Thomas Neumann is a full-time Professor at the Department of Database Systems within the TUM School of Computation, Information and Technology at the Technical University of Munich . Research Focus : Query optimization and processing in database systems Hardware-aware database engine design Scalable RDF/semantic data management Hybrid OLTP/OLAP processing Join optimization algorithms Scientific Recognition : Gottfried Wilhelm Leibniz Prize (2020), Germany's most prestigious research award Academic Contributions : His recent publications focus on high-performance query execution strategies, hybrid transactional/analytical systems using virtual memory snapshots, and scalable graph processing techniques. These works demonstrate consistent advancements in database engine optimization for modern hardware architectures and large-scale semantic data management.
Prof. Dr. Andre Schöning is a Full Professor (W3) at the Physics Institute of Heidelberg University since 2009, specializing in experimental particle physics. He serves as Co-Spokesperson of the Mu3e Collaboration and leads research in detector development and high-energy physics experiments. Research Interests: Search for the decay μ→eee with the Mu3e Experiment at PSI Development of High-Voltage Monolithic Active Pixel Sensors (HV-MAPS) Track trigger systems for ATLAS and future colliders Physics analysis with ATLAS and historical H1 experiment data Wireless data transmission technologies for particle detectors His recent publications demonstrate strong focus on detector technology development, particularly for muon experiments and high-rate tracking systems, alongside significant contributions to Standard Model physics measurements at the LHC. The research spans both hardware development and sophisticated data analysis techniques. Scientific Recognition: CERN Fellowship (1997-1999) University of Hamburg dissertation award (1997) Association of the Friends and Sponsors of DESY dissertation award (1997) Prof. Schöning has secured substantial research funding from DFG and BMBF from 2009-2025, including leadership of the DFG Research Unit on Lepton Flavor Violation with Mu3e. He maintains active collaborations including WADAPT, Mu3e, ATLAS, and the long-standing H1 collaboration. His research group operates within the High-Energy Physics division of Heidelberg's Physics Institute, working on cutting-edge detector systems for current and future particle physics experiments, with particular emphasis on precision measurements requiring novel detector technologies.
Prof. Wolfgang Blochinger is a Professor in the Department of Computer Science at Reutlingen University, specializing in Services Computing and IT Security. He leads teaching programs in Wirtschaftsinformatik (Business Informatics) at both Bachelor and Master levels, focusing on foundational topics such as Programming Basics, Operating Systems, IT Security, Cloud Computing, and Big Data Technologies. His research emphasizes Cloud Computing and High Performance Computing, particularly in elasticity control, parallel processing, and cloud resource optimization. His research projects include developing elastic parallel systems for HPC applications, cloud migration strategies, and automated cloud service generation. Notable contributions involve frameworks like TASKWORK for elastic task parallelism and the Elasticity Description Language for cloud applications. He has published extensively on serverless computing, cost-efficient cloud resource utilization, and container-based isolation techniques. Prof. Blochinger collaborates with industry partners through the university's labs, including the AI-Reallabor AIDA and Cloud Lab. His work bridges academia and industry, addressing real-world challenges in distributed systems and cloud infrastructure. Current research trends focus on self-tuning cloud services and adaptive parallel algorithms for scalable computing environments.
Yue Li is an Associate Professor at the School of Computer Science, Nanjing University, where they co-run the PASCAL Research Group with Tian Tan. Their work focuses on static program analysis techniques and tools for programming languages, software engineering, security, and hardware verification. PhD in Computer Science from UNSW Sydney (2016) Postdoctoral research at Aarhus University (Denmark) and UNSW Sydney B.Eng and M.Eng from Northwestern Polytechnical University (2010, 2012) Research interests center on Program Analysis and Programming Languages , with a focus on: Pointer analysis for database-backed applications Context sensitivity optimization Reflection analysis in Java/Android Operational semantics for hardware languages Distributed dataflow analysis frameworks Developer-friendly static analysis tools Key publication trends (2016-2025) span static analysis , pointer precision , reflection handling , and tool frameworks across conferences like OOPSLA, PLDI, ICSE, ISSTA, and journals including TOPLAS and IEEE TSE. Notable artifacts include Tai-e and Chianina systems. 2025: ICSE Best Artifact & Distinguished Paper Awards 2024: IEEE TSE Publication on Generic Sensitivity 2023: OOPSLA Distinguished Artifact, SPLASH/ECOOP committees 2021: National Youth Talent Support Program, ZiJin Scholar 2016: ECOOP Distinguished Paper, CGO Best Paper As co-PI of PASCAL Research Group, they lead projects on precision-guided analysis, microservice systems, and cloud-based dataflow frameworks, with teaching awards for SICP and Software Analysis courses.
Christian Tominski serves as an apl. Professor (non-tenured) at the University of Rostock, holding the außerplanmäßige Professur for Human-Data Interaction within the Institute for Visual and Analytic Computing. His academic work spans teaching in Visual Computing and Computer Science programs, with active research contributions in data visualization and visual analytics. His research focuses on multi-variate data visualization, time-series and geo-visualization, graph visualization, and coordinated multiple views. He investigates interaction techniques including interactive lenses, visual comparison, navigation, and guidance mechanisms, alongside computational aspects such as efficient algorithms and asynchronous processing for visualization systems. Recent work emphasizes task-driven approaches and analytic support for interactive exploration. Analysis of his publication trends reveals strong emphasis on visual analytics for complex data structures, particularly in process mining and multivariate graphs. His work consistently explores guidance frameworks, progressive computation models, and novel interaction paradigms for large high-resolution displays, bridging theoretical foundations with practical applications in visual data analysis. Tominski holds professional roles as a member of the Faculty Council of IEF and the System Technical Group of Computer Science Institutes at the University of Rostock. He actively participates in the Informatik-Forum Rostock (INFO.RO), contributing to the regional computer science community through collaborative initiatives and knowledge sharing.
Prof. Dr. Rolf Wanka is a Professor at the Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), specializing in efficient algorithms and combinatorial optimization. His research focuses on swarm intelligence, discrete optimization algorithms, and scheduling problems, particularly in timetabling and robotics applications. Education : Sc.D. (Dr. rer. nat.) in Computer Science His work includes theoretical and experimental analyses of particle swarm optimization (PSO) algorithms, addressing runtime complexity, stagnation behavior, and convergence properties. He has developed novel heuristics for timetabling and sorting problems, with applications in multi-robot systems and medical imaging. Notable collaborations include studies on Markov chain-based PSO and fairness in academic scheduling. Key trends in his recent publications span swarm intelligence , discrete optimization , and scheduling heuristics , with a focus on robust timetabling , runtime analysis , and stochastic algorithm behavior . While no explicit scientific awards are listed, his mentorship in the Max Weber-Programm highlights his advisory role in academia. His publications demonstrate interdisciplinary applications of algorithms in robotics , medical imaging , and parallel computing , leveraging both theoretical rigor and practical experimentation. The full description below provides exhaustive details on his academic contributions and affiliations.
Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.
Prof. Dr. Andreas Herkersdorf is a Full Professor and Chair of Integrated Systems at the Technical University of Munich (TUM) School of Computation, Information and Technology. His research focuses on application-specific multicore processors (MPSoC), FPGA-based prototyping, fault-tolerant systems, and energy-efficient architectures, with applications in IP packet processing, automotive systems, and visual computing. He has received multiple IBM innovation awards and serves on editorial boards including the DFG Review Board for computer architecture. Education: Dipl.-Ing. Electrical Engineering (TUM, 1987), Dr. techn. Electrical Engineering (ETH Zurich, 1991) Research: MPSoC architectures, autonomic computing, NoC resilience, FPGA acceleration, and self-optimizing systems. Awards: IBM Master Inventor (1998), IBM Outstanding Technical Achievement Award (2001), multiple IBM Innovation Achievement Awards (1996-2003) His recent publications emphasize hardware/software co-design, machine learning integration for runtime optimization, and network-on-chip innovations. He collaborates on projects involving 6G systems, smartNICs, and automotive communication protocols.
Marc Teboulle is a distinguished Professor holding The Eric and Sheila Samson Chair of Optimization in the School of Mathematical Sciences at Tel Aviv University. With a career spanning over three decades, he has established himself as a leading figure in optimization theory and applications. His work bridges theoretical foundations with practical implementations across multiple scientific domains. Professor Teboulle's research focuses on continuous optimization, with particular emphasis on convex optimization, complexity analysis of algorithms, Lagrangian and dual decomposition methods, variational inequalities, and nonconvex nonsmooth large-scale optimization. His work has significant applications in engineering science, machine learning, and finance, demonstrating the interdisciplinary impact of optimization techniques. He has developed novel frameworks for center-based clustering algorithms and contributed to the theoretical understanding of first-order methods beyond traditional Lipschitz gradient continuity assumptions. His publication record shows a clear evolution toward increasingly sophisticated optimization frameworks, with recent work focusing on nonconvex composite optimization, Lagrangian-based methods, and complexity analysis of gradient-based algorithms. The trend indicates growing interest in non-Euclidean geometries for optimization and applications to high-dimensional data problems, reflecting the evolving challenges in modern optimization. As an educator and mentor, Professor Teboulle has supervised numerous PhD and MSc students since 1990, including prominent researchers like Amir Beck, Ron Shefi, and Yoel Drori. His graduate courses include Convex Analysis and Optimization, Advanced Topics in Modern Optimization, Algorithms for Continuous Optimization, and Advanced Seminar in Continuous Optimization. He has been exceptionally active in the academic community, delivering invited lectures at major international conferences from 2003 through 2024 across Asia, Europe, and North America. His book 'Asymptotic Cones and Functions in Optimization and Variational Inequalities' (co-authored with A. Auslender) has become a standard reference in the field, while his edited volume 'Grouping Multidimensional Data: Recent Advances in Clustering' has influenced data science applications.
Mohammad Sadoghi is a Professor at the University of California, Davis, with former affiliations at Purdue University, IBM T.J. Watson Research Center, and the University of Toronto. His research focuses on distributed systems, blockchain technologies, consensus protocols, and fault-tolerant computing. He has contributed extensively to transaction processing, stream processing architectures, and the integration of edge-cloud systems with blockchain frameworks. Current Affiliation: University of California, Davis Former Affiliations: Purdue University, IBM, University of Toronto Research Interests include consensus algorithms, Byzantine fault tolerance, distributed ledger technologies, and scalable data processing. He has pioneered systems like ResilientDB and ByShard, addressing challenges in global-scale distributed systems and blockchain fabrics. His work bridges theoretical foundations with practical implementations, emphasizing real-world applications in edge computing and hybrid cloud-edge environments. Key publications highlight advancements in consensus protocols, blockchain scalability, and fault-tolerant architectures. Recent trends in his work focus on concurrent consensus mechanisms, DAG-based systems, and secure geo-replication. Contributions span both academic publications and industry-oriented solutions, such as the Bedrock platform for BFT protocol analysis. Grants and advising roles are implied through his extensive research output, though specific grants are not detailed in the provided text. His collaborations include projects on self-curating databases (e.g., L-Store) and systems like SplitJoin for stream processing.
Alin Deutsch is a Professor of Computer Science at the University of California, San Diego (UCSD), specializing in database systems, graph databases, and formal verification. He has contributed significantly to research areas including query optimization, data integration, and privacy-preserving systems. His work spans theoretical foundations and practical implementations, such as the Linked Data Benchmark Council (LDBC) and the TigerGraph database system. He co-authored over 100 papers and has been involved in major conferences like SIGMOD and VLDB. Research interests include graph query processing, parallel computing, data-centric business processes, and automated system verification. Recent work focuses on scalable hybrid analytics and graph databases. Deutsch is also active in database education, co-authoring a paper on UCSD's database curriculum. He has led projects in privacy-aware systems, such as policy-aware location-based services, and contributed to tools like CLIDE for interactive query formulation in service-oriented architectures. His collaborations involve industry partners like TigerGraph and academic institutions globally.
Peter Buneman is a prominent academic affiliated with the University of Edinburgh, UK. His research primarily focuses on databases, data provenance, XML, and semistructured data. He has held significant roles in advancing computational models for data citation, provenance tracking, and distributed systems. Key areas of expertise include database systems, graph algorithms, and collaborative data curation platforms. Buneman's work integrates theoretical foundations with practical applications in pharmacology data, digital libraries, and scientific data management. His contributions to data provenance and citation systems have shaped standards in academic and industrial settings. Notable projects include the Database Wiki platform for collaborative data management and the MITra framework for graph traversal. Buneman collaborates extensively with co-authors like Susan Davidson, Wenfei Fan, and Val Tannen. Publications span 1974–2025, emphasizing methodologies for data integration, distributed computing, and computational biology. His work bridges theoretical computer science with real-world applications in biomedicine and information systems.
Michael Ortiz is the Dotty and Dick Hayman Professor of Aeronautics and Mechanical Engineering at the California Institute of Technology (Caltech). He holds the Hans Fischer Senior Fellowship at the Technical University of Munich's Institute for Advanced Study (TUM-IAS) since 2010. His research focuses on developing the Advanced Cardiac Mechanics Emulator (ACME) to model human heart function in healthy and diseased states. He has a BS from the Polytechnic University of Madrid, and MS/PhD from UC Berkeley. Previously, he was at Brown University (1984–1995). Professor Ortiz leads Caltech’s DoE/PSAAP Center on High-Energy Density Dynamics of Materials. His honors include the IUTAM Rodney Hill Prize (2008), election to the American Academy of Arts & Sciences (2007), and Humboldt Research Award (2002). He has advised national labs like Lawrence Livermore and Sandia on predictive science and engineering review panels. His work spans computational mechanics, materials science, and multiscale modeling. Recent research emphasizes cardiac biomechanics, fracture mechanics, and quantum materials simulations. Over 100 highly cited papers showcase contributions to meshfree methods, variational fracture, and orbital-free DFT.
Hai (Helen) Li is a Professor and Clare Boothe Luce Associate Chair at Duke University's Electrical and Computer Engineering department. She was a TUM-IAS Hans Fischer Fellow (2017) hosted by Prof. Ulf Schlichtmann in the Neuromorphic Computing focus group. Education: B.S./M.S. from Tsinghua University, Ph.D. from Purdue University Positions: Qualcomm, Intel, Seagate, Polytechnic Institute of New York University, University of Pittsburgh Her research spans neuromorphic computing systems , machine learning acceleration , emerging memory technologies , and low-power circuits . Publications demonstrate expertise in ReRAM/memristor-based accelerators, sparse neural networks, and processing-in-memory architectures. Key contributions include cross-layer optimization frameworks and robust neuromorphic designs. Her awards include: 9 Best Paper Awards (ASPDAC, ICMLA, ISVLSI, etc.) NSF Career Award DARPA Young Faculty Award IEEE Fellow (2019) ACM Distinguished Member (2017) IEEE TCSDM Outstanding Leadership Award (2021)
Martin Diehl is a computational materials scientist affiliated with KU Leuven (Departments of Computer Science and Materials Engineering) and the Max-Planck-Institut für Eisenforschung GmbH in Germany. His work focuses on crystal plasticity simulations, computational materials engineering, and multi-physics modeling of metallic systems. Research interests include: Crystal plasticity finite element method (CPFEM) and spectral solvers Microstructure evolution and damage mechanics Machine learning applications in materials design Development of the DAMASK simulation toolkit Multi-phase steel alloys and heterogeneous deformation Integrated computational materials engineering (ICME) Key trends in his publications since 2021 highlight advancements in: Multi-physics DAMASK framework for coupled chemo-mechanical and thermal simulations AI-driven inverse design of steel microstructures Damage modeling in dual-phase steels Collaborative software development for materials science Experimental-simulation integration for stress-strain partitioning High-resolution spectral methods for finite strain analysis He actively collaborates with institutions like Harbin Institute of Technology, University of Oxford, and research groups across Europe and Asia.
