جست‌وجوی استاد و پژوهشگر

Sebastian Kuckukمشاهده پروفایل

پژوهشگر

• High Performance Computing
• GPU Programming
• Parallel Computing
• +۷ مورد دیگر

Dr. Sebastian Kuckuk is a researcher and head of training at the Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg. He is affiliated with the Department of Computer Science and contributes to the Chair of System Simulation. His work bridges research, training, and software development in high-performance computing. Education: PhD in Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (2019) His research focuses on enhancing performance portability and programmer productivity using domain-specific languages, code generation, automatic parallelization, and GPU programming. These techniques are applied to develop massively parallel numerical solvers for computational fluid dynamics, particularly for the shallow water equations. He is a core developer of the ExaStencils framework, which enables automated generation of efficient multigrid solvers for structured and patch-structured grids. Analysis of his recent publications (2020–2025) reveals a consistent focus on code generation, GPU acceleration, and solver optimization for fluid dynamics problems. Key themes include heterogeneous computing, block-structured grids, and adaptive methods. His work integrates advanced compiler techniques with numerical mathematics to improve scalability and performance on modern HPC architectures. Scientific Recognition: NVIDIA Deep Learning Institute (DLI) University Ambassador Certified Instructor for DLI courses in GPU programming and CUDA He actively contributes to teaching and training through courses such as Programming Techniques for Supercomputers and High-End Simulation in Practice . He conducts workshops and tutorials on GPU programming and performance optimization. While no formal students are listed, his mentoring role is evident through collaborative research and training activities. He has no recorded grants in the provided text, but his involvement in NHR and KONWIHR projects indicates active participation in funded HPC initiatives. Laboratories and Projects: Lead developer of ExaStencils , a code generation framework for multigrid solvers Contributor to GHODDESS , a module for higher-order discretizations in shallow water modeling Active in NHR@FAU and KONWIHR projects focused on GPU computing and performance optimization

Johannes Karstensenمشاهده پروفایل

عضو هیئت علمی

• Physical Oceanography
• Ocean Circulation
• Climate Dynamics
• +۳ مورد دیگر

Johannes Karstensen is a Research Scientist at GEOMAR Helmholtz Centre for Ocean Research Kiel since 2002. His work spans Physical Oceanography , Climate Dynamics , and Marine Observing Systems . Since 2025, he leads the Seagoing Technology unit at GEOMAR. Education: Ph.D. (1999) and Diploma (1994) in Physical Oceanography from University of Hamburg International Roles: Chairperson of OceanPractices AISBL, German representative in G7 FSOI, and leadership in OceanGliders and OSNAP programs His research focuses on ocean circulation-climate interactions , including mesoscale eddy dynamics , air-sea fluxes , and oxygen export mechanisms . Recent work examines Greenland meltwater impacts on North Atlantic circulation and deep ocean variability off Chile. The 15 most recent publications (2023-2025) demonstrate expertise across Physical Oceanography (6 articles) Climate Dynamics (5 articles) Observational Systems (4 articles) Biogeochemical Coupling (3 articles) Key subfields: North Brazil Current Rings , Labrador Sea Convection , Baltic Sea Circulation , Barrier Layer Dynamics , Ocean Best Practices , Deep Western Boundary Currents . Editorial Roles: Chief Editor, Frontiers in Marine Science (2025–present) Editorial Board, Copernicus Ocean State Report (2022–present) Former Research Topic Editor on Ocean Ethics and Best Practices He coordinates OceanSITES time series stations and contributes to SWOT satellite calibration . Leadership extends to UN Ocean Decade initiatives and European Ocean Integration frameworks.

• Human-AI Interaction
• Social Media Psychology
• Cognitive Psychology
• +۷ مورد دیگر

Jürgen Buder is a research scientist at the Leibniz-Institut für Wissensmedien (IWM) in Tübingen, Germany, and has been a key member since 2008. He serves as Deputy Head of the Perception and Action lab since 2022 and is also the ombudsman for good scientific practice at IWM. He is affiliated with Eberhard Karls University Tübingen, where he completed his habilitation in psychology in 2021. His research focuses on human-AI interaction, processing of conflicting information on social media, attitude polarization, knowledge communication, and computer-supported collaborative learning (CSCL). His work bridges cognitive psychology, educational technology, and social media dynamics. He has led major projects such as 'Info-noise', 'How do people deal with opposing opinions?', and 'ArchiveGPT', exploring the psychological effects of digital environments. The most recent articles highlight trends in AI perception, trust in artificial agents, polarization in online discourse, and cognitive mechanisms in digital learning. These publications appear in high-impact journals such as Computers in Human Behavior , Psychological Science , and New Media & Society , reflecting interdisciplinary engagement across psychology, education, and human-computer interaction. Scientific Awards: Dissertation Award, Faculty of Social Sciences, University of Tübingen (2002) He actively supervises and collaborates with PhD students and postdocs, though specific advisees are not listed. He has secured multiple research grants through DFG and Leibniz ScienceCampus funding. He has organized numerous conferences and workshops, including the 'WCT meets HCI' series and symposia on belief polarization. He teaches courses such as 'Knowledge media in education, work, and leisure' and seminars on anthropomorphism at the University of Tübingen. He is a core member of the Perception and Action lab at IWM, contributing to its mission of understanding human cognition in digital environments. His work emphasizes both theoretical frameworks and practical applications in educational and public policy contexts.

Ralf Hielscherمشاهده پروفایل

استاد

• Mathematical Imaging
• Crystallography
• Texture Analysis
• +۷ مورد دیگر

Ralf Hielscher is a Professor at the Institute of Applied Analysis within the Faculty of Mathematics and Computer Science at the Technical University of Freiberg, Germany. His research lies at the intersection of applied mathematics, materials science, and imaging, with a strong focus on crystallographic texture analysis and electron backscatter diffraction (EBSD). He is a core developer and leading figure behind MTEX, a widely used open-source MATLAB toolbox for texture and orientation data analysis. Research Interests: His work centers on mathematical methods for analyzing crystallographic orientations, including spherical harmonic transforms, kernel density estimation on rotation groups, manifold-valued data processing, and inverse problems in tomography and texture reconstruction. He develops algorithms for parent grain reconstruction, orientation mapping, denoising, and visualization of microstructures. The recent publications reveal a consistent trend in advancing computational techniques for EBSD and texture analysis, particularly through the MTEX platform. His work bridges theoretical mathematics with practical materials characterization, enabling more accurate and efficient analysis of polycrystalline materials across geology, metallurgy, and engineering. Email: ralf.hielscher@math.tu-freiberg.de Scientific Contributions: While no formal awards are listed, his extensive publication record in high-impact journals such as SIAM Journal on Imaging Sciences , Journal of Applied Crystallography , and Inverse Problems underscores his significant contributions to the field. He has developed foundational algorithms now embedded in MTEX, which is used globally by researchers in materials science and geology. Teaching and Advising: He teaches courses such as Function Theory, Analysis 3, and Mathematics for Engineers. Although specific students are not mentioned, his leadership in MTEX and numerous collaborative publications suggest he mentors researchers and contributes to training the next generation of scientists in computational materials analysis. Labs and Teams: He is part of the team at the Institute of Applied Analysis and leads research efforts related to signal and image processing in crystallography. The MTEX project serves as a virtual research platform involving international collaborators in Germany, France, the UK, and beyond, facilitating open science in texture analysis.

• Social Media Analysis
• Online Political Polarization
• Misinformation Detection
• +۷ مورد دیگر

Ashwin Rao is a Research Professor at the University of Southern California's Information Sciences Institute (ISI) within the Viterbi School of Engineering. With a research career spanning over two decades, his work bridges computer science, social sciences, and political science, focusing on understanding human behavior through digital footprints. His academic journey began with signal processing research in the 1990s before evolving into network protocols and mobile computing, and most recently into computational social science and AI ethics. Rao's research interests encompass Social Media Analysis, Online Political Polarization, Misinformation Detection, Network Protocols, Mobile Computing, Privacy in Mobile Applications, Natural Language Processing, and AI Ethics. His work demonstrates a consistent trajectory from technical networking research to the societal implications of technology. His most recent publications reveal a strong focus on understanding political discourse online, particularly examining polarization, emotional responses to events, and the impact of social media algorithms on information ecosystems. His interdisciplinary approach combines computational methods with social science theories to address pressing issues in digital society. Rao has published extensively across top venues including ICWSM, WWW, ACL, IEEE Transactions, and numerous conferences in networking and systems. His work often appears with Kristina Lerman, with whom he collaborates closely at USC ISI. His recent research portfolio shows a sophisticated integration of machine learning techniques with social science questions, particularly examining how language models reflect and potentially amplify societal biases. Rao has made significant contributions to understanding privacy issues in mobile applications, network protocols, and social media dynamics. His research has been influential in both technical communities studying network performance and social science communities examining online behavior. His work on BitTorrent performance, mobile privacy, and social media analysis has been widely cited across disciplines. His laboratory work appears to focus on computational social science methodologies, developing tools and frameworks for analyzing large-scale social media data while addressing ethical considerations in AI and data analysis. Recent projects suggest strong connections with public health research through social media analysis during the pandemic.

Karl Bringmannمشاهده پروفایل

استاد

• Theoretical Computer Science
• Fine-Grained Complexity
• Algorithm Design
• +۵ مورد دیگر

Karl Bringmann is a Professor at Saarland University since November 2019 and is affiliated with the Max Planck Institute for Informatics, where he works in the Department of Algorithms and Complexity. He has established himself as a leading researcher in theoretical computer science, particularly in fine-grained complexity and algorithm design. His work bridges theoretical insights with practical applications in optimization problems. Bringmann's research focuses on conditional lower bounds (often based on the Strong Exponential Time Hypothesis) and algorithm design, with particular emphasis on optimization problems, string algorithms, and computational geometry. His work has significant implications for fundamental problems like Subset Sum, Knapsack, and Integer Programming, with applications ranging from scheduling to post-quantum cryptography. He develops innovative approaches combining modern algorithmic techniques, mathematical structure theory, and fine-grained complexity to design faster algorithms and establish optimality. His publication record shows a consistent trend toward developing near-optimal algorithms for fundamental problems, with significant contributions to fine-grained complexity theory. His work often establishes tight conditional lower bounds while simultaneously providing matching upper bounds, creating a comprehensive understanding of problem complexity. He has made notable advances in string algorithms (particularly edit distance), geometric problems, and optimization. ERC Starting Grant 2019: Technology Transfer between Integer Programming and Efficient Algorithms (TIPEA) EATCS Presburger Award for Young Scientists 2019 Heinz Maier-Leibnitz-Prize 2019 EATCS Distinguished Dissertation Award 2015 Google European Doctoral Fellowship 2012-2014 Bringmann leads the ERC-funded TIPEA project (2019-2024), which investigates fundamental optimization problems with the goal of developing next-generation industrial solvers. He advises several PhD students including Nick Fischer, Alejandro Cassis, and Vasileios Nakos, and has served on numerous program committees for top theoretical computer science conferences including STOC, FOCS, SODA, and ICALP. His teaching includes advanced courses on Fine-Grained Complexity Theory and Competitive Programming.

Yulia Alexandrمشاهده پروفایل

استادیار مدعو

• Algebraic Statistics
• Applied Algebraic Geometry
• Mathematical Machine Learning
• +۳ مورد دیگر

Dr. Yulia Alexandr is a Hedrick Assistant Adjunct Professor at UCLA and a Postdoctoral Fellow in Applied Mathematics at Harvard University . She completed her PhD in Mathematics at UC Berkeley in 2023 under the supervision of Bernd Sturmfels and Serkan Hoşten , with a thesis titled "From Voronoi Cells to Algebraic Statistics" . She holds a Bachelor's degree in Mathematics from Wesleyan University (2019), where she was advised by Karen Collins .

• Machine Learning
• Artificial Intelligence
• Speech Processing
• +۶ مورد دیگر

Saeed Amizadeh is a prominent researcher specializing in artificial intelligence and machine learning, currently affiliated with Microsoft's research division. His work spans multiple domains of AI including speech processing, natural language understanding, computer vision, and time series analysis, with a particular focus on developing novel frameworks that bridge symbolic reasoning with neural approaches. Over the past decade, he has established himself as a significant contributor to the field through publications in top-tier conferences including ICASSP, ICLR, AAAI, KDD, and IJCAI. Dr. Amizadeh's research interests primarily center around advancing the theoretical foundations and practical applications of machine learning systems. His work on neuro-symbolic visual reasoning has contributed to understanding how to effectively disentangle visual perception from logical reasoning in AI systems. He has made significant contributions to differentiable programming, particularly in making classical machine learning pipelines fully differentiable, which enables end-to-end optimization of complex ML workflows. His research on speech enhancement using GANs represents cutting-edge work in audio processing, while his time series anomaly detection frameworks have practical applications in numerous industry settings. Analysis of his publication trends reveals a consistent trajectory from theoretical machine learning foundations toward increasingly applied research with practical industrial relevance. His early work (2010-2015) focused on fundamental algorithms and probabilistic modeling approaches, while his more recent publications (2019-2025) demonstrate a shift toward practical AI systems with direct applications in speech processing, audio separation, and enterprise machine learning. A notable theme throughout his career is the development of frameworks that enable more efficient, scalable, and interpretable AI systems. As a key contributor to Microsoft's ML.NET framework, Dr. Amizadeh has played an important role in developing tools that make machine learning more accessible to enterprise developers. His collaborative work spans academia and industry, with notable partnerships with researchers from various institutions as well as within Microsoft Research.

Daniela Heddaeusمشاهده پروفایل

پژوهشگر

• Mental Health Care
• Collaborative Care Models
• Stepped Care Approaches
• +۵ مورد دیگر

Daniela Heddaeus is a researcher at the Institute and Polyclinic for Medical Psychology at the University Medical Center Hamburg-Eppendorf. With a doctoral degree in biological human sciences (Dr. rer. biol. hum.) and a diploma in psychology (Dipl.-Psych.), she has established herself as a prominent researcher in mental health care systems. Her work primarily focuses on collaborative and stepped care models for mental disorders, particularly depression, with extensive research on implementation in primary care settings. Her research interests center on innovative mental health service delivery models, with particular emphasis on stepped care approaches that match treatment intensity to patient needs. She investigates collaborative care models that integrate mental health services into primary care, examining effectiveness, cost-effectiveness, and implementation factors. Her work also explores telemedicine applications in mental health, particularly telephone-administered cognitive behavioral therapy for depression. Through numerous publications and clinical trials, she has contributed significantly to understanding how to optimize mental health service delivery for better patient outcomes and efficient resource utilization. Analysis of her publication record reveals a consistent research trajectory focused on improving mental health care through structured, evidence-based service models. Her work spans clinical trials, implementation research, economic evaluations, and qualitative studies examining provider perspectives. The COMET study represents a major contribution to the field, investigating collaborative and stepped care models for various mental disorders in outpatient settings. Daniela Heddaeus works within the Hamburg healthcare research network, collaborating with multiple institutions and researchers across Germany. Her research has been published in high-impact journals including BMC Psychiatry, PLOS ONE, and Journal of Affective Disorders, demonstrating her significant contributions to mental health services research.

Stefan Mathiasمشاهده پروفایل

استاد

• Ultrafast Phenomena
• Solid-State Physics
• Surface Physics
• +۷ مورد دیگر

Prof. Dr. Stefan Mathias is a faculty member at the University of Göttingen , specializing in ultrafast phenomena in solids, surfaces, interfaces, and nanostructures. His research leverages ultrashort X-ray pulses generated via high-harmonic generation combined with time-resolved spectroscopy to probe dynamic processes at fundamental time and length scales. His work spans disciplines such as condensed matter physics , optics , and materials science , focusing on systems like graphene , 2D materials , and magnetic heterostructures . Recent publications highlight advancements in time-resolved photoelectron spectroscopy , exciton dynamics , and all-optical spin control . Stefan Mathias's research group employs cutting-edge techniques including femtosecond extreme ultraviolet light sources and momentum microscopy to investigate topics such as hybrid excitons , electron-phonon interactions , and topological states in quantum materials. Contact: smathias@uni-goettingen.de

Volker Kaibelمشاهده پروفایل

استاد

• Discrete Optimization
• Polyhedral Combinatorics
• Extended Formulations
• +۵ مورد دیگر

Prof. Dr. Volker Kaibel is a full-time Professor for Mathematical Optimization at Otto-von-Guericke Universität Magdeburg since 2007. His research focuses on Discrete Optimization , Polyhedral Combinatorics , and Extended Formulations , with applications in combinatorial optimization , quantum computing , and genomic epidemiology . His recent work includes studies on Polytope Extensions with Linear Diameters , Steiner Cut Dominants , and Scale-Free Spanning Trees . Publications span journals like SIAM Journal on Discrete Mathematics , Mathematics of Operations Research , and Journal of Computational Biology , emphasizing theoretical advancements in mathematical programming and polyhedral theory . Key projects include the long-running Mathematical Complexity Reduction (2017–2026) and earlier DFG-funded initiatives on Extended Formulations and Orbitopes . His collaborations extend to institutions like the Zuse-Institute Berlin and DFG Research Center MATHEON .

• Hydrological modeling
• Flood forecasting
• Drought monitoring
• +۴ مورد دیگر

Robert Schweppe is a Researcher at the Department of Computational Hydrosystems , Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. He has held this position since 2017, following prior roles as Project Engineer at HYDRON GmbH (2014-2017) and Research Assistant at UFZ (2013). Expertise in hydrological modeling using mHM, ECLand, LARSIM Developed pyflow configuration tools and MPR software for environmental data processing Key contributor to 4DHyro project integrating Earth Observation data with hydrological models His research interests span hydrological modeling , drought forecasting , machine learning applications , and climate change impacts on water systems. His 2022 publications on parameter regionalization and floodplain modeling demonstrate methodological innovations in environmental data analysis. Notable scientific recognitions include the Deutschlandstipendium scholarship (2012-2013) and Helmholtz Field Study Fellowship (2022). He collaborates extensively with European institutions on compound environmental risks and smart monitoring technologies .

Olivier Buffetمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Automated Planning and Scheduling
• Reinforcement Learning
• +۷ مورد دیگر

Olivier Buffet is a Researcher at INRIA, working at the INRIA Center at Université de Lorraine / LORIA since November 2007. He is affiliated with the LORIA laboratory (Lorraine Laboratory of Computer Science and its Applications), which focuses on computer science research. His work spans multiple institutions, having previously held positions at NICTA's Statistical Machine Learning program (2004-2006), RSISE at ANU (2004-2006), and LAAS at CNRS (2006-2007). Dr. Buffet received his engineering degree from Supélec and a DEA (Diplôme d'Etudes Approfondies) from Henri Poincaré University. He completed his PhD in computer science under the supervision of François Charpillet and Alain Dutech at LORIA / INRIA Nancy Grand-Est, defended on September 10, 2003. He later defended his habilitation to supervise research (HDR) on December 18, 2017. Dr. Buffet's research focuses on artificial intelligence, particularly in the areas of automated planning and scheduling, reinforcement learning, and decision-making under uncertainty. His work extensively explores Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs), and Decentralized POMDPs (Dec-POMDPs), with applications ranging from multi-agent systems to traffic management and adaptive conservation strategies. His research often bridges theoretical foundations with practical applications, developing algorithms that can handle complex decision problems in uncertain environments. His publication record demonstrates a consistent focus on advancing methods for planning and decision-making under uncertainty. Over the past decade, his work has increasingly addressed decentralized and multi-agent settings, developing novel approaches for coordination among multiple decision-makers with partial information. More recently, his research has explored interpretable solutions for adaptive management problems, particularly in environmental contexts, and advanced theoretical understanding of properties like Lipschitz continuity in POMDP value functions. Dr. Buffet has received recognition for his contributions to the field, including: Winner of the probabilistic track in the Fifth International Planning Competition (IPC-06) Best Paper award at AAMAS-14 for "Exploiting separability in multi-agent planning with continuous-state MDPs" Best Paper award at JFSMA-13 for "Synchronisation de véhicules autonomes aux croisements d'un réseau de routes" Best Paper award at CAp'11 for "Une extension des POMDP avec des récompenses dépendant de l'état de croyance" As an educator and mentor, Dr. Buffet has supervised numerous PhD students including Arnaud Glad, Mauricio Araya-Lòpez, Mohamed Tlig, Arsène Fansi, and Manel Tagorti. He has also guided many interns and research projects. His teaching experience includes tutored sessions on discrete and deterministic optimization, decision making under uncertainty, and computer science for industrial engineering at École des Mines de Nancy, as well as courses on Unix shell and C programming at Université Henri Poincaré. Dr. Buffet has been actively involved in the academic community, serving as Co-Conference Chair of the 30th International Conference on Automated Planning and Scheduling (ICAPS 2020) in Nancy. He has organized multiple meetings of the French workgroup JFPDA (formerly PDMIA) and chaired several workshops on planning and scheduling under uncertainty. He previously served on the editorial boards of Revue d'Intelligence Artificielle (RIA) and Journal of Artificial Intelligence Research (JAIR), and has been a reviewer for numerous prestigious journals and conferences in artificial intelligence.

• International Macroeconomics
• Monetary Economics
• Applied Mathematics in Economics
• +۳ مورد دیگر

Prof. Dr. Michael Evers is a faculty member at the College of Economics, leading the Field of Economics, especially Money and Currency (520A). He is actively involved in teaching courses like International Macroeconomics and Geld & Währung (Money & Currency) for the WiSe 2025/26 semester. His research focuses on mathematical methods in economics, particularly in macroeconomic modeling and monetary theory. Recent publications explore advanced computational techniques, including Faà Di Bruno's formula and Bell polynomials, applied to economic analysis. His work spans fields such as international finance, quantitative economics, and theoretical economic frameworks. While no explicit scientific awards or student advisement records are listed here, his academic contributions are reflected in his publications and institutional leadership.

Hubertus Th. Jongenمشاهده پروفایل

استاد

• Nonlinear Optimization
• Parametric Optimization
• Semi-Infinite Programming
• +۱۰ مورد دیگر

Hubertus Th. Jongen is a full Professor of Mathematics at RWTH Aachen University and has held an ALMA Professorship at Maastricht University since 2001. He is affiliated with the Department of Mathematics within the Faculty of Mathematics, Computer Science and Natural Sciences at RWTH Aachen. His academic career includes professorships at the University of Hamburg and visiting positions at institutions such as the Humboldt University of Berlin, University of Rome, Tokyo Institute of Technology, and ICSI Berkeley. Full Professor, RWTH Aachen University (since 1987) ALMA Professor, Maastricht University (since 2001) Professor, University of Hamburg (1980–1982) Special Professor, University of Hamburg (from 1983) Guest Professor, Humboldt University of Berlin (1985) His primary research interests lie in nonlinear optimization, parametric optimization, semi-infinite and generalized semi-infinite programming, bilevel optimization, and global optimization. His work integrates deep theoretical analysis with applications in operations research and mathematical programming. He has made foundational contributions to the structural analysis of optimization problems, stability of feasible sets, and critical point theory. The most recent articles show a consistent focus on advanced topics in optimization, particularly in generalized semi-infinite programming, bilevel problems, and equilibrium models. His publications emphasize structural analysis, stability, smoothing techniques, and computational aspects of nonconvex and constrained optimization. There is a strong trend toward understanding the geometry and topology of feasible sets and the behavior of solution concepts under perturbations. Doctor honoris causa, Shanxi University (2003) Honorary Foreign Member, Mongolian Mathematical Society Miembro Honorario, Sociedad Colombiana de Investigación de Operaciones Visitante Distinguido de la Ciudad Puebla, Mexico Special Issue in Optimization journal (2008) honoring his 60th birthday Professor Jongen has advised numerous PhD students and collaborators, including Oliver Stein, Jan-J. Rückmann, and Vladimir Shikhman. He has been deeply involved in research grants and editorial work, serving as co-editor of major journals such as Journal of Global Optimization , Optimization , and Mathematical Methods of Operations Research . He co-founded the SIGOPT interest group and has organized multiple international conferences. He has also authored influential books on nonlinear optimization and optimization theory. He has led significant academic initiatives, including serving as Chairman of the Department of Mathematics at RWTH Aachen (1992–1994, 1996–1998) and as a Senator of the university (1998–2000). His research group and collaborations form a well-established network in parametric and global optimization.