جست‌وجوی استاد و پژوهشگر

• Computational Complexity
• Cryptography
• Zero-Knowledge Proofs
• +۵ مورد دیگر

Boaz Barak is a Professor at the Weizmann Institute of Science in the Department of Computer Science, Faculty of Mathematics and Computer Science. With an h-index of 64 and over 17,301 citations, he is a leading researcher in theoretical computer science with significant contributions spanning computational complexity, cryptography, and machine learning theory. His research interests include: Computational Complexity Cryptography Zero-Knowledge Proofs Program Obfuscation Interactive Proofs Privacy-Preserving Computation Machine Learning Theory Barak's publication record reveals a trajectory from foundational work in theoretical computer science to contemporary research at the intersection of theory and practice. His early work established impossibility results for program obfuscation and advanced techniques for zero-knowledge proofs beyond black-box simulation. His influential textbook "Computational Complexity: A Modern Approach" has become a standard reference in the field. More recently, his research has expanded into machine learning phenomena like double descent and scaling laws for language models, demonstrating the evolving nature of his theoretical contributions. His work consistently bridges deep theoretical insights with practical implications for computing. His notable collaborations include extensive work with Sanjeev Arora (77 publications, 6,879 citations), David Steurer (95 publications, 4,945 citations), and Oded Goldreich, among others. Professor Barak leads a research group at the Weizmann Institute focused on theoretical aspects of computer security and complexity theory. His work has been consistently supported by major research funding, enabling significant contributions to the theoretical foundations of computer science. He maintains an active research program with publications spanning over two decades, demonstrating sustained impact in multiple subfields of theoretical computer science.

Jan Lellmannمشاهده پروفایل

استاد

• Variational Image Processing
• Optimization on Manifolds
• Image Registration
• +۷ مورد دیگر

Jan Lellmann is a Professor at the Institute of Mathematics and Image Computing of the University of Lübeck, with affiliations to Fraunhofer MEVIS . His research focuses on variational image processing , emphasizing the systematic formulation of prior knowledge into energy functions for improved accuracy and data efficiency. Applications span medical imaging , earth sciences , and biological data analysis . He develops non-smooth optimization methods for problems with combinatorial aspects like image segmentation . His recent work includes manifold-constrained optimization and quantum algorithms for imaging tasks. He has contributed software libraries like MFOPT (for manifold optimization) and COAL (for convex energy minimization). Scientific Awards: Best Student Paper Award at SSVM 2021 Honorable Mention at CVPR 2016 Recent Research Trends: Integration of quantum computing with classical image registration. Advancements in Riemannian geometry for protein dynamics and cryo-EM. Development of meta-learning frameworks for adaptive image alignment. Focus on non-smooth and higher-order regularization for sparse data reconstruction.

• Formal Methods
• Graph Databases
• Property Graph Schema
• +۵ مورد دیگر

Ştefania-Gabriela Dumbravă is an Associate Professor at Institut Polytechnique de Paris (ENSIIE/Samovar Laboratory). Her research focuses on formal methods for graph database algorithms , including schema design, query processing, and verification. She contributes to international standards through co-chairing VLDB 2026 Demonstration Track and organizing workshops like VLDB Summer School 2025 . Dumbravă serves on editorial boards (e.g., ACM Transactions on Database Systems ) and program committees for top-tier conferences ( SIGMOD , ICDE ). Research Interests include formal verification of graph databases , property graph schema constraints , certified inference engines , and distributed graph systems . She integrates proof assistants (Coq, Isabelle) with practical database development, emphasizing reliability and mechanical correctness . Her work spans theoretical foundations (e.g., category theory , type theory ) and real-world applications (e.g., transit accessibility analysis , SDN controllers ). Selected Scientific Awards : EASST Best Software Science Paper Award (ICGT 2025) SIGMOD Best Paper Award (2023) VLDB Best Regular Paper Runner-Up (2022) D4Gen Hackathon 3rd Prize Teaching includes Graph Databases , Functional Programming , and Formal Languages at ENSIIE, with additional roles at Université Paris-Saclay, ENS Lyon, and Université Rennes 1. She supervises research internships and participates in gender parity initiatives (e.g., Paris-Saclay Women & Science Mentorship Program).

Disha Purohitمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Data Mining
• Knowledge Graphs
• +۴ مورد دیگر

Disha Purohit is a Research Fellow at the Scientific Data Management Research Group of TIB (German National Library of Science and Technology) and a Ph.D. candidate in Computer Science at Leibniz Universität Hannover. She holds an MSc in Internet Technology and Information Systems from Leibniz Universität Hannover and a BSc in Computer Engineering from Dwarkadas J. Sanghvi College of Engineering, India. Current research focuses on Transparent AI over Knowledge Graphs with Causality Collaborates in TrustKG (Leibniz Association-funded) and P4-LUCAT (ERAMed-funded) projects Key research areas: Artificial Intelligence, Data Mining, Hybrid AI systems, and Medical Informatics Contact: disha.purohit@tib.eu , Phone: 0511 762-12153 Her recent publications prioritize neuro-symbolic AI integration, knowledge graph validation frameworks, and medical applications of hybrid systems. She has contributed to open-access datasets for lung cancer research and bias detection mechanisms in ML pipelines. Current projects emphasize explainability and causal reasoning in healthcare knowledge graphs. Research infrastructure includes the Leibniz Universität Hannover's Data Science & Digital Libraries framework and TIB's Scientific Data Management lab. Her work bridges symbolic reasoning with machine learning for clinical decision support systems.

Gullal Cheemaمشاهده پروفایل

پژوهشگر

• Multimodal Analysis
• News Analysis
• Fake News Detection
• +۵ مورد دیگر

Gullal Cheema is a Researcher at the Visual Analytics department of Technische Informationsbibliothek (TIB), the German National Library of Science and Technology. Based in Hannover, Germany, Cheema is actively engaged in cutting-edge research at the intersection of computer vision, natural language processing, and multimedia analysis with a particular focus on news media and social platforms. Research interests primarily center around multimodal analysis, with specialization in news video processing, fake news detection, claim verification, and image-text relation understanding. Cheema's work bridges the gap between technical AI approaches and real-world media analysis challenges, particularly in the context of misinformation detection and verification. The research spans multiple modalities including text, images, and video, with applications in social media analysis, news verification, and hate speech detection. Cheema's publication record shows a consistent trajectory of impactful research in multimodal AI, with a strong emphasis on practical applications for media verification. Recent work has focused on speaker role identification in news videos, multimodal claim detection, and the analysis of image-text relations in news contexts. The research demonstrates expertise in both theoretical approaches and practical system development for complex multimodal challenges. Best Paper Award at International Conference on Multimedia Retrieval (ICMR 2024) for work on speaker roles and situation types in news videos Multiple publications at premier venues including ACM conferences, IEEE workshops, and Frontiers in Artificial Intelligence Active participation in major evaluation labs like CLEF CheckThat! and MediaEval Cheema is an active member of the Visual Analytics research group at TIB, contributing to projects that address contemporary challenges in media analysis and verification. The work has significant implications for understanding and combating misinformation in digital media landscapes.

• Software Security
• Complex Systems
• Artificial Intelligence
• +۶ مورد دیگر

Pierre Parrend is a researcher at INSA de Lyon's College of Engineering, focusing on software security, complex systems, and artificial intelligence applications. He has published extensively on topics including OSGi platform security, graph-based attack detection, and biologically inspired computing models. His research bridges cybersecurity with machine learning, emphasizing explainable AI for threat detection. Recent work includes Agentic AI frameworks for offensive security Graph processing methodologies for ML Multilayer architectures in cyber-physical systems Key collaborations include Pierre Collet, Stéphane Frénot, and Julio Navarro. While no formal education details appear in the data, his work spans 2006-2025 with significant contributions to zero-day attack detection and security architecture.

• Algorithms
• Graph Theory
• Computational Complexity
• +۴ مورد دیگر

Ivona Bezáková is a Professor in the Department of Computer Science at the B. Thomas Golisano College of Computing and Information Sciences, Rochester Institute of Technology. With a research career spanning nearly two decades, she has established herself as a prominent figure in theoretical computer science and computer science education, with over 70 publications from 2005 to 2025. Her academic journey shows progression from foundational work in theoretical algorithms to broader educational applications. Early research focused on sampling methods and counting problems in graphs, while recent work emphasizes innovative educational approaches using pencil puzzles and board games as teaching contexts. Bezáková's research spans algorithms , graph theory , and computational complexity , with particular contributions to sampling methods and counting problems. Her educational work has significantly influenced computer science pedagogy, especially through puzzle-based learning approaches. The evolution of her publications shows a strategic integration of theoretical foundations with practical educational applications. Her publication record demonstrates consistent productivity across top venues including SIAM Journal of Computing, ACM Transactions, and leading conferences such as STOC and SIGCSE. Recent work shows increasing focus on the intersection of theoretical computer science with educational applications and AI-enhanced learning tools. Over 20 publications in SIGCSE (computer science education conference) Multiple publications in top theoretical venues (SIAM, STOC, SODA) Long-standing collaborations with researchers like Daniel Stefankovic (19 papers), Leslie Ann Goldberg (13 papers), and Zack Butler (10 papers) As an educator and mentor, Bezáková has guided several students including Angelina Brilliantova, Hannah Miller, and Wenbo Sun. Her work on feedback tools for theoretical CS courses demonstrates commitment to improving student learning experiences. She leads research at the intersection of theoretical computer science and educational innovation, developing methods that help students grasp complex concepts through engaging activities.

• Machine Learning
• Big Data
• Computational Linguistics
• +۴ مورد دیگر

Jakob Andersen is a Researcher at the Department of Computer Science, Faculty of Engineering and Computer Science, Hamburg University of Applied Sciences. His work focuses on machine learning, big data, and computational linguistics, with specific emphasis on text classification, uncertainty sampling, and human-in-the-loop systems. His recent publications explore real-time active learning, sustainable text classification, and edge device reliability. He contributes to open-source frameworks like Forum 4.0 and MINTFIT, focusing on user comment analysis and computer science education. Andersen is a member of the Association for Computational Linguistics (ACL) and has published extensively in top-tier conferences like ACL-IJCNLP, EACL, COLING, and IEEE BigData Congress since 2016.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Xue Yang is a Professor in the Department of Computer Science and Engineering at Shanghai Jiao Tong University's School of Electronic Information and Electrical Engineering. With an extensive publication record spanning from 2023 to 2026, Dr. Yang has established herself as a prominent researcher in multiple interdisciplinary fields at the intersection of computer science and practical applications. Dr. Yang's research interests span a wide spectrum of cutting-edge computational fields including Artificial Intelligence , Machine Learning , Quantum Computing , Image Processing , and Healthcare Applications of AI . Her work demonstrates a remarkable ability to bridge theoretical computer science with practical applications across diverse domains such as biomedical informatics, oceanography, and e-commerce. The interdisciplinary nature of her research is evident in publications that combine computer vision with medical applications, quantum computing with image processing, and machine learning with environmental monitoring. An analysis of Dr. Yang's recent publications reveals a strong focus on developing novel algorithms and computational frameworks that address complex real-world problems. Her work shows particular strength in generative models , feature learning architectures , and data-driven optimization approaches. The consistent publication output across multiple high-impact venues demonstrates sustained research productivity and influence in her fields of expertise. Exponential stability analysis for nonlinear systems Advanced machine learning architectures for biomedical applications Quantum-classical hybrid computing models Blockchain applications for network security Computer vision techniques for specialized imaging scenarios

• Quantum Key Distribution
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Dr. Yijun Wang is a researcher affiliated with the Chinese Academy of Sciences, State Key Laboratory on Integrated Optoelectronics, and Institute of Semiconductors in Beijing, China. His work spans multiple disciplines including quantum key distribution, brain-computer interfaces, and machine learning applications in cybersecurity and biomedical imaging. Key Research Areas: Quantum cryptography, Neural signal processing, Transformer-based models, Network security, and Digital health systems. Recent publications highlight advancements in EEG authentication systems 2026 , radar-based activity recognition 2025 , and quantum communication security 2025 . His work demonstrates interdisciplinary expertise bridging theoretical algorithms and practical engineering solutions. Scientific contributions include: Developing attention mechanisms for image restoration 2025 Creating reliability-enhanced BCIs via graph-driven fusion 2025 Designing semi-supervised solvers for CAPTCHA breaking 2021 Dr. Wang's collaborations appear across journals like Pattern Recognition , IEEE Transactions , and conferences including AAAI 2025 , ICLR 2025 , and EMBC 2023 . Current projects integrate LLMs with digital phenotyping for hypertension management 2025 and explore self-taught reasoning mechanisms 2025 .

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Jiawei Zhang is an active researcher with primary affiliations at institutions like the University of California, Davis and other universities in China and the USA. His work spans Computer Science , Artificial Intelligence , and Biomedical Engineering , focusing on applications in image processing , autonomous vehicles , and remote sensing . Research interests include deep learning , graph neural networks , 3D reconstruction , and medical image analysis . Recent publications emphasize transformer models , lightweight AI frameworks , and multimodal data fusion . His work has been published in prestigious venues such as IEEE Transactions on Biomedical Engineering , CVPR , and IEEE Internet of Things Journal . Collaborations include researchers from institutions in China, the US, and Europe. Key trends in his 2024-2025 articles involve industrial anomaly detection , liver tumor segmentation , autonomous vehicle control , and AI-driven sensor networks . Methodologies often integrate attention mechanisms , generative models , and real-time optimization .

• Relational Machine Learning
• Graph Neural Networks
• Complex Networks
• +۴ مورد دیگر

Dr. Rebekka Burkholz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. Her work bridges relational machine learning with biological applications , focusing on theoretical improvements to deep learning through complex network analysis. Education: Studied Mathematics and Physics at TU Darmstadt, followed by a PhD at ETH Risk Center under Frank Schweitzer and Hans J. Herrmann. Postdoctoral Experience: Worked at Harvard's Biostatistics Department (2019-2021) and ETH Zurich's Machine Learning Institute (2017-2019). Her research explores graph neural networks , sparse training , and biologically informed models , aiming to enhance algorithm robustness and interpretability. Recent publications analyze: 2025 : Implicit sparsification, hyperbolic regularization, and network balance techniques. 2024 : Spectral pruning, gene regulatory modeling, and dynamic rescaling for GNNs. Scientific recognition includes: Zurich Dissertation Prize for systemic risk research (2016). CSF Best Contribution Award for maize trade analysis (2016). She collaborates with institutions like Harvard T.H. Chan School of Public Health and ETH Zurich, focusing on trustworthy information processing and empirical security at CISPA.

• Algorithmic Game Theory
• Stochastic Games
• Distributed Systems
• +۵ مورد دیگر

Hugo Gimbert is a Researcher at the LaBRI (Laboratoire Bordelais de Recherche en Informatique) within the Méthodes Formelles Team . His work bridges computational theory and practical applications, focusing on algorithmic game theory, stochastic processes, and formal methods in computer science. Research interests include: Decidability problems in distributed and stochastic games Partial observation in multi-player systems Cooperation and competition in bridging scenarios Social choice theory implementation Precision farming automation Probabilistic logics and humanoid robot control via MDPs Advising roles: PhD advisor for Soumyajit Paul, Edon Kelmendi, Quentin Rouxel, and Ludovic Hofer PhD co-advisor for Simon Mauras Former advisor for Youssouf Oualhadj Key projects: Stamina - Stabilisation monoids and regular language analysis Marmotte - CNRS committee digitalization platform Rhoban System - Real-time robotics for agriculture and soccer Parcoursup - Algorithmic college admission system for French government

Yixin Caoمشاهده پروفایل

دانشیار

• Algorithmic Graph Theory
• Structural Graph Theory
• Fine-grained Complexity
• +۸ مورد دیگر

Yixin Cao is an Associate Professor in the Department of Computing at Hong Kong Polytechnic University, Faculty of Engineering. He conducts research in theoretical computer science with a focus on algorithmic and structural graph theory, combinatorial optimization, and their applications. He leads a research group and is actively involved in the theoretical computer science community through publications, talks, and conference organization. Research Interests: Algorithmic and structural graph theory, particularly on interval and circular-arc graphs Fine-grained complexity and parameterized algorithms Graph modification problems (editing, deletion, completion) Combinatorial optimization with applications in bioinformatics and social networks His recent publications highlight a strong trend in characterizing complex graph classes (e.g., proper Helly circular-arc graphs, chordal circular-arc graphs) and developing efficient parameterized algorithms, especially kernelization techniques for graph editing problems. He frequently publishes in top venues such as Algorithmica, Theoretical Computer Science, ICALP, ESA, and MFCS. Grants: PI, "(Parameterized) complexity of graph edge editing problems", NSFC, 01/2024–12/2027 PI, "Approximation algorithms for phylogenetic networks", RGC, 01/2021–12/2023 PI, "Algorithmic study on chordal and related graphs", NSFC, 01/2020–12/2023 PI, "Super-polynomial approximation of graph problems", RGC, 01/2018–12/2020 Multiple earlier grants from RGC, NSFC, and NLSDE on graph algorithms and complexity Research Group: Advises graduate students including Ying Xu, Haowei Chen, and Shenghua Wang Hosts visiting researchers such as Tomasz Krawczyk (Warsaw University of Technology) Collaborates extensively with researchers in China and internationally

Fahad Panolanمشاهده پروفایل

مدرس

• Parameterized Algorithms and Complexity
• Graph Theory and Graph Algorithms
• Approximation Algorithms
• +۷ مورد دیگر

Fahad Panolan is a Lecturer in the Algorithms and Complexity group at the School of Computing, University of Leeds, UK, a position he has held since August 2023. Prior to this, he was an Assistant Professor in the Department of Computer Science and Engineering at IIT Hyderabad, India, from July 2019 to August 2023. He conducted postdoctoral research at the Department of Informatics, University of Bergen, Norway, between 2016 and 2019. Education: PhD in Theoretical Computer Science, The Institute of Mathematical Sciences, HBNI, Chennai, India (2012–2015) MSc in Theoretical Computer Science, The Institute of Mathematical Sciences, HBNI, Chennai, India (2010–2012) Master of Computer Applications, National Institute of Technology, Calicut, India (2006–2009) BSc in Physics, DGM MES Mampad College, University of Calicut, India (2002–2005) His research lies at the intersection of theoretical computer science and algorithm design, with core interests in Parameterized Algorithms and Complexity , Graph Theory , Approximation Algorithms , and Streaming Algorithms . He has made significant contributions to kernelization, matroid-based techniques, and the development of subexponential-time algorithms for NP-hard graph problems. His work often bridges structural graph theory with algorithmic efficiency, particularly on sparse and geometric graphs. The recent publications show a strong trend in advancing the frontiers of fixed-parameter tractability, including efficient kernelization, approximation schemes for matrix problems, and reconfiguration algorithms. His work frequently appears in top-tier venues such as STOC, SODA, ICALP, and journals like JACM and Algorithmica. Scientific Service: PC Member: WALCOM 2026, IPEC 2023, ESA 2022, IPEC 2021, AAAI 2021 Scientific Coordinator: Parameterized Complexity 201 Workshop Referee for journals including TALG, SIDMA, TCS, Algorithmica, and conferences like STOC, SODA, ICALP, ESA. Fahad Panolan advises PhD students and interns, including Shubhada Suresh Aute and Seshikanth Varma. He has secured research grants through collaborative projects and has delivered invited talks at international workshops and seminars, including Dagstuhl, DIMAP, and Parameterized Complexity workshops. He has taught courses such as Algorithms, Design and Analysis of Algorithms, and Parameterized Algorithms at both University of Leeds and IIT Hyderabad. He is actively involved in the parameterized complexity and algorithms research community, organizing workshops and contributing to the theoretical foundations of efficient computation on hard problems.