جست‌وجوی استاد و پژوهشگر

Wolfgang Greinerمشاهده پروفایل

استاد

• Health Economics
• Health Technology Assessment (HTA)
• Quality of Life Research
• +۵ مورد دیگر

Prof. Dr. Wolfgang Greiner, born in 1965, is the Chair of Health Economics and Health Management at Bielefeld University's Faculty of Health Sciences. He has held this position since April 2005 and leads Working Group 5 (Health Economics and Health Management). Current roles: Dean of Faculty, Chairman of Structure and Budget Commission, Habilitation Committee member. Affiliated with Bielefeld Center for Data Science (BiCDaS). His research focuses on health technology assessment , economic evaluation of health services , quality of life metrics , and disease management frameworks . Recent projects include AI-driven clinical decision support systems for sepsis, financial burden analysis for cancer patients, and smart home emergency call system effectiveness. Key awards include the Austrian Prize for Health Economics, Hannover University Science Prize, and Medvantis Research Prize. He serves as Managing Editor of the European Journal of Health Economics and sits on scientific advisory boards for health insurers. His 2025 articles address dengue burden, AI in critical care, and dyslipidemia epidemiology. Earlier works span meningococcal vaccines, HPV disease costs, and chronic disease management in Germany.

Manuel Penschuckمشاهده پروفایل

پژوهشگر ارشد

• Algorithm Engineering
• Random Graphs
• Parallel and Distributed Computing
• +۳ مورد دیگر

Manuel Penschuck is a Research Fellow at the Institute of Computer Science , Goethe University Frankfurt, Germany. His research focuses on algorithm engineering, graph theory, and scalable network generation, with emphasis on parallel computing, I/O-efficient algorithms, and random graph models. He actively contributes to conferences like ESA, SEA, and IPDPS, and has co-authored publications in top venues including LIPIcs , IEEE Transactions , and SIAM . His work includes engineering algorithms for non-linear preferential attachment , parallel shuffling , and hyperbolic graph generation . He has co-organized program committees for ESA, EuroPar, and SEA, and his collaborations span institutions such as MPI-INF, TU Darmstadt, and Australian National University. Recent publications highlight advances in uniform graph sampling, geometric network models, and distributed systems. His research integrates theoretical rigor with practical implementation, addressing challenges in big data and high-performance computing. He is a key contributor to the Networkit toolkit for large-scale network analysis.

Andrea Barthمشاهده پروفایل

استاد

• Stochastic Partial Differential Equations
• Uncertainty Quantification
• Monte Carlo Methods
• +۴ مورد دیگر

Andrea Barth is a W3-Professor of Computational Methods for Uncertainty Quantification at the University of Stuttgart, leading the Research Group for Computational Methods for Uncertainty Quantification within the Excellence Cluster for Simulation Technology. She holds a Ph.D. from the University of Oslo (2009) and has held positions at ETH Zürich and the University of Stuttgart. Her work focuses on stochastic partial differential equations, numerical methods for uncertainty quantification, and applications in engineering and natural sciences. Education: Ph.D. in Mathematics, University of Oslo (2006–2009) Lecturer/Postdoc at ETH Zürich (2010–2013) Junior Professor at University of Stuttgart (2013–2017) Research Interests: Stochastic PDEs, uncertainty quantification, Monte Carlo methods, Bayesian inverse problems, and numerical analysis of random fields. Her work bridges stochastic analysis and numerical simulations, addressing challenges in modeling and simulating complex systems with uncertainties. Grants & Funding: Principal Investigator in projects like 'Data-Integrated Simulation Science' (ExC 2075) and 'Quantitative Methods for Visual Computing' (SFB/TRR 161). Her research also explores applications in porous media, carbon dioxide storage, and optical flow analysis. Supervision: Advised PhD students including Oliver König, Fabio Musco, and Robin Merkle. Current students focus on topics like deep learning for stochastic PDEs and continuous level Monte Carlo methods.

• Medical Biometry
• Health Services Research
• Mental Health
• +۵ مورد دیگر

Anne Daubmann is a Research Fellow at the Institute of Medical Biometry and Epidemiology within the Faculty of Medicine at the University Medical Center Hamburg-Eppendorf (UKE) . Her work focuses on Medical Biometry and Health Services Research , with significant contributions to Mental Health , Multiple Sclerosis , and Primary Care domains. Her research spans: Digital health interventions for cancer patients Nutritional strategies in MS management Family-oriented mental health policies AI reliability in medical guidelines Resuscitation training methodologies Scientific Awards: Certificate in Medical Biometry (2020) from GMDS and IBS-DR She holds memberships in the International Biometric Society, German Region (IBS-DR) . Her methodological expertise includes statistical analysis in clinical trials and health outcomes evaluation .

• Electronic Structure Theory
• Machine Learning in Materials Science
• Data Driven Materials Science
• +۶ مورد دیگر

Professor Patrick Rinke leads the Chair of AI-based Materials Science at the Technical University of Munich (TUM), within the TUM School of Natural Sciences and Department of Physics. His research group develops advanced electronic structure and machine learning methods to address critical challenges in materials science, surface science, physics, chemistry, and nanoscience. Professor Rinke's research spans multiple cutting-edge domains including electronic structure theory development, machine learning applications for materials science, data-driven materials discovery, biomaterials engineering, atmospheric science applications, clean energy materials, and hybrid materials systems. His work integrates advanced computational methods with practical applications across diverse scientific fields, particularly focusing on how artificial intelligence can transform traditional materials research. Analyzing his recent publications reveals strong trends in applying machine learning techniques to materials discovery, with particular emphasis on Bayesian optimization methods, active learning approaches for molecular data, and efficient dataset generation strategies. His research spans from fundamental electronic structure theory to practical applications in biomaterials, atmospheric science, and renewable energy technologies. Professor Rinke has received several prestigious awards including the August-Wilhelm Scheer visiting professorship (2017), a German Science Foundation research scholarship (2007), the Outstanding Postdoctoral Research Achievement Award from UC Santa Barbara (2009), recognition as an Outstanding Referee for Physical Review journals (2014), and the Institute of Physics Computational Physics Group Thesis Prize (2003). Professor Rinke actively contributes to the academic community through teaching and supervision. For the Winter term 2025/26, he is teaching courses including Academic Writing Skills, Introduction to Machine Learning for Materials Science, Current Topics in AI-Based Materials Science, and Machine Learning for Natural Sciences. His research group includes several team members working on diverse projects spanning the intersection of AI and materials science.

Abdelhak M. Zoubirمشاهده پروفایل

استاد

• Statistical Signal Processing
• Cooperative Sensor Communications
• Radar Imaging
• +۵ مورد دیگر

Abdelhak M. Zoubir is a Professor of Signal Processing and Head of the Signal Processing Group at Technische Universität Darmstadt, Germany. He has held leadership roles including Head of the Department of Electrical Engineering and Information Technology (2012–2014 and 2020–2022), and President of the European Association for Signal Processing (EURASIP, 2017–2018). His research focuses on statistical signal processing with applications in radar imaging, biomedical engineering, and automotive systems. Zoubir has authored over 500 publications and is a Fellow of IEEE and EURASIP. He currently leads projects on radar communication integration, robust signal processing algorithms, and radiation-hardened sensor development. Education: Dipl.-Ing. (BSc/MSc) from Fachhochschule Niederrhein and Ruhr-Universität Bochum, followed by a Dr.-Ing. (PhD) in Electrical Engineering from Ruhr-Universität Bochum (1992). Research Interests: Bootstrap techniques, robust detection/estimation, cooperative sensor networks, radar for landmine detection, and automotive safety systems. He has pioneered methods in robust statistical signal processing, including low-rank matrix completion and sparsity-aware algorithms. Recognition: Recipient of the IEEE Meritorious Service Award (2018), IEEE Signal Processing Magazine Best Paper Award (2017), and the M. Barry Carlton Award (2014). He has been a keynote speaker at major conferences such as ICASSP and EUSIPCO, and served as Editor-in-Chief of the IEEE Signal Processing Magazine (2012–2014). Current Projects: Focus on automotive radar signal processing, radiation-hardened sensors (MALTA), and distributed learning robustness. His work bridges theoretical advancements with practical applications in defense, healthcare, and automotive industries.

Jason Abaluckمشاهده پروفایل

استاد

• Health Economics
• Insurance Markets
• Behavioral Economics
• +۵ مورد دیگر

Jason Abaluck is a Professor of Economics at Yale School of Management, specializing in health economics and behavioral economics. His research focuses on consumer choice in health insurance markets, particularly Medicare Part D, and the effects of health interventions. Abaluck has led major research initiatives including a cluster randomized trial in Bangladesh involving 350,000 people to assess the impact of community masking on COVID-19 transmission. Abaluck's research interests span health economics, insurance markets, behavioral economics, and public health interventions. His work examines how consumers make decisions in complex health insurance markets, with a particular focus on Medicare Part D. He investigates choice inconsistencies, welfare implications, and potential policy solutions to improve decision-making. His research on mask-wearing during the pandemic demonstrated significant reductions in COVID transmission, particularly among vulnerable elderly populations. Analysis of Abaluck's publications reveals a consistent focus on healthcare decision-making across multiple contexts. His work spans theoretical modeling of consumer choice, empirical analysis of insurance markets, and field experiments testing public health interventions. Key themes include the impact of information on decision quality, the welfare consequences of choice inconsistencies, and the design of market structures to improve outcomes. Abaluck's scientific contributions have been recognized with several prestigious awards: 2023 Top 10 Clinical Research Award for his work on community masking during the pandemic 2017 NIHCM Honorable Mention for the Best Paper in Health Economics 2012 NIHCM Winner for the Best Paper in Health Economics Abaluck has secured significant research funding for projects including 'Differential Mortality in Medicare Advantage,' 'Mortality Effects of Health Insurance Networks and Providers,' and 'HEARTSPOT: Implementation and Rigorous Evaluation of a Machine Learning System to Aid Decision-Making in the Emergency Department.' His current work includes large-scale cluster randomized trials in multiple countries examining vaccination rates and the impact of trial representativeness on doctor treatment decisions. Abaluck leads collaborative research teams involving dozens of researchers from Yale, Stanford, Berkeley, and other institutions. His Bangladesh mask-wearing study, for example, involved coordination across multiple organizations and 600 villages. His work often bridges economics, public health, and clinical medicine, reflecting his interdisciplinary approach to solving complex healthcare problems.

Muhammad Bilal Zafarمشاهده پروفایل

استاد

• Human-Centric AI/ML
• Fairness in Machine Learning
• Explainable Artificial Intelligence
• +۳ مورد دیگر

Professor Muhammad Bilal Zafar holds a faculty position at Ruhr University Bochum (RUB), where he heads the Chair of AI and Society within the Research Center for Trustworthy Data Science and Security. He is also a Principal Investigator (PI) at the Cluster of Excellence CASA and a member of the Horst Görtz Institute for IT Security. Previously, he worked as a Senior Applied Scientist at Amazon Web Services and a Research Scientist at Bosch Center for Artificial Intelligence. He completed his PhD at the Max Planck Institute for Software Systems (MPI-SWS) and Saarland University, advised by Krishna P. Gummadi and Manuel Gomez Rodriguez. His research focuses on human-centric AI/ML, emphasizing fairness, explainability, and robustness of AI systems. Key contributions include mechanisms for fair classification, bias detection in algorithms, and trustworthy AI applications. He actively engages in mentoring PhD and Master’s students, with current openings for candidates in Computer Science, Mathematics, and related fields. His work bridges theoretical advancements with practical solutions for ethical AI deployment. Notable collaborations include projects with AWS and Bosch, addressing challenges in algorithmic fairness and AI transparency. His publications span top venues like ACL, KDD, NeurIPS, and WWW, highlighting contributions to both foundational and applied aspects of AI ethics.

Markus Heydenreichمشاهده پروفایل

استاد

• Probability Theory
• Statistical Mechanics
• Random Graphs
• +۷ مورد دیگر

Markus Heydenreich is a Professor at the Institute of Mathematics, Faculty of Mathematics, Natural Sciences and Technology, University of Augsburg. His research focuses on probability theory, particularly discrete random objects in statistical mechanics, mean-field behavior, lace expansion, and spatial random graph models. 2025: Workshop Bayrischzell 2024: Workshop Frauenchiemsee His recent publications highlight critical phenomena in random connection models, percolation, and spatial networks. He contributes to editorial boards and DFG-funded projects, including Random Geometric Systems and Mathematics of Many-Body Quantum Systems . Teaching includes courses on stochastic processes and seminars on probability and forensics. Key research themes: phase transitions, high-dimensional random walks, and mathematical physics applications. Collaborators include Remco van der Hofstad, Christian Hirsch, and Kilian Matzke. The team at Augsburg comprises doctoral researchers and postdocs.

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.

Zakhar Kabluchkoمشاهده پروفایل

استاد

• Stochastic Processes
• Convex and Integral Geometry
• Random Trees and Branching Processes
• +۵ مورد دیگر

Prof. Zakhar Kabluchko is a faculty member at the University of Münster, affiliated with the Institut für Mathematische Stochastik and the Mathematics Münster cluster of excellence. His research focuses on stochastic processes, convex and integral geometry, and probabilistic number theory. He holds a professorship and has contributed to high-impact publications in areas like random polytopes, stochastic geometry, and extreme value theory. His research interests include the study of random analytic functions, stochastic processes in high dimensions, and geometric probability. Notably, he has explored beta-star polytopes, Poisson zero cells, and the interplay between convex hulls and random walks. Kabluchko has also investigated applications of stochastic geometry in statistical mechanics and number theory. Recent work includes studies on high-dimensional limit theorems, propagation of chaos in spin systems, and the geometry of random simplices. He collaborates actively with researchers like Christoph Thäle and Vladimir Vysotsky, contributing to advancements in geometric probability and stochastic analysis.

Jörg Schürmannمشاهده پروفایل

استاد

• Arithmetic Geometry
• Representation Theory
• Algebraic Geometry
• +۵ مورد دیگر

Jörg Schürmann is an Adjunct Professor at the Faculty of Mathematics and Computer Science , University of Münster, affiliated with the Mathematical Institute . He serves as an Investigator in the Mathematics Münster cluster and a member of the CRC 1442 Geometry: Deformations and Rigidity project, specifically subproject D03: Integrability . His research focuses on Arithmetic Geometry and Representation Theory , with key contributions to topics in K-theory , moduli spaces , field theory , and randomness in mathematical structures . His work intersects algebraic geometry , topology , and equivariant theories . Recent publications highlight his work on Chern-Schwartz-MacPherson classes , equivariant toric geometry , generic vanishing theorems , and topological coherence of characteristic classes . Collaborations span leading mathematicians like Paolo Aluffi , Leonardo Mihalcea , and Changjian Su . He is involved in research projects under CRC 1442 , including studies on K-groups and cohomology , moduli spaces in arithmetic and geometry , and field theory and randomness .

Julien Poisatمشاهده پروفایل

مدرس

• Probability Theory
• Statistical Mechanics
• Disordered Systems
• +۴ مورد دیگر

Julien Poisat is a Lecturer (equivalent to Assistant Professor) at CEREMADE, Paris-Dauphine University, where he has been affiliated since 2014. Previously, he was a postdoctoral researcher at Leiden University (2012-2014) and completed his Ph.D. at Lyon 1 University (2008-2012). His research focuses on probability theory and statistical mechanics, with specific interests in disordered systems, polymers, random walks, and large deviations. He investigates phenomena such as localization, pinning, and phase transitions in various models including copolymers, charged polymers, and random environments. His recent publications primarily explore rigorous analyses of stochastic systems, with recurring themes including large deviations for random walks, critical behavior of polymer models, and asymptotic properties of disordered systems. Research often involves mathematical techniques from renewal theory, potential theory, and weak convergence methods. He leads the ANR LOCAL grant (2022-2027) focused on localization phenomena in polymers and random walks. Currently advises two doctoral students: Nicolas Bouchot (2021-2024) and Elric Angot (2022-2025). Active in academic community, recently co-organized the 2023 Workshop on Random Walks, Localization and Reinforcement in Paris.

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).

• Cloud Computing
• Container Orchestration
• Cloud-Native Applications
• +۴ مورد دیگر

Nane Kratzke is a Professor at Lübeck University of Applied Sciences, specializing in cloud computing and cloud-native applications. His research addresses practical challenges in container orchestration, cloud security, and vendor lock-in for small and medium enterprises. He holds a Diplom in Computer Science and a Doctorate in Natural Sciences, though specific institutions are not documented in available sources. Research interests include cloud-native architecture design, Kubernetes orchestration, moving target defenses for cloud security, and cost modeling of cloud services. His work bridges academic research and industry needs, particularly for SMEs seeking cloud portability through multi-cloud strategies and runtime transferability. Analysis of recent publications (2022-2024) reveals a strategic shift toward AI-driven cloud management techniques like prompt engineering, building on foundational contributions in cloud observability, security mechanisms, and transferability frameworks established between 2016-2021. Key recurring themes include mitigating vendor lock-in and enabling seamless application migration across cloud environments. No scientific awards are documented in the provided information sources. Details regarding graduate student advising, research grants, and laboratory facilities are not specified in current datasets, though his publications on programming assessment tools indicate engagement with computer science education.