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Stefan ZellmannView profile

Associate Professor

• Scientific Visualization
• Computer Graphics
• High-Performance Computing
• +4 more

Stefan Zellmann is an Associate Professor and Principal Investigator of the DFG Project 'VTV-AMR' at the Institute of Computer Science , University of Cologne. His research focuses on the intersection of large-scale scientific visualization and high-performance computing, particularly in developing real-time rendering algorithms for adaptive mesh refinement (AMR) data. He leads projects such as ExaBrick (AMR rendering framework) and Visionaray (cross-platform ray tracing library). Education & Roles: Completed his PhD in 2014 on 'Interactive High-Performance Volume Rendering'. Currently teaches courses on graphics processor architectures and programming, including Practical Computer Science: Architecture and Programming of Graphics and Coprocessors . Research Interests: Direct volume rendering, physically based rendering, AMR visualization, GPGPU computing, and FPGA programming. His work emphasizes low-latency interaction and efficient algorithms for supercomputers/multi-GPU systems. Awards: Honorable Mention @EGPGV 2020, Best Paper Awards @EGPGV 2018 (for Rapid k-d Tree Construction ), @VDA 2017 (for Ray Clipping ), and @PDCS 2012 (for Distributed Volume Rendering Architecture ). Key Projects: ExaBrick (AMR rendering), Visionaray (ray tracing), Virvo (volume rendering library), and HPSC TerrSys (high-performance computing in terrestrial systems). Grants & Services: Co-Chair of IEEE LDAV 2022 Posters, Program Committee member for IEEE VIS and EGPGV. Regular reviewer for journals/conferences like TVCG and IEEE VR. Labs/Teams: Part of the Center for Data and Simulation Science (CDS), focusing on visualization algorithms for exascale computing and large-scale scientific data.

Dominique SugnyView profile

Professor

• Quantum Control
• Molecular Dynamics
• Nuclear Magnetic Resonance (NMR)
• +3 more

Dominique Sugny is a Professor at Université de Bourgogne and a Hans Fischer Fellow at the TUM Institute for Advanced Study (2015 appointment). He leads the Optimal Control and Medical Imaging focus group, collaborating with Prof. Steffen Glaser at TUM. His primary affiliation is the Laboratoire Interdisciplinaire Carnot de Bourgogne (ICB). Education & Career: PhD in Theoretical Physics (2002), Laboratory of Spectrométrie Physique, Grenoble Habilitation à Diriger des Recherches (2009) Maître de Conférence (2003–2014) and Professor (2014–present) at Université de Bourgogne Visiting Researcher at TUM (2012) Research Focus: Dominique Sugny develops optimal control methods for quantum and classical systems, with applications to molecular dynamics, nuclear magnetic resonance (NMR), and medical imaging. His work bridges theoretical physics, applied mathematics, and collaborations with experimentalists. Key areas include: Quantum control of spin systems and molecular processes Optimal pulse design for MRI contrast enhancement Nonlinear optics and terahertz spectroscopy Time-optimization algorithms for quantum systems Publications & Impact: His recent work emphasizes MRI optimization, quantum control algorithms, and molecular dynamics. Notable contributions include time-optimal RF pulse designs, quantum-classical analogues (e.g., tennis racket effect), and terahertz-driven molecular orientation. Labs & Collaborations: Affiliated with the ICB lab, he collaborates with mathematicians, physicists, and engineers to advance control theory applications in both fundamental and applied sciences.

Klaus DiepoldView profile

Professor

• Machine Learning
• Signal Processing
• Ethical AI
• +2 more

Prof. Dr.-Ing. Klaus Diepold is a Full Professor and Chair of Data Processing at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He earned his PhD in Electrical Engineering from TUM in 1992. His research focuses on machine learning, multimedia signal processing, moral machines, and affect modeling for cognitive systems. He actively promotes entrepreneurship through the Center for Digital Technology and Management (CDTM). Academic Career: Diepold held visiting professorships at the University of Alberta, Canada, and NICTA, Australia. He served as Dean of Studies at TUM (2005–2010) and Senior Vice President for Diversity and Talent Management (2013–2015). He has contributed to MPEG standards and led projects in telepresence (SFB-453) and cognitive systems (CoTeSys). Research Interests: His work spans ethical AI, reinforcement learning, biomedical applications of machine learning, and interdisciplinary education. Notable projects include CellFace, Sirrel, and the development of AI-driven diagnostic tools using digital holographic microscopy. Awards: He has received the TUM Teaching Prize (2004), Fellowship for Innovation in University Teaching (2012), and the 2024 IEEE German Education Conference Best Paper Award. His work on 'Komputer & Creativität' highlights innovative teaching at the intersection of engineering, AI, and theater. Labs & Teams: His team includes researchers like Sven Gronauer and Alice Hein, focusing on projects like GenAI chatbot agents, clinical applications of computational medicine, and cybersecurity. The Chair of Data Processing collaborates with industry partners like fortiss and Rohde & Schwarz.

Mingshuai ChenView profile

Assistant Professor

• Formal Verification
• Programming Theory
• Logical Aspects of Computer Science
• +5 more

Mingshuai Chen is an Assistant Professor at Zhejiang University, leading the Formal Verification Group. He previously held a postdoctoral position at RWTH Aachen University. Education: Ph.D. in Computer Science from Institute of Software, Chinese Academy of Sciences (2019) B.Sc. in Computer Science from Jilin University (2013) His research focuses on formal verification, synthesis, programming theory, and probabilistic/quantum systems. Notable contributions include Exact Bayesian Inference and Lower Bounds for Probabilistic Programs . Scientific awards include: NSFC Excellent Young Scientists Fund Program (Overseas) Distinguished Paper Award at ATVA 2018 Best Paper Award at FMAC 2019 CAS-President Special Award (2019) 2nd Prize@ChinaSoft'24 He serves on program committees for OOPSLA 2026, TACAS 2026, and multiple other conferences.

Marcelo ArenasView profile

Professor

• Data Management
• Semantic Web
• Database Theory
• +3 more

Marcelo Arenas is a Professor at the Department of Computer Science and the Institute for Mathematical and Computational Engineering at the Pontifical Catholic University of Chile. He is a Fellow of the Association for Computing Machinery (ACM), former director of the Millennium Institute for Foundational Research on Data, and co-founder of the Center for Semantic Web Research. His Ph.D. in Computer Science was obtained from the University of Toronto in 2005. Research interests include data management , applications of logic in computer science , and Semantic Web technologies. He has published extensively on topics such as SPARQL query complexity , graph database systems , and incomplete database theory , with notable works like MillenniumDB and Foundations of Data Exchange . Scientific accolades: 2016 SWSA Ten-Year Award for "Semantics and Complexity of SPARQL" IBM Ph.D. Fellowship (2004) Nine Best Paper Awards across PODS, ISWC, ICDT, ESWC, WWW, and NeurIPS His work on approximate counting algorithms (e.g., FPRAS for #NFA) and explainable AI frameworks has influenced database theory, while serving on program committees for ICDT 2015, ISWC 2015, and PODS 2018 demonstrates leadership in the field. Current projects focus on probabilistic explanations for decision trees and temporal regular path queries in knowledge graphs.

Ansgar ScherpView profile

Professor

• Data Science
• Big Data Analytics
• Text Analytics
• +7 more

Professor Ansgar Scherp is a leading academic in Data Science and Big Data Analytics at Ulm University , Germany. His career spans multiple institutions including University of Essex, University of Stirling, and Kiel University, where he led the EU Horizon 2020 MOVING project. He holds a habilitation thesis on Semantic Media Management and has pioneered research in combining symbolic and statistical methods for data analysis. Current: Professor for Data Science & Big Data Analytics (Ulm University) Previous: Professor for Knowledge Discovery (Kiel University), Juniorprofessor roles (Mannheim, Koblenz-Landau) Skills: Semantic Web, Graph Mining, Text Analytics, Multimedia Systems His research interests focus on integrating Information Retrieval, Data Mining, Machine Learning, and Semantic Web technologies. He has developed innovative methods like HCF-IDF for semantic profiling and SchemEX for schema-level indexing of Linked Data. His work addresses challenges in media companies' digital transformation and large-scale Knowledge Graph analysis. Scientific awards include: Best Paper Award, Symposium on Document Engineering (2021) Best Student Paper Award, Symposium on Document Engineering (2019) ACM SIGMM Rising Stars Symposium Speaker (2016) Double Winner of Billion Triple Challenge (2008, 2011) klickTel Award (2013) He has supervised 12 PhD theses and over 15 Master's theses , with students now working at Google, Capgemini, and academic institutions. His email addresses are ansgar.scherp@uni-ulm.de and mail@ansgarscherp.net .

Dominik SchmidView profile

Professor

• Probability Theory
• Markov Chains
• Interacting Particle Systems
• +3 more

Dominik Schmid is a junior professor at the University of Augsburg , affiliated with the Faculty of Mathematics, Natural Sciences and Technology and the Institute of Mathematics under the Chair of Stochastics and Its Applications . Prior to this role, he held postdoctoral positions at Columbia University , University of Bonn , and Princeton University . His research focuses on probability theory , particularly mixing times for Markov chains , limit theorems for interacting particle systems , and stochastic processes in random media . Education: PhD in Mathematics, Technical University of Munich (2021) Research Trends: His work on exclusion processes , random walks in dynamic environments , and fragmentation models has been published in leading journals such as Annals of Probability , Communications in Mathematical Physics , and Annals of Applied Probability . Key themes include non-equilibrium statistical mechanics , random graph dynamics , and hydrodynamic limits . Scientific Contributions: Developed sharp mixing time bounds for exclusion processes in various boundary conditions Extended cutoff theory to tree structures and Galton-Watson random media Derived limit profiles for the Bernoulli-Laplace urn and ASEP models Academic Engagement: Organizes workshops at institutions like the Simons Center and Oberwolfach , and has delivered invited talks in Poland , Hungary , Italy , and China . Teaches advanced courses on discrete probability and KPZ universality at the University of Augsburg.

• Privacy-preserving machine learning
• Adversarial robustness
• Differential privacy
• +5 more

Saeed Mahloujifar is a researcher in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. He has established himself as a prominent researcher in machine learning security, privacy, and robustness, with extensive collaborations with leading researchers including Prateek Mittal, Mohammad Mahmoody, and Somesh Jha. His research primarily focuses on security and privacy challenges in machine learning systems. Mahloujifar has made significant contributions to understanding and mitigating membership inference attacks, developing defenses against poisoning and backdoor attacks, advancing differential privacy techniques, and exploring the fundamental limits of robust machine learning. His work bridges theoretical foundations with practical applications, often developing novel algorithms and frameworks that have been widely adopted in the security and privacy communities. The analysis of his recent publications reveals a strong research trajectory centered on privacy-preserving machine learning, with particular emphasis on large language models, differential privacy guarantees, and security vulnerabilities. His work spans theoretical foundations of privacy through practical security mechanisms like watermarking and robustness benchmarking. A notable trend is his increasing focus on large language models and their security implications, alongside continued contributions to foundational privacy theory. Mahloujifar has co-authored over 90 publications since 2017, with a significant acceleration in output since 2020. His work appears consistently in top-tier venues including NeurIPS, ICML, IEEE S&P, and USENIX Security, demonstrating both breadth and depth in his research contributions.

• Wireless Communication Networks
• Distributed Systems
• Sensor Networks
• +8 more

Prof. Horst Hellbrück is an esteemed academic at the Lübeck University of Applied Sciences , where he holds a professorship in Communication Systems and Distributed Systems since 2008. He leads the Center of Excellence CoSA (Communications - Systems - Applications) and focuses on wireless mobile networks, sensor networks, and their applications in medical and underwater technologies. His work bridges theoretical research with practical implementations, including projects like 5G-TELK-NF (autonomous driving/flying), Baltic Future Port (5G drone-based data optimization), and DAVE (digital twin for inland vessel navigation). Education: Diplom (equiv. to Master’s) in Electrical Engineering (1994, University of Saarland); PhD in Computer Science (2004, TU Braunschweig). Research Interests: Modern network structures, sensor systems for healthcare, underwater object detection, and 5G/AI integration. Scientific Contributions: His recent publications focus on UWB-based localization, 5G industrial deployments, and multisensor platforms for underwater applications. He has developed tools like ANSim (Ad-Hoc Network Simulator) and JGPSMap (GPS visualization). Leadership Roles: Active in GI/ITG Fachgruppe KuVS , program committee memberships, and long-term support for internationalization as part of the university’s governance bodies.

Burkhard HeerView profile

Professor

• Public Economics
• Distributional Macroeconomics
• Optimal Taxation
• +3 more

Burkhard Heer is a full Professor of Public Finance at the University of Augsburg 's Faculty of Business and Economics . He previously held academic positions at the Universities of Bolzano (2004-2012), Bamberg (2001-2004), Innsbruck (2001), and Ludwig Maximilian University of Munich (2000-2001). He earned his Ph.D. and habilitation from the University of Cologne in 1996 and 2000 respectively. Research Affiliations : CESifo (Munich), Netspar (Tilburg) Research Interests span public economics , distributional macroeconomics , and dynamic general equilibrium (DSGE) modeling . His work explores optimal taxation , social security systems , business cycles , and macroeconomic policy analysis through computational methods. Recent Publications demonstrate expertise in DSGE modeling (3rd ed., 2024), demographic economics , taxation-automation dynamics , and pension systems across aging economies. He frequently employs computational methods for macroeconomic analysis and policy simulation . Scientific Recognition : Listed among top 100 German international economists by Handelsblatt (2010, 2011). Affiliated with leading research networks: CESifo and Netspar . Teaching includes masters-level macroeconomics , public finance , and predictable generational models at undergraduate/graduate levels. The chair offers thesis supervision in public economics and macroeconomic policy topics.

Robert KohrsView profile

Lecturer

• Smart Grids
• Communication Technologies
• Distributed Energy Resources (DER)
• +5 more

Dr. Robert Kohrs is a researcher and lecturer with expertise in smart grid communication technologies, distributed energy resource (DER) management, and electric vehicle (EV) grid integration. He currently leads the Smart Grids department at Fraunhofer ISE and contributes to teaching at Albert-Ludwigs-University of Freiburg. Education: PhD in Semiconductor Detectors for High-Energy Particle Physics (University of Bonn, 2008) Diploma in Physics (University of Bonn, 2002) His research focuses on smart grid communication protocols, demand-side management, and bidirectional EV charging. He has led major projects like DEMAX and iZEUS, developing predictive control systems for photovoltaic-battery integration and vehicle energy networks. Recent publications highlight his work on stochastic energy frameworks, blockchain-based management systems, and grid-forming inverters, spanning topics from smart metering to renewable energy communities . He collaborates widely with teams across Fraunhofer ISE and industry partners.

• Computerized Tomography
• Radon Transform
• Mathematical Imaging
• +4 more

Matthias Beckmann is an active researcher specializing in mathematical imaging, with a strong focus on the Radon Transform and its applications in computerized tomography and image processing. His work spans theoretical developments, algorithm design, and practical implementations in medical and computational imaging. He has established significant collaborations with researchers across multiple institutions, particularly with Ayush Bhandari, Felix Krahmer, and Robert Beinert. Beckmann's research primarily centers on the Modulo Radon Transform, a specialized area within mathematical imaging that addresses challenges in high-dynamic-range tomography and limited data scenarios. His work bridges theoretical mathematics with practical computational methods, developing novel inversion formulas, reconstruction algorithms, and classification techniques. He has made significant contributions to understanding the properties of the Modulo Radon Transform, developing Fourier-domain inversion methods, and creating normalized transforms that provide invariance and robustness in image classification tasks. Analysis of Beckmann's publication trends reveals a consistent progression from theoretical foundations to practical applications. His early work established the mathematical framework for the Modulo Radon Transform, while more recent publications demonstrate applications in machine learning, optimal transport theory, and specialized imaging scenarios like thermogram analysis of watermarks in manuscripts. His research shows increasing integration of neural network approaches with traditional mathematical imaging techniques, particularly in addressing indirect measurement problems. Beckmann has contributed to multiple prestigious journals including SIAM Journal on Imaging Sciences, SIAM Journal on Mathematics of Data Science, and IEEE Transactions on Computational Imaging, demonstrating the significance and recognition of his work within the mathematical imaging community. His publications span conference proceedings, journal articles, and technical reports, showing versatility in communicating research findings to different academic audiences. Through his collaborations and publication record, Beckmann appears to be actively involved in research teams focused on computational imaging and mathematical methods for image processing. His work on lecture notes suggests involvement in educational activities related to computerized tomography, though specific teaching roles or institutional affiliations are not explicitly documented in the available publication records.

Emanuele D'OsualdoView profile

Professor

• Programming Languages
• Verification
• Security
• +8 more

Emanuele D'Osualdo is a Tenure-Track Professor of Formal Methods for Software Engineering at the University of Konstanz, Department of Computer and Information Science. Previously, he was a Postdoctoral Researcher at Max Planck Institute for Software Systems (MPI-SWS) in Saarbrücken until April 2024, working with Derek Dreyer on verification of concurrent software. Before that, until September 2020, he was a Marie Curie Fellow at Imperial College London, working with Prof. P. Gardner. From 2015 to 2017, he was a PostDoc in the Concurrency Theory Group at the University of Kaiserslautern, working with Prof. Roland Meyer. His educational background includes: PhD (DPhil) in Computer Science from the University of Oxford (2015), supervised by Prof. C.-H. Luke Ong Undergraduate and master's studies at the University of Udine, Italy, graduating with honors and supported by the Scuola Superiore scholarship Emanuele D'Osualdo's research focuses on Formal Methods for the verification of software, with particular emphasis on two main approaches: automatic verification methods using static analysis and infinite-state model checking; and compositional methods, focusing on program logics for properties beyond safety. His work spans several areas including Programming Languages, Verification, Security, Concurrency Theory, Probabilistic Programs, Types, Static Analysis, Process Algebra, and Model Checking. His research has led to the development of tools such as Soter for automatic safety verification of Erlang programs and James Bound for analyzing π-calculus. His recent publications demonstrate a strong focus on advanced verification techniques for concurrent and distributed systems, with particular attention to linearizability, session types, security protocols, and probabilistic reasoning. His work bridges theoretical foundations with practical applications in software verification, showing a progression from foundational work on π-calculus to more applied verification of concurrent data structures and probabilistic systems. He has received notable recognition for his work, including: Winner of the 2016 CPHC/BCS Distinguished Dissertation award Marie Curie Fellow Emanuele actively supervises students and leads research in formal methods. He is involved in several research projects related to software verification and has presented his work at top-tier conferences including POPL, OOPSLA, ESOP, and CAV. He is scheduled to give a keynote at HYPER'25 in Zagreb and will be attending POPL'25. His research group appears to focus on developing theoretically sound yet practical verification techniques for modern concurrent and distributed software systems.

• Edge Computing
• 5G Networks
• Serverless Applications
• +4 more

Dr. Balázs Sonkoly is a researcher specializing in edge computing, serverless applications, and 5G network optimization. His work focuses on latency-sensitive systems, multi-user augmented reality coordination, and cloud-native microservice management across distributed architectures. Key research themes include network function virtualization (NFV), software-defined networking (SDN), and programmable packet-optical networks Recent publications analyze AI-driven autoscaling, P4-assisted serverless migration, and multi-domain service orchestration Article trends reveal expertise in mixed reality frameworks, federated learning for vehicular networks, and edge-cloud convergence. His collaborations span institutions like IEEE, SIGCOMM, and the UNIFY project. Contributions to conferences like NOMS, ISMAR, and GLOBECOM highlight practical implementations of edge cloud platforms, SLAM integration, and latency-constrained resource allocation.

Mirco SternView profile

Researcher

• Sensor Networks
• Database Systems
• Service Composition
• +2 more

Mirco Stern is a researcher at the Institute of Information Management (IPD Böhm) at Karlsruhe Institute of Technology. His work focuses on sensor networks, database systems, and service composition technologies. Key research areas include distributed query processing, data quality guarantees, and optimization of join operations in resource-constrained environments. Role : Researcher, Institute of Information Management Location : Karlsruhe, Germany Research trends highlight his expertise in sensor network optimization (join processing, wavelet transforms) and service composition (semantic matchmaking, automated integration). Publications span ACM SIGMOD, VLDB, and ECOWS conferences, emphasizing algorithm design and system efficiency.