstructure-property relations, and high-performance simulation techniques. Notable contributions involve FFT-based homogenization, reduced order modeling, and GPU-accelerated methods. He has authored over 60 peer-reviewed papers, including works on surrogate models for microstructure forecasting and thermoelastic material analysis. Awards include the KIT PhD Award (2012) and recognition as a GAMM Junior (2012–2014). His teaching includes courses on data processing for engineers and model order reduction. Funded projects include DFG grants (EXC-2075, HE 7919/1) and the Heisenberg Professorship (FR2702/8).
Michael Bader is a Professor in the Department of Computer Science at the Technical University of Munich (TUM), part of the TUM School of CIT. He leads the research group on hardware-aware algorithms and software for high-performance computing at the Leibniz Supercomputing Center. His work focuses on developing efficient algorithms and software for supercomputing platforms, particularly in geosciences and simulation of earthquakes and tsunamis. His research interests include high-performance computing, simulation software development (e.g., SeisSol and ExaHyPE), parallel numerical algorithms, adaptive mesh refinement, and large-scale geophysical simulations such as earthquake dynamics and tsunami modeling. He emphasizes optimizing algorithms for modern supercomputing architectures to handle complex computational challenges. Professor Bader has supervised numerous PhD students, including Lukas Krenz, Ravil Dorozhinskii, and Sebastian Wolf, among others. His research has been supported by grants from the EuroHPC JU, BMBF, DFG, and other institutions. Notable projects include ChEESE-2P for exascale computing in solid earth sciences and the targetDART project for adaptive task distribution on exascale systems. He is actively involved in teaching, offering courses such as Numerical Algorithms for High Performance Computing and Scientific Computing 1 . His group collaborates extensively with institutions like the Leibniz Supercomputing Center to advance computational methods for simulating natural disasters and geophysical phenomena.
Tobias Meuser is a Researcher at the Multimedia Communications Lab of Technische Universität Darmstadt, leading the "Adaptive Communication Systems" group since 2020. He holds a PhD (2019) focused on vehicular network data management and has been a central figure in the third phase of the Collaborative Research Center (CRC) MAKI as a principal investigator in subproject B1. His work emphasizes resilient 5G networks, edge AI, and distributed systems. Education: B.Sc. Business Informatics (Fernuniversität Hagen) M.Sc. Informatics (TU Darmstadt) Research Interests: Resilience in 5G and beyond Edge AI and distributed machine learning Information assessment in vehicular networks Collaborative perception systems Hardware acceleration for network functions Key Projects: Principal Investigator in CRC MAKI's B1 (Monitoring and Analysis) Collaborations with Opel (cooperative maneuvering) and Deutsche Bahn (5G resilience) Labs/Teams: Head of Adaptive Communication Systems group at Multimedia Communications Lab Member of Distributed Sensing Systems group (2016–2020)
Lukas Arnold is a Professor and Head of the Fire Dynamics Division at Forschungszentrum Jülich GmbH, affiliated with the Institute for Advanced Simulation (IAS) and its Civil Safety Research Group (IAS-7). He holds a chair in Computational Civil Engineering at the University of Wuppertal and leads major research initiatives in fire safety science using computational methods. Research Focus: Fire Dynamics Simulation Academic Rank: Professor Key Collaborations: University of Wuppertal, DFG, BMBF His research spans fire dynamics simulation, visibility modeling in smoke environments, and flame spread prediction. He develops advanced numerical methods like CFD-based models and inverse modeling techniques for pyrolysis kinetics, smoke propagation, and material decomposition analysis. His work integrates experimental data from real-scale fires with computational tools to improve evacuation safety and risk assessment. Recent publications highlight his expertise in smoke visibility, PMMA pyrolysis, and GPU-accelerated fire simulations. He supervises PhD students in projects involving TGA experiments, multi-scale modeling, and emergency management systems. Arnold's work has been supported by third-party grants from BMBF, DFG, and State NRW, focusing on AI-driven fire modeling, high-performance computing, and disaster resilience. He organizes bi-annual summer schools on fire modeling and contributes to open-access scientific resources.
Ermeson Carneiro de Andrade is a Professor at the Department of Systems and Computer Engineering within the Center of Informatics at the Federal University of Pernambuco (UFPE) in Brazil. His research focuses on dependability engineering, performability analysis, and fault tolerance in distributed and embedded systems. Over his career spanning more than 15 years, he has established himself as a prominent researcher in the field of system reliability through numerous publications in top-tier journals and conferences. Dr. Andrade's research interests primarily center on the analysis and modeling of system dependability, with particular expertise in UAV-based monitoring systems, cloud computing environments, and IoT architectures. His work bridges theoretical modeling with practical applications, particularly in environmental monitoring, disaster recovery solutions, and mission-critical systems. He has made significant contributions to understanding software aging phenomena in various computing environments and developing performability-aware solutions for real-time systems. The analysis of his recent publications reveals a strong focus on UAV systems for environmental monitoring, particularly deforestation detection, with increasing attention to weather impacts and vehicle density-aware traffic monitoring. His research demonstrates a consistent pattern of applying stochastic modeling techniques to solve practical problems in distributed systems, with recent work expanding into NoSQL database performance, satellite constellation dependability, and the performance-interpretability trade-offs in machine learning models. This evolution shows his ability to adapt to emerging technologies while maintaining core expertise in system reliability. Dr. Andrade has been actively involved in mentoring students and collaborating with researchers across Brazil and internationally. His work often involves interdisciplinary teams addressing complex system challenges. While specific awards aren't detailed in the available publication records, his consistent output in high-impact venues demonstrates recognition within the dependability engineering community. His laboratory work appears to focus on system modeling and analysis, with particular emphasis on experimental validation through simulation and real-world testing. Current projects suggest involvement in UAV-based monitoring systems for environmental applications, with strong connections to public sector institutions in Pernambuco state.
Srishti Yadav is a Research Fellow at the University of Copenhagen and University of Amsterdam , affiliated with the Pioneer Centre for AI and ILLC respectively. She is advised by Dr. Serge Belongie and Dr. Ekaterina Shutova . Education: M.Sc. (Research-Track) in Computing Science, Simon Fraser University , Canada Research Interests: AI and Society Cross-Cultural Competency in Multimodal Models AI Safety and Evaluation Frameworks Model Interpretability and Dataset Creation Scientific Awards: ELLIS PhD Fellowship Advising & Community: Board Member, Women in Computer Vision (WiCV) Advisor for WiCV@ICCV2023 and WiCV@CVPR 2021 Chaired workshops at CVPR 2024, CVPR 2023, CVPR 2020 Labs & Teams: Belongie Lab (University of Copenhagen) Shutova Lab (University of Amsterdam) Collaborator at MILA Biodiversity Monitoring Project
Dr. Holger Eichelberger is part of the Academic Staff in the Software Systems Engineering (SSE) department at the University of Hildesheim's Institute of Computer Science. He is affiliated with Faculty 4: Mathematics, Natural Sciences, Economics and Computer Science. His roles include membership in the Managing Committee of the Institute of Computer Science and the Committee for Student Scholarships. He has extensive experience in model-based software development, Industry 4.0 platforms, and performance engineering. Research Interests: Software Engineering for adaptive systems, Asset Administration Shells (AAS), IIoT platforms, MLOps, container orchestration, and open-source tools like EASy-Producer and SPASS-meter. His work focuses on bridging research and industrial needs, particularly in smart manufacturing and edge computing. Publications highlight contributions to IIoT platform analysis, AI integration in Industry 4.0, and performance benchmarking of communication protocols. He has organized conferences like ICPE and SSP and reviewed for top journals such as IEEE Transactions on Software Engineering. Key projects include the IIP-Ecosphere platform and contributions to standards like AAS. Collaborations involve institutions like the University of the West Indies and industry partners through funded projects like BMBF AI-Lab HAISEM. His research emphasizes reproducibility, interoperability, and scalable solutions for industrial challenges.
Dr. Seongmin Lee is a researcher at the Max Planck Institute for Security and Privacy, specializing in software security and program analysis. Their work bridges theoretical and practical aspects of software testing, with a particular focus on automated testing techniques, dependency modeling, and genetic improvement. Research interests include: Software Security Software Testing and Fuzzing Program Analysis and Slicing Machine Learning Applications in Software Engineering Genetic Algorithms for Code Optimization Statistical and Causal Analysis of Code Behavior Recent publications (2016–2025) demonstrate a trajectory from foundational work on GPU parameter optimization to cutting-edge research on LLM-driven regression testing. Key trends include: Statistical modeling of software behavior Machine learning for bug classification and optimization Approximate analysis techniques for scalability Advancements in greybox fuzzing and coverage prediction Application of causal inference to mutation testing
Henning Franke is a Researcher (Postdoc) at the Max Planck Institute for Meteorology's Department of Climate Physics, focusing on the Climate Surface Interaction group. His research leverages km-scale general circulation models to explore atmospheric circulation systems, from thunderstorms to global wind patterns. He investigates processes like gravity waves and their role in phenomena like the Quasi-Biennial Oscillation (QBO), while advancing model development to address climate change impacts. Education PhD in Earth System Sciences (2020–2024): Max Planck Institute for Meteorology M.Sc. Meteorology (2017–2020): University of Hamburg B.Sc. Meteorology (2014–2018): University of Hamburg Research Focus His work emphasizes understanding climate dynamics through high-resolution modeling, particularly the QBO's behavior under global warming and interactions between ocean mesoscale structures and tropical convection. He explores model dependencies and aerosol effects on atmospheric circulation. Publications Recent studies highlight advancements in QBO simulation accuracy, effects of global warming on gravity waves, and stratospheric aerosol modeling during volcanic eruptions. His work bridges theoretical insights with computational innovations, such as GPU-based climate modeling frameworks. Labs/Teams He contributes to the Climate Surface Interaction group and collaborates on projects like the ICON model development and RV SONNE ocean-atmosphere measurement campaigns.
