جست‌وجوی استاد و پژوهشگر

• 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.

Torsten Mützeمشاهده پروفایل

استاد

• Discrete Mathematics
• Theoretical Computer Science
• Graph Theory
• +۵ مورد دیگر

Torsten Mütze is a Professor at the Institute of Mathematics at the University of Kassel. Previously, he held positions as an Assistant Professor at the University of Warwick (2019–2024) and was affiliated with Charles University Prague's Department of Theoretical Computer Science and Mathematical Logic. His academic journey includes a postdoc under Martin Skutella at TU Berlin, research stays at Georgia Tech and ETH Zurich, and a software engineering role at Supercomputing Systems Zurich. Education: PhD in 2011 from ETH Zurich under Angelika Steger's supervision Master's and Bachelor's degrees in related fields Research Interests: Focuses on discrete mathematics and theoretical computer science, including combinatorial algorithms, graph theory, computational geometry, order theory, Ramsey theory, and combinatorial games. His work bridges foundational theory and real-world applications, particularly in algorithm design and combinatorial generation. Advising & Grants: Supervised students: Francesco Verciani, Nastaran Behrooznia, Namrata, Arturo Merino, Frieder Smolny, Karl Däubel, Jerri Nummenpalo, and Ondřej Mička Funded projects: DFG Heisenberg grant 522790373, Chancellor's International Scholarships, and EU funding Labs & Collaborations: Organizes workshops like 'Combinatorics, Algorithms and Geometry' and contributes to research networks such as the 'Combinatorial Optimization and Graph Algorithms' group. His work involves collaborations with institutions globally, including ETH Zurich, Georgia Tech, and TU Berlin.

• Structural Reliability Engineering
• Power System Resilience
• Seismic Risk Assessment
• +۳ مورد دیگر

Qiang Xie is a Professor in the Department of Electrical Engineering at Zhejiang University's College of Engineering. With an active research career spanning over two decades, Dr. Xie has established himself as a leading researcher in structural reliability engineering with significant contributions to power system resilience and seismic risk assessment. Dr. Xie's research interests center on structural reliability engineering with particular focus on power infrastructure resilience. His work bridges civil and electrical engineering domains, developing innovative frameworks for seismic risk analysis of electrical substations, post-earthquake power system restoration, and wind-related risk assessment for transmission systems. In recent years, he has expanded his research into medical imaging applications, particularly in gastrointestinal endoscopy assistance systems and ultrasound bone imaging. Analysis of Dr. Xie's recent publications (2021-2026) reveals a strategic expansion of research scope while maintaining core expertise in reliability engineering. The earliest works focused on wireless sensor networks and computer science applications, while the 2018-2021 period saw a strong emphasis on medical imaging and bioinformatics. Since 2022, there's been a pronounced return to power system resilience with increasingly sophisticated methodologies incorporating Bayesian networks, copula models, and multi-strategy frameworks. This evolution demonstrates both depth in core competencies and adaptability to emerging interdisciplinary opportunities. Multiple publications in Reliability Engineering & System Safety (6 papers 2022-2026) Contributions to medical imaging in IEEE Transactions and Frontiers journals Collaborative work with international researchers across engineering and medical domains Dr. Xie maintains active collaborations across multiple institutions, as evidenced by his diverse co-authorship patterns. His research demonstrates strong translational potential, particularly in critical infrastructure protection and medical diagnostic technologies. While specific grant information isn't available in the publication record, the consistent output and diverse funding sources implied by publication venues suggest sustained research support.

• Causal Inference
• Machine Learning
• Social Media Analysis
• +۳ مورد دیگر

Prof. Drew Dimmery is the Professor of Data Science for the Common Good at the Hertie School's Data Science Lab. He holds a PhD in Politics from New York University (2016), with a focus on causal inference and machine learning. Prior to joining Hertie, he served as Scientific Coordinator at the University of Vienna’s Data Science Research Network (2021–2023) and worked on Facebook’s Core Data Science team (2016–2020), developing experimentation tools and methods. His research emphasizes methodological innovation in causal inference and machine learning, particularly applied to social media and internet studies. Key areas include experimental design, algorithmic transparency, and policy applications of data science. He has published in top-tier journals like ICML, KDD, and Science, and his work bridges computational methods with societal challenges such as electoral integrity, digital governance, and public welfare provision. Recent publications analyze the impact of social media platforms on elections, user behavior, and mental health, leveraging large-scale experimentation and causal analysis. His endowed professorship, supported by the Dieter Schwarz Foundation, includes scholarships for the Master of Data Science for Public Policy program, reflecting his commitment to applying data science for societal benefit.

Alex Blackمشاهده پروفایل

پژوهشگر

• Algebraic Combinatorics
• Geometric Combinatorics
• Optimization
• +۳ مورد دیگر

Alex Black is currently a Hermann Weyl Instructor at ETH Zurich, focusing on algebraic and geometric combinatorics and optimization. He will join Bowdoin College as a tenure-track assistant professor in the mathematics department starting Fall 2025. His research emphasizes the simplex method and its applications in optimization and polytope theory. Black’s research explores the interplay between algebraic and geometric combinatorics, with a particular focus on optimization and the simplex method. His work addresses topics such as polytopes, matroid theory, and pivot rules, aiming to advance understanding in discrete and computational geometry. He investigates issues like exponential lower bounds for pivot rules, monotone paths on polytopes, and the structure of flag polymatroids. INFORMS 2022 George Nicholson Paper Competition Finalist for Small Shadows of Lattice Polytopes No advising roles or grants are explicitly mentioned in the provided information.

• Numerical Linear Algebra
• Differential-Algebraic Equations
• Control Theory
• +۳ مورد دیگر

Prof. Tatjana Stykel is a Professor of Computational Mathematics at the University of Augsburg, affiliated with the Faculty of Mathematics, Natural Sciences, and Materials Engineering. She holds positions in the Institute of Mathematics and leads the Computational Mathematics research group. Her academic journey includes a PhD from Technische Universität Berlin (2002) and a Habilitation (2009), both in numerical mathematics. Prior to her current role, she led a junior research group at TU Berlin’s MATHEON Research Center and held visiting professorships at TU Berlin and Humboldt-Universität zu Berlin. Stykel’s research focuses on Numerical Linear Algebra (e.g., generalized eigenvalue problems, matrix equations), Differential-Algebraic Equations (numerical methods and stability), and Control Theory (descriptor systems, model reduction). She has pioneered energy-adaptive optimization methods and Riemannian geometry techniques for complex systems, including applications in quantum chemistry and electromagnetism. Her awards include the Richard von Mises Prize (2007), Leslie Fox Prize (2003), and multiple fellowships from the Pacific Institute for the Mathematical Sciences and Soros programs. She has led major projects like the EU-funded EU-MORNET (2014–2018) and contributed to BMBF networks in model reduction for coupled systems. Stykel teaches advanced numerical methods, including courses on differential equations, optimization, and model reduction. Her work emphasizes interdisciplinary collaboration, with partnerships in robotics, semiconductor technology, and fluid dynamics. She advises several students, including alumni who have become prominent in applied mathematics and engineering.

• Geometry
• Mathematical Physics
• Complex Systems
• +۴ مورد دیگر

Jürgen Jost is an Honorary Professor in the Department of Mathematics at the University of Leipzig and a retired Director at the Max Planck Institute for Mathematics in the Sciences. He is a member of multiple prestigious academies including the Leopoldina and the Santa Fe Institute for the Sciences of Complexity. His research focuses on geometric analysis, complex systems, network science, and applications in chemistry and neuroscience. Key areas include Ricci curvature in networks, Dirac-harmonic maps, and the evolution of chemical knowledge systems. Jost has received major awards such as the Leibniz Prize (1993) and an ERC Advanced Grant (2010). His work bridges pure mathematics with interdisciplinary applications, including topological data analysis and mathematical biology. He leads the Max Planck School of Cognition and has contributed to foundational studies on minimal surfaces, geometric flows, and information geometry.

• Computer Science
• Bioinformatics
• Remote Sensing
• +۷ مورد دیگر

Ying Liang is a multidisciplinary researcher with significant contributions across computer science, bioinformatics, and remote sensing. Their work spans AI-driven medical diagnostics, environmental monitoring, and computational biology, with recent focus on precision nutrition, skin lesion recognition, and urban heat island analysis. Research Interests : Machine learning for biomedical applications (e.g., skin cancer detection, pancreatic segmentation), remote sensing (landslide mapping, urban structure dynamics), and computational biology (RNA-protein interactions, microRNA regulation). Notable Article Trends : 2025 publications highlight self-supervised remote sensing classification, fractal reservoir modeling, and biomedical AI. 2024 works emphasize cross-domain gait recognition, panoptic segmentation, and health informatics via Wi-Fi sensing. Grants & Collaborations : Co-authored studies with institutions like Open University and Chinese universities, focusing on thermal cycling reliability, multi-omics data fusion, and wireless health monitoring. Labs & Teams : Collaborated on interdisciplinary projects involving neural network architectures, hybrid AI models for environmental and biomedical applications, and biomedical signal processing systems.

Thomas Schierlمشاهده پروفایل

پژوهشگر

• Video Coding
• Wireless Communication Protocols
• Multimedia Systems
• +۳ مورد دیگر

Dr. Thomas Schierl is the Head of the Video Communication and Applications Department at Fraunhofer Heinrich Hertz Institute (HHI) in Berlin. Since 2010, he has led research groups in multimedia communications and video coding, co-developing key video coding standards such as H.264 SVC and HEVC. He currently heads the Video Coding & Analytics department since 2015, focusing on video compression, wireless transmission, and standardization. Education: Diplom-Ingenieur (Computer Engineering) from Berlin University of Technology, 2003 Dr.-Ing. in Electrical Engineering and Computer Science, Berlin University of Technology, 2010 Research interests span video over wireless networks, system integration of video codecs, and cellular network protocols. He contributed to MPEG-2 Transport Stream standards and co-authored IETF RFCs for video payload formats. In 2014, he received the Emmy Award for MPEG-2 Transport Stream development. Active in standardization bodies: JCT-VC, MPEG, IETF, 3GPP, and DVB. His work includes high-level syntax for HEVC parallelism and V2X resource pooling for 5G NR. Labs/Teams: Leads the Video Coding & Analytics team at HHI, specializing in cutting-edge video compression and communication technologies.

• IoT and Sensor Networks
• Quantum Computing
• RFID Systems
• +۴ مورد دیگر

Bartolomeo Montrucchio is a full Professor at the Polytechnic University of Turin's Department of Electrical, Computer and Biomedical Engineering. His research focuses on IoT systems, quantum computing applications, RFID technologies, and sensor networks. He leads interdisciplinary projects involving environmental monitoring, cybersecurity, and embedded systems design. Key research areas include: Development of low-cost air quality monitoring systems leveraging IoT and sensor fusion Quantum algorithm design for financial risk analysis and game theory applications RFID security protocols and anti-collision mechanisms for dense networks Energy-efficient image processing techniques for mobile displays Recent work highlights quantum computing's potential in vulnerability analysis and financial modeling, while maintaining strong contributions to sensor network reliability and environmental applications. His publications span prestigious journals like IEEE Internet of Things Journal and IEEE Transactions on Dependable and Secure Computing. Collaborations with industry partners and academic institutions emphasize practical implementation of theoretical advancements, particularly in smart city technologies and healthcare monitoring systems.

Chinmoy Bhattacharjeeمشاهده پروفایل

استاد

• Stochastic Geometry
• Distributional Approximations
• Stein's Method
• +۴ مورد دیگر

Prof. Chinmoy Bhattacharjee is a Professor of Mathematical Stochastics at the University of Hamburg's Department of Mathematics, part of the Faculty of Mathematics, Computer Science and Natural Sciences. His research focuses on stochastic geometry, distributional approximations via Stein's method, stochastic analysis, random graphs, continuum percolation, and nonparametric inference. He holds a PhD from the University of Southern California and previously held postdoctoral positions at the University of Luxembourg and University of Bern. His work is supported by the German Research Foundation (DFG) through Project No. 531540467. Education: PhD in Applied Mathematics, University of Southern California (2013–2018) M.Stat. and B.Stat. (Hons.), Indian Statistical Institute, Kolkata (2008–2013) Teaching: Recent courses include Stochastic Processes, Probability and Statistics, and seminars on percolation theory and probabilistic methods. Research Highlights: Prof. Bhattacharjee's work bridges stochastic geometry and Stein's method, with applications in spatial random structures, non-Gaussian limit theory, and noise sensitivity in percolation models. Key contributions include Gaussian and Dickman approximation techniques, spectral analysis of Poisson functionals, and statistical applications in random forests. Upcoming Events: Co-organizing session on Spatial Stochastics at German Probability and Statistics Days (2025) Invited talks at Aarhus University and workshops in Germany/Croatia (2025)

Wolfgang Mulzerمشاهده پروفایل

استاد

• Theoretical Computer Science
• Computational Geometry
• Algorithms
• +۳ مورد دیگر

Wolfgang Mulzer is a Professor in the Department of Computer Science at Freie Universität Berlin, within the Faculty of Mathematics and Computer Science. He leads the Theoretical Computer Science research group (AG Theoretische Informatik) and holds a PhD from Princeton University (2010), advised by Bernard Chazelle. His research focuses on computational geometry, algorithms, and discrete mathematics, with notable contributions to geometric algorithms, data structures, and combinatorial optimization. Education: PhD in Computer Science, Princeton University (2010) Advisor: Bernard Chazelle Research Interests: Mulzer's work spans theoretical computer science with an emphasis on computational geometry. Key areas include geometric algorithms (e.g., Voronoi diagrams, Fréchet distance), dynamic graph algorithms (e.g., disk graphs, connectivity problems), and algorithmic complexity. His research also addresses discrete geometry challenges like Tverberg theorems and geometric intersection graphs. Grants & Labs: Leading the Theoretical Computer Science group at FU Berlin Active in collaborative projects on geometric algorithms and algorithmic foundations Professional Activities: Mulzer has authored or co-authored over 100 peer-reviewed papers, with work appearing in top venues like Discrete & Computational Geometry , SIAM Journal on Computing , and Algorithmica . His research bridges theoretical foundations with practical algorithm design.

Ullrich Kötheمشاهده پروفایل

دانشیار

• Machine Learning
• Neural Networks
• Explainable AI
• +۶ مورد دیگر

Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .

• Computational Geometry
• Discrete Geometry
• Convex Optimization
• +۶ مورد دیگر

Francisco Criado is a researcher with significant contributions to computational geometry, discrete mathematics, and applied algorithm design. His work spans theoretical and applied domains, including convex optimization, Voronoi diagrams, and geometric modeling in gas dynamics and earthquake decision-making frameworks. Collaborators : Francisco Santos, Sebastian Pokutta, Michael Joswig Key Venues : Discrete & Computational Geometry , Foundations of Computational Mathematics , NeurIPS Research Interests focus on geometric algorithms, polyhedral complexes, and applications of fuzzy logic. His 2025 papers explore convex hulls, zonotopes, and tropical geometry, while earlier works address gas dynamics and decision-making models. Article Trends reveal expertise in high-dimensional geometry, combinatorial optimization, and numerical methods. He frequently employs randomized and linear convergence algorithms in 2022–2025. Advising and Grants : No explicit data provided, but his extensive co-authorship network suggests collaborative research leadership.