جست‌وجوی استاد و پژوهشگر

Marcelo Arenasمشاهده پروفایل

استاد

• Data Management
• Semantic Web
• Database Theory
• +۳ مورد دیگر

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

Ansgar Scherpمشاهده پروفایل

استاد

• Data Science
• Big Data Analytics
• Text Analytics
• +۷ مورد دیگر

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

• Tensor Decompositions
• Quantum Computing
• Fredholm Integral Equations
• +۷ مورد دیگر

Patrick Gelß is a Postdoctoral Researcher at Zuse Institute Berlin, working in the AI in Society, Science, and Technology department. He leads the iol.QUANT research group focused on quantum-inspired and tensor-based methods, with affiliations to Freie Universität Berlin and collaborative projects like MATH+ Cluster of Excellence. Diploma in Mathematics/Physics (2013, Freie Universität Berlin) PhD in Mathematics (2017, summa cum laude, Freie Universität Berlin) Postdoctoral roles at Zuse Institute Berlin (2020-present) and CRC 1114 (2017-2022) His research bridges tensor decompositions, quantum computing, and dynamical systems, with notable contributions to: Graph Isomorphism: Continuous optimization approaches via doubly stochastic matrices and Frank-Wolfe algorithms Quantum Simulation: Tensor network representations for quantum circuits and the open-source WaveTrain package KvN Mechanics: Mathematical analysis of existence/uniqueness solutions for bounded domains Fredholm Networks: Novel training paradigms for neural networks using integral equations Scientific achievements include: Developing Scikit-TT for tensor-train computations Organizing major workshops (QML@SC2024, TMQS 2024) Leading the Thematic Einstein Semester 2024 on Mathematics for Quantum Technologies His work spans chemical kinetics, quantum dynamics, and quantum machine learning, with applications to CO oxidation models, exciton-phonon systems, and mutational hierarchies in medicine.

Andreas Ingerlمشاهده پروفایل

مدرس

• New Media Design
• Applied Media Art
• Audiovisual Media
• +۲ مورد دیگر

Andreas Ingerl serves as a University Lecturer within the Department of Design and Culture at HTW Berlin - University of Applied Sciences, primarily affiliated with the Communication Design program. He holds key administrative roles including Department Council member, Deputy Chair of the Selection Committee, Chair of the Examination Board, and BAföG Representative, while managing the Physical and Ubiquitous Computing laboratory from his office at Wilhelminenhof Campus WH Building A, 419. His research centers on the intersection of technology and artistic expression, specializing in audiovisual media, new media design, and applied media art. He actively explores creative computing methodologies and ubiquitous computing applications to transform physical environments into interactive experiences, with strong emphasis on virtual reality systems, sensory design, and AI integration for immersive media installations that challenge conventional perception and interaction paradigms. Analysis of his recent publications reveals a cohesive trajectory toward experimental media forms and intelligent systems, exemplified by projects like CINEMIRACLE (public-space VR), Lucid Dream (consciousness exploration), and CFI – Craving Future Intelligence (AI-driven art). These works collectively demonstrate his commitment to bridging technical innovation with critical artistic inquiry, particularly in addressing societal implications of emerging media technologies and redefining educational frameworks through digital experimentation. As Laboratory Manager for Physical and Ubiquitous Computing, Ingerl directs a multidisciplinary research environment focused on developing novel interfaces for immersive storytelling and sensor network integration in urban contexts. The lab functions as a nexus for the Creative Computing research cluster, facilitating collaborations between design, art, and engineering disciplines to pioneer tangible media interactions that dissolve boundaries between digital and physical realms.

• Privacy-preserving machine learning
• Adversarial robustness
• Differential privacy
• +۵ مورد دیگر

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

• Phenomenology
• Intersubjectivity
• Online Sociality
• +۷ مورد دیگر

Lucy Osler is a Lecturer in the Department of Philosophy at Cardiff University's School of English, Communication and Philosophy. She completed her PhD at the University of Exeter in 2021 and has established herself as a significant contributor to contemporary philosophical discourse, particularly at the intersection of phenomenology, online sociality, and embodiment. Her research focuses on phenomenological approaches to intersubjectivity, online sociality, shared space, embodiment, perception, emotions, and psychopathology, as well as analytic philosophy of mind and 4E (embodied, embedded, extended, enactive) approaches to cognition and affectivity. Dr. Osler has made notable contributions to understanding how digital technologies shape social experiences, including empathy, belonging, and narrative formation in online contexts. Her recent publications reveal a strong focus on how digital environments transform social interactions, with particular attention to loneliness, empathy, and the phenomenology of social anxiety disorder. She has developed innovative frameworks for understanding how objects can embody loneliness through 'obsolete affordances' and how digital technologies function as 'narrative devices' that shape our self-understanding. Her work demonstrates how seemingly disembodied online interactions can still facilitate meaningful social connection and how environments—both physical and digital—never remain neutral in supporting certain bodies, behaviors, and emotions while suppressing others. Dr. Osler has co-edited the forthcoming volume For, Against, Together: Antagonistic Political Emotions with Thomas Szanto (Cambridge University Press) and is working on Phenomenology in Action for Researching Networked Learning Experiences . Her research has appeared in leading journals including Topoi , Philosophy, Psychiatry, and Psychology , Inquiry: An Interdisciplinary Journal of Philosophy , and Philosophy and Technology . Her work demonstrates significant interdisciplinary reach, connecting philosophy with psychology, cognitive science, and digital studies.

• Graph Theory
• Combinatorics
• Theoretical Computer Science
• +۳ مورد دیگر

Christian Elbracht is a Researcher in the Department of Mathematics within the Faculty of Mathematics, Informatics and Natural Sciences at the University of Hamburg. He is based in Geomatikum (Bundesstraße 55), Room 232, Hamburg, with office hours conducted online by appointment. His primary academic activities involve teaching exercise sessions for mathematics courses targeting computer science students and physicists. His research focuses intensely on advanced graph theory, particularly tangle theory, separation systems, and structural decompositions in both finite and infinite graphs. Key themes include tree structures, connectivity hierarchies, submodular systems, ubiquity conjectures, and combinatorial optimization. His work bridges theoretical mathematics with applications in clustering algorithms and data science, as evidenced by collaborations extending into machine learning domains. Christian's publication trends reveal a concentrated specialization in tangle-tree duality and structural graph theory since 2018, with significant contributions to abstract separation systems and infinite graph theory. His collaborative work frequently involves researchers like J. Kneip and M. Teegen, with publications appearing in journals such as the Journal of Combinatorial Theory and Advances in Combinatorics . While no scientific awards are publicly documented, his research output demonstrates consistent contributions to combinatorial theory. His teaching responsibilities include leading exercise sessions for foundational mathematics courses for computer science students (Mathematics I/II) and specialized topics like Graph Theory I and Optimization. Christian maintains active research collaborations across multiple institutions, particularly evident in his work on graph ubiquity theorems and clustering applications. His technical work centers on structural analysis of graphs, with implications for theoretical computer science and discrete mathematics.

• Computerized Tomography
• Radon Transform
• Mathematical Imaging
• +۴ مورد دیگر

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

Hannah Williamsمشاهده پروفایل

پژوهشگر

• Movement Ecology
• Bio-logging
• Animal Behavior
• +۵ مورد دیگر

Dr. Hannah Williams is a Movement Ecologist and Affiliated Scientist at the Max Planck Institute of Animal Behavior , where she investigates soaring bird behavior using bio-logging, UAVs, and simulation modeling. She is affiliated with the University of Konstanz and leads research within the Research Group Safi under the Department of Migration . Current Focus: Social eavesdropping in soaring birds, mapping airflow landscapes through conspecific behavior Previous Institutions: Swansea University (postdoc), University of Exeter (MSc), University of St Andrews (BSc) Research Interests center on movement ecology, animal behavior, and bio-logging. Her work explores how soaring birds use social and environmental cues for navigation, combining field data with computational modeling to decode dynamic movement strategies. Publications (2015-2019) demonstrate expertise in bio-logging technology, flight energetics, and social information theory. Key themes include thermal soaring mechanics, movement pattern recognition, and ecological data visualization tools.

Susanne Draheimمشاهده پروفایل

پژوهشگر

• Affective Computing
• Smart Systems
• Ambient Displays
• +۸ مورد دیگر

Dr. Susanne Draheim is the Managing Director of the Research and Transfer Center 'Smart Systems' at the Hamburg University of Applied Sciences , within the Faculty of Engineering and Computer Science and the Department of Computer Science . Her work bridges cutting-edge technology with ethical considerations, focusing on smart environments and interdisciplinary research. Managing Director, Research and Transfer Center 'Smart Systems' Department of Computer Science, Hamburg University of Applied Sciences Laboratory: 'Living Place' and 'Creative Space for Technical Innovations' Her research spans Affective Computing , Ambient Displays , Ethical AI , and Hybrid Work Technologies . She explores how technology integrates into human contexts, emphasizing ethics, small data challenges, and environmental awareness. Recent studies include the 'EmotionBike Project' and frameworks for long-term field research. Her work demonstrates a strong focus on Human-Computer Interaction in agile environments, leveraging Machine Learning for real-time health monitoring and stress detection. She investigates the sociotechnical dynamics of ambient displays in professional settings, aiming to enhance workplace efficiency and user-driven innovation. Key trends in her publications include Smart Systems for ethical agency, Environmental Monitoring via interactive interfaces, and Digital Ethics in academic labs. Her interdisciplinary approach merges Computer Science Education with Sustainability and Public Engagement strategies.

• Theoretical Computer Science
• Property Testing
• Approximation Algorithms
• +۸ مورد دیگر

Yuichi Yoshida is a Professor at the National Institute of Informatics (NII), affiliated with the Principles of Informatics Research Division. He also holds a concurrent position as Senior Researcher at Preferred Networks. His academic career at NII spans from Assistant Professor (2012–2015), Associate Professor (2015–2022), to full Professor since 2022. He serves as Vice Director of the Global Research Center for Big Data Mathematics at NII and has held advisory and research roles at Preferred Infrastructure and the Ministry of Education, Culture, Sports, Science and Technology (MEXT). Ph.D. in Informatics, Kyoto University, 2012 Master of Informatics, Kyoto University, 2009 Bachelor of Engineering, Kyoto University, 2007 His research focuses on theoretical computer science, particularly property testing , approximation algorithms , sublinear-time algorithms , and constraint satisfaction problems . He investigates how theoretical insights can be applied to real-world graphs, with recent emphasis on average sensitivity of algorithms and Lipschitz continuity in combinatorial optimization . His work bridges theory and practical scalability in large network analysis and machine learning. The most recent 15 publications (2022–2025) demonstrate a strong trend in algorithmic stability, spectral methods for hypergraphs, influence propagation in networks, and learning-augmented algorithms. Key venues include FOCS, SODA, ICALP, and NeurIPS, reflecting high impact in theoretical computer science and machine learning. Topics such as Lipschitz continuity, average sensitivity, spectral sparsification, and sublinear algorithms recur, indicating a cohesive research program on robust and efficient computation. Best Paper Award, AISTATS (2018) MEXT Commendation for Science and Technology – Young Scientists’ Prize (2017) Inoue Research Award for Young Scientists (2014) KDDI Foundation Award (2024) Funai Information Technology Award (2024) Yuichi Yoshida advises PhD students and hosts numerous postdocs, RAs, and international interns. He leads major research projects funded by JSPS and JST, including Grants-in-Aid for Scientific Research (S) and PRESTO programs. His service includes program committee roles for ICML, NeurIPS, SODA, and ICALP. He has co-authored a book on Property Testing and contributed to encyclopedias on big data technologies. He leads research teams on algorithm desensitization and large-scale graph algorithms, and has been program co-chair for GRADES-NDA'23. His lab fosters international collaboration, hosting interns from top universities worldwide.

• Precision Medicine
• Rare Disease Diagnosis
• Data Science
• +۷ مورد دیگر

Matthew Might is a Professor of Internal Medicine and Computer Science at the University of Alabama at Birmingham's Heersink School of Medicine, where he serves as Director of the Hugh Kaul Precision Medicine Institute and Co-Director of the Center for Precision Animal Modeling. He also holds a position as Senior Lecturer in the Department of Biomedical Informatics at Harvard Medical School. His career spans academia, government service at The White House (2016-2018), and industry as co-founder of Pairnomix, LLC and Q State Biosciences, Inc. Might's research program uniquely bridges computer science and precision medicine, born from his personal quest to diagnose his son Bertrand who was the first identified case of a novel genetic disorder. His work applies data science, artificial intelligence, machine learning, and formal reasoning to accelerate biomedical science in service of patients. His broader interests include oligonucleotide therapeutics, model organisms, drug screening, and medicinal chemistry. As Director of the Precision Medicine Institute, he oversees a comprehensive research portfolio that includes significant bench science targeted at tailoring therapeutics to individual patients. Analysis of Might's recent publications reveals a strong focus on rare genetic disorders, particularly NGLY1 deficiency, with extensive work on disease modeling using induced pluripotent stem cells (iPSCs). His research spans multiple disciplines including genomics, proteomics, neurology, and drug discovery, with a notable emphasis on the Undiagnosed Diseases Network. Recent work has also addressed the COVID-19 pandemic through computational drug discovery and investigations into viral pathogenesis. Throughout his career, Might has maintained a dual focus on theoretical computer science and practical medical applications. His early work included significant contributions to programming language theory, abstract interpretation, and control-flow analysis, which he has successfully translated into biomedical applications. He has been instrumental in developing computational approaches to rare disease diagnosis and matchmaking patients with similar conditions. Might has been actively involved with the Undiagnosed Diseases Network, contributing to numerous collaborative studies that have advanced understanding of rare genetic conditions. His work emphasizes patient-centered approaches to precision medicine, with projects focused on genomic healthcare empowerment and family communication of genetic results. He has also developed innovative computational methods for RNA-seq analysis and drug screening. His leadership extends to directing research teams working on multiple fronts of precision medicine, including laboratory-based investigations of genetic disorders, computational approaches to drug discovery, and patient matchmaking strategies for rare disease communities. Might's interdisciplinary approach has created bridges between computer science theory and practical clinical applications, demonstrating how computational methods can directly impact patient care for those with rare and undiagnosed conditions.

• Adult Education
• Digital Learning
• Educational Technology
• +۴ مورد دیگر

Lars Kilian is a Researcher at the German Institute for Adult Education - Leibniz Centre for Lifelong Learning (DIE) in Bonn, where he serves as Editor-in-Chief of wb-web, a portal for teachers of adult and continuing education. His academic career spans multiple German institutions with a focus on adult education research and digital learning methodologies. Dr. Kilian earned his doctorate (Dr. phil.) from the Technical University of Kaiserslautern, a Diploma in Educational Sciences with a focus on adult education and educational planning from the University of Erfurt, and a first state examination in primary school teaching for mathematics and art education from the Erfurt University of Education. His research interests center on teaching and learning with digital media in higher education settings, competence-oriented didactic approaches, and quality development/school development. His work explores how digital technologies transform pedagogical practices and institutional structures in adult education. He has particular expertise in e-learning frameworks, competence development models, and the integration of digital media into educational practice. Analysis of his recent publications reveals a strong trajectory toward digital transformation in adult education, with increasing focus on national education platforms, open educational resources (OER), artificial intelligence applications, and pandemic-driven shifts to remote learning. His work demonstrates a consistent concern with practical implementation challenges, user experiences, and the development of sustainable digital learning ecosystems within adult education contexts. Dr. Kilian serves in multiple leadership capacities including as Treasurer of the Society for Media in Science (GMW) since 2016, Member of the Steering Committee for Digitalization in Further Education for the Ministry of Culture and Science in North Rhine-Westphalia since 2019, and Advisory Board member for OERinfo/DIPF since 2023. His wb-web portal has become a significant resource for adult educators seeking professional development. He leads the Portals Team at DIE and contributes to multiple national initiatives focused on digitalization in adult education, including the National Education Platform development and Education for Sustainable Development (ESD) quality development. His collaborative work extends across institutional boundaries to connect research with practice in adult education settings.

Daniel Wachsmuthمشاهده پروفایل

استاد

• Optimal Control Theory
• Partial Differential Equations
• Nonsmooth Optimization
• +۴ مورد دیگر

Professor Daniel Wachsmuth holds the Chair of Mathematics VII (Optimal Control) at the University of Würzburg, where he has been a professor since 2012. He is affiliated with the Faculty of Mathematics and Computer Science and maintains his office in Building 30 (Mathematics West), Room 02.011. Prior to his current position, he completed his academic training with positions as a postdoc at RICAM in Linz, Austria (2008-2012) and as a research assistant at TU Berlin (2002-2008). Wachsmuth's research focuses on optimal control theory , particularly concerning partial differential equations, nonsmooth optimization problems, and regularization of problems with bang-bang control. His work bridges theoretical mathematical analysis with practical numerical methods for solving complex control problems. He has made significant contributions to the understanding of sparse optimization, topological derivatives, and non-convex optimization problems in function spaces. His recent publications demonstrate a strong trend toward addressing L 0 constraints in optimal control, developing advanced numerical methods for non-smooth problems, and exploring connections between optimal control theory and deep neural networks. His work spans both theoretical developments (such as second-order conditions and stability analysis) and practical algorithmic implementations for solving challenging optimization problems. Among his notable recognitions is the Dimitrie-Pompeiu Preis awarded in 2016. His research has been published in top-tier journals including SIAM Journal on Control and Optimization, Computational Optimization and Applications, and Inverse Problems. Current Position: Professor of Mathematics at University of Würzburg (since 2012) Previous Positions: Postdoc at RICAM, Linz (2008-2012); Research Assistant at TU Berlin (2002-2008) Contact: daniel.wachsmuth@uni-wuerzburg.de; +49 931 31-89071

Annette Kinderمشاهده پروفایل

استاد

• Cognitive Psychology
• Artificial Grammar Learning
• Numerical Processing
• +۳ مورد دیگر

Annette Kinder is a Professor of Psychology of Learning (W2) at the Department of Education and Psychology, Freie Universität Berlin. She has held this position since 2010, following a Heisenberg Fellowship (2004–2010) at the University of Potsdam and a substitute professorship at the same institution in 2008. Her academic journey includes roles as a university assistant at Philipps-Universität Marburg (1999–2004) and research associate in a DFG project on associative learning (1995–1999). She obtained her habilitation in psychology (specializing in general psychology) in 2003 and completed her PhD in 1996 at Philipps-Universität Marburg with a scholarship from Hessian Promotion for Junior Researchers. Research Interests: Cognitive Psychology Artificial Grammar Learning Numerical Processing Educational Diagnostics Machine Learning in Education Neurocognitive Poetics Scientific Awards: Heisenberg Fellowship (2004–2010) Teaching: She has taught courses like 'Pädagogische Diagnostik' (Educational Diagnostics) and research colloquia for qualifying theses since 2018/2019, focusing on cognitive and educational psychology.