جست‌وجوی استاد و پژوهشگر

Lorenzo Grassiمشاهده پروفایل

پژوهشگر

• Symmetric Cryptography
• Cryptanalysis
• Hash Functions
• +۵ مورد دیگر

Lorenzo Grassi is a postdoctoral researcher in symmetric cryptography at Ruhr University Bochum's Cluster of Excellence CASA and a consultant at Ponos Technology. He holds a PhD in Mathematics from Graz University of Technology (2019) and a degree in Mathematics from the University of Milano. Funded by the CASA Jump.Start Program for Postdocs since 2023, he focuses on cryptography for future applications, including quantum-resistant protocols and MPC-/FHE-/ZK-friendly schemes. Education: University of Milano (Mathematics), Graz University of Technology (PhD in Mathematics) Positions: Ruhr University Bochum (CASA Cluster), Ponos Technology (Consultant), Radboud University Nijmegen (2020-2023) His research spans symmetric encryption, hash function design, algebraic attacks, and cryptanalysis of modern cryptographic primitives. Recent work includes Gröbner basis attacks, side-channel secure implementations, and quantum cryptanalysis. Key trends in his publications include the development of zero-knowledge proof-friendly hash functions (e.g., Poseidon2), structural analysis of symmetric primitives (e.g., Subspace Trails, Feistel networks), and optimization of cryptographic schemes for MPC and FHE applications. Quantum cryptanalysis and truncated differential attacks also feature prominently. Scientific Awards: ERC Starting Grant 2024 Grassi contributes to advancing cryptographic security through innovative designs like Monolith and Reinforced Concrete, while also analyzing vulnerabilities in schemes like AES and Grendel. His work bridges theoretical analysis with practical implementations, emphasizing efficiency and resistance to emerging threats.

• Protein Structure Prediction
• Protein Interactions
• Drug Design
• +۴ مورد دیگر

Yuedong Yang is a Professor at the School of Data and Computer Science, Sun Yat-sen University since August 2017. His academic journey includes a Research Assistant Professor position at Indiana University (2011-2013) and Research Fellow at Griffith University's Institute for Glycomics (2013-2017), following postdoctoral training at Indiana University School of Medicine (2006-2011). His primary research focuses on Protein Structure Prediction and Refinement , Protein Interactions with biomolecules and ligands, and Classification of Human Genetic Variations . His work bridges computational biology, machine learning, and structural bioinformatics, with emphasis on developing AI-driven methods for biomolecular analysis. Recent publications demonstrate strong integration of geometric deep learning, language models, and physics-based approaches. Analysis of his 15 most recent publications (2024-2025) reveals dominant trends in spatial transcriptomics , protein structure-function prediction , foundation models for genomics , and interpretable AI for drug discovery . His work increasingly combines multimodal data integration with causal learning frameworks. Over 50 publications in top journals including PNAS, Genome Biology, Nucleic Acids Research, and Bioinformatics Active community service as Associate Editor for BMC Bioinformatics Extensive peer review activity across 46 publications/grants in 35+ journals His research is supported by multiple major grants including National Natural Science Foundation of China (61772566), National Health and Medical Research Council (1121629), and Australian Research Council (LP150100137). He has advised numerous researchers through collaborative projects in computational biology and bioinformatics. Dr. Yang leads research in protein informatics and multi-omics integration, with current projects focusing on geometric deep learning for biomolecular structure prediction and foundation models for single-cell genomics. His lab maintains strong international collaborations between China, USA, and Australia.

Lars Krecklauمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Procedural Modeling
• Real-Time Rendering
• +۳ مورد دیگر

Dr. Lars Krecklau is a researcher at the Department of Computer Science , RWTH Aachen University . He specializes in procedural modeling, real-time rendering, and urban reconstruction. His work includes algorithms for efficient 3D visualization of cityscapes and interactive modeling tools for non-programmers. Research Focus : Procedural modeling, real-time rendering, urban visualization Key Contributions : Procedural facade rendering, historical city interpolation, interconnected structure modeling His publications focus on GPU-accelerated procedural graphics, temporal interpolation of historical data, and intuitive 3D modeling interfaces. He has contributed to frameworks like Houdini and developed techniques for memory-efficient rendering. Email : krecklau@informatik.rwth-aachen.de

• Psychological Assessment
• Personality Psychology
• Intelligence Research
• +۷ مورد دیگر

Matthias Ziegler is a Professor of Psychological Diagnostics at the Humboldt University of Berlin, Faculty of Life Sciences. He has held a W3 professorship at the Institute of Psychology since April 2012, following a junior professorship at the same institution from 2008-2010 and again 2011-2012. His academic career includes positions at the Ludwig-Maximilians-Universität München where he worked as a research assistant and held substitute professorships. Professor Ziegler's work spans psychological assessment, personality psychology, intelligence research, and test development. Dr. Ziegler pursued his academic education with studies at the University of Marburg, culminating in a doctorate from the University of Munich under Prof. Dr. Markus Bühner on situational demand and its impact on personality questionnaire validity. He completed his habilitation at the University of Munich with mentors including Prof. Dr. Markus Bühner, Prof. Dr. Josef Zihl, and Prof. Dr. Del Paulhus, focusing on the assessment of personality and its interaction with intelligence in predicting academic performance. Professor Ziegler's research interests center on psychological assessment methodologies, personality psychology (particularly the Dark Triad/Tetrad constructs), intelligence research, situational perception, and vocational interests. His work explores the dynamic interplay between personality traits and situations, the measurement of domain-specific knowledge, and the development of robust psychological assessment tools. He has made significant contributions to understanding how personality traits interact with cognitive abilities to predict academic and professional outcomes. His recent publications demonstrate a consistent focus on innovative assessment approaches, with particular attention to situational perception, personality-state dynamics, and cross-cultural measurement issues. Ziegler's work bridges theoretical personality research with practical assessment applications, often incorporating advanced statistical techniques and multi-method approaches to examine complex person-situation interactions across diverse contexts. Editor in chief of European Journal of Psychological Assessment (2013-2016) Holds DIN 33430 license (professional qualification in psychological assessment) Member of German Diagnostic- and Test Committee Board member of Interdisciplinary Center for Educational Research Member of DGPs, EAPA, EAPP, ISSID professional societies Professor Ziegler has served on numerous editorial boards including European Journal of Psychological Assessment, Personality and Individual Differences, and Journal of Intelligence. His methodological expertise is evident in his extensive reviewer work for over 30 psychological journals. His research program demonstrates strong continuity in developing and validating assessment instruments while exploring the complex relationships between personality, intelligence, and situational factors in predicting meaningful life outcomes.

Artem Sokolovمشاهده پروفایل

استاد

• Machine Translation
• Natural Language Processing
• Structured Prediction
• +۴ مورد دیگر

Artem Sokolov serves as an Honorary Professor in the Department of Computational Linguistics at Heidelberg University and as a Research Scientist at Google Berlin. His primary research focuses on machine translation and structured prediction within natural language processing. Previously, he held positions at Amazon, the Statistical NLP Group at Heidelberg University led by Prof. Stefan Riezler, LIMSI, and Orange Labs in France, contributing to advancements in statistical and neural machine translation systems. He earned his PhD in Computer Science and Artificial Intelligence from the IRTCITS research center in Kyiv. His doctoral thesis investigated randomized algorithms for locality-sensitive embeddings of the Levenstein edit distance, establishing foundational work for efficient string similarity search in computational linguistics and intrusion detection systems. Dr. Sokolov's research expertise spans machine translation, imitation learning, bandit algorithms, and weakly supervised learning. He has pioneered methods for learning from partial feedback in structured prediction tasks, particularly addressing exposure bias in sequence generation and multi-facet evaluation of translation systems. His work bridges theoretical machine learning with practical NLP applications, emphasizing robustness against noisy data and scalable optimization techniques for real-world deployment. Analysis of his recent publications reveals trends toward scalable influence functions for model interpretability, multi-attribute control in machine translation, and rigorous auditing of multilingual datasets. His research consistently intersects natural language processing, machine learning optimization, and data quality assessment, with increasing emphasis on ethical AI considerations and efficient learning from weak supervision signals. Scientific awards include: 1st place at ECML/PKDD Discovery Challenge 2010 (English quality task) 2nd place at ECML/PKDD Discovery Challenge 2010 (general task) 2nd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 3rd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 As co-Principal Investigator for the 2015-2017 grant "Weakly Supervised Learning of Cross-Lingual Systems", Dr. Sokolov developed techniques for learning cross-lingual rankings from weakly supervised data sources like patent citations and Wikipedia hyperlinks. He has mentored students through teaching advanced courses including Imitation Learning, Stochastic Learning, and Statistical Machine Translation at Heidelberg University, supervising seminar projects on structured prediction and optimization algorithms. Dr. Sokolov is an active member of the Statistical NLP Group at Heidelberg University and collaborates with research teams at Google Berlin. His current work focuses on advancing production-scale machine translation systems through scalable inverse reinforcement learning and robust training methodologies, building on his extensive background in both academic research and industrial applications.

• Theoretical Computer Science
• Database Theory
• Formal Logic
• +۵ مورد دیگر

Nicole Schweikardt is a Professor at the Institute of Computer Science within Humboldt University of Berlin . Her research focuses on Theoretical Computer Science , particularly in Database Theory , Formal Logic , and Algorithmic Meta-Theorems . Academic Rank: Professor Contact: schweikn@informatik.hu-berlin.de Research Interests : Nicole investigates logical characterizations of database query languages, algorithmic meta-theorems for sparse graphs, and efficient enumeration techniques. Her work bridges formal logic, computational complexity, and practical database systems. Scientific Awards : 2018 ACM PODS Alberto O. Mendelzon Test-of-Time Award Recent Article Trends : Nicole's recent publications emphasize schema matching , spanner evaluation , first-order logic extensions , and query enumeration . Her work spans theoretical foundations (e.g., counting quantifiers, Hanf normal forms) and practical applications (e.g., event stream analysis, document compression).

• Probability Theory
• Machine Learning
• Stochastic Optimization
• +۴ مورد دیگر

Stephan Eckstein is a junior professor in the Department of Mathematics at the University of Tübingen and a member of the university's machine learning cluster. His research bridges probability theory and machine learning with particular focus on stochastic optimization and numerical approximation. Research interests include: Optimal transport theory and its computational aspects Regularization techniques for high-dimensional problems Causal models and probabilistic structures Graphical models in machine learning Graph neural networks Recent publications analyze dimensional stability in optimal transport, exponential convergence rates for Sinkhorn algorithms, and causal modeling in financial time series generation. Contact: stephan.eckstein@uni-tuebingen.de

• Cryptography
• Computational Complexity
• Pseudorandomness
• +۵ مورد دیگر

Andrej Bogdanov is a Professor in the Department of Computer Science at the Weizmann Institute of Science's Faculty of Mathematics and Computer Science. With a prolific publication record spanning over two decades from 2002 to 2025, he has established himself as a leading researcher in theoretical computer science and cryptography. His research interests span multiple areas of theoretical computer science, with a particular focus on cryptography, computational complexity, pseudorandomness, and secret sharing. His work often bridges theoretical foundations with practical cryptographic applications, exploring the mathematical underpinnings of secure computation and cryptographic primitives. His research has evolved to address contemporary challenges in quantum computing security and machine learning evaluation. Bogdanov's publication record shows consistent contributions to top-tier conferences including FOCS, STOC, CRYPTO, TCC, and ITCS. His work demonstrates deep theoretical insights while maintaining relevance to practical cryptographic applications. Recent publications indicate expanding interests into quantum computing security and machine learning evaluation frameworks. Bogdanov has collaborated extensively with leading researchers in theoretical computer science, most notably with Alon Rosen (31 joint publications), as well as Siyao Guo, Yuval Ishai, and Chin Ho Lee. His collaborative work spans multiple institutions and reflects the interdisciplinary nature of modern theoretical computer science research. His academic contributions include foundational work on pseudorandom generators, secret sharing schemes, hardness amplification, and more recently, contributions to post-quantum cryptography and quantum security. His research has been supported by multiple grants that have enabled his team to explore the theoretical boundaries of cryptographic security.

• Distributed Computing
• Communication Complexity
• Quantum Computing
• +۴ مورد دیگر

Rotem Oshman is affiliated with Tel-Aviv University, Israel , where he contributes to research in theoretical computer science, focusing on distributed computing, communication complexity, and quantum algorithms. His work addresses fundamental challenges in network protocols, concurrent data structures, and cryptographic verification.

Timothy Berkelbachمشاهده پروفایل

دانشیار

• Quantum Chemistry
• Materials Science
• Computational Physics
• +۶ مورد دیگر

Timothy Berkelbach is an Associate Professor in the Department of Chemistry at Columbia University and a Research Scientist at the Flatiron Institute's Center for Computational Quantum Physics. He holds a B.A. from New York University (2009) and a Ph.D. from Columbia University (2014), followed by a postdoctoral fellowship at Princeton University (2014–2016). His research focuses on developing and applying computational methods in quantum chemistry and materials science, particularly excited states and spectroscopy. He has received prestigious awards including the AFOSR Young Investigator Award, Sloan Fellowship, NSF CAREER Award, and PECASE. His work bridges theoretical chemistry and computational physics, with applications to plasmons, excitons, and electronic structure in materials. Berkelbach’s contributions include advancements in coupled-cluster theory, GW/BSE methods, and software like PySCF. His research explores phenomena such as anharmonic vibrations in clathrates, plasmon-exciton coupling, and superconductivity in cuprates. He also investigates quantum transport in organic crystals and catalytic effects of electric fields. Education: B.A., NYU (2009); Ph.D., Columbia University (2014) Labs/Teams: Center for Computational Quantum Physics (Flatiron Institute), Columbia Chemistry Department Grants: NSF CAREER Award, AFOSR funding

Luciano Spinelloمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Robotics
• SLAM (Simultaneous Localization and Mapping)
• +۳ مورد دیگر

Luciano Spinello is a Research Fellow affiliated with the University of Freiburg's Department of Computer Science, working within the AIS Lab led by Prof. W. Burgard. Previously, he held roles at Amazon Research (Seattle), ETH Zurich (PhD under Prof. Roland Siegwart), and EPFL Lausanne as a research assistant. His research focuses on the intersection of computer vision and robotics, specializing in robot perception, SLAM, and autonomous systems. He has contributed to projects involving RGB-D data processing, terrain classification, and socially-aware navigation algorithms. Education: PhD in Computer Science from ETH Zurich (2009), Electrical Engineering degree from Rome, Italy. Academic activities include organizing workshops (RSS 2014, IROS 2012), serving on program committees for robotics conferences, and editorial roles (IROS associate editor). His work emphasizes multimodal sensing, object detection in 3D environments, and robust localization across dynamic conditions. Key technical contributions include methods for RGB-D fusion, adaptive domain adaptation, and large-scale place recognition. His research bridges theoretical advancements with practical applications in autonomous robotics, including navigation systems and human-robot interaction protocols.

• Coding Theory
• Telecommunications
• Signal Processing
• +۴ مورد دیگر

Dr. Vladimir Sidorenko is a Senior Researcher at the Institute for Communications Engineering, Technical University of Munich (TUM), since January 2015, on leave from the Institute for Information Transmission Problems (IITP), Russian Academy of Sciences. His academic career includes roles at Ulm University (2003–2014) and the Computer Center of the Russian Health Ministry (1975–1983). He holds an M.S. in Electrical Engineering (1972) and a Ph.D. in Mathematics (1975) from the Moscow Institute for Physics and Technology. Research Interests: Coding theory, telecommunications, signal processing, cryptology, and applications. His work focuses on error correction, network security, quantum computing, and cryptographic protocols. He has authored over 140 papers in these areas. Recent Research Trends: Dr. Sidorenko’s recent work emphasizes quantum stabilizer codes decoding , network coding security , and PUF-based key agreement systems . His publications in 2023–2024 highlight advancements in polymorphic Gabidulin codes , data protection in networks , and minimal trellis structures for quantum codes . Awards: Recipient of the In-centi Award for Best Lecturing (Ulm University, 2003–2014). Collaborations: Invited researcher at institutions including Lund University (Sweden), Darmstadt TU (Germany), and Southwest Jiaotong University (China). Active in projects like Secure Key Agreement with PUFs and Concatenated Convolutional Codes .

• Graph Theory
• Combinatorics
• Algorithmic Problem Solving
• +۲ مورد دیگر

Felix Joos leads the Theoretical Computer Science and Discrete Mathematics research group at Heidelberg University. His research examines graphs and hypergraphs through algorithmic, extremal, and structural lenses, incorporating probability theory methods. Currently serving as Vice Dean of the Faculty for Mathematics and Computer Science. Key research areas: Combinatorial optimization Random graph structures Discrete probability applications Structural graph theory Significant recognition includes: European Prize in Combinatorics (2023) Lauterschläger Prize for Young Researchers (2020) DFG Emmy Noether grant (2019) Prior academic positions include professorships at Hamburg University and postdoctoral research at University of Birmingham. Research contributions advance fundamental combinatorial methods with applications in computer science and discrete mathematics.

• Geospatial Data Management
• Distributed Computing
• Machine Learning
• +۴ مورد دیگر

Prof. Werner Martin is a Professor of Big Geospatial Data Management at the Technical University of Munich (TUM), affiliated with the School of Engineering and Design and the Department of Aerospace and Geodesy. His research focuses on georeferenced data processing, distributed computing, quantum algorithms, and machine learning applications in geospatial contexts. Prof. Martin holds a doctorate from LMU Munich and has held academic and research positions at institutions including LMU Munich, Leibniz-University Hannover, the German Aerospace Center (DLR), and UniBW Munich. His work bridges theoretical advancements with practical applications in spatial data analysis, visualization, and high-performance computing. Awards ACM SIGSPATIAL GIS Certificate of Appreciation (2019) 1st place ACM SIGSPATIAL GIS Cup (2015) IPIN Best Paper Award (2014) His recent publications emphasize geospatial AI, quantum computing for data processing, and environmental monitoring systems. Collaborations with industry partners like DLR highlight his focus on real-world geospatial challenges.

• Numerical Analysis
• Computational Fluid Dynamics
• Partial Differential Equations
• +۳ مورد دیگر

Aleksey Sikstel is a part-time faculty member at RWTH Aachen University , holding the title of Vertr.-Prof. (Part-Time Lecturer). His research focuses on advanced numerical methods for hyperbolic conservation laws, including Discontinuous Galerkin schemes, stochastic Galerkin formulations, and error estimation techniques. Key research areas: Numerical Analysis, Computational Fluid Dynamics, and Applied Mathematics Primary affiliation: Faculty 10 Institutions at RWTH Aachen University Contact: aleksey.sikstel@rwth-aachen.de His work emphasizes coupling hyperbolic systems, entropy-stable discretizations, and adaptive grid methods. Recent publications analyze Baer-Nunziato-type models, multiresolution strategies, and boundary control for hyperbolic equations.