جست‌وجوی استاد و پژوهشگر

Jan Hamaekersمشاهده پروفایل

پژوهشگر

• Numerical Algorithms
• High-Dimensional Problems
• Sparse Grids
• +۷ مورد دیگر

Dr. Jan Hamaekers is a researcher at the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) in Bonn, Germany. He is affiliated with the development of advanced numerical methods for high-dimensional problems in computational science, particularly in quantum mechanics and molecular dynamics. His work is centered on efficient algorithms such as HCFFT, ESPACK, and TREMOLO, and he collaborates on major research projects funded by DFG and EU programs. Research Interests: Development of hierarchical methods for high-dimensional problems Sparse grid and fast Fourier transform techniques Quantum mechanical and molecular mechanical (QM/MM) methods Numerical algorithms for the electronic Schrödinger equation Parallel software for nanoscale dynamics simulations His recent publications reflect a strong trend in computational materials science, multiscale modeling, and machine learning applications in chemistry. He has contributed to software packages like TREMOLO and ATK-ForceField, and his work bridges applied mathematics, physics, and engineering. He teaches practical labs in numerical simulation, indicating an active academic engagement. Scientific Projects: Molecular Dynamics Simulations for Material Science Applications CODICE - Computationally Driven design of Innovative Cement-based materials (EU FP7) Development of efficient algorithms for nanoscale dynamics simulations Multiscale QM/MM simulations of nanotubes and composites (DFG) Dr. Hamaekers has advised or collaborated with multiple researchers, as seen in his extensive co-authorship. He has not received any explicitly mentioned awards in the provided text. He is involved in software development and algorithmic innovation, with a focus on scalability and efficiency in scientific computing.

Dingenis Roelant Michiel Rengerمشاهده پروفایل

پژوهشگر

• Variational calculus
• Convex analysis
• Partial differential equations
• +۶ مورد دیگر

Dr. D.R. Michiel Renger is a mathematical researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology in the Department of Mathematics. He also maintains connections with the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, where he has presented lectures on advanced mathematical topics. Position: Researcher in the Department of Mathematics Institution: Technical University of Munich (TUM) Location: Boltzmannstraße 3, 85748 Garching bei München Contact: d.r.m.renger@tum.de Dr. Renger's research spans multiple areas of mathematical analysis and theoretical physics, with particular expertise in variational calculus, convex analysis, partial differential equations, and non-equilibrium thermodynamics. His work bridges pure mathematics with applications in physics and chemistry, especially through his investigations of gradient flows, Hamiltonian systems, large deviations theory, and chemical reaction networks. His research has significant implications for understanding complex systems far from equilibrium. His publication record shows consistent scholarly output from 2013 through 2025, with recent work focusing on the connections between macroscopic fluctuation theory and large deviations principles. His research often involves collaborative work with other leading mathematicians across European institutions. Recent teaching: Modeling and Simulation with Ordinary Differential Equations (TUM, Winter 2025/26) Past specialized courses: Large Deviations (TU Berlin, 2020), Convex Analysis (TU Berlin, 2018) Dr. Renger completed his PhD at Eindhoven University of Technology in 2013 with a dissertation titled "Microscopic interpretation of Wasserstein gradient flows," establishing his foundation in the mathematical theory of gradient flows which continues to inform his current research directions.

Longbing Caoمشاهده پروفایل

استاد

• Data mining
• Machine learning
• Anomaly detection
• +۷ مورد دیگر

Longbing Cao is a prominent academic researcher in data science and artificial intelligence, currently holding dual affiliations with Macquarie University's School of Computing in Sydney, Australia, and the University of Technology Sydney (UTS). He leads the Data Science Lab and has established himself as a leading authority in machine learning, data mining, and anomaly detection research with a prolific publication record spanning over two decades. Dr. Cao's research encompasses multiple critical domains within data science, with particular expertise in deep learning for anomaly detection, time series analysis, recommender systems, and behavior informatics. His scholarly work bridges theoretical foundations with practical applications across diverse industries including finance, cybersecurity, and e-commerce. He has pioneered innovative approaches in coupled behavior analysis, representation learning, and cross-domain collaborative filtering that have significantly advanced these fields. His research demonstrates a consistent trajectory from foundational data mining techniques to sophisticated deep learning methodologies. His publication record shows remarkable impact with 240 publications as of 2025 and 4,725 total citations. His highly influential survey papers have shaped research directions across multiple domains, with "Deep Learning for Anomaly Detection: A Review" (2021) accumulating over 1,600 citations and 25,586 downloads, establishing it as a seminal reference in the field. His recent work (2025) focuses on cutting-edge topics including diffusion models (SepDiff, ProgDiffusion), dynamic spectral graph analysis, and non-stationary time series modeling, reflecting his position at the forefront of AI research evolution. Data Science: A Comprehensive Overview (2017) - 298 citations, 41,677 downloads Data science: challenges and directions (2017) - 87 citations, 31,525 downloads AI in Finance: Challenges, Techniques, and Opportunities (2022) - 201 citations, 21,892 downloads A Survey on Session-based Recommender Systems (2021) - 346 citations, 4,563 downloads As a research leader, Dr. Cao has mentored numerous students and collaborators, contributing significantly to the development of next-generation data science researchers. His Data Science Lab serves as an interdisciplinary hub where computer scientists, statisticians, and domain experts collaborate to tackle complex data challenges. The lab's research spans theoretical advancements in machine learning algorithms to practical implementations with tangible real-world impact across multiple sectors.

• Database Systems
• Data Mining
• Machine Learning
• +۷ مورد دیگر

K. Selçuk Candan is a Professor at Arizona State University, Tempe, Arizona, with an extensive research career spanning over two decades in database systems, data mining, and machine learning. His work demonstrates deep expertise in tensor decomposition, causal inference, spatio-temporal analysis, and time series modeling. Candan has maintained a prolific publication record with significant contributions to premier venues including SIGMOD, ICDE, VLDB, and CIKM. His research interests focus on the intersection of data management and machine learning, particularly in developing novel methods for tensor decomposition, causal discovery, and efficient data representation. Candan's work addresses critical challenges in handling high-dimensional data, building robust predictive models, and developing systems for complex decision-making scenarios. Recent research directions include spatio-causal modeling for environmental systems, causal benchmarks, and learned data mapping techniques that bridge traditional database systems with modern machine learning approaches. Analysis of Candan's recent publications reveals a strong trend toward causal inference applications across multiple domains including hydrology, environmental science, and healthcare. His work increasingly integrates deep learning techniques with traditional data management principles, as evidenced by research on diffusion models for time series imputation and learned data mapping for compression. The research demonstrates practical applications in wetland conservation, streamflow forecasting, building fault detection, and medical diagnostics, showing a commitment to solving real-world problems through advanced data science techniques. Candan has established significant collaborative relationships, particularly with Maria Luisa Sapino, Huan Liu, and several other researchers across multiple institutions. His work often appears in top-tier conferences and journals, reflecting the high impact and quality of his research contributions. While specific grant information isn't detailed in the available text, the scope and depth of his research suggest substantial research funding supporting his work in data science and causal inference.

• Computer Science
• Artificial Intelligence
• Decision Support Systems
• +۳ مورد دیگر

Björn Ebbinghaus is a Researcher at the Chair of Computer Networks and Communication Systems within the Department of Computer Science at Heinrich Heine University Düsseldorf . Since January 2019, he has focused on extending argumentation systems with decision-making mechanisms and integrating them into natural language environments like chatbots. Education: Bachelor of Science in Computer Science (2017) Master of Science in Computer Science (May 2019) Research Interests: Collaborative decision-making through argumentation systems Human-computer interaction in participatory processes Applications of argumentation in online environments (e.g., budgeting, chat systems) Fairness evaluation of decision-making algorithms Publications highlight his work on participatory budgeting, proposal refinement, and argumentation graph models.

Junyu Qiمشاهده پروفایل

پژوهشگر

• Condition Monitoring
• Predictive Maintenance
• Rotating Machinery Diagnostics
• +۶ مورد دیگر

Dr.-Ing. Junyu Qi serves as a Scientific Researcher at the Reutlingen Research Institute within the Faculty of Technology at Reutlingen University, Germany. His primary affiliation is with the Drive Technology research group under the Electronics & Drives division, where he conducts advanced research in condition monitoring and predictive maintenance for industrial machinery. Education: B.Sc. in Mechatronics from Shanghai Maritime University (2008-2012), M.Sc. in Mechatronics from Friedrich-Alexander-Universität Erlangen-Nürnberg (2013-2016) Professional Experience: Researcher at KU Leuven's Department of Mechanical Engineering (2017-2023), Bosch Rexroth engineer (2015-2016) Qi's research centers on AI-driven predictive maintenance systems, specializing in vibration analysis and cyclostationary signal processing for rotating machinery. His work bridges statistical modeling with deep learning to develop robust diagnostic frameworks for wind turbine drivetrains, roller chain systems, and planetary gearboxes. Key innovations include sensorless monitoring techniques using motor current data and multi-sensor fusion architectures for remaining useful life prediction. His publication portfolio shows a clear trajectory toward integrating physics-informed AI with industrial applications, with recent work emphasizing few-shot learning for data-scarce scenarios and federated learning for distributed industrial systems. Over 70% of his publications focus on wind turbine diagnostics and bearing prognostics. Best Paper Award, American Society of Mechanical Engineers (ASME) 2020 for wind turbine gearbox research Regular reviewer for Mechanical Systems and Signal Processing, Reliability Engineering and System Safety, IEEE/ASME Transactions on Mechatronics Qi actively contributes to the academic community through peer review and conference participation, with no formal teaching responsibilities documented. His current research leverages the Reutlingen University's specialized laboratories for drive technology and vibration analysis to develop deployable industrial solutions.

Jens M. Schmidtمشاهده پروفایل

استاد

• Algorithm Engineering
• Graph Theory
• Theoretical Computer Science
• +۳ مورد دیگر

Jens M. Schmidt is a Professor at the Institute of Computer Science, University of Rostock. His research focuses on Algorithm Engineering , Graph Theory , and Connectivity , particularly in planarity, Hamiltonicity, and Schnyder woods. He teaches courses such as Introduction to Theoretical Computer Science , Efficient Algorithms , and Algorithmic Graph Theory . Current Affiliation: University of Rostock (Chair of Algorithms and Complexity) Research Themes: Algorithm design, planar graph analysis, and Schnyder woods applications. His recent work emphasizes graph structures, edge-connectivity, and predictive modeling in mechanical and electrical systems. Key collaborations include studies on polyhedral graphs and Grünbaum's conjecture. Scientific Contributions: He has authored numerous publications on graph algorithms, including proofs for 3-connected planar graphs and advancements in cut-tree representations. His interdisciplinary projects address efficiency in low-voltage motors and plasma jet analysis.

Henrik Walterمشاهده پروفایل

استاد

• Psychiatry
• Neuroscience
• Psychology
• +۸ مورد دیگر

Henrik Walter is a Full Professor (W3) of Psychiatry with a focus on Psychiatric Neuroscience and Neurophilosophy at Charité – Universitätsmedizin Berlin. He serves as Director of the Mind and Brain Research Division and Deputy Medical Director (Research) at the Department of Psychiatry and Psychotherapy, Charité Campus Mitte. Walter is also a faculty member at the Berlin School of Mind and Brain , a faculty member of the Bernstein Computational Center Berlin , and a principal investigator at the Berlin Center for Advanced Neuroimaging . Clinical expertise: Schizophrenia and affective disorders Empirical research: Working memory, volition, reward mechanisms, emotion regulation, mentalization, imaging genetics, connectomics Philosophical research: Philosophy of mind, neurophilosophy, neuroethics, philosophy of psychiatry Research trends in his 15 most recent publications (2009-2024) emphasize connectome-based machine learning , dynamic network reconfiguration , self-control mechanisms , and predictive models for psychiatric relapse . His work explores the intersection of neural network organization , emotional processing , and philosophical frameworks in mental health. Walter oversees major projects including environMENTAL (data harmonization in large cohorts) and FOR5187 PREACT (personalized psychotherapy). His team actively trains students and researchers through internships, theses supervision, and doctoral programs.

Arne Meierمشاهده پروفایل

استاد

• Complexity Theory
• Parameterised Complexity Theory
• Logic in Computer Science
• +۳ مورد دیگر

Arne Meier is a Professor at Leibniz Universität Hannover, affiliated with the Faculty of Electrical Engineering and Computer Science and the Institute of Theoretical Computer Science. He heads the Algorithms research group, focusing on theoretical aspects of computer science with applications to artificial intelligence and database systems. Meier obtained all his academic degrees—Bachelor's, Master's, PhD, and Habilitation—at Leibniz Universität Hannover, establishing a strong foundation in theoretical computer science. His academic journey at the same institution reflects his deep commitment to advancing research in computational theory. Meier's research spans several interconnected areas in theoretical computer science. His primary focus is on complexity theory, particularly the parameterized complexity of problems in non-classical logics with applications to AI. He also investigates enumeration algorithms and the logical foundations of artificial intelligence. His work bridges theoretical computer science with practical applications in knowledge representation and reasoning systems. He has a notable interest in LaTeX and typography, having developed the 'timeline' package for creating timelines in LaTeX documents. His recent publications (2023-2025) demonstrate a consistent focus on the intersection of logic, complexity, and artificial intelligence. Meier's work shows progression from foundational research in dependence and team logics toward more applied areas in argumentation theory and database systems. His research increasingly addresses computational challenges in AI systems, particularly in reasoning under uncertainty and handling inconsistent information. Meier actively contributes to the academic community through extensive program committee service for major conferences including AAAI (2021, 2023, 2024, 2025), IJCAI (2021-2025), and FoIKS (2024 as Co-Chair, 2026). He has also served as a reviewer for numerous conferences and journals in theoretical computer science and artificial intelligence. His current research projects include the DAAD-funded 'Applications and Complexity of Logics in Semiring-Team-Semantics' (2024-2025) and the DFG project 'Team Logics: New Bridges to Database Repairs' (2023-2026). Previously, he led the DFG project 'Nonclassical logics: parametrised and enumeration complexity' (2013-2022) and the MWK project 'Innovation Plus: Komplexität von Algorithmen' (2020-2022). Meier leads the Algorithms research group at Leibniz Universität Hannover, which focuses on theoretical aspects of algorithms with applications to logic and artificial intelligence. The group's work spans complexity theory, logical formalisms, and their applications to computational problems in knowledge representation and database systems.

Bodo Rosenhahnمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Big Data Analysis
• +۷ مورد دیگر

Bodo Rosenhahn is a Full Professor at Leibniz University Hannover, heading the Institute for Information Processing since September 2008. His research focuses on automated image interpretation with profound expertise in Computer Vision, Machine Learning, and Big Data Analysis. He has established himself as a leading researcher through extensive contributions to the field and successful industry transfer of his work. Rosenhahn received his Computer Science education at the University of Kiel, earning his Dipl.-Inf. in 1999 and Dr.-Ing. in 2003. His academic journey included a postdoctoral position at the University of Auckland (2003-2005), funded by the German Research Foundation, followed by senior researcher work at the Max-Planck Institute for Informatics in Saarbruecken (2005-2008). His research interests span multiple cutting-edge areas including Computer Vision, Machine Learning, 3D Human Pose Estimation, Motion Capture, Object Tracking, Anomaly Detection, and Reinforcement Learning. His work bridges theoretical foundations with practical applications, particularly in medical imaging, autonomous systems, and industrial quality control. The group he leads has developed innovative approaches for video-based motion capture, semantic scene analysis, and multi-object tracking that have achieved state-of-the-art results in numerous challenges. His most recent publications demonstrate strong trends toward explainable AI systems, uncertainty quantification in vision models, robust multi-model fitting techniques, and the integration of quantum principles with machine learning. These works reflect his commitment to developing both theoretically sound and practically applicable computer vision solutions that address real-world challenges in industry and medicine. DAGM-Prize 2002 Dr.-Ing. Siegfried Werth Prize 2003 DAGM-Main Prize 2005 ERC-Starting Grant 2011 (EUR 1.43 million) CVPR 2017 Multi-Object Tracking Challenge PhysRev-A Editors Suggestion 2023 TÜV-Süd Innovation award 2018 As head coach of the LUH AI competition team, Rosenhahn has mentored numerous students who have achieved success in international competitions. His research has been supported by prestigious grants including the ERC Starting Grant and POC Grant. He has also received the Erskine Fellowship for research at the University of Canterbury. Since 2023, he serves as associate editor for IEEE TPAMI, the highest-ranked journal in computer science. Rosenhahn leads a vibrant research group focused on automated image interpretation with multiple ongoing projects including Multiple People Tracking, Relational Object Tracking, Physics-based modeling, Video-based Motion Capture, and Quantum Learning. His group has developed significant datasets such as the Multimodal Motion Capture Indoor Dataset (MPI08) and Multimodal Motion Capture Dataset (TNT15) that have become valuable resources for the computer vision community. The group maintains strong industry connections, successfully transferring research into practical applications while continuing to push the boundaries of fundamental research in computer vision and machine learning.

Thomas Broxمشاهده پروفایل

استاد

• Computer Vision
• Deep Learning
• Visual Representation Learning
• +۴ مورد دیگر

Thomas Brox is a Professor for Pattern Recognition and Image Processing at the University of Freiburg , where he has headed the Computer Vision Group since 2010. He holds a PhD in computer science from Saarland University (2005) and has held postdoctoral positions at UC Berkeley, TU Dresden, and University of Bonn. Research Interests Education Publications & Awards Teaching & Leadership Roles His research focuses on computer vision and deep learning , particularly visual representation learning , video analysis , and training deep networks without manual supervision . He investigates how deep networks learn capabilities and applies these insights to action-perception cycles in robotics . Recent publications highlight trends in vision-language models , diffusion-based 3D generation , and anomaly detection . His work includes U-Net (seminal biomedical image segmentation) and FlowNet (optical flow estimation). Koenderink Prize (2014) ERC Starting Grant (2011) Longuet-Higgins Best Paper Award (2004) Multiple GCPR/ICCV/CVPR Best Paper Honors He teaches courses in Optimization , Statistical Pattern Recognition , and Computer Vision . He has served as Dean of Studies , Director of Computer Science Department , and will become Dean in 2026. His lab mentors 25+ PhD/MSc students and collaborates with institutions like Amazon (2020-2024).

Robert T. Königمشاهده پروفایل

دانشیار

• Quantum Communication Theory
• Fault-Tolerant Quantum Information Processing
• Quantum Computation
• +۴ مورد دیگر

Prof. Dr. Robert T. König is an Associate Professor at the Technical University of Munich (TUM) , holding the Professorship of Theory of Complex Quantum Systems in the TUM School of Computation, Information and Technology and Department of Mathematics. His research focuses on quantum information theory, with particular emphasis on mathematical methods for quantum communication, fault-tolerant quantum computing, and quantum many-body systems. Education: Diploma in Theoretical Physics, ETH Zurich (1998–2003) PhD in Applied Mathematics and Theoretical Physics, University of Cambridge (2005–2007) Key research areas include quantum communication theory , fault-tolerant quantum information processing , and quantum computation , with significant contributions to topological quantum computing, quantum error correction, and quantum channel capacity analysis. His recent work explores hybrid quantum-classical algorithms, non-abelian anyon manipulation, and bosonic code optimizations. Scientific awards include Swiss National Science Foundation Fellowship (2010) Smith/Rayleigh-Knight Prize, Cambridge (2006) ETH Medal and Willi-Studer Prize (2003) Pólya Prize, ETH Zurich (2003) He co-leads the Quantum Information Theory research group with Prof. Michael Wolf at TUM, supported by grants like the ERC Consolidator Grant 'Enhanced quantum information processing targeting the near term (EQUIPTNT)' and the Munich Quantum Valley initiative . His work has applications in quantum hardware design, noise resilience strategies, and fundamental limits of quantum communication.

Andreas Wieseمشاهده پروفایل

دانشیار

• Combinatorial optimization
• Approximation algorithms
• Operations research
• +۳ مورد دیگر

Prof. Dr. Andreas Wiese is an Associate Professor at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Mathematics. He previously held academic positions at the Vrije Universiteit Amsterdam (2021-2022), Universidad de Chile (2016-2021), and the Max-Planck-Institut für Informatik (2012-2016). Andreas Wiese earned his PhD in Mathematics from TU Berlin (2008-2011) and studied Mathematics and Computer Science at TU Berlin (2002-2008). His research focuses on combinatorial optimization , approximation algorithms , and geometric problem-solving , particularly for NP-hard problems in packing, scheduling, and network flow. His recent work includes A 2024 SODA publication on outlier-aware minimum sum of radii approximation A 2023 STOC paper improving weighted flow time minimization Foundational contributions to unsplittable flow and geometric knapsack problems with consistent appearances at top-tier conferences like STOC, FOCS, and SODA. Andreas Wiese leads the Discrete Optimization research group at TUM, supervising PhD students Alexander Armbruster, Elisa Dell'Arriva, and Sandy Heydrich. He has organized major conferences including LATIN 2024 and ADFOCS 2015 , and served on program committees for STOC, FOCS, and SODA.

• Optoelectronic Materials
• Organic Semiconductors
• Machine Learning for Materials Science
• +۳ مورد دیگر

Michael Rinderle is a researcher at the Technical University of Munich, affiliated with the TUM School of Computation, Information and Technology. He works under the Associate Professorship of Computational Photonics (headed by Prof. Jirauschek) and the Associate Professorship Simulation of Nanosystems for Energy Conversion (headed by Prof. Alessio Gagliardi). His research focuses on computational modeling of optoelectronic materials and devices, integrating machine learning with multiscale simulations. Research Interests: Michael specializes in applying machine learning techniques to materials discovery and simulation, particularly for organic semiconductors, perovskite solar cells, and electrocatalytic systems. His work bridges computational methods like kinetic Monte Carlo, density functional theory, and graph neural networks with practical applications in photovoltaics, IoT energy autonomy, and nanoscale device engineering. Teaching Roles: He contributes to courses including Computational Photonics Laboratory , Python for Engineering Data Analysis , and Simulation of Quantum Devices . His teaching emphasizes practical skills in programming, device simulation, and data visualization for engineering students. Projects: Involved in DFG e-Conversion clusters (I-III), TUM Innovation Network ARTEMIS, EU Lion-Hearted, and BMWi-funded initiatives, focusing on interfaces, energy conversion, and machine learning-driven materials optimization.

Henry Försterمشاهده پروفایل

پژوهشگر

• Graph Drawing
• Network Visualization
• Algorithm Design
• +۴ مورد دیگر

Dr. Henry Förster is a Post-Doc researcher at the Technical University of Munich (TUM) within the Chair of Efficient Algorithms, working under Prof. Stephen Kobourov. His research focuses on graph embeddings and network visualization , with applications in computer engineering, economics, and everyday contexts like metro maps. Ph.D. (Dr. rer. nat.) in Computer Science, summa cum laude, University of Tübingen (2020) M.Sc. in Computer Science, University of Tübingen (2016) B.Sc. in Engineering & Computing, TU Bergakademie Freiberg (2014) His work explores algorithmic efficiency , theoretical bounds for aesthetic criteria , and practical solutions for visualizing relational data. He has contributed to domains such as VLSI layouting, floor planning, and UML/Petri net diagram generation. Henry has received multiple accolades, including: Best Paper Award at GD 2024 First Place in GD Contest Live Challenges (2018–2022) Best Presentation Award at GD 2022 He actively contributes to academic communities as a PC member for GD2025, WG2025, and SafeToC advocate for GD. His teaching includes courses like Advanced Algorithms and Practical Course on Network Visualization .