جست‌وجوی استاد و پژوهشگر

Stefan Kratschمشاهده پروفایل

استاد

• Parameterized complexity
• Efficient preprocessing
• Computational complexity

Stefan Kratsch is a Professor of Theoretical Computer Science at the Institute of Computer Science, Humboldt University of Berlin. He holds this position since September 2017 and has previously held academic roles at the University of Bonn (2015–2017) and Technical University Berlin (2012–2014). His research focuses on parameterized complexity, efficient preprocessing, and computational complexity, with a strong emphasis on theoretical foundations and algorithm design. University: Humboldt University of Berlin Department: Institute of Computer Science (Algorithm Engineering group) Academic Rank: Professor Email: stefan.kratsch@hu-berlin.de, kratsch@informatik.hu-berlin.de Kratsch’s recent publications highlight his work on advanced algorithmic techniques for graph problems. Key areas include kernelization methods, flow-augmentation for connectivity problems, and tight complexity bounds for classical problems parameterized by structural measures like clique-width and cutwidth. His theoretical contributions aim to bridge preprocessing efficiency with computational hardness. Stefan actively engages in academic service, including organizing workshops and serving on program committees for leading conferences such as IPEC, LATIN, and SWAT. He has reviewed for numerous journals and grant panels, including the European Research Council and German Research Foundation. Advising: He supervised PhD candidate Michael Piechotta, whose defense is scheduled for September 2025.

Alexander Mayمشاهده پروفایل

استاد

• Cryptanalysis
• Post-Quantum Cryptography
• Lattice-Based Cryptography
• +۴ مورد دیگر

Alexander May is a Professor at Ruhr-University Bochum, affiliated with the Faculty of Mathematics and the Horst Görtz Institute for IT-Security. His research focuses on cryptanalysis, post-quantum cryptography, and lattice-based security, with extensive work on cryptographic attacks targeting schemes like LWE, NTRU, McEliece, and RSA. Research Interests: May specializes in: Developing optimized attacks on lattice-based cryptosystems (e.g., Kyber, Dilithium) Side-channel vulnerability analysis in post-quantum schemes Quantum algorithm applications in cryptanalysis Efficient decoding attacks for code-based cryptography His recent publications (2021-2025) demonstrate consistent focus on: Practical cryptanalysis with reduced computational resources Novel approaches to breaking NIST post-quantum candidates Quantum-speedup techniques for key recovery Side-channel attacks on hardware implementations PhD Supervision: May has supervised over 15 doctoral students including recent graduates like Carl Schneider (2024), Önder Askin (2024), and Floyd Zweydinger (2023). Their work spans lattice cryptography, coding theory, and quantum cryptanalysis.

• Natural Language Processing
• Dialectology
• Speech Processing
• +۷ مورد دیگر

Lea Fischbach is a Research Fellow and PhD candidate at the Research Center Deutscher Sprachatlas (DSA) within the Faculty of German Studies and Arts at Philipps University of Marburg. She is employed in the long-term Regionalsprache.de project funded by the Academy of Sciences and Literature in Mainz and also contributes to the BMBF project AnDy . Since January 2021, she has been working at the DSA, initially as a student assistant before advancing to her current PhD candidate position under the supervision of Lucie Flek and Alfred Lameli. Her educational background includes both Bachelor's and Master's degrees in Informatics from Philipps University of Marburg. Her Bachelor's thesis focused on Efficient Serialization of Objects while her Master's thesis addressed Applied Learning Analytics Process Based on a Language Learning App , building upon an Android app she helped develop as part of a student team project. Lea's research centers on the classification of German dialects using audio recordings and deep learning techniques. Her work explores how deep learning can be optimized for dialect classification, which phonetic features are crucial for distinguishing between dialects, and how insights from both deep learning and phonetic analysis can be combined for enhanced classification. This research sits at the intersection of computational linguistics, speech processing, and dialectology, with particular focus on German dialects and low-resource audio data scenarios. Her technical approach integrates programming expertise with linguistic knowledge to develop practical solutions for dialect recognition challenges. Her publications demonstrate a strong focus on practical applications of deep learning to dialect classification challenges. She has explored voice conversion techniques for data augmentation, analyzed the importance of acoustic features in deep learning models, compared speaker diarization approaches for German dialectal speech, and contributed to digital language geography projects. Her work consistently addresses the challenges of working with limited dialectal data and seeks to improve classification accuracy through innovative technical approaches that minimize speaker-related variability while highlighting dialect-specific features. At Philipps University of Marburg, Lea has served as a tutor teaching courses in programming, technical informatics, and database systems. During her time at the DSA, she has contributed to various projects including the creation of the Hessenplattform and developed the second version of the Welcome to Bavaria app, a language guide for Bavarian dialects. Her technical work bridges the gap between computational methods and traditional linguistic research, creating practical tools for dialect documentation and analysis. Her research is conducted within the Research Center Deutscher Sprachatlas, a leading institution for German dialect research that provides both the theoretical framework and computational resources for her technical investigations into dialect classification. She actively participates in international conferences including Interspeech, ACL, and NoDaLiDa, presenting her work on deep learning pipelines for dialect recognition and the challenges in developing technical solutions for dialect classification tasks.

• Theoretical Computer Science
• Algorithms
• Approximation Algorithms
• +۷ مورد دیگر

Shaofeng Jiang is an Assistant Professor at Peking University, affiliated with the School of Computer Science and the Center on Frontiers of Computing Studies. He previously held an assistant professor position at Aalto University and was a postdoctoral researcher at the Weizmann Institute of Science under Robert Krauthgamer. He earned his PhD from the University of Hong Kong under Hubert Chan and completed his bachelor's at Shandong University. PhD: University of Hong Kong, supervised by Hubert Chan Bachelor's: Shandong University His research lies in theoretical computer science, focusing on algorithm design for massive and high-dimensional data. Key interests include approximation algorithms, online algorithms, streaming algorithms, clustering, coresets, and dimensionality reduction. His work often bridges theoretical guarantees with practical applications in distributed and parallel computing environments. The recent publications reveal a strong trend in developing efficient and scalable algorithms for clustering problems, particularly through coreset construction, dimensionality reduction, and distributed models like MPC. There is a growing emphasis on fairness, robustness, and dynamic settings, indicating a forward-looking research agenda in adaptive and responsible algorithmic systems. He actively mentors PhD students and teaches both undergraduate and graduate courses in programming and computational social science. He has served on program committees for top conferences including NeurIPS, ICML, and STOC, and co-organized a Dagstuhl seminar on clustering. Scientific Service and Leadership: Co-organizer, Dagstuhl Seminar on New Trends in Clustering (2026) Area Chair, NeurIPS 2024 and 2025 Program Committee, IPDPS 2019, SWAT 2022, COCOA 2024, ICALP 2026 He has received no explicitly mentioned scientific awards. His grants and funding sources are not detailed in the text. He leads a research group focused on theoretical foundations of data-efficient algorithms, collaborating with students on cutting-edge problems in high-dimensional and streaming data analysis.

Sebastian Schmidlمشاهده پروفایل

پژوهشگر

• Distributed Computing
• Data Profiling
• Time Series Anomaly Detection
• +۳ مورد دیگر

Sebastian Schmidl is a Ph.D. researcher at the Information Systems Group (University of Potsdam, Digital Engineering Faculty ). He is also affiliated with the Big Data Analytics Group at Philipps-Universität Marburg . Research Interests : Distributed computing, time series anomaly detection, data profiling, and scalable reactive systems using actor programming. Academic Roles : Supervised multiple master's and bachelor's theses, delivered guest lectures on reproducibility and order dependencies, and contributed to academic reviewing for conferences like SIGMOD and journals like SoftwareX. His recent publications focus on time series anomaly detection , data profiling query languages , and distributed algorithm design . He has collaborated extensively on benchmarking frameworks and optimization techniques for univariate and multivariate time series data.

Arne Schmitzمشاهده پروفایل

پژوهشگر

• Radio Wave Propagation
• Computer Graphics
• Wireless Network Simulation
• +۳ مورد دیگر

Dr. Arne Schmitz is a researcher at the Department of Computer Science, RWTH Aachen University, specializing in computational methods for wireless communications and computer graphics. His work bridges radio wave propagation physics with advanced visualization techniques, focusing on practical applications for mobile network planning and mobile device interfaces. His primary research domains include: Radio wave propagation modeling in urban environments GPU-accelerated beam/ray tracing algorithms Antenna pattern compression using spherical harmonics Real-time 3D visualization for resource-constrained mobile devices Ad-hoc multi-display systems for collaborative mobile applications Analysis of his publication timeline reveals an evolution from foundational radio propagation models (2006) toward increasingly sophisticated urban simulation frameworks (2011-2012), with consistent emphasis on computational efficiency. His most impactful contributions integrate computer graphics rendering techniques with telecommunications engineering, particularly in adapting beam tracing for radio wave simulation and developing novel mobile visualization paradigms that overcome hardware limitations through client-server architectures. Dr. Schmitz maintains a strong collaborative relationship with Professor Leif Kobbelt and colleagues at RWTH Aachen, with co-authorship appearing in 9 of his 10 documented publications. His research demonstrates consistent funding support through participation in IEEE and ACM conferences, though specific grant details are not disclosed in the available materials.

• Multi-agent systems
• Reinforcement learning
• Trust management
• +۵ مورد دیگر

Athirai Aravazhi Irissappane is an Assistant Professor at Nanyang Technological University's College of Computing and Data Science, School of Computer Science and Engineering. With a PhD in Trust oriented decision making via POMDPs completed in 2016, they have established themselves as a prominent researcher in multi-agent systems and reinforcement learning. Their academic journey shows a clear progression from foundational work in trust management to cutting-edge research in deep reinforcement learning frameworks. Dr. Irissappane's research interests focus on multi-agent systems , reinforcement learning , trust management , and recommender systems . They have made significant contributions to the field of multi-objective reinforcement learning, developing frameworks that address complex policy distributions and cooperative behavior in multi-agent environments. Their work bridges theoretical advances with practical applications, particularly evident in their contributions to the RecSys Challenge 2023. Analysis of their recent publications (2020-2024) reveals a strong emphasis on Advanced reinforcement learning architectures for multi-agent cooperation Privacy-preserving recommendation systems Automated data quality improvement for recommendation tasks Scalable frameworks for reinforcement learning research These works demonstrate increasing sophistication in handling complex decision-making scenarios while addressing practical constraints like computational efficiency and privacy concerns. Dr. Irissappane has been actively involved in major academic initiatives including the RecSys Challenge 2023 and has contributed to comprehensive guides on multi-objective reinforcement learning that have become valuable resources for the research community. Their collaborative work spans multiple institutions and has resulted in publications in top-tier venues including IEEE Transactions, Autonomous Agents and Multi-Agent Systems journal, and conference proceedings of AAAI, IJCAI, and AAMAS.

• Computer Science
• Quantum Computing
• Machine Learning
• +۲ مورد دیگر

Atul Kumar is a Researcher in computer science with publications spanning quantum computing, machine learning, and cybersecurity. His work appears in journals including IEEE Transactions on Visualization and Computer Graphics, Knowledge-Based Systems, and IEEE Access. His research focuses on quantum machine learning algorithms, medical image analysis, hardware security, and chaotic encryption systems. Recent investigations explore quantum support vector machines, hardware Trojan attacks, and uncertainty-aware neural representations for scientific visualization. Analysis of his publication portfolio reveals consistent methodological innovation in applying quantum computing principles to traditional computing problems, particularly in image processing and classification tasks. His security-focused research examines vulnerabilities in hardware systems and develops cryptographic solutions using chaotic maps.

Till Tantauمشاهده پروفایل

استاد

• Computational Complexity
• Parameterized Algorithms
• Bioinformatics
• +۴ مورد دیگر

Prof. Dr. Till Tantau is a Full Professor of Theoretical Computer Science at the University of Lübeck , Germany. He has served as Dean of Studies for the Faculty of Technology and Natural Sciences (MINT sections) since 2008 and chairs the Scientific Advisory Board of the German National Computer Science Competition from 2014. His research focuses on computational complexity, parameterized algorithms, and bioinformatics, particularly logspace problems and descriptive complexity theory. Born 1975 in Berlin, Germany 1994–1999: Studied at TU Berlin 1999–2005: Research Assistant at TU Berlin 2003: Doctorate in Natural Sciences (Dr. rer. nat.) at TU Berlin 2004: Research stay at ICSI, Berkeley with Richard Karp 2005: Appointed W2 Professor at University of Lübeck 2015: Promoted to W3 Professor (highest professorial rank in Germany) His research interests include: Computational Complexity: Logspace problems, weak cardinality theorems, and structural similarities across computation models Parameterized Algorithms: Color coding, kernelization techniques, and parallelization of fixed-parameter tractable problems Bioinformatics: Haplotyping problems under perfect phylogeny models Descriptive Complexity: Logical characterizations of computational problems Graph Algorithms: Shortest/longest paths in series-parallel graphs and smoothed analysis of binary search trees Publications since 2000 demonstrate expertise across theoretical computer science, with 15 recent works focusing on parameterized complexity, parallel algorithms, and applications in bioinformatics. He contributes to algorithmic metatheorems, color coding techniques, and kernelization for hitting set problems. Scientific Awards & Scholarships : 1992: Federal winner of German computer science competition 1993: Silver medal at International Olympiad in Informatics 1993: German National Academic Foundation scholarship 1994: Dr. Habbena Prize for Abitur excellence 1999: Erwin Stephan Prize, TU Berlin 2002: Best student paper award at MFCS 2003: GI Dissertation Prize nomination 2007–2021: Heliprof Teaching Awards (5x recipient) His teaching includes courses on algorithm design, complexity theory, LaTeX/TikZ, and theoretical computer science. He chairs the Scientific Advisory Board of the German National Computer Science Competition and participates in academic governance as Dean of Studies.

Anil Nagathilمشاهده پروفایل

پژوهشگر

• Speech Signal Processing
• Audio Signal Processing
• Auditory Modeling
• +۵ مورد دیگر

Anil Nagathil is a Senior Research Scientist at the Institute of Communication Acoustics (Ruhr University Bochum), currently on leave while holding a Marie Skłodowska-Curie Global Fellowship at McMaster University (2025-2027). He holds a Dr.-Ing. degree from RUB (2016) following Dipl.-Ing. (2009), with prior academic experience at University of Birmingham (UK). Education: Dr.-Ing. (2016) and Dipl.-Ing. (2009) in Electrical Engineering, RUB Professional roles: Postdoc at RUB (2016-2023), Senior Research Scientist (2023-present), Visiting Scholar at McMaster University (2025-2027) His research focuses on statistical and machine learning-based speech/audio signal processing , with specific interests in auditory modeling , neural modeling , and EEG analysis for hearing instrument applications. Recent work includes WaveNet-based cochlear modeling , machine learning for spectral complexity reduction , and interactive music preprocessing for cochlear implant users. Publications span deep neural network approximation , music signal enhancement , and acoustic signal classification with 15+ peer-reviewed contributions since 2007. Current projects include neural network modeling of auditory transduction and individualized music remixing for hearing-impaired listeners.

• Embedded Machine Learning
• FPGA Design
• Neural Networks
• +۴ مورد دیگر

Leo Buron, M.Sc., is a Researcher and doctoral student at the Intelligent Embedded Systems Lab, part of the School of Computer Science at the University of Duisburg-Essen. He holds a Master's degree in Electrical Engineering and Information Technology with a focus on embedded systems. University of Duisburg-Essen (Master's in Electrical Engineering and Information Technology) Hochschule Düsseldorf (Dual studies in Electrical Engineering with Siemens) His research focuses on optimizing deep neural networks for resource-constrained hardware like FPGAs, with applications in brain-computer interfaces (BCIs) and time series analysis. Key areas include spike sorting, feature extraction, clustering, and AI implementation on sensors/smart devices to improve data privacy. Recent publications highlight his work on quantized neural network training, FPGA-based AI hardware for BCIs, and memristor applications in spike sorting. These works span computer science, biomedical engineering, and electrical engineering disciplines. Leo contributes to knowledge transfer initiatives like the ZaKI.D project, aiming to democratize AI adoption for regional companies. He collaborates with researchers including Gregor Schiele and Andreas Erbslöh on embedded machine learning and adaptive systems.

• Cryptography
• Information Security
• Formal Verification
• +۳ مورد دیگر

Ralf Küsters is a Professor at the University of Stuttgart , leading the Institute of Information Security . His research focuses on cryptographic protocols, formal verification, and privacy-enhancing technologies. Research Trends: His recent work addresses critical challenges in e-voting systems, including bulletin board vulnerabilities and zero-knowledge proofs for ballot validity , as well as accountability mechanisms for distributed systems. He also investigates security analysis of web authentication standards like OpenID FAPI 2.0 and develops efficient multi-party computation (MPC) protocols (e.g., Multipars) with reduced communication overhead. Scientific Contributions: Küsters has formalized security models for web infrastructure , exposed attacks on global standards (e.g., audience injection attacks ), and designed provably secure implementations for cryptographic building blocks. His collaborations span institutions in Germany, Australia, and Brazil, with applications in open banking, healthcare, and secure multi-party computation. Technical Leadership: As head of the Institute of Information Security, he drives research on privacy-preserving technologies , secure data aggregation , and standardization-compliant security analysis , bridging theoretical cryptography with practical implementations.

• Spatial Databases
• Big Spatial Data
• Location-Based Services
• +۶ مورد دیگر

Mohamed F. Mokbel is a Professor at the University of Minnesota with a distinguished career in spatial databases and big spatial data management. His research spans over two decades with more than 260 publications in top-tier venues including ICDE, SIGMOD, VLDB, and GIS conferences. He has established himself as a leading researcher in trajectory data management, mobility data science, and scalable spatial processing systems. Dr. Mokbel's research interests focus on the intersection of spatial databases, big data, and machine learning. He has pioneered work in trajectory data management systems, developing frameworks like ST-Hadoop for processing spatio-temporal data at scale. His recent research explores the application of large language models to trajectory analysis, spatial data cleaning systems with spatial awareness, and innovative approaches to mobility data science. His work addresses fundamental challenges in handling massive spatial datasets while maintaining efficiency and accuracy. His publication record reveals significant trends toward integrating machine learning with spatial data management, particularly in trajectory imputation using BERT models (KAMEL system) and applying NLP techniques to trajectory analysis. Recent work shows increasing focus on urban mobility applications, privacy considerations in location data, and the development of specialized frameworks for processing different types of spatial data at scale. Dr. Mokbel has mentored numerous students who have become productive researchers in their own right, with many publications featuring students as first authors. His collaborative network is extensive, with frequent co-authorship with researchers like Walid G. Aref, Ahmed Eldawy, and Amr Magdy, indicating strong research group leadership. He has contributed significantly to the spatial database community through systems like RASED for monitoring OpenStreetMap updates, KAMEL for trajectory imputation, and Sparcle for spatial data cleaning. His work bridges theoretical database concepts with practical applications in transportation, urban planning, and location-based services.

• Efficient Algorithms
• Computational Complexity
• Network Foundations
• +۵ مورد دیگر

Dr. George B. Mertzios is an Associate Professor at the Department of Computer Science, Durham University , UK. His career spans roles including Senior Lecturer (2015-2017) and Lecturer (2011-2015) at Durham, with additional positions as an Invited Assistant Professor at LaBRI, University of Bordeaux/CNRS, France (2012), and postdoctoral research fellowships at the University of Haifa and Technion, Israel (2010-2011). He earned his PhD in Computer Science from RWTH Aachen University (2009) and a Diplom in Mathematics from Technische Universität München (2005). Education PhD in Computer Science, RWTH Aachen University, 2009 Diplom in Mathematics (minor in Computer Science), Technische Universität München, 2005 Research Interests focus on efficient algorithms and computational complexity in temporal graphs , with significant contributions to dynamic network optimization , evolutionary graph theory , and parameterized complexity . His work bridges combinatorial optimization , algorithmic game theory , and intersection graph models . Scientific Activities include organizing Co-Chair of the PC for SAND 2026 Co-Organizer of Dagstuhl Seminar 26251 (2026) Organizer of the Algorithmic Aspects of Temporal Graphs workshops (2018-2025) His 15 most recent articles (2025-2021) address problems in temporal graph realization , dynamic network optimization , Hamiltonian cycles , and epidemic control on temporal networks , published in top venues like Theoretical Computer Science , Journal of Computer and System Sciences , and Algorithmica . Scientific Awards include a Gold Medal in the 1998 Balkan Mathematical Olympiad, Distinguished Diploma in the 1998 Bulgarian National Mathematical Competition, and Best Paper Awards at ALGOWIN 2025 and ICALP 2010 Track C. Research Supervision includes advising PhD students: David Fairbairn (in progress), David Kutner (2025), Nina Klobas (2024), Charles Murray (2021), Sepehr Meshkinfamfard (2016), Ioannis Lignos (2016) Postdoctoral researchers: Christoforos Raptopoulos (2020), Viktor Zamaraev (2017-2019), André Nichterlein (2016-2017), Archontia Giannopoulou (2014-2015), Konrad Dabrowski (2012-2013) Grants led include EPSRC grants EP/P020372/1 (2017-2020) on algorithmic aspects of temporal graphs and EP/K022660/1 (2013-2015) on intersection graph models. He also contributed to the EU IP MULTIPLEX (2012-2016).

• Building Performance Optimization
• Sustainable Construction Materials
• Building Automation Systems
• +۳ مورد دیگر

Prof. Dr. Jan Mehnert is a Professor specializing in building performance optimization, sustainable construction materials, and data-driven building automation systems. His research bridges the gap between theoretical design and operational performance through innovative quality management frameworks and big data analytics. Current work focuses on algorithmic toolkits for technical monitoring, eco-materials like additive manufactured earth, and carbon-neutral campus initiatives. His primary research domains include building performance verification (addressing the design-operation gap), sustainable material systems (earth-based construction and paper composites), and data-intensive building diagnostics (leveraging big data for energy efficiency). Key methodologies involve functional testing protocols, algorithmic performance monitoring, and lifecycle assessment of novel materials. Recent projects demonstrate practical applications in campus sustainability and neighborhood energy systems. Analysis of his 2017-2023 publications reveals three converging trends: (1) Integration of real-time data analytics into building commissioning processes, (2) Development of eco-material systems with validated thermal and structural properties, and (3) Creation of modular toolkits for scalable performance management. His work consistently targets the operational phase of buildings, emphasizing measurable outcomes in energy conservation, indoor environmental quality, and carbon reduction across both new construction and retrofits.