Andrew Ho is an active academic researcher with publications spanning computer science, electrical engineering, and interdisciplinary applications. His recent work focuses on hybridizable discontinuous Galerkin methods for plasma simulations (2024) and AI/LLM applications in scholarly knowledge organization. 2025: Project Alexandria (LLM for copyright-free knowledge) 2024: Hybridizable DG plasma methods, GPU-accelerated kinetic simulations 2023: Low-resource translation techniques 2022: Multimodal VR interfaces 2003-2006: High-speed serial link transceivers and radiography artifact detection His research interests include: Computer science applications in plasma physics and medical imaging LLM-based scholarly knowledge graphs and literature reviews High-speed communication systems Educational technology implementations Co-authors include Vladimir Stojanovic (Stanford, 2003-2005), Carl W. Werner (2003-2005), and Genia Vogman (GPU plasma simulations, 2024).
Muhammad Arslan Ansari is a researcher at the Department of Computer Architecture and Parallel Systems, Technical University of Munich. He contributes to hybrid quantum-classical computing models and high-performance computing (HPC) integration. Research Focus: Hybrid Quantum-Classical Computing, HPC-QC Integration, Compilation Toolchain Teaching: Seminar leadership on quantum-HPC integration and performance modeling in SS2023-SS2025 Projects: Involved in EuroHPC initiatives like SEANERGYS, PlasmaPEPS, and MUNIQC-ATOMS Software: QMPI, LAIK, and other HPC toolchain developments His publications address quantum accelerator integration challenges and programming tool reviews. He participated in conferences like IEEE QCE 2023.
Judith Fauth is a researcher at the Technical University of Munich's Chair of Computing in Civil and Building Engineering, where she pioneers digital transformation in building permit processes through Building Information Modeling (BIM) and digital twin technologies. Her work bridges civil engineering, computer science, and public policy to streamline regulatory compliance across international contexts. Her research expertise spans Building Permit Processes , Digital Building Permits , Ontology Engineering , and Process Modeling , with emphasis on developing standardized frameworks for global permit system benchmarking. She investigates how semantic technologies and digital twins can automate regulatory compliance while accommodating regional variations in construction law and administrative practices. Analysis of her 2023-2025 publications reveals three dominant trends: (1) integration of digital building permits with digital building logbooks for lifecycle data management, (2) development of ontologies like OBPA for automated permit reviews, and (3) creation of taxonomies and process models (e.g., PACE-BP framework) enabling cross-national comparisons of permit systems. This work increasingly incorporates sustainability metrics and stakeholder-specific KPIs. Within TUM's research infrastructure, Dr. Fauth contributes to the BIM Lab and Digital Twinning research groups, collaborating on projects involving Gaia-X-based data frameworks and semantic modeling of the built environment. She teaches practical software development skills through the SoftwareLab course, preparing engineering students for digital construction workflows.
Prof. Akash Kumar is a Professor at the Chair of Embedded Systems at Ruhr University Bochum, Germany. He previously held professorships at TU Dresden (2015–2024) and the National University of Singapore (NUS; 2011–2015). His research focuses on design automation of embedded systems, reliability optimization, and approximate computing, with a strong emphasis on FPGA and emerging technologies. He leads projects such as Lean-MICS (DFG-funded) and SecuREFET-II, addressing cross-layer reliability and secure circuits. Education: PhD in Multimedia Multiprocessor Systems from Eindhoven University of Technology (TUe) and NUS (2005–2009), Master of Technological Design (Embedded Systems) from NUS (2003–2004), and B.Eng (Computer Engineering) from NUS (1999–2002, First Class Honours). Research interests span embedded systems, reconfigurable architectures, and hardware-software co-design. His work includes optimizing energy efficiency, fault tolerance, and cross-layer approximation techniques. Recent publications highlight advancements in FPGA-based accelerators, machine learning optimizations, and mixed-criticality systems. Active in grants and leadership, Kumar is Principal Investigator on multiple DFG and industry-funded projects, emphasizing collaborative research in distributed computing and approximate architectures. His contributions bridge theory and practice, with applications in edge AI, IoT, and cybersecurity.
Prof. Dr. Thomas Ludwig is the Director of the German Climate Computing Center (DKRZ) and a Professor at the Universität Hamburg. He holds a doctoral degree and habilitation from the Technische Universität München, with expertise in High-Performance Computing (HPC), energy efficiency, and data storage systems. His research focuses on optimizing parallel systems, storage technologies, and computational efficiency for climate science applications. He leads projects like AIMES and PeCoH, advancing HPC storage and energy-aware computing. Education: Doctoral degree and habilitation from TU München (1988–2001). Chair in Parallel Computing at Universität Heidelberg (2001–2009). Research Interests: HPC, data reduction techniques, energy-efficient systems, parallel I/O optimization, and climate modeling infrastructure. Recent Research Trends: His work emphasizes storage system efficiency, machine learning in HPC, and convergence between HPC and Big Data. Key contributions include frameworks for portability (Vecpar), automated performance tools, and energy-aware storage solutions. Awards: Some publications received recognition, e.g., a Best Paper award in 2014 for work on energy efficiency. However, no personal awards are explicitly listed. Advising & Grants: Supervised numerous theses in HPC, I/O optimization, and energy efficiency. Leads major projects funded by national and international initiatives. Labs/Teams: Heads the DKRZ team providing supercomputing and data management for climate research, collaborating with global institutions like the University of Hamburg and European research networks.
Stefan Wildermann is a Professor at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), where he leads the Reconfigurable Computing Group within the Chair of Computer Science 12 (Hardware-Software Co-Design) in the Department of Computer Science. He has maintained continuous research activity at FAU since 2006, progressing from researcher to his current leadership position. Dr. Wildermann earned his Diploma degree in Computer Science from FAU in 2006 and completed his doctorate (Dr.-Ing.) in Computer Science at the same institution in July 2012. His academic career has been entirely rooted at FAU, demonstrating a strong institutional commitment and progression through the ranks. His research spans multiple cutting-edge areas in computer science and engineering, with particular emphasis on reconfigurable systems and hardware-software co-design. Wildermann's work in edge computing explores efficient processing at the network periphery, while his research in organic computing investigates self-organizing systems that can adapt to changing environments. His expertise extends to optimization techniques for embedded systems, applying game theory principles and convex optimization methods to solve complex resource allocation problems. More recently, he has integrated reinforcement learning approaches to enhance system adaptability and performance. His teaching portfolio includes courses on event-driven systems, computer engineering fundamentals, embedded systems, and hardware-software co-design. Analysis of Wildermann's publication record from 2021-2025 reveals a strong focus on hardware acceleration, security, and embedded systems. His work demonstrates consistent evolution from foundational research in reconfigurable architectures toward practical applications in IoT, robotics, and secure computing. A significant portion of his recent work addresses near-data processing using FPGAs for database acceleration, while maintaining parallel research streams in side-channel security analysis and energy-efficient embedded systems design. His publications frequently appear in top-tier conferences including DATE, FPL, ASP-DAC, and HOST, reflecting strong recognition within the computer architecture and embedded systems communities. Wildermann has held significant leadership roles including Head of the Reconfigurable Computing Group since 2015 and previously served as Head of the Self-organizing Systems Group (2012-2015) and Lab Leader of the Automotive Lab within the Embedded Systems Initiative (2016-2020). His research has been consistently funded through multiple projects investigating invasive computing, reconfigurable architectures, and embedded systems design methodologies. Currently based in Room 02.116 at Cauerstr. 11, 91058 Erlangen, Wildermann continues to lead active research in the Hardware-Software Co-Design group, supervising projects that bridge theoretical computer science with practical hardware implementation challenges.
Prof. Dr.-Ing. Richard Membarth is a Research Professor for System-on-a-Chip and AI at the Edge Computing at Technische Hochschule Ingolstadt (THI). He is affiliated with the Hardware-Software Co-Design group and holds a secondary position at the German Research Center for Artificial Intelligence (DFKI) Saarbrücken. Co-creator of DSL frameworks like AnyDSL and Hipacc Key contributor to MetaDL (AI metaprogramming) and PRIME (predictive rendering) His research bridges GPU computing , domain-specific languages , and compiler technology , with recent work on Vulkan SPIR-V compilation and device-driven SpMV algorithms . Notable awards include the HiPEAC Paper Award (2018) and multiple Best Paper Awards for his compiler frameworks.
Dr. Erma Perenda serves as Professor and Chair of Distributed Signal Processing at RWTH Aachen University, Germany, leading research within the Department of Distributed Signal Processing. Her contact details include email perenda@dsp.rwth-aachen.de and phone +49 241 80-27879, with office location at Kopernikusstraße 16, 52074 Aachen in the ICT Cubes facility. Her research spans: Distributed Signal Processing Wireless Communications Machine Learning (Deep Reinforcement Learning, Federated Learning) Modulation Classification AI-driven Network Optimization She focuses on solving real-world challenges in wireless systems including hardware impairments, channel variations, and energy efficiency through advanced AI techniques. Analysis of her 2018-2024 publications reveals consistent innovation in applying multi-agent deep reinforcement learning to wireless power allocation, developing robust modulation classification methods resilient to channel impairments, and implementing federated learning for industrial edge computing. Her work bridges theoretical machine learning with practical wireless communication constraints. Scientific Awards: No awards documented in available sources Advising and Grants: No student advisees or grant information provided Labs and Teams: Leads Distributed Signal Processing research group at RWTH Aachen University Based in ICT Cubes building focusing on wireless AI systems
Prof. Dr. rer. nat. Rainer Leupers is a faculty member at RWTH Aachen University, chairing the Department of Software for Systems on Silicon. His research focuses on embedded systems, hardware-software co-design, virtual prototyping, and security in computing-in-memory architectures. He has published extensively on RRAM accelerators, logic locking, and neuromorphic security. Chair of Software for Systems on Silicon Research in hardware security and deep learning accelerators Recent publications on cross-tool virtual frameworks and thermal side-channel attacks His work bridges system-level modeling with practical security implementations, emphasizing reliability and performance in heterogeneous computing environments. Key trends in his 2025-2023 articles include compute-in-memory optimization, neural network inference efficiency, and security vulnerabilities in emerging hardware. Awards and formal recognitions are not explicitly detailed in the provided materials. He has not directly mentioned advising students or research grants in the given text fragments. The chair's contact information includes an office at ICT Cube 1, Electrical Engineering, Aachen, with direct email and website links.
Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.
Daniel Hernández is a Postdoctoral Researcher at the Institute for Artificial Intelligence (KI) under the Cluster of Excellence IntCDC at the University of Stuttgart. He is part of the Analytic Computing group within the Institute for Parallel and Distributed Systems (IPVS). His work focuses on Semantic Web technologies , particularly SPARQL , RDF , and knowledge graph applications in interdisciplinary design workflows . Research Trends : His publications (2015–2025) emphasize semantic query processing , provenance computation , and interoperability between architectural data and knowledge graphs . Key innovations include the eSPARQL language for epistemic queries, NPCS for native provenance in SPARQL, and BHoM to bhOWL for integrating building data with ontologies. Teaching & Collaborations : He has held teaching roles at the University of Stuttgart ( Human-Computer Interaction with Knowledge Graphs ), University of Aalborg ( Group Supervisor ), and University of Chile ( Lecturer for The Web of Data ). Collaborations span institutions like Buro Happold , TU Wien , and INRIA , with publications in journals like Proceedings of the VLDB Endowment and conferences such as WWW and ISWC .
Volker Markl is a Professor at Technische Universität Berlin in the Institute of Software Engineering and Theoretical Computer Science, with additional affiliations at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the German Research Center for Artificial Intelligence (DFKI). His research spans database systems, stream processing, and distributed data management with significant contributions to both theoretical foundations and practical implementations. Markl's research interests focus on next-generation data management systems, particularly for streaming and IoT environments. His work addresses critical challenges in distributed query processing, system integration, and performance optimization. He has pioneered approaches for stream processing in volatile infrastructures and developed innovative techniques for GPU-accelerated database operations. His NebulaStream project represents a major contribution to distributed stream processing systems. His publication record demonstrates consistent impact across top database venues including VLDB, SIGMOD, and ICDE. Recent work shows increasing focus on machine learning integration with database systems, privacy-preserving query processing, and educational approaches for teaching large-scale data management. Markl has mentored numerous researchers who have become prominent in the database community, with frequent collaborators including Steffen Zeuch, Tilmann Rabl, and Philipp Grulich. His leadership extends to major research initiatives and collaborations across European institutions.
Jianfeng Gu is a Ph.D. Candidate and researcher at the Technical University of Munich (TUM), affiliated with the Department of Computer Science and specifically the Chair of Computer Architecture and Parallel Systems led by Prof. Martin Schulz. He maintains an active research profile with numerous publications and contributes to the academic community through teaching seminars on Cloud Computing. His academic path began with a Bachelor of Software Engineering from Sun Yat-sen University in China (2014-2018), followed by a Master of Engineering from the same institution (2018-2020). Since April 2021, he has been pursuing his Ph.D. at TUM, advancing research in computing systems and architectures. Gu's research focuses on Heterogeneous Serverless Computing for Deep Learning applications, specializing in GPU, FPGA, and NPU technologies within serverless environments. His work addresses critical challenges in resource allocation, auto-scaling, and performance optimization for serverless inference systems. Additionally, he investigates Real-time Autonomous Driving Systems , developing advanced perception techniques through sensor fusion (particularly stereo-LiDAR fusion) for high-precision depth sensing and object detection in autonomous vehicles. His interdisciplinary approach bridges hardware acceleration, cloud infrastructure, and AI applications. His publication trajectory shows a progression from foundational computer vision and autonomous driving research (2018-2020) toward increasingly sophisticated work on serverless computing and federated learning (2021-2025). Recent publications focus on efficient resource sharing in heterogeneous serverless environments, with particular attention to GPU and FPGA allocation strategies that maintain service level objectives while optimizing costs. His work demonstrates strong technical depth across multiple computing domains. Best Paper Award at IEEE/ACM DATE 2021 15+ publications with 185+ citations Research featured in top venues for computer architecture and cloud computing As a Ph.D. researcher, Gu teaches seminars on Cloud Computing (IN2107) and contributes to multiple research projects at TUM's Chair of Computer Architecture and Parallel Systems. His work is supported by the department's research infrastructure and collaborations with faculty including Prof. Martin Schulz and Prof. Michael Gerndt. Gu works within TUM's advanced computing research environment, contributing to projects related to high-performance computing, serverless architectures, and autonomous systems. His research group maintains specialized hardware and software infrastructure for evaluating modern HPC architectures and accelerators, including FPGA clusters and GPU resources for deep learning research.
Prof. Dirk Schneider is a Full Professor (W3) of Biochemistry at Johannes Gutenberg University Mainz since 2010, with previous appointments at the University of Freiburg (2003-2009) and postdoctoral training at Yale University. His research spans membrane biochemistry, biophysics, and transmembrane protein folding/assembly, focusing on thylakoid membrane biogenesis and protein-lipid interactions in cyanobacteria and chloroplasts. Current roles: Full Professor, University Mainz Previous roles: Assistant Professor (W1), University of Freiburg Education: PhD (summa cum laude) from Ruhr-University Bochum His research interests include: Membrane protein folding and stability ESCRT-III/Vipp1/PspA family structural dynamics ABC transporter activity regulation (e.g., BmrA) Protein-lipid interaction mechanisms Thylakoid membrane remodeling Comparative membrane biology between prokaryotes and eukaryotes Development of spectroscopic and computational methods Recent publications reveal trends in bacterial membrane remodeling (SynDLP, PspA), lipid effects on transporter activity (BmrA), and IM30/Vipp1-mediated membrane fusion. His work combines structural biology, biophysics, and functional assays to elucidate membrane dynamics. Awarded the Dr. Heinrich Kost Award (2001) and Leopoldina Fellowship (2001) , he has held leadership roles including Study Section Speaker (2010-2014) , Director of Institute of Pharmacy and Biochemistry (2013-2015) , and Dean of Faculty of Chemistry (2015-2020) . His scientific advisory roles include editorial board memberships and study section leadership.
Dr. Wanja Hofer is a former research staff member at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). She specialized in embedded systems, real-time operating systems, and aspect-oriented programming. Her research focused on optimizing hardware-centric systems like Sloth and CiAO, addressing challenges in interrupt handling, scheduling, and software product line variability. Her academic journey includes a PhD in 2014 titled Sloth: The Virtue and Vice of Latency Hiding in Hardware-Centric Operating Systems . She contributed to key projects such as Sloth (time-triggered RTOS) and CiAO (aspect-oriented OS family), emphasizing scalability and configurability for automotive and embedded domains. Hofer also held roles like Web chair for EuroSys 2009 and co-maintained the EuroSys Research Directory. Her teaching involved Basics of Systems Programming in C and OS-related seminars. She advised over 15 graduate students on topics ranging from MPU-based task isolation to filesystem-level variability management. Notable contributions include hardware-accelerated interrupt handling, aspect-oriented OS design, and embedded system energy optimization. Hofer currently works at Brose Fahrzeugteile, applying her expertise in embedded systems and real-time computing to automotive technologies. Her work bridges academic innovation with industrial applications, particularly in safety-critical and resource-constrained environments.
