جست‌وجوی استاد و پژوهشگر

• Big Data
• Artificial Intelligence
• Cloud Computing
• +۵ مورد دیگر

Prof. Dr.-Ing. Christoph P. Neumann is an Associate Professor at Amberg-Weiden University of Applied Sciences (OTH) in the Faculty of Electrical Engineering, Media Technology and Computer Science, holding a W2 professorship in Big Data and Cloud Computing for AI since 2021. Previously, he served as a guest lecturer at Friedrich-Alexander-University Erlangen-Nuremberg (FAU) from 2013 to 2020, accumulating over thirteen years of university teaching experience. Education: Neumann earned his doctorate (Dr.-Ing.) from FAU, where his dissertation on distributed data management, system integration, and process management received a top grade and nomination for the GI Dissertation Award. Research Interests: His work focuses on Big Data infrastructure , AI-driven analytics , and cloud-native software architecture , with emphasis on scalable data-centric applications. He aims to establish nationally visible Big Data research integrated with OTH's Faculty EMI, ensuring AI students receive industry-relevant curricula. Key innovations include data lake technologies for BI modernization and graph databases for content management systems. Scientific Awards: Nomination for GI Dissertation Award Two-time recipient of FAU's best course award (Exercises/GOP subject) Ranked among OTH's Top-5 scientists (out of 200+ professors) Best Paper Award (2024, with Patrick Levi) IARIA Fellowship (2025) Best Paper Award (2025, with Philipp Stangl) Advising and Grants: Supervised 25 student theses at FAU and secured approximately 900,000€ in external funding through three cluster projects, creating additional PhD positions. Industry projects included 768,000€ revenue contribution at EXASOL and 2.7 million € budget responsibility at Teambank AG for BI modernization. Labs and Teams: Speaker of the Research Cluster Robotics & Big Data (RBD-OTH) and member of the Innovation and Competence Center Artificial Intelligence (IKKI). Also serves on INDIGO consortium's steering committee and advises four profile schools for computer science (PIZ), with a guest professorship at Hefei University (China) teaching Big Data principles.

Günter Neumannمشاهده پروفایل

استاد

• Computational Linguistics
• Artificial Intelligence
• Deep Learning
• +۵ مورد دیگر

Günter Neumann is a Professor of Computational Linguistics at Saarland University and a Research Fellow at the German Research Center for Artificial Intelligence (DFKI). His work spans computational linguistics, artificial intelligence, and biomedical informatics, with a focus on information extraction, question answering systems, and knowledge graph reasoning. Education: PhD in Computer Science (1994) and Computational Linguistics (2004), both from Saarland University. Neumann's research interests include multilingual natural language processing, low-resource language modeling, and neural architectures for information retrieval and biomedical relation extraction. He pioneered techniques in dense passage retrieval for Urdu, feature textualization for BERT interpretability, and low-rank temporal knowledge graph embeddings. Recent publications focus on cross-lingual transfer learning, code-mixed clinical text de-identification, and robust biomedical benchmarks. He has led EU/national projects in language technology and served on program committees for ACL, EMNLP, and LREC. His work with the EXCITEMENT Open Platform and DOMLIN system demonstrates expertise in textual entailment and fact verification, achieving top rankings in competitions like CLEF and TAC.

• Computer Graphics
• Geometry Processing
• Differentiable Rendering
• +۳ مورد دیگر

Marc Alexa is a Professor at the Technische Universität Berlin , leading the Department of Computer Graphics within the Institute of Computer Engineering and Microelectronics . His research spans computational geometry, rendering, and human-computer interaction, with a focus on differentiable rendering, mesh processing, and geometric modeling. PhD (2002) and MSc (1997) from Technische Universität Darmstadt under advisors Prof. Encarnacao and Prof. Gross (ETH Zurich) Research interests include: Differentiable rendering frameworks Geometric stylization and manifold reconstruction Optimization of polygonal and tetrahedral meshes Eye-tracking applications in mental imagery and visual perception Scientific awards include: ACM Fellow (2024) SIGGRAPH Academy inductee (2022) ERC Advanced Grant for geometric computation (2022) Eurographics Fellow (2018) Engineering Science Prize (2012) His recent publications (2023-2025) focus on differentiable acoustic path tracing, Poisson manifold reconstruction, and shadow mapping optimization. He has served as Editor-in-Chief of ACM Transactions on Graphics (2018-2021) and chaired key conferences like SIGGRAPH 2013 Technical Papers .

Sarah Morellمشاهده پروفایل

پژوهشگر

• Combinatorial Optimization
• Network Flow Problems
• Approximation Algorithms
• +۳ مورد دیگر

Sarah Morell is a Researcher at the University of Bremen, where she began her postdoctoral position in April 2025 under the mentorship of Prof. Dr. Nicole Megow. Previously, she completed her PhD at TU Berlin under Prof. Dr. Martin Skutella, focusing on Combinatorial Optimization. Her research emphasizes network flow problems, approximation algorithms, and diversity maximization in algorithms. She holds an M.Sc. in Mathematics from EPFL, Switzerland, with a minor in Theoretical Computer Science. Her master’s thesis, advised by Prof. Dr. Friedrich Eisenbrand, explored algorithms for diversity maximization. Key research contributions include work on the Submodular Santa Claus Problem (SODA 2025), unsplittable flows with arc constraints (Math. Program. 2022), and minimum-cost integer circulations in homology classes (SODA 2021). She has actively participated in workshops and summer schools on combinatorial optimization, theoretical computer science, and discrete mathematics. Professional activities include research stays at Maastricht University (2023) and TU Munich (2020), as well as presentations at institutions like MPI Saarbrücken and the University of Bremen. Her interdisciplinary work bridges discrete algorithms, optimization, and applications in fair resource allocation.

Manfred Hauswirthمشاهده پروفایل

استاد

• Distributed Systems
• Internet of Things (IoT)
• Stream Processing
• +۴ مورد دیگر

Prof. Manfred Hauswirth is the Managing Director of Fraunhofer Institute for Open Communication Systems (FOKUS) and holds the Chair for Open Distributed Systems at Technical University of Berlin. His research focuses on distributed systems, IoT, stream processing, quantum computing, and blockchain. He has held roles including Vice Director at Digital Enterprise Research Institute (DERI) and professor at National University of Ireland, Galway. He leads multiple strategic initiatives, including the Fraunhofer Quantum Technologies Research Field and the Weizenbaum Institute. His work bridges academia and industry, emphasizing digitalization, quantum computing, and IoT. Education: Dipl.-Ing. (1993), Dr. techn. (1999) in Computer Science from Vienna University of Technology. Postdoctoral work at École Polytechnique Fédérale de Lausanne (EPFL). Research Interests: Prof. Hauswirth’s work spans distributed systems, semantic web technologies, quantum algorithms, and IoT edge computing. He emphasizes real-world applications like smart cities, autonomous driving, and secure data management. Recent trends in his publications include quantum programming frameworks (e.g., Qrisp), scalable graph distillation, and edge-based AI systems. Awards: Not explicitly listed, but his work has been recognized through leadership roles in IEEE, ACM, and Fraunhofer committees. Advising & Grants: Active in funding initiatives like the Berlin Institute for Learning and Data (BIFOLD) and Einstein Center Digital Future (ECDF). Leads projects on quantum benchmarking, energy flexibility markets, and semantic stream processing. Labs/Teams: Directs the Fraunhofer High Performance Center for Digital Networking and chairs the Quantum Computing Competence Network, integrating interdisciplinary teams across quantum computing, IoT, and AI domains.

Sybille Hellebrandمشاهده پروفایل

استاد

• Testing and Diagnosis of Electronic Systems
• Fault Tolerance
• VLSI Design and Testing
• +۳ مورد دیگر

Sybille Hellebrand is a Professor and Head of the Data Technology (DATE) department at the University of Paderborn, within the Faculty of Electrical Engineering, Computer Science and Mathematics. She leads research in testing methodologies, fault tolerance, and reliability engineering. Her work focuses on advanced testing techniques for nanoelectronic systems, including BIST (Built-In Self-Test), delay fault analysis, and interconnect reliability under process variations. Key research areas include: Testing and diagnosis of electronic systems Robust testing under PVT (Process-Voltage-Temperature) variability Functional safety of interconnects Approximate communication for error-tolerant systems Hardware fault modeling and simulation Recent publications emphasize advancements in storage-based BIST optimization, defect vs. fault coverage analysis, and cross-talk delay modeling using polynomial regression. She has contributed to international conferences such as European Test Symposium (ETS), IEEE Latin American Test Symposium (LATS), and International Symposium of EDA (ISEDA). Teaching responsibilities include courses on systems engineering, quality assurance in microelectronics, and algorithms for test and diagnosis of systems-on-chip. Her work aligns with emerging challenges in nanoelectronics, including reliability under variations and fault-tolerant communication. No specific awards or grants are listed in the provided texts, but her extensive publication record underscores her leadership in testing and reliability research. The DATE lab at Paderborn University serves as a core research hub for her activities.

Manuel Bodirskyمشاهده پروفایل

استاد

• Constraint Satisfaction Problems (CSP)
• Algebraic Methods in Computer Science
• Computational Logic
• +۵ مورد دیگر

Prof. Manuel Bodirsky is a Professor of Algebra and Discrete Structures at Technische Universität Dresden since August 2014. He leads the Algebra and Discrete Structures group within the Faculty of Computer Science and is affiliated with the International Center for Computational Logic (ICCL). His research focuses on constraint satisfaction problems (CSP), algebraic methods in computer science, computational logic, Ramsey theory, model theory, and discrete mathematics. Notable projects include exploring the algebraic tractability of CSPs and applying universal algebra to classify computational complexity. His work frequently intersects with combinatorics, graph theory, and theoretical computer science. Recent publications address advanced topics like temporal CSPs, spectrahedral shadows, and resilience problems using valued CSP frameworks. He maintains active collaborations in computational algebra and logic, contributing to both theoretical foundations and algorithmic applications. Education: Not explicitly listed in provided texts but inferred to include advanced studies in mathematics and computer science. Research interests span foundational areas such as: Constraint satisfaction problem complexity classification Applications of universal algebra to computational problems Model theory and finite structures Combinatorial properties of graphs and tournaments Algorithmic approaches to algebraic and logical systems His recent articles emphasize methodological innovations, including reductions to semidefinite programming, Ramsey-theoretic techniques, and gadget-based transformations. Despite no listed academic awards in the provided data, his prolific publication record reflects significant contributions to theoretical computer science and discrete mathematics. No student advisees or grant details were explicitly mentioned, though his group likely engages in funded research projects given the institutional context.

Wilhelm Beckerمشاهده پروفایل

پژوهشگر

• Nonlinear Optics
• Atomic Physics
• Quantum Optics
• +۳ مورد دیگر

Wilhelm Becker is a Researcher at the Max Born Institute for Nonlinear Optics and Short Pulse Spectroscopy, where he leads the Strongfield Theory Group. His research focuses on fundamental aspects of laser-matter interactions, particularly in strong-field physics, nonlinear optics, and quantum dynamics. He explores phenomena such as high-order harmonic generation, photoelectron spectra, electron dynamics in laser-driven systems, and the effects of tailored laser fields on atomic and molecular processes. Key areas of investigation include the theoretical analysis of strong-field ionization mechanisms, symmetry properties in photoelectron spectra, and the generation of attosecond pulses using two-color laser fields. His work bridges quantum mechanics and classical models to understand electron trajectories and ionization pathways in ultra-intense laser environments. Becker’s contributions span theoretical advancements in molecular alignment effects, polarization-dependent processes, and the application of saddle-point methods to ionization dynamics. His research has implications for novel light sources and ultrafast spectroscopy techniques.

Stefan Ruzikaمشاهده پروفایل

استاد

• Multi-criteria Optimization
• Integer and Combinatorial Optimization
• Mathematical Modeling
• +۳ مورد دیگر

Prof. Dr. Stefan Ruzika is a full professor (W3) in the Department of Mathematics at the Rheinland-Palatinate Technological University Kaiserslautern-Landau. He leads the Competence Center for Mathematical Modelling in MINT Projects in Schools (KOMMS) and chairs the DFG-funded Graduate School 'Mathematics of Interdisciplinary Multiobjective Optimization' (MIMO), starting in 2024. His research focuses on multi-criteria optimization, integer programming, mathematical modeling, and network optimization. He teaches courses such as 'Multicriteria Optimization' and supervises Bachelor’s and Master’s theses. Education: PhD (2007, TU Kaiserslautern), Master of Science (2002, Clemson University), Diplom in Mathematics (2003, TU Kaiserslautern). Positions include professorships at TU Kaiserslautern (2017–present) and University of Koblenz-Landau (2012–2017), and postdoctoral roles at TU Kaiserslautern (2003–2007). Research interests include optimization on networks, approximation algorithms, and decision support systems for sustainable urban planning. Projects include 'Ageing Smart' (decision support for elderly quality of life) and 'GRK 2982: MIMO' (multiobjective optimization research).

Jiameng Puمشاهده پروفایل

پژوهشگر

• Cybersecurity
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Jiameng Pu is a Researcher in the Department of Computer Science at Virginia Tech, part of the College of Engineering. Their research focuses on cybersecurity, deep learning, and adversarial machine learning, with notable contributions to deepfake detection and AI robustness. Pu holds a Ph.D. in Computer Science and Applications from Virginia Tech (2017–2022) and a B.E. in Computer Science and Technology from Wuhan University (2013–2017). Education : Ph.D. in Computer Science and Applications, Virginia Tech (2017–2022) B.E. in Computer Science and Technology, Wuhan University (2013–2017) Research Interests : Pu’s work addresses cutting-edge challenges in cybersecurity, including detecting deepfake content (text, images, and videos), defending against adversarial attacks on AI models, and improving robustness in medical imaging systems. Their research combines techniques from machine learning, computer vision, and data mining to enhance security and reliability in AI applications. Key Publications : Pu has authored influential papers on deepfake detection (IEEE S&P 2023, WWW 2021) and AI security (USENIX Security 2021, EuroS&P 2020). Their work spans theoretical advancements and practical tools like NoiseScope for blind deepfake image detection. Awards : Dr. Dennis Kafura Graduate Fellowship (2022) Nominated for 2021 PhD Fellowship Awards (Facebook Visa Research Scholarship) IEEE S&P 2020 Student Travel Grant NDSS 2019 Student Travel Grant National Endeavor Fellowship (Twice), Chinese Ministry of Education Pu’s professional experience includes software engineering at Facebook (2021) and data science roles at IBM and Wuhan University labs. They actively collaborate with industry and academia to advance AI security and ethical AI practices.

• Network Management
• Software-Defined Networking
• Network Function Virtualization
• +۶ مورد دیگر

Steven Latré is a Professor in the Department of Computer Science at the University of Antwerp, specializing in network management, software-defined networking, and network function virtualization. His research spans wireless communications, Internet of Things, machine learning for networking, and quality of experience optimization. With over 246 publications from 2007 to 2025, he has established himself as a leading researcher in network intelligence and management. His research focuses on developing intelligent approaches for network management, with particular emphasis on applying machine learning techniques to solve complex networking challenges. His work covers adaptive video streaming, resource allocation in heterogeneous networks, wireless sensor networks, and vehicular communications. His research often bridges theoretical advances with practical implementations, as evidenced by numerous collaborations with industry partners and participation in European research projects. His recent publications demonstrate a growing interest in applying AI and machine learning to networking problems, with significant contributions in network function virtualization, software-defined radio access networks, and intelligent resource allocation. His work shows a clear trajectory from traditional network management toward more intelligent, self-optimizing network systems that leverage the latest advances in artificial intelligence. His scientific contributions have been recognized through numerous publications in top-tier networking venues including IEEE Communications Magazine, IEEE Transactions on Network and Service Management, and IEEE Internet of Things Journal. His research has consistently addressed real-world networking challenges while pushing theoretical boundaries in network management and orchestration. Professor Latré has actively supervised numerous PhD students and collaborated extensively with researchers worldwide, particularly with colleagues at the University of Antwerp including Jeroen Famaey, Filip De Turck, and Tom De Schepper. His collaborative network spans multiple continents, reflecting the international impact of his research.

Zeynep Akataمشاهده پروفایل

استاد

• Explainable Machine Learning
• Artificial Intelligence
• Machine Vision
• +۳ مورد دیگر

Prof. Zeynep Akata is a distinguished professor of computer science at the Technical University of Munich (TUM) and director of the Institute for Explainable Machine Learning at Helmholtz Munich. Her research focuses on developing transparent AI systems that can interact reliably with humans, integrating methods from machine vision, natural language processing, and learning. She holds the Liesel Beckmann Distinguished Professorship and has held academic positions at institutions including the University of Tübingen, University of Amsterdam, and UC Berkeley. Education: PhD from INRIA Rhone Alpes (2014), followed by postdoctoral research at the Max Planck Institute for Informatics and UC Berkeley. Career highlights include ERC Starting Grant (2019) and the European Computer Vision Young Researcher Award (2022). Research interests span explainable AI, zero-shot learning, vision-language models, and mitigating biases in AI systems. Her work emphasizes ethical AI deployment and human-centered AI design. Awards: Alfried Krupp Promotion Prize (2023), German Pattern Recognition Award (2021), and Werner von Siemens Ring Young Researcher Award (2019). Her laboratory at TUM and Helmholtz Munich focuses on advancing interpretable machine learning frameworks, with projects addressing societal impacts and technical challenges in AI transparency.

• Artificial Intelligence
• Game Theory
• Multiagent Systems
• +۴ مورد دیگر

Amy Greenwald is a Professor affiliated with Brown University. Her research focuses on artificial intelligence, game theory, and multiagent systems. She has contributed extensively to areas such as market design, auction mechanisms, reinforcement learning, and empirical game-theoretic analysis. Her work bridges theoretical foundations with practical applications, including automated negotiation platforms and supply chain management systems. Key contributions include the development of methodologies for analyzing equilibria in complex games, designing efficient mechanisms for resource allocation, and advancing algorithms for multiagent learning. She has collaborated on projects like the TAC Travel Agent competition and the NegMAS negotiation framework. Her publication record spans over 135 works from 1999 to 2025, appearing in top venues such as AAAI, AAMAS, EC, NeurIPS, and Games and Economic Behavior. She has also contributed to textbook writing and editorial roles in the field of autonomous bidding agents.

• Parameterized Algorithms
• Structural Graph Theory
• Discrete Mathematics
• +۲ مورد دیگر

Michał Pilipczuk is an Associate Professor at the Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw. His research focuses on parameterized algorithms , structural graph theory , and logic in computer science , with significant contributions to algorithm design for sparse and planar graphs. Current affiliation: University of Warsaw Research areas: Parameterized Complexity, Graph Structure, Algorithmic Logic Grant leadership: ERC Starting Grant (CUTACOMBS), NCN SONATA BIS (PI) Recent work explores quasi-polynomial algorithms for induced subgraphs in H-free graphs, geometric independent set approximation , and kernelization techniques for nowhere dense graph classes. He co-authored the definitive textbook Parameterized Algorithms (Springer 2015). Contact: michal.pilipczuk@mimuw.edu.pl

• Machine Learning
• Computer Vision
• Medical Imaging
• +۵ مورد دیگر

Yi Cheng is an active academic researcher with a prolific publication record spanning multiple disciplines in computer science and engineering. Their work demonstrates strong affiliations with research institutions in China, frequently collaborating with researchers from Chinese Academy of Sciences and other Chinese universities. The publication pattern shows consistent high-quality output across top venues in machine learning, computer vision, and control systems. Yi Cheng's research interests center around artificial intelligence applications across diverse domains. Their work bridges theoretical advances in machine learning with practical applications in medical imaging, robotics, control systems, and natural language processing. Recent publications show increasing focus on multimodal learning approaches, domain adaptation techniques, and interpretable AI systems. The research demonstrates both theoretical depth in algorithm development and practical implementation in real-world scenarios. Analysis of recent publications reveals a strong trend toward developing robust, efficient AI systems that can operate across different domains with minimal adaptation. The work spans from fundamental control theory to applied medical imaging, showing versatility across the AI spectrum. Key methodological contributions include novel transformer architectures, multi-view learning approaches, and stability analysis for complex systems. Yi Cheng has received recognition through publications in top-tier venues including IEEE transactions, ACL, AAAI, and other prestigious conferences and journals. While specific awards aren't detailed in the publication record, the consistent acceptance in high-impact venues indicates peer recognition of research quality. The research trajectory shows increasing collaboration with interdisciplinary teams, particularly in medical applications where AI techniques are applied to healthcare challenges. Current work emphasizes practical deployment considerations including computational efficiency, robustness to domain shifts, and interpretability of models - addressing key challenges in real-world AI deployment.