جست‌وجوی استاد و پژوهشگر

• Algorithms
• Computational Geometry
• Discrete Mathematics
• +۸ مورد دیگر

Mordecai J Golin is a Professor in the Department of Computer Science and Engineering at The Hong Kong University of Science and Technology (HKUST), School of Engineering. His research lies at the intersection of theoretical computer science, algorithms, and discrete mathematics, with strong applications in information theory and computational geometry. His research interests include algorithms , computational geometry , data structures , dynamic programming , coding theory , and combinatorics . He has made significant contributions to the design and analysis of optimal search trees, prefix-free coding, and minmax regret optimization in dynamic flow networks. His work often combines probabilistic analysis with algorithmic efficiency. The recent publications highlight a sustained focus on optimization problems in graphs and trees, particularly in dynamic flow networks for applications like evacuation modeling, and in data compression via advanced Huffman and AIFV coding techniques. The research spans from theoretical foundations to algorithmic innovation, with recurring themes of efficiency, robustness, and structural analysis. Scientific Awards No specific awards mentioned in the provided text. Advising and Grants : While specific students and grants are not listed, Dr. Golin has an extensive record of collaborative research with colleagues at HKUST and internationally, suggesting active supervision and project leadership. His frequent publications in top-tier venues indicate sustained funding and research activity. Labs and Teams : Though not explicitly named, his work is likely conducted within theoretical computer science or algorithms research groups at HKUST, possibly associated with centers focusing on discrete mathematics or information sciences.

• Computational Algebra
• Finite Group Theory
• Combinatorics
• +۷ مورد دیگر

Alice Niemeyer is a Professor at the Algebra Teaching and Research Area of RWTH Aachen University , specializing in computational algebra, finite group theory, and interdisciplinary applications in civil engineering. She has co-authored over 30 publications in journals like PNAS Nexus , Linear Algebra and Its Applications , and International Journal of Solids and Structures , with recent work bridging abstract algebra and topological interlocking concrete systems. Research Interests : Finite groups, matrix decomposition algorithms, combinatorial design, and applications to sustainable construction. Key Collaborators : Cheryl Praeger, Tomasz Popiel, Wilhelm Plesken, Daniel Robertz, Reymond Akpanya. Notable Grants : Funding from the Simons Foundation for computational algebra research. Awards : No explicit awards mentioned in the provided text.

• Financial Mathematics
• Insurance Mathematics
• Model Uncertainty
• +۴ مورد دیگر

Prof. Dr. Katharina Oberpriller is a faculty member at the Department of Mathematics, University of Munich, working in the Financial and Insurance Mathematics research group. Her research focuses on model uncertainty, insurance risk markets, and credit risk modeling. She collaborates extensively on publications related to stochastic processes, affine models, and financial risk management.

Artur Czumajمشاهده پروفایل

استاد

• Theoretical Computer Science
• Randomized Algorithms
• Parallel and Distributed Computing
• +۴ مورد دیگر

Artur Czumaj is a Professor of Computer Science and Director of the Center for Discrete Mathematics and its Applications (DIMAP) at the University of Warwick, United Kingdom. He is a member of the Global Faculty at the University of Cologne and serves as President of the European Association for Theoretical Computer Science (EATCS). PhD and Habilitation from University of Paderborn Recipient of the IBM Award and Fellow of EATCS His research focuses on theoretical computer science, particularly in the design of randomized algorithms, analysis of large data, and applications to parallel/distributed computing, property testing, sublinear algorithms, optimization algorithms, and algorithmic game theory. His work has been funded by major institutions including EPSRC, NSF, Royal Society, European Union, Simons Foundation, and IBM. The 15 most recent publications reflect expertise in graph algorithms, distributed systems, approximation techniques, and algorithmic complexity. His research spans foundational algorithm design to practical applications in large-scale data processing. Fellow of the European Association for Theoretical Computer Science (EATCS) IBM Award recipient Czumaj has held leadership roles in prominent conferences, including Program Committee Chair for STOC, ICALP, SODA, and HALG. His academic career is supported by grants from multiple international research councils and organizations.

Göran Kauermannمشاهده پروفایل

استاد

• Nonparametric Models
• Semi- and nonparametric analysis
• Generalized Linear Models
• +۲ مورد دیگر

Prof. Dr. Göran Kauermann is a Full Professor of Statistics at the Ludwig-Maximilians-University Munich , holding the Chair of Applied Statistics in Social Sciences, Economics and Business . His research spans nonparametric models, generalized linear models, and network data analysis, with applications in economics, epidemiology, and data science. Education: Diplom in Economic Mathematics (1991, TU Berlin), PhD in Statistics (1994), Habilitation (Venia Legendi) in Statistics (2000) Kauermann’s research interests focus on penalized regression , network analysis , and statistical modeling in economics, social sciences, and public health. Recent work explores label uncertainty in machine learning , spatio-temporal conflict diffusion , and dynamic network models for economic and social data. Scientific trends in his publications include penalized splines for nonlinear modeling, network flow estimation in social and economic contexts, and label variation analysis in machine learning. His collaborations span climate zone classification , Covid-19 mortality modeling , and smart city parking analytics . Scientific Awards: Bruce Russett Award (2020) for political network research Leadership Roles: He served as Dean of the Faculty of Mathematics, Informatics and Statistics (2019–2021), Speaker of the Elite Master Program in Data Science (2016–2026), and Chair of the German Statistical Society (2005–2013). He also held editorial roles in journals like AStA Advances in Statistical Analysis and Statistical Modelling .

Felix Schneiderمشاهده پروفایل

پژوهشگر

• Structural Dynamics
• Uncertainty Quantification
• Bayesian Methods
• +۳ مورد دیگر

Felix Schneider is a Researcher at the Chair of Structural Mechanics at Technical University of Munich since 2018. He holds an M.Sc. in Civil Engineering from TU Munich (2018) and completed a semester abroad at the Norwegian University of Science and Technology. His research focuses on uncertainty analysis in structural and acoustic models, Bayesian updating techniques, and stochastic reduction methods in the frequency domain. He has contributed to advancements in rational polynomial chaos expansions and sparse Bayesian learning for structural dynamics applications. Education: 2011-2018: Study of Civil Engineering at Technical University of Munich 2018: Master of Science (M.Sc.) in Civil Engineering 2016: Semester abroad at Norwegian University of Science and Technology 2009: Abitur at Humboldtgymnasium Solingen Research emphasizes computational methods for structural dynamics, including: Bayesian parameter estimation for linear systems Rational surrogate models for uncertainty quantification Frequency-domain stochastic reduction techniques Applications in seismic risk assessment and vibroacoustic simulations Key contributions include a Maurer Söhne Prize for his Master's thesis and a published MATLAB toolbox for rational polynomial chaos expansions on the LRZ Git repository. Teaching responsibilities include courses on random vibrations, structural dynamics, and continuum mechanics. He has participated in research stays at ETH Zurich (2023) and collaborates with international institutions on uncertainty quantification projects.

Bernard Haasdonkمشاهده پروفایل

استاد

• Model Reduction
• Parametrized Partial Differential Equations (PDEs)
• Kernel Methods
• +۷ مورد دیگر

Bernard Haasdonk is a Professor at the University of Stuttgart, affiliated with the Institute of Applied Analysis and Numerical Simulation (IANS), part of the Faculty of Mathematics and Computer Science. His research focuses on model reduction techniques for parametrized partial differential equations (PDEs), kernel-based methods, numerical analysis, and machine learning applications in scientific computing. He leads a research group in numerical mathematics and has contributed to software tools like RBMatlab and KerMor. Affiliations: Institute of Applied Analysis and Numerical Simulation, University of Stuttgart Roles: Academic Researcher, Software Developer, Grant Principal Investigator His work bridges numerical simulation, machine learning, and reduced basis methods, addressing challenges in optimal control, fluid dynamics, and biomechanics. Haasdonk has held multiple funded projects, including those on kernel methods for model reduction and certified RB-ML-ROM surrogate models. Research Interests: Model reduction for PDEs, kernel methods, greedy algorithms, numerical analysis, optimal control, and applications in fluid dynamics and porous media. He emphasizes structure-preserving methods for Hamiltonian systems and data-driven approaches for surrogate modeling. Publications: Over 200 articles in journals like SIAM, BIT Numerical Mathematics, and Physica D, focusing on convergence analysis, kernel-based approximation, and reduced-order modeling. Recent trends include adaptive greedy algorithms, symplectic model reduction, and energy-conserving surrogates. Awards: IEEE PerCom 2017 Best Paper Award, Teaching Excellence Awards (2012-2017), and early-career research grants. Grants: DFG-funded projects on model reduction, SimTech Cluster contributions, and collaborations on fuel cells and biomechanics. Teams: Leads the Numerical Mathematics Research Group at IANS, collaborating with interdisciplinary teams on projects like MORCOS (Model Order Reduction of Coupled Systems) and KerMor (Kernel Methods for Model Reduction).

• Database Systems
• Information Retrieval
• Probabilistic Databases
• +۴ مورد دیگر

Martin Theobald is a Professor in the Department of Computer Science at the University of Luxembourg's Faculty of Science, Technology and Communications. Previously affiliated with University of Ulm, Germany, his research spans database systems, information retrieval, and knowledge extraction with over 120 publications since 2002. His work bridges theoretical database foundations with practical applications in large-scale data processing. His research focuses on: Probabilistic and uncertain database systems Stream processing frameworks (notably the AIR architecture) Knowledge extraction from heterogeneous data sources Integration of machine learning with database systems Efficient query processing for structured and semi-structured data Recent publications demonstrate an evolving research trajectory toward real-time data stream processing with machine learning integration. His work on the AIR (Asynchronous Iterative Routing) framework and its extensions (TensAIR, OPTWIN) addresses critical challenges in concept drift detection, neural network training on streaming data, and efficient resource utilization. These contributions sit at the intersection of database systems, distributed computing, and machine learning, with applications in knowledge graph construction and question answering systems. Martin Theobald has mentored numerous researchers including Mauro Dalle Lucca Tosi, Alessandro Temperoni, and Vinu E. Venugopal, who have become active contributors to the database community. His collaborative network spans institutions across Europe, with frequent partnerships with researchers from University of Ulm, Max Planck Institute, and other European universities. His laboratory work focuses on developing scalable systems for processing evolving data streams, with particular emphasis on creating lightweight architectures that maintain high performance while minimizing resource consumption. Current projects involve integrating knowledge graphs with real-time analytics and developing adaptive systems that can handle concept drift in streaming environments.

Saharon Rossetمشاهده پروفایل

استاد

• Statistics
• Data Science
• Machine Learning
• +۳ مورد دیگر

Saharon Rosset is a Professor and Chair in Modern Statistics and Data Science at the Department of Statistics and Operations Research, School of Mathematical Sciences, Tel Aviv University, since 2007. His expertise spans statistical learning theory, machine learning, and computational biology, with a focus on integrating statistical methods into biological and data science applications. Research Interests: Statistical Learning, Computational Biology, Genomics, Data Science Teaching: Statistics of Big Data, Statistical Genetics, Bootstrap Methods, Data Science His recent publications explore diverse applications including ATP production mechanisms, SARS-CoV-2 evolution, gene-environment interactions, and music structure analysis. He employs methodologies from high-dimensional statistics, random effects modeling, and multiple hypothesis testing. Scientific Awards : Best paper award at KDD-2011 KDD Cup 2007 Winner Best application paper award at KDD-02 He has mentored numerous PhD and MSc students, including Oren Yuval, Giora Simchoni, and Rajesh Karmakar. His collaborative work appears in journals like PNAS , Biometrics , and Journal of Machine Learning Research .

• Urban Climate Research
• Thermal Comfort
• Urban Air Quality
• +۶ مورد دیگر

Prof. Dr. Uwe Schlink is a leading Professor at the Institute of Meteorology, University of Leipzig, and Senior Researcher at the Department of Urban & Environmental Sociology, Helmholtz Centre for Environmental Research - UFZ. His work focuses on urban climate research , thermal comfort , urban air quality , and statistical modelling with Bayesian inference. He leads the working group on urban climate and personal exposure, bridging environmental science with societal resilience. Affiliation: University of Leipzig (since 2009) and UFZ (since 2013) Research Themes: Urban heat islands, personal exposure to environmental stressors, statistical climate models, and health impacts of air pollution His research spans environmental health , urban climatology , and resilient city planning , with significant contributions to understanding thermodynamic interactions between urban structures and climate. He has pioneered methods for high-resolution land surface temperature analysis and green infrastructure performance in mitigating heat stress. Recent publications (2023-2025) highlight his work on PM2.5-bound PAH exposure , anthropogenic heat impacts in Beijing, and Asian plateau climate dynamics . Collaborative projects address urban heat stress , green roofs , and health-focused urban planning .

• Innovation Management
• Technology Strategy
• Organizational Economics
• +۳ مورد دیگر

Alfonso Gambardella is a Professor of Corporate Management at Bocconi University in Milan, holding a PhD in Economics from Stanford University (1991). He serves as Department Editor of Business Strategy at Management Science and leads high-impact research in technology strategy and innovation management. His educational foundation at Stanford underpins decades of influential scholarship, including the widely cited MIT Press book Markets for Technology (co-authored with Arora and Fosfuri). Gambardella's research integrates theoretical models with empirical analysis to explore causal reasoning in organizational performance, theory-driven strategic decisions, and microfoundations of high-impact choices. He pioneers evidence-based approaches through randomized trials and large-scale replications in entrepreneurial contexts. Recent publications reveal a cohesive trajectory toward scientific innovation management, emphasizing experimental validation of strategic frameworks and decision-making processes. His work bridges organizational economics with practical business applications, particularly in technology-intensive sectors. ERC Advanced Grant (2020) for "A Scientific Approach to Innovation Management" Extensive participation in European Commission research projects Gambardella secures competitive funding through major grants including the ERC award and EU research initiatives. While student mentorship details are unreported, his leadership in multi-author projects (e.g., with Camuffo, Pignataro) indicates active research team coordination and junior scholar development.

André Uschmajewمشاهده پروفایل

استاد

• Numerical Analysis
• Tensor Methods
• Low-Rank Approximation
• +۷ مورد دیگر

Prof. Dr. André Uschmajew is a full professor and holds the Chair of Mathematical Data Science at the Institute of Mathematics, Faculty of Mathematics, Natural Sciences, and Materials Engineering, University of Augsburg, Germany. He has held prominent research and academic positions at institutions including the Max Planck Institute for Mathematics in the Sciences (Leipzig), University of Bonn, and EPF Lausanne. 2022–present: Chair of Mathematical Data Science, University of Augsburg 2017–2022: Research Group Leader, Max Planck Institute MiS Leipzig 2014–2017: Bonn Junior Fellow Professorship, University of Bonn 2013: Ph.D. in Mathematics, TU Berlin His research centers on the theoretical and computational aspects of low-rank tensor and matrix approximations, with deep connections to Riemannian optimization, functional analysis, and high-dimensional scientific computing. He investigates the geometry of low-rank varieties, convergence of alternating algorithms, and applications in data science and dynamical systems. His work combines rigorous mathematical analysis with algorithmic innovation. The recent publications (2023–2025) reflect a strong focus on optimization methods for low-rank structures, dynamical low-rank approximation for PDEs like the Vlasov-Poisson equation, randomized SVD, Sinkhorn-type algorithms with overrelaxation, and Kronecker product operator approximation. Key themes include convergence analysis, algorithmic acceleration, and applications in scientific computing and signal processing. Although no specific awards are listed, his publication record in top-tier journals such as Numerische Mathematik , SIAM Journal on Optimization , and Foundations of Computational Mathematics indicates significant recognition in applied mathematics and numerical analysis. He advises students and researchers in mathematical data science and numerical analysis, though specific advisees are not named. He teaches courses such as Kernel Methods and Linear Algebra II. He has collaborated with leading researchers including Bart Vandereycken, Daniel Kressner, and Wolfgang Hackbusch. His work is supported through institutional affiliations and likely research grants, though specific grants are not listed. He is actively involved in the development of numerical methods for high-dimensional problems, particularly using tensor networks and manifold optimization. He is affiliated with research teams at the University of Augsburg and previously led a group at the Max Planck Institute MiS Leipzig, focusing on mathematical aspects of data science and tensor methods.

Cristina G. Fernandesمشاهده پروفایل

دانشیار

• Algorithms
• Combinatorial Optimization
• Graph Theory
• +۷ مورد دیگر

Cristina G. Fernandes is an Associate Professor in the Department of Computer Science at the Institute of Mathematics and Statistics, University of São Paulo (IME-USP), where she conducts research in theoretical computer science, combinatorial optimization, and graph theory. She teaches advanced courses such as Advanced Data Structures and Topics in Algorithm Analysis. Her research interests include algorithms, combinatorial optimization, graph theory, approximation algorithms, data structures (persistent, retroactive, kinetic, succinct), and computational geometry. She applies theoretical methods to solve complex problems in network design, clustering, and discrete structures. The recent publications reflect a strong focus on structural and extremal graph theory, approximation algorithms, and combinatorial optimization. Key themes include tree and path packing, dominating sets, Steiner-type problems, and combinatorial properties of graphs. Her work often involves deep structural analysis and algorithmic design for NP-hard problems. Cristina G. Fernandes has not been mentioned with any specific scientific awards in the provided texts. She has supervised numerous postdoctoral researchers, PhD, MSc, and undergraduate students, many funded by FAPESP, CAPES, and CNPq. She leads significant research projects, including CAPES/MATH/STIC/CLIMAT-AMSU on energy efficiency in distributed computing and CNPq Universal projects on partitioning and connectivity. She is actively involved in academic advising and grant-funded research. She leads or participates in the Research Group in Theoretical Computer Science, Combinatorics and Combinatorial Optimization at IME-USP, fostering collaborative research in algorithms and discrete mathematics.

Laxman Dhulipalaمشاهده پروفایل

استادیار

• Parallel Algorithms
• Graph Processing
• Parallel Clustering
• +۷ مورد دیگر

Laxman Dhulipala is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park, and a research scientist at Google Research in the Graph Mining team. He holds a Ph.D. from Carnegie Mellon University, advised by Guy Blelloch, and was a postdoctoral researcher at MIT with Julian Shun. Ph.D., Carnegie Mellon University Postdoctoral Research, MIT His research focuses on efficient parallel algorithms, particularly for graph processing and clustering. He explores theoretical and practical models of parallel computation aligned with modern hardware. His work spans parallel graph algorithms, computational geometry, and scalable systems for massive datasets. The recent publications demonstrate a strong trend in scalable and dynamic graph algorithms, with a focus on hierarchical clustering, connectivity, and benchmarking. Key themes include batch-dynamic updates, memory-efficient data structures, and practical parallel implementations for massive-scale problems. Many works appear in top venues such as SPAA, VLDB, NeurIPS, and SIGMOD. Best Paper Award at VLDB'25 Best Paper Award at SPAA'22 Best Paper Runner Up at VLDB'22 Distinguished Paper Award at PLDI'19 Best Paper Award at SPAA'18 Memorable Paper Award Finalist at NVMW'20 Honorable Mention, CMU SCS Dissertation Award Nominated for ACM Dissertation Award Dhulipala has advised and collaborated with numerous students and researchers, including Quinten De Man, Shangdi Yu, Jessica Shi, and others, contributing to influential projects such as Aspen, ParGeo, GBBS, and ParlayLib. He has received recognition for both theoretical and practical contributions to parallel computing. He teaches courses such as CMSC858N (Scalable Parallel Algorithms and Data Structures) and CMSC451 (Design and Analysis of Computer Algorithms). He is actively involved in building tools and frameworks for parallel algorithm development and evaluation, including benchmark suites and graph processing systems. His dual affiliation with academia and Google Research enables impactful, scalable research bridging theory and practice.

• Biological Physics
• Active Matter
• Soft Condensed Matter
• +۵ مورد دیگر

Dr. Thorsten Auth is a researcher at Forschungszentrum Jülich GmbH, affiliated with the Institute for Advanced Simulation (IAS) and its Theoretical Physics of Living Matter (IAS-2) division. His work bridges physics and biology, focusing on lipid-bilayer membranes, active matter, and cellular mechanics. Research Interests : Biological Physics, Active Matter, Soft Condensed Matter, Membrane Biophysics, and Computational Biophysics. He investigates membrane interactions with nano/microstructures and simulates active matter dynamics in cellular environments. Publications Trends : His recent work emphasizes active matter modeling, membrane-particle interactions, and computational approaches to non-equilibrium biological systems. Keywords include Biophysics , Nanotechnology , Non-equilibrium Physics , and Soft Matter . Contact : Available via phone (+49 2461/61-1735) or online profile ( Link ). ORCiD: 0000-0002-6618-2316 . Labs/Teams: Theoretical Physics of Living Matter (IAS-2), Forschungszentrum Jülich