Burak Tagtekin is a researcher in computer science with significant contributions to optimization algorithms, recommender systems, and machine learning applications. His work spans both theoretical and practical domains, including evolutionary algorithms for compiler flag tuning, Bayesian personalized ranking models for recommendation systems, and genetic algorithm-based approaches to job scheduling and resource allocation challenges. Key Research Areas : Optimization Algorithms, Recommender Systems, Machine Learning, Compiler Engineering, Scheduling Problems Collaborations : Frequently collaborates with researchers like Tuna Çakar, Mahiye Uluyagmur Öztürk, and M. Sezer Recent Publications (2021-2024) demonstrate expertise in genetic algorithms, particle swarm optimization, and Bayesian modeling, achieving notable improvements in execution time and resource efficiency across diverse applications such as C++ compilation, job prioritization, and implicit feedback-based recommendation systems. Co-Author Network includes 18+ collaborators across computer science and engineering fields, with institutional connections to IEEE conferences and academic research communities.
Prof. Dr. Nicolas R. Gauger is a Full Professor and Chairholder for Scientific Computing at the University of Kaiserslautern-Landau (RPTU), holding dual appointments in the Department of Mathematics and Department of Computer Science. Since February 2015, he has also served as Director of the Computing Center (RHRZ) at RPTU. His academic career includes positions as Assistant Professor at Humboldt University Berlin (2005-2010), Associate Professor at RWTH Aachen University (2010-2014), and a Visiting Professorship at MIT (March-August 2014). Prof. Gauger earned his Master in Mathematics from Leibniz University of Hanover in 1998 and his Ph.D. in Applied Mathematics from Braunschweig University of Technology in 2003. Prior to his professorial positions, he worked as a Research Scientist in Numerical Methods for Aerodynamics at the German Aerospace Center (DLR) in Braunschweig from 1998 to 2010, while also being a Member of the DFG Research Center MATHEON in Berlin from 2006 to 2010. His research spans multiple disciplines within computational science and engineering, with primary interests in Nonlinear Optimization, Numerical Optimization, Optimization and Control with PDEs, Aerodynamic Shape Optimization, Computational Fluid Dynamics (CFD), Computational Aeroacoustics (CAA), Algorithmic Differentiation (AD), Machine Learning (ML), and High-Performance Computing (HPC). His work bridges theoretical mathematics with practical engineering applications, particularly in aerospace and medical physics. Recent publications show a strong trend toward integrating differentiable programming techniques with traditional computational methods, especially in optimizing experimental setups in fundamental physics and medical applications like proton therapy. Among his notable recognitions are being named an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA) in August 2018, receiving a Teaching Award (June 26, 2025), and a Best Student Paper Award at AIAA Aviation 2020. He serves on the Managing Board of ERCOFTAC (European Research Community on Flow, Turbulence and Combustion) and the Steering Committee of the ERCOFTAC Special Interest Group on Design Optimization. Prof. Gauger has been actively involved in multiple research initiatives including the Research and Development Lab 'Data Analysis and Artificial Intelligence' of the Fraunhofer Performance Center (vice spokesperson since March 2016), AICES (2010-2019), (CM)^2 (2014-2019), MathApp (2019-2024), and currently MSO (Modelling, Simulation and Optimisation) since 2024. He leads a research team of approximately 15 members working on projects related to algorithmic differentiation, computational fluid dynamics, and optimization methods. His laboratory, the Scientific Computing research group at RPTU, is involved in multiple interdisciplinary projects including SIVERT (pCT) for fighting cancer with AI and the 'AI Care' project. The team has developed several important software tools including CoDiPack, OpDiLib, and SU2, which are widely used in the computational science community for algorithmic differentiation and aerodynamic optimization.
Dr. Stefan Hendricks works as a Senior Scientist at the Alfred Wegener Institute for Polar and Marine Research, specializing in Sea Ice Physics within the Climate Sciences division. His primary research focuses on the global sea ice mass balance using satellite remote sensing, particularly with CryoSat-2 and Sentinel-3 radar altimetry systems. Key Responsibilities: Lead PI for CryoSat-2 thematic sea ice products (Cryo-TEMPO), Advisor for Sentinel-3 and future CRISTAL missions, Developer of the Python-based sea ice radar altimetry toolbox (pysiral) Expertise: Satellite radar altimetry, Algorithm development, Data validation, Arctic/Antarctic ice dynamics His research involves extensive algorithm development for converting altimeter waveforms into sea ice thickness metrics, fieldwork participation in polar expeditions, and integration of multi-sensor data for climate data records. Stefan maintains active collaborations with international institutions and contributes to ESA projects like SIN’XS. Recent publications highlight innovations in FMCW radar deconvolution techniques, sub-ice platelet layer mapping, and decadal-scale sea ice classification using machine learning. He also leads efforts in creating drift-aware sea ice thickness maps and validating SMOS-derived Antarctic ice datasets.
Pallav Kumar Shrestha is a postdoctoral researcher at the Helmholtz Centre for Environmental Research (UFZ) since 2017, focusing on Computational Hydrosystems in Leipzig, Germany. His work centers on resolving challenges in global hydrological modeling, particularly for small catchments and reservoir systems. Developed subgrid catchment conservation method for gridded hydrological models Created new reservoir module for mHM Contributor to Nature Communications flood early warning system paper Research Themes : Flood forecasting at global/regional scales Reservoir modeling in regulated basins Climate change impacts on water resources Computational hydrosystems development Environmental informatics for global hydrology Key Projects : ULYSSES (Copernicus), SaWaM (BMBF), 4DHydro (ESA), and state of global water resources (WMO). His 2025 ASCE-EWRI award paper on Great Lakes runoff intercomparison highlights his collaborative impact. Modeling Expertise : Active member of the mHM development team , contributing to debugging, user support, and training across Europe and Asia. His technical skills bridge Fortran programming for skill assessment tools and ecFlow automation systems.
Debin GAO is a Full-time Professor of Computer Science at the Singapore Management University , affiliated with the School of Computing and Information Systems (SCIS) . He serves as Co-Director of the Centre on Security, Mobile Applications & Cryptography and Faculty Manager for the SMU BSc (IS)-CMU Fast-Track Programme . His research focuses on Android security , trusted execution environments , and malware detection . PhD from Carnegie Mellon University (2006) Supervisor to SCIS undergraduate instructors Research Advisor to EE Fook Ming GAO's research explores security vulnerabilities in mobile platforms , with emphasis on cache side-channel attacks and Android app debloating . His recent work investigates LLM-driven malware classification and secure code partitioning for smart contracts . His publications demonstrate a focus on mobile security (15/15), including malware analysis (9/15), trusted execution environments (5/15), and side-channel attack mitigation (4/15). Notable contributions include DynDebloater (2025), AutoTEE (2025), and CacheAlarm (2025). As Co-Director of the Centre on Security, Mobile Applications & Cryptography , GAO leads initiatives in trustworthy app delegation (AGChain, 2024) and user-centric security (OTO, 2012). His teaching covers Information Security & Trust , Networking , and Software Engineering .
Steffen Zeuch is a researcher at Humboldt University of Berlin, Germany, with a strong research focus on database systems, stream processing, and Internet of Things (IoT) data management. He is a key contributor to the NebulaStream platform, an extensible, high-performance system for multi-modal edge applications. His work spans query optimization, GPU acceleration, fault tolerance, and distributed state management in stream processing environments. Research interests include database systems, stream processing, IoT data management, query optimization, GPU computing, and hardware-aware execution. His research addresses challenges in real-time analytics, efficient data placement, adaptive compilation, and complex event processing in distributed and edge environments. The publication trends in his recent articles highlight a consistent focus on stream processing systems—especially NebulaStream—with increasing attention to GPU acceleration, adaptive optimization, fault tolerance, and complex event processing. His work bridges theoretical query optimization with practical system implementation, emphasizing performance, scalability, and deployment in real-world IoT and edge infrastructures. Steffen Zeuch has made significant contributions to top-tier venues such as VLDB, SIGMOD, EDBT, and DEBS. His collaborative work, primarily with Volker Markl and other members of the database group at Humboldt University, demonstrates strong research leadership and technical depth in data-intensive systems. He has led and contributed to projects involving system design, performance benchmarking, and real-world deployment of stream processing platforms. His work on tutorials and system demonstrations indicates active engagement in community education and dissemination. While no specific lab or team name is mentioned, his research is centered around the NebulaStream project, a comprehensive platform for managing and analyzing data across fog, edge, and cloud environments. This platform supports complex analytics beyond traditional cloud boundaries, enabling scalable and efficient IoT applications.
Isabel Valera is a full Professor in the Department of Computer Science at Saarland University, Saarbrücken, Germany. She also holds an adjunct faculty position at the Max Planck Institute for Software Systems (MPI-SWS) and is a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), contributing to the Robust Machine Learning Program and the Saarbrücken AI & Machine Learning (Sam) Unit. Research Interests: Her work lies at the intersection of machine learning, fairness, and causality. She focuses on developing methods for algorithmic recourse , fair decision-making , causal modeling , and robust learning . She is particularly interested in designing models that provide actionable explanations and ensure equitable outcomes in AI systems. Her research leverages probabilistic modeling, variational inference, and deep generative architectures. Publication Trends: Her recent publications (2021–2023) show a strong emphasis on algorithmic recourse , fairness under uncertainty , causal representation learning , and multimodal and graph-based generative models . She frequently publishes in top-tier venues such as NeurIPS, ICML, ICLR, AAAI, and FAccT, often in collaboration with leading researchers like Bernhard Schölkopf and Zoubin Ghahramani. Scientific Awards and Fellowships: Humboldt Post-Doctoral Fellowship Minerva Fast Track Fellowship (Max Planck Society) ELLIS Fellow Advising and Grants: She has mentored several early-career researchers who are now active contributors in machine learning, including Adrián Javaloy and Amir-Hossein Karimi. While specific grant details are not listed, her leadership roles and fellowships indicate sustained funding support from major research organizations. She has served as a co-editor for major conferences such as AISTATS and ECML PKDD, demonstrating her active role in the academic community. Labs and Research Groups: She leads or has led research groups at both Saarland University and the Max Planck Institutes (MPI for Intelligent Systems and MPI for Software Systems), focusing on foundational and applied aspects of machine learning with societal impact.
Prof. Dr.-Ing. Christian Hochberger is a Professor at Technische Universität Darmstadt, affiliated with the Department of Computer Science. His research focuses on reconfigurable computing, FPGA architecture, embedded systems, and hardware/software co-design. He teaches courses such as 'Rechnersysteme I/II' and 'High-Level Synthese.' University: Technische Universität Darmstadt Department: Department of Computer Science Research interests span FPGA-based acceleration, CAD tools for reconfigurable systems, and energy-efficient computing. His work frequently addresses challenges in hardware design, parallelization, and fault tolerance. Publications from 2022-2024 highlight contributions to memristive devices, genetic circuit design automation, and CGRA optimization. His recent work emphasizes experimental methodologies and novel techniques in resistive switching and memristor-based FPGAs. Awards: None explicitly listed in the provided text.
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.
Joel S. Emer is a Professor of the Practice in MIT's Department of Electrical Engineering and Computer Science (EECS) and a Senior Distinguished Research Scientist at NVIDIA. His research focuses on computer architecture, processor micro-architecture, and performance modeling. He has contributed to advancements in simultaneous multithreading, cache optimization, and reliability analysis. Emer holds over 25 patents and has published over 60 papers, earning awards like the IEEE Rau Award and induction into the National Academy of Engineering. Education: Ph.D., Electrical Engineering, University of Illinois Urbana-Champaign, 1979 M.S., Electrical Engineering, Purdue University, 1975 B.S., Electrical Engineering, Purdue University, 1974 (highest honors) Research interests include accelerator architectures for sparse computation and deep learning, spatial processing, memory hierarchy design, and reliability analysis. His work on Eyeriss and other accelerators has shaped energy-efficient neural network hardware. Recent projects explore hierarchical structured sparsity (HSS) and compute-in-memory (CIM) techniques. Key awards include the ISCA Best Paper Session (2024), IEEE Micro Top Picks (2024), and the SIGMICRO Test of Time Award (2022). He co-advises students with Prof. Vivienne Sze, focusing on sparse tensor acceleration and energy-efficient designs. Awards: 2023 IEEE Rau Award 2022 IASED Lifetime Achievement Award 2020 National Academy of Engineering Membership 2009 Eckert-Mauchly Award Grants and collaborations span industry partnerships (e.g., NVIDIA) and academic initiatives. Emer leads the Emze Group, exploring hardware-software co-design for emerging architectures. Current work includes sparse tensor accelerators (e.g., HighLight, Tailors) and modeling tools like Sparseloop and Accelergy.
Andrés Goens is an Assistant Professor at the University of Amsterdam since 2023. He holds a Ph.D. (Dr.rer.nat.) in Computer Science from TU Dresden (2021) and an M.Sc. in Mathematics from RWTH Aachen University (2014). His research focuses on programming languages, formal methods, and theorem proving, with applications to compilers and heterogeneous systems. He investigates efficient execution of concurrent programs in multicore architectures and explores machine learning techniques for compiler optimization. Key research areas include concurrency, weak memory models, and bridging abstract mathematics with practical compiler design. His work often employs theorem provers like Lean to formalize program behavior. Recent publications address e-graphs for variable handling, guided equality saturation, and optimizing virtual networks in distributed systems. Publications span venues like PLDI, POPL, and ISCA, reflecting contributions to programming language theory and compiler infrastructure. His research bridges theoretical foundations with practical system design, aiming to improve both programmer productivity and computational efficiency. Contact details: a.goens@uva.nl (professional) and andres@goens.org (personal).
Enrique Morancho Llena is a Professor in the Department of Computer Architecture at the Barcelona School of Informatics (FIB), Polytechnic University of Catalonia (UPC). He is an active member of the UPC PM - Programming Models research group and has been affiliated with the CAP - Grup de Computació d'Altes Prestacions (High-Performance Computing Group), one of UPC's most productive research teams with over 120 publications. His academic career spans more than 30 years, with continuous research output from 1994 to the present. Dr. Morancho Llena's research spans multiple areas in computer systems, with particular expertise in high-performance computing, parallel programming models, computer architecture, operating systems, and compiler optimization. His work has evolved from early research in instruction scheduling and memory access optimization to current work on heterogeneous computing, RISC-V architecture, hardware security, and malware detection. He has made significant contributions to OpenMP programming models, GPU acceleration, and virtualization technologies, with publications in top-tier venues including IEEE Transactions on Parallel and Distributed Systems and the Journal of Parallel and Distributed Computing. His recent publications (2021-2024) show a clear trend toward heterogeneous computing architectures, security aspects of computer systems, and RISC-V based technologies. He has published extensively on OpenMP extensions for heterogeneous systems, hypervisor implementations for RISC-V, and security applications including malware detection using opcode analysis. His work bridges theoretical computer architecture concepts with practical implementations for scientific computing and security applications. Dr. Morancho Llena has supervised PhD students, with R. Gran's thesis on "Non-speculative enhancements for the scheduling logic" being one documented example. He has participated in numerous competitive research projects funded by Spanish and Catalan governments, including multiple iterations of the "Computació d'Altes Prestacions" (High-Performance Computing) projects and RISC-V related initiatives under the RIS3CAT program. His collaborations extend across UPC departments and include partnerships with the Barcelona Supercomputing Center. He is an active member of the CAP research group and the PM - Programming Models group, focusing on advanced programming techniques for high-performance systems. His work on the UPC-Computación de Altas Prestaciones series demonstrates long-term leadership in establishing UPC as a center of excellence in high-performance computing research. Current projects include work on RISC-V based accelerators, security in the computing continuum, and trustworthy development environments for cloud services.
Prof. Dr. Sergei Gorlatch is a full professor at the University of Münster, Germany, in the Department of Mathematics and Computer Science, where he holds the Chair of Practical Computer Science (Parallel and Distributed Systems) within the Institute of Computer Science. He has been a leading figure in high-performance and parallel computing since joining the university in 2003. University: University of Münster School: Department of Mathematics and Computer Science Department: Institute of Computer Science Academic Rank: Professor His research focuses on algorithm and software development for modern computer systems, particularly in parallel and distributed computing, high-performance computing (HPC), GPU-based systems, cloud and grid computing, and performance optimization. His work bridges theoretical formal methods and practical applications, especially in real-time online interactive systems such as online games and simulations. He has pioneered frameworks like SkelCL, dOpenCL, and the Real-Time Framework (RTF) to simplify parallel programming and improve performance portability. The recent publications (2020–2024) reflect a strong trend in GPU programming, performance optimization, formal verification, and distributed systems. Key themes include the development of safe and high-level GPU languages (e.g., Descend), autotuning and model checking for performance, multi-cloud orchestration, and performance modeling of legacy and real-time systems. His work often combines compiler techniques, functional programming, and systematic transformations to achieve efficient and portable code. Best Poster Award – PUMPS+AI, 2019 Best Paper Award – CGO, 2018 Alexander von Humboldt Research Fellowship, 1991 Prof. Gorlatch has supervised numerous students and researchers, many of whom are frequent co-authors on his publications. He has led multiple funded projects from DFG, EU (e.g., CoreGrid, MONICA), and industry (e.g., NVIDIA Graduate Fellowship). His work includes both theoretical contributions (e.g., algorithmic skeletons, formal verification) and applied systems development, demonstrating a strong record of advising, grant acquisition, and interdisciplinary collaboration. He is actively involved in several research labs and teams at the University of Münster, particularly those focused on parallel computing, GPU programming, and real-time systems. His group develops high-level programming models and tools to make parallel computing more accessible and efficient across diverse architectures.
Dr. Jakub Yaghob is a researcher at the Faculty of Mathematics and Physics , Charles University , specializing in computer science and parallel computing. He teaches advanced programming topics including Compiler Principles , Parallel Programming , and Cloud Computing . Research Interests : Parallel data stream processing, virtualization technologies, semantic web infrastructures, and performance optimization Teaching : Advanced C++ programming, virtualization administration, and computer systems architecture Technical Expertise : Design of parallelization frameworks, astrophysical data analysis, and hybrid CPU-GPU systems His publications focus on: Optimizing stream data processing across distributed architectures Developing domain-specific languages like Bobolang Performance evaluation in educational programming contexts Applications of parallel computing in astrophysics
Xhemal Zenuni is a Full Professor and current Dean at the Faculty of Contemporary Sciences and Technologies, South East European University (Tetovo, North Macedonia). His academic career spans multiple roles from IT Administration Assistant (2003-2004) to progressively senior positions including Young Assistant (2004-2006), Assistant (2006-2008), Assistant Professor (2013-2018), Associate Professor (2018-2023), and now Full Professor since 2023. Education: PhD in Computer Sciences (2006-2012, Technical University of Sofia), specialization in Computer Systems, Complexes and Networks Master of Sciences in Informatics (2004-2005, New Bulgarian University), specialization in Internet Software Technologies Bachelor of Sciences in Computer Sciences (2001-2003, SEEU) With expertise in Machine Learning , IoT , Cloud Computing , and Database Systems , his research focuses on applying AI/ML techniques across multiple domains. Recent work includes predictive modeling in education, hate speech detection for Albanian social media, smart agriculture systems, and microservices architecture migration. His publications demonstrate consistent engagement with modern computational challenges, particularly around low-resource language processing , smart city infrastructure , and educational technology . While serving as Dean and Vice-Dean, he has maintained active research output through collaborations with colleagues like Mentor Hamiti, Jaumin Ajdari, and Florije Ismaili.
