جست‌وجوی استاد و پژوهشگر

• Mathematical Biology
• Epidemiology
• Dynamic Systems
• +۶ مورد دیگر

Prof. Dr. Johannes Müller is an Associate Professor of Applied Mathematics in Ecology and Medicine at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Mathematics . His research bridges mathematics and life sciences, focusing on dynamic systems, stochastic processes, and their applications in biology and medicine. He holds a doctorate and habilitation from the University of Tübingen and has held academic positions at institutions like the Helmholtz Zentrum München. Research Interests: Mathematical modeling of infectious diseases, including vaccination strategies and control measures. Ecological population dynamics and bacterial communication (quorum sensing). Evolutionary dynamics under fluctuating environments and stochastic processes. Applications in public health, social sciences, and SDG projects in Sub-Saharan Africa. Key Achievements: Recipient of the Erwin Schrödinger Prize (2007) . Author of influential books such as Methods and Models in Mathematical Biology (Springer, 2015) and Cellular Automata – Theory and Applications (Springer, 2017). Lead researcher in projects like CoNeMEsSA , fostering African mathematical epidemiology through DAAD support. Teaching: Active in courses on mathematical epidemiology, biomathematics, and modeling, with a focus on interdisciplinary applications.

Nils Thuereyمشاهده پروفایل

استاد

• Computer Graphics
• Fluid Dynamics
• Machine Learning
• +۳ مورد دیگر

Prof. Nils Thuerey is a Professor of Physically Based Simulation at the Technical University of Munich (TUM), within the TUM School of Computation, Information and Technology. He specializes in computer graphics, fluid dynamics, and machine learning applications in physics-based simulations. His work focuses on developing algorithms for fluid simulations, turbulence modeling, and differentiable physics solvers. He earned his doctorate in fluid simulations from the University of Erlangen-Nuremberg (2006) and held positions at ETH Zurich, ScanlineVFX, before joining TUM in 2013. Research Interests: Fluid Dynamics and Turbulence Modeling Machine Learning for Physics Simulations Deep Learning in Computer Graphics Differentiable Simulations and Inverse Problems Key Awards: Technical Oscar (2012) for contributions to fluid effects in films/games ERC Starting Grant (2014) for 'realFlow' research University of Erlangen Staedtler Graduation Award (2008) His recent work emphasizes integrating deep learning with physical models, such as differentiable solvers for fluid dynamics and probabilistic methods for weather forecasting. He leads projects like WeatherBench and develops tools like φ-ML and phiflow for scientific computing.

Ludwig Ferdinandمشاهده پروفایل

استاد

• Architectural Design
• Baubotanik (Plant-based Construction)
• Urban Green Infrastructure
• +۳ مورد دیگر

Prof. Ludwig Ferdinand is a Professor of Green Technologies in Landscape Architecture at the Technical University of Munich (TUM), within the TUM School of Engineering and Design. His research integrates botanical principles into architectural design, focusing on climate adaptation, growth dynamics, and ecological sustainability. He earned his doctorate from the University of Stuttgart with a thesis on botanical design principles and pioneered the field of Baubotanik (plant-based construction). Prof. Ferdinand co-founded the Office for Living Architecture (OLA), applying his research to urban planning and landscape projects. His work emphasizes interdisciplinary approaches, blending computational design with ecological strategies. Notable contributions include the development of tools like GroTree for urban tree management and studies on living root bridges. He has received prestigious awards such as the Federal Ecodesign Award (2017) and the Prize for Courageous Science (2016). His publications span topics like urban microclimate optimization, machine learning in tree growth prediction, and vertical greening systems. Current projects explore 'blue-green architecture' synergies and adaptive urban infrastructure. Awards include the Federal Ecodesign Award (2017), Prize for Courageous Science (2016), University of Stuttgart Prize (2013), and Day After Tomorrow Maker Award (2012). His advisory and teaching roles at TUM emphasize design-build workflows and sustainable densification strategies.

• Social Network Analysis
• Natural Language Processing
• Data Science
• +۶ مورد دیگر

Prof. Dr. Jana Diesner holds the Human-Centered Computing professorship at the TUM School of Social Sciences and Technology, Technical University of Munich. Her research focuses on computational social science, network analysis, and ethical data practices. She explores topics such as gender bias in AI, crisis communication networks, and the societal impact of scientific research. Her work bridges computer science and social sciences, addressing challenges in natural language processing, data ethics, and interdisciplinary collaboration. Notable areas include analyzing coauthorship networks, detecting biases in large language models, and improving disaster response through network data. Recent publications highlight her contributions to understanding structural balance in signed networks, cross-lingual alignment for LLM evaluation, and the societal impact of documentaries. She emphasizes methodological rigor in computational impact detection and ethical considerations in data usage.

Stefan Weltgeمشاهده پروفایل

استاد

• Combinatorial Optimization
• Integer Programming
• Polyhedral Combinatorics
• +۳ مورد دیگر

Prof. Dr. Stefan Weltge is a Professor of Discrete Mathematics at the Department of Mathematics, Technical University of Munich (TUM). He holds a PhD in Mathematics from Otto von Guericke University Magdeburg (2016) and was a postdoctoral researcher at ETH Zurich. His research focuses on combinatorial optimization, integer programming, and polyhedral combinatorics, with notable contributions to extension complexity and mixed-integer programming. He has received multiple teaching awards at TUM, including the TUM Supervisory Award (2022) and Best Lecturer recognitions in 2019 and 2021/22. His work has been published in leading journals such as the Journal of the ACM and Journal of Combinatorial Theory B. Prof. Weltge’s academic contributions include groundbreaking research on the complexity of mixed-integer programs, polyhedral representations, and combinatorial optimization problems. He has organized conferences like OR 2024 and the Cargese Workshops on Combinatorial Optimization, and serves on program committees for IPCO, MIP, and ISCO. His research is supported by DFG grants, including the Individual Grant (NextGen) and the AdONE PhD Program. He advises PhD students working on topics such as integer programming, algorithm design, and optimization theory. His publications span theoretical advancements in convex optimization, linear programming relaxations, and applications in logistics and operations research. Notable articles include work on bounded subdeterminants in integer programs and the complexity of stable set problems. His research bridges discrete mathematics with practical algorithmic solutions, influencing both theoretical and applied domains.

Mahalakshmi Sabanayagamمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning Theory
• Graph-based Learning
• +۳ مورد دیگر

Mahalakshmi Sabanayagam is a Researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Theoretical Foundations of Artificial Intelligence. Her research focuses on the theory of deep learning and graph-based learning, with notable contributions to adversarial robustness, neural network certification, and graph representation learning. She currently serves as a Teaching Assistant for the course 'Gems of Informatics 3 (IN2176): Modelling and analysis of real-world graphs.' Her work bridges theoretical foundations and practical applications, including analysis of neural tangent kernels in graph networks, robustness certification against adversarial attacks, and cross-domain machine learning techniques. Key research directions include understanding decision boundaries via Hessian analysis, tensorized autoencoder architectures, and graphon-based network clustering. Publications span topics from adversarial machine learning to interdisciplinary applications in physics and finance. While currently no awards or grants are listed, her active publication record reflects ongoing contributions to artificial intelligence theory and graph-based methodologies. She is part of Prof. Debarghya Ghoshdastidar's research group at TUM's Faculty of Computer Science. Lab affiliations include the Theoretical Foundations of Artificial Intelligence (I7) group, focusing on rigorous mathematical analysis of AI systems and their real-world graph applications. No advising relationships or student collaborations are explicitly documented in available materials.

Krasimira Tsaneva-Atanasovaمشاهده پروفایل

استاد

• Biomathematics
• Dynamical Systems
• Data Driven Modelling
• +۶ مورد دیگر

Krasimira Tsaneva-Atanasova is a Professor of Mathematics for Healthcare at the University of Exeter, affiliated with the Living Systems Institute and the Department of Mathematics. She holds a joint appointment as Associate Dean for International and Development within the College of Engineering, Mathematics and Physical Sciences. Her research focuses on applying mathematical models to healthcare challenges, including personalized medicine, clinical decision-making, and understanding biological systems like hormone signaling and neuronal dynamics. Her academic journey includes a MSc from the University of Plovdiv (Bulgaria) and a PhD from the University of Auckland (New Zealand). She has held prestigious roles such as Guest Professor at Huazhong University of Science and Technology (China) and Honorary Senior Research Fellow at the University of Bristol. Key awards include the EPSRC Hub Directorship and Fellowship with the Institute of Mathematics and its Applications. Research interests span biomathematics, dynamical systems, and data-driven modeling, with applications to cellular processes (calcium dynamics, hormone signaling) and movement science. She has pioneered interdisciplinary work bridging mathematics, neuroscience, and clinical healthcare. Recent publications explore topics like seizure dynamics, hormone regulation, and mathematical models of cardiac excitability. Awards highlight her leadership in advancing quantitative approaches to healthcare challenges. She leads collaborative projects such as the EPSRC Hub, fostering innovation in healthcare technologies and disease modeling.

Robert Königمشاهده پروفایل

دانشیار

• Quantum Information Theory
• Quantum Computation
• Quantum Cryptography
• +۳ مورد دیگر

Robert König is an Associate Professor at the Technical University of Munich (TUM), leading the Theory of Complex Quantum Systems group within the Department of Mathematics. He is affiliated with the TUM Institute for Advanced Study (TUM-IAS) and focuses on quantum information theory, quantum computation, and their applications to many-body physics. His work explores the fundamental limits of quantum information processing and novel quantum algorithms. Education & Positions: PhD in Applied Mathematics and Theoretical Physics, University of Cambridge (2007) Postdoctoral positions at Caltech's Institute for Quantum Information and IBM's TJ Watson Research Center Assistant Professor at the University of Waterloo (2012–2014) Joined TUM as a Rudolf Mößbauer Tenure Track Professor in 2015, promoted to Associate Professor in 2020 Research Interests: König investigates quantum communication, quantum cryptography, and fault-tolerant quantum computing. His studies include quantum advantage, error-correcting codes, and the interplay between quantum systems and condensed matter physics. His work bridges theoretical foundations and practical applications, such as optimizing quantum algorithms and enhancing quantum hardware resilience. Key Contributions: Proven quantum computational advantage in shallow circuits Developed error-detection protocols for bosonic systems Advanced theoretical frameworks for quantum channel capacities Contributed to the mathematical foundations of quantum many-body systems Awards: NSERC Discovery Grant (2013) Swiss National Science Foundation Fellowship (2010) Smith/Rayleigh-Knight Prize (University of Cambridge, 2006) ETH Medal and Willi-Studer Prize (2003) Labs & Collaborations: König's research group collaborates with institutions globally, focusing on quantum information theory, topological quantum computing, and hybrid quantum-classical algorithms. His work is supported by TUM-IAS and international grants.

Frank Hopfgartnerمشاهده پروفایل

استاد

• Information Retrieval
• Gamification in Education
• Machine Learning
• +۵ مورد دیگر

Prof. Frank Hopfgartner is a Professor at the University of Koblenz's Institute of Computer Science, Department 4. His research focuses on information retrieval, gamification in education, machine learning ethics, digital preservation, and user modeling. He leads projects involving AI-driven healthcare triage systems, bias-aware search engines, and participatory web archiving initiatives. His academic work bridges technical innovation with societal impact, addressing challenges in pandemic response systems, generational engagement in education, and ethical AI development. Key areas of contribution include lifelog data retrieval, knowledge graph alignment, and misinformation analysis during crises. Notable ongoing projects include: FAIR clustering algorithms for ethical machine learning Privacy-preserving email archive exploration Design of gamified IR systems for Gen Z learners He advises external doctoral student Dr. Matthias Lohr and collaborates extensively on international evaluation frameworks for information systems (e.g., NTCIR, CLEF). His research group maintains strong ties with institutions like National Museums Liverpool and NHS data networks.

Patrick Delfmannمشاهده پروفایل

استاد

• Business Process Management
• Process Mining
• Predictive Process Monitoring
• +۵ مورد دیگر

Prof. Dr. Patrick Delfmann is a University Professor at the Department of Computer Science (FB4) of the University of Koblenz, leading the Process Science research group. His roles include Research Dean of the Department and chairman of the Institute for Business and Administrative Information Systems. He holds a Dr. rer. pol. from the University of Münster (2006) and has held academic positions since 2002, including senior academic councillor roles and acting professorships before his current position since 2017. His research focuses on technological aspects of business process management, including process mining, predictive process monitoring, and ontology-based process engineering. Current projects include AI-DPA (funded by Rhineland-Palatinate) and DFG-funded MIB (declarative process models). Methodological foundations include algorithmic graph theory, computational linguistics, and quantum machine learning. Key achievements include the 2024 Best Paper Award at ICPM’s PODS4H workshop for process-oriented cancer data analysis. He advises on interdisciplinary theses requiring strong algorithmic and modeling skills, and collaborates with industry partners to ensure practical applicability of research outcomes. Education: PhD in Business Administration (2006), University of Münster; earlier roles as research assistant (2001–2013). Grants: DFG MIB Project (2023–), RLP AI-DPA Research College (2023–). Labs/Teams: Process Science Group develops tools like declare-js and ProPoneRe, focusing on predictive modeling and process compliance.

Christian Thiesمشاهده پروفایل

استاد

• Medical Information Systems
• Telemedicine Infrastructure
• Medical Image Retrieval
• +۳ مورد دیگر

Prof. Dr. Christian Thies is a Professor at Reutlingen University's Faculty of Informatics, specializing in Medical Information Systems. He co-founded the Medizinisch-Technische Informatik (Medical Technical Informatics) bachelor's program and contributes to the Human-Centered Computing master's program. His research focuses on telemedicine infrastructure, medical imaging systems, and clinical data integration, particularly through projects like the aRTeMIS framework and the bwHealthApp platform. He is a key member of the Computerassistierte Medizin (CAmed) research group at Reutlingen Research Institute. Education & Career: Thies has held roles at ixmid GmbH (2009-2012), Forschungszentrum Jülich (2006-2008), and RWTH Aachen University (2001-2006), focusing on medical software development, image processing, and clinical systems. His career spans over 25 years in healthcare IT, including freelance software development since 1996. Research Focus: His work combines clinical informatics with technological innovation, emphasizing interoperability in healthcare systems, telemedicine implementation, and personalized medicine platforms. Notable projects include the TeleDerm teledermatology trial and infrastructure for practice-based research networks (e.g., FoPraNet-BW). Articles Trends: Recent publications emphasize telehealth infrastructure, clinical trial methodologies, and wearable health monitoring systems. Earlier work concentrated on medical image retrieval algorithms and hierarchical data processing frameworks. Grants & Labs: Active in developing lab infrastructure such as the Ambient Assisting Lab and Future Mobility Lab. No specific grant details listed, but his projects indicate sustained institutional and collaborative funding.

Antje Krauseمشاهده پروفایل

استاد

• Bioinformatics
• Computational Molecular Biology
• Genomics
• +۵ مورد دیگر

Prof. Antje Krause is the current President of TH Bingen since November 2021. She has held academic leadership roles including Vice President for Research (2012-2015) and Professor of Bioinformatics at TH Bingen since 2007. Her professional journey includes prior roles as Deputy/Temporary Professor at TH Wildau and research positions at the Max Planck Institute for Molecular Genetics and the German Cancer Research Center. Education: Dr.rer.nat. (PhD) in Natural Sciences from University of Bielefeld (2002) Studies in Computer Science in Natural Sciences at University of Bielefeld (1990-1996) Research Focus: Bioinformatics with emphasis on computational molecular biology, protein sequence analysis, genome-wide studies, and bioinformatics database development. Key projects include SYSTERS protein family database and contributions to understanding vertebrate genome evolution. Recent work involves AI-driven immunology research and applied bioinformatics education programs. Projects & Grants: 2021: Dual degree program in Applied Bioinformatics funded by Rhineland-Palatinate 2020: AI-based immunology research for SARS-CoV-2 funded by MWWK 2017: MethGen (DNA methylation studies with Mainz University Medical Center) 2011: BINGS project on next-gen sequencing Advisory Roles: Member of Rhineland-Palatinate Biotechnology Advisory Board, University Evaluation Network Southwest Board, and multiple university councils. Active in shaping bioinformatics education and regional innovation policy. Labs/Initiatives: Involved in Hermann Hoepke Institute for cooperative research projects and the Biotechnology Academy RLP. Leads innovative teaching initiatives like MinION sequencing integration in curricula.

Paul Seiferthمشاهده پروفایل

پژوهشگر

• Computational Geometry
• Algorithms
• Data Structures
• +۳ مورد دیگر

Paul Seiferth is affiliated with the Department of Computer Science at Freie Universität Berlin. His research focuses on computational geometry, algorithms, data structures, and graph theory. He has contributed to topics like Voronoi diagrams, dynamic planar graphs, unit disk routing, and spanner construction. His work emphasizes algorithmic efficiency, time-space trade-offs, and geometric data structures. Education : PhD (2012–2016), Freie Universität Berlin Master's (2010–2012), Freie Universität Berlin Research Interests : His research addresses fundamental problems in computational geometry and graph theory, with applications to wireless networks and geometric algorithms. Key themes include dynamic data structures for proximity problems (e.g., Voronoi diagrams), routing in unit disk graphs, and efficient spanner constructions for directed transmission graphs. Key Contributions : His work on time-space trade-offs for Voronoi diagrams and dynamic planar Voronoi diagrams has advanced algorithmic techniques for geometric problems. He also developed efficient routing schemes for unit disk graphs and reachability oracles for transmission graphs, balancing theoretical guarantees with practical efficiency. Teaching : He teaches ProInformatik I: Logik und Diskrete Mathematik . Labs/Teams : Associated with the AG Theoretische Informatik research group at Freie Universität Berlin.

Thomas Kalinowskiمشاهده پروفایل

استاد

• Applied Mathematics
• Optimization
• Multi-Agent Systems
• +۲ مورد دیگر

Thomas Kalinowski is a Professor at the University of Rostock, affiliated with the Faculty of Mathematics and Natural Sciences and the Institute of Mathematics's Optimization group. He teaches courses like 'Mathematics 2 for ET and INF - Linear Algebra' and 'Mathematical Foundations of Machine Learning.' Email: thomas.kalinowski@uni-rostock.de His research focuses on mathematical methods for multi-agent systems, funded by the European Regional Development Fund (ERDF) from 2025 to 2027. He works in optimization, network theory, and applied mathematics, with teaching responsibilities bridging linear algebra and machine learning. He is involved in projects related to the analysis and optimization of multi-agent systems, indicating expertise in mathematical modeling of complex networks and algorithmic game theory. Current affiliations include the Optimization research group at the Institute of Mathematics, University of Rostock.

• Visualization
• Virtual Reality
• Augmented Reality
• +۵ مورد دیگر

Steffen Koch is a Senior Lecturer at the Institute for Visualization and Interactive Systems (VIS), University of Stuttgart. His work bridges visualization, virtual reality, and digital humanities, focusing on tools for analyzing text, spatiotemporal data, and cultural datasets. He collaborates extensively with institutions like the Visualisierungsinstitut der Universität Stuttgart and IEEE. Research Interests : Visualization techniques, virtual/augmented reality applications, digital humanities, text analytics, and human-in-the-loop systems. Key Publications : 2024 ChoreoVis (dance formation analysis), 2024 ViSCitR (hotel review comparison), 2023 Animated Transitions (small-scale visualization design), 2021 Sparse Spherical K-Means (document clustering), and 2016 VA 2 (evaluation methodology). Techniques Developed : Triangulation of eye-tracking and interaction data, pyramid tag maps for document exploration, multifocus navigation in text analysis, and real-time social media monitoring tools. His recent work trends emphasize human-centric visualization , combining machine learning with interactive interfaces for complex tasks in cultural analysis, social media monitoring, and temporal data exploration. While no explicit awards are listed, his prolific output in top venues like IEEE Transactions and Computer Graphics Forum indicates recognition in visualization and digital humanities communities. He frequently collaborates with Thomas Ertl, Johannes Knittel, and Markus John, spanning applications from religious history to cinematic storytelling.