جستجوی استاد راهنمای دانشگاهی

• Visual Perception
• Color Vision
• Sensorimotor Integration
• +۴ مورد دیگر

Karl R. Gegenfurtner is a Professor of General Psychology at the Department of Psychology, Justus Liebig University Giessen. His research focuses on information processing in the visual system, particularly the interplay between low-level sensory processes, high-level visual cognition, and sensorimotor integration. He investigates how complex scenes are perceived, represented in the brain, and used to drive motor systems, with a specialization in color perception, material property recognition, and eye movement dynamics. Ph.D. in Experimental Psychology, New York University (1990) Diploma in Psychology, University of Regensburg (1986) Habilitation in Medical Psychology and Behavioral Neurobiology, University of Tübingen (1998) His work bridges visual neuroscience with computational modeling, examining color categorization in neural networks, cortical mechanisms of color vision, and dynamic recalibration of visual perception during eye movements. Recent projects include Color 3.0: An object-oriented approach to color (ERC Advanced Grant) and Dynamics in Vision and Touch (Marie Curie Actions). Publications highlight advancements in understanding saccadic suppression, predictive eye movements, and chromatic adaptation timelines. Scientific awards include the Wilhelm Wundt Medal (2016), Rank Prize Funds Lecture (2014), and ERC Advanced Grant (2020–2025). He has served on editorial boards of Journal of Vision , Vision Research , and Perception , and led initiatives like the Neuroscientific Workflow Assistance (NOWA) project. Collaborations span institutions in Germany, the U.S., Australia, and the U.K., with a focus on perception-action loops and neural mechanisms underlying visual stability.

Yafang Chengمشاهده پروفایل

پژوهشگر

• Aerosol Chemistry
• Atmospheric Science
• Environmental Science
• +۴ مورد دیگر

Yafang Cheng is Director of the Aerosol Chemistry Department at the Max Planck Institute for Chemistry since 2024, with concurrent appointments as Guest Professor at Peking University (2023-) and Distinguished Guest Professor at University of Science and Technology of China (2021-). Her research integrates experimental methods , multi-scale modeling , and machine learning to advance understanding of aerosol particle dynamics and their impacts on air quality , public health , and climate change . Ph.D. in Environmental Sciences (Peking University, 2007) B.Sc. in Environmental Sciences (Wuhan University, 2001) Her work focuses on reactive nitrogen chemistry , aerosol acidity , black carbon effects , and planetary boundary layer interactions . She has developed novel instrumentation for aerosol analysis and pioneered machine learning applications in atmospheric science. Recent publications emphasize black carbon mitigation strategies (One Earth 2023), aerosol microdroplet pH (Chem 2023), and SARS-CoV-2 transmission modeling (Science 2021). These studies demonstrate interdisciplinary approaches spanning environmental chemistry , climate physics , and public health policy . Fellow: AAAS (2023), AGU (2022) Joanne Simpson Medal (AGU, 2022) Science Breakthroughs of the Year (Falling Walls, 2021) Highly Cited Researcher (Web of Science, 2021-2022) Minerva Outstanding Female Scientist Award (2014) She has mentored 38 early-career researchers (21 postdocs, 17 PhD students) who have achieved professorships , tenured positions , and international awards . Her institutional leadership includes initiating academic exchange programs between European and Chinese institutions.

Heiner Giefersمشاهده پروفایل

استاد

• Cloud Computing
• Hardware Acceleration
• Field-Programmable Gate Arrays
• +۷ مورد دیگر

Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.

Andreas Herkersdorfمشاهده پروفایل

استاد

• Computer Architecture
• Embedded Systems
• Network-on-Chip (NoC)
• +۴ مورد دیگر

Prof. Dr. Andreas Herkersdorf is a Full Professor and Chair of Integrated Systems at the Technical University of Munich (TUM) School of Computation, Information and Technology. His research focuses on application-specific multicore processors (MPSoC), FPGA-based prototyping, fault-tolerant systems, and energy-efficient architectures, with applications in IP packet processing, automotive systems, and visual computing. He has received multiple IBM innovation awards and serves on editorial boards including the DFG Review Board for computer architecture. Education: Dipl.-Ing. Electrical Engineering (TUM, 1987), Dr. techn. Electrical Engineering (ETH Zurich, 1991) Research: MPSoC architectures, autonomic computing, NoC resilience, FPGA acceleration, and self-optimizing systems. Awards: IBM Master Inventor (1998), IBM Outstanding Technical Achievement Award (2001), multiple IBM Innovation Achievement Awards (1996-2003) His recent publications emphasize hardware/software co-design, machine learning integration for runtime optimization, and network-on-chip innovations. He collaborates on projects involving 6G systems, smartNICs, and automotive communication protocols.

Marc Teboulleمشاهده پروفایل

استاد

• Continuous Optimization
• Convex Optimization
• Complexity Analysis of Optimization Algorithms
• +۵ مورد دیگر

Marc Teboulle is a distinguished Professor holding The Eric and Sheila Samson Chair of Optimization in the School of Mathematical Sciences at Tel Aviv University. With a career spanning over three decades, he has established himself as a leading figure in optimization theory and applications. His work bridges theoretical foundations with practical implementations across multiple scientific domains. Professor Teboulle's research focuses on continuous optimization, with particular emphasis on convex optimization, complexity analysis of algorithms, Lagrangian and dual decomposition methods, variational inequalities, and nonconvex nonsmooth large-scale optimization. His work has significant applications in engineering science, machine learning, and finance, demonstrating the interdisciplinary impact of optimization techniques. He has developed novel frameworks for center-based clustering algorithms and contributed to the theoretical understanding of first-order methods beyond traditional Lipschitz gradient continuity assumptions. His publication record shows a clear evolution toward increasingly sophisticated optimization frameworks, with recent work focusing on nonconvex composite optimization, Lagrangian-based methods, and complexity analysis of gradient-based algorithms. The trend indicates growing interest in non-Euclidean geometries for optimization and applications to high-dimensional data problems, reflecting the evolving challenges in modern optimization. As an educator and mentor, Professor Teboulle has supervised numerous PhD and MSc students since 1990, including prominent researchers like Amir Beck, Ron Shefi, and Yoel Drori. His graduate courses include Convex Analysis and Optimization, Advanced Topics in Modern Optimization, Algorithms for Continuous Optimization, and Advanced Seminar in Continuous Optimization. He has been exceptionally active in the academic community, delivering invited lectures at major international conferences from 2003 through 2024 across Asia, Europe, and North America. His book 'Asymptotic Cones and Functions in Optimization and Variational Inequalities' (co-authored with A. Auslender) has become a standard reference in the field, while his edited volume 'Grouping Multidimensional Data: Recent Advances in Clustering' has influenced data science applications.

• Spatial Modeling
• Temporal Modeling
• Semantic Modeling
• +۷ مورد دیگر

Prof. Thomas H. Kolbe serves as Chair of Geoinformatics at the Technical University of Munich (TUM), where he leads research in spatial, temporal, and semantic modeling of urban environments. His work focuses on developing foundational frameworks for 3D/4D city models, digital twins, and smart city applications through international standardization efforts including CityGML and IndoorGML. His research spans virtual city modeling, urban system simulation, and GIS integration with emerging technologies. Current projects emphasize AI-driven urban scenario generation, semantic streetspace modeling, and IoT integration in digital twin ecosystems. Recent publications demonstrate strong interdisciplinary connections between computer vision, urban planning, and geospatial data science, with particular emphasis on practical implementations of 3D city models for sustainability challenges. Prof. Kolbe actively contributes to professional organizations including the Round Table GIS eV (as Chairman since 2013) and the Munich Data Science Institute (as core member since 2021). His leadership extends to the Leonhard Obermeyer Center for digital methods in the built environment and the Hans Eisenmann Forum for agricultural sciences. His work bridges theoretical geoinformatics with practical urban applications through numerous collaborative projects with municipal governments and industry partners.

• Financial Mathematics
• Stochastic Processes
• Risk Management
• +۳ مورد دیگر

Prof. Dr. Francesca Biagini is a full Professor at the Department of Mathematics, University of Munich (LMU Munich) , leading the Stochastics and Financial Mathematics working group. She serves as Vice President for International Affairs and Diversity at LMU Munich since October 1, 2019, and as President of the Bachelier Finance Society (2022–2023). She is also a Correspondent of the Deutsche Aktuarvereinigung (DAV) and a member of the Executive Board of the Munich Risk and Insurance Center (MRIC) since 2017. Her research focuses on stochastic processes in financial markets , particularly asset price bubbles , default risk modeling , and robust hedging under model uncertainty. Recent work includes deep learning applications to bubble detection and non-linear affine processes for market dynamics. She actively contributes to academic leadership through teaching and publications, including 15+ recent articles on topics like liquidity-induced bubbles, machine learning calibration, and systemic risk transfer equilibrium. Her workgroup collaborates on quantLab initiatives and DAV certificate programs .

Jan Salmenمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Neural Networks
• +۳ مورد دیگر

Dr. Jan Salmen is a researcher at Ruhr University Bochum's Faculty of Computer Science, affiliated with the Institute of Neuroinformatics (INI). His work focuses on real-time systems, computer vision, and machine learning. Doctoral thesis: Efficient video-based driver assistance systems Salmen's research spans autonomous driving, traffic sign recognition, stereo vision, and sports analytics. He has contributed to benchmarks in traffic sign detection and soccer analysis. Publications highlight his expertise in image processing, pattern recognition, and sensor fusion for autonomous systems. Key trends include optimization of machine learning algorithms for real-time applications. He collaborates with interdisciplinary teams at INI, which integrates experimental psychology, neurophysiology, and robotics into artificial cognitive systems research.

Mira Schedensackمشاهده پروفایل

استادیار

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +۳ مورد دیگر

JProf. Dr. Mira Schedensack is a faculty member at the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science , University of Münster. Her expertise lies in Numerical Analysis, Machine Learning, and Scientific Computing , with a focus on finite element methods and numerical solutions for partial differential equations. Research Interests: Numerical methods for PDEs, mixed finite element formulations, adaptive algorithms, and thermo-optical interactions in computational physics. Teaching: Offers courses in Numerical Partial Differential Equations and Adaptive Finite Element Methods. Students: Mentors doctoral student Jonas Ketteler . Publications: Key contributions to non-conforming FEM, Stokes equations, and gradient elasticity.

• Information Theory
• Quantum Communication
• Wireless Networks
• +۳ مورد دیگر

Pau Colomer is a Researcher at the Technical University of Munich , affiliated with the Theoretical Information Technology department. His work focuses on quantum communication, information theory, and wireless networks, contributing to foundational research in quantum channel identification and network information processing. His research interests span quantum information theory , neural networks , and signal processing , with recent publications addressing deterministic identification over quantum channels, coherence theory in quantum systems, and multi-user quantum communication. These works highlight advancements in quantum channel coding , decentralized processing , and security in wireless networks . Publications demonstrate a trend toward quantum communication and network information theory , emphasizing challenges in interference modeling , entanglement transmission , and information-theoretic security . Key subfields include superlinear rates in finite output channels , decoupling mechanisms , and quantum-enhanced sensing .

Carolin Müllerمشاهده پروفایل

استادیار

• Computational Chemistry
• Molecular Dynamics
• Machine Learning
• +۷ مورد دیگر

Carolin Müller is a Juniorprofessor for the Theory of Electronically Excited States at the Friedrich-Alexander University Erlangen-Nuremberg since November 2023. Previously, she was a Feodor Lynen Postdoctoral Researcher at the University of Luxembourg (June 2022-October 2023) and a Postdoctoral Researcher at Friedrich Schiller University Jena (March 2021-May 2022). Dr. Müller received her B.Sc. (2016) and M.Sc. (2018) in Chemistry from Friedrich Schiller University Jena, followed by her Ph.D. (Dr. rer. nat) in 2021 from the same institution. Her doctoral research focused on "Towards Operando Spectroscopy of Supramolecular Photocatalysts – A Case Study on Ru-dppz-derived Systems" under the supervision of Prof. B. Dietzek-Ivanšić. Dr. Müller's research focuses on the theoretical understanding of photoinduced processes in molecules and materials. Her group (CPC Group) investigates electron transfer processes, isomerization reactions, and excited-state dynamics with the goal of controlling and optimizing light-driven processes for increased reactivity and efficiency. Her work combines computational chemistry, spectroscopy, and machine learning approaches, specifically utilizing methods like TD-DFT, CASSCF, molecular/quantum dynamics, and cheminformatics techniques including SVD, MCR, and global/target lifetime analysis. Her recent publications demonstrate a strong interdisciplinary approach spanning computational chemistry, spectroscopy, and machine learning. Key themes include nonadiabatic molecular dynamics, excited-state simulations, photoswitch design, photocatalysis, and the development of computational tools like KiMoPack for kinetic modeling. Her work often bridges theoretical predictions with experimental validation through close collaboration with spectroscopy research groups. Feodor Lynen Research Fellowship (Alexander von Humboldt Foundation) Thuringian Research Award 2023 for Applied Research Albert-Weller Award (German Chemical Society) Dissertation Award (Faculty of Chemistry and Earth Sciences) FCI Kekulé PhD fellowship As a Juniorprofessor, Dr. Müller leads the CPC Group at FAU, where she mentors students in computational chemistry research. She has developed expertise in combining spectroscopic techniques (resonance Raman, transient absorption, and time-resolved emission spectroscopy) with computational methods and cheminformatics approaches. She also actively contributes to the scientific community through service roles including co-organizing the ESTML 2023 Workshop and serving as an active member in the yPC organization of the German Bunsen Society. Dr. Müller is actively developing the CPC Group research program at the Computer Chemistry Center, focusing on light-induced physical processes and chemical reactions. Her group combines quantum chemistry, chemoinformatics, and experimental spectroscopy to reveal mechanisms behind photoinduced phenomena and optimize light-driven processes.

Maurits Haverkortمشاهده پروفایل

استاد

• Strongly Correlated Electron Systems
• Quantum Many-Body Physics
• X-ray Spectroscopy
• +۶ مورد دیگر

Maurits Haverkort is a Professor at the Institute for Theoretical Physics, Heidelberg University (Germany). His research focuses on quantum many-body systems , strongly correlated electrons , and X-ray spectroscopy of complex materials under strong fields. University of Cologne (PhD in Physics, 2005) University of Groningen (M.Sc. in Physics, 2002) Research Interests : He investigates orbital and magnetic properties in heavy fermion systems , actinide materials , and correlated oxides using resonant inelastic X-ray scattering (RIXS) , ARPES , and computational tools like Quanty . His work spans crystal field theory , spin-orbit coupling , and ultrafast electron dynamics . Scientific Awards & Activities : 2018 – Editorial Board Member, Physical Review Letters 2017 – Beam Time Allocation Panel, ESRF Grenoble 2016–2018 – Swedish Research Council Panel NT-4 2012–2016 – Scientific Selection Panel, Helmholtz-Zentrum Berlin Recent Publications highlight 5f electron counting , photon-modulated bonding , and precision neutrino mass experiments , reflecting his expertise in quantum materials and advanced spectroscopy .

• Wireless Communication
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

• Epistemology
• Philosophy of Science
• Philosophical Logic
• +۷ مورد دیگر

Wolfgang Spohn is a Senior Professor at the University of Tübingen (since 2019) and Principal Investigator of the Cluster of Excellence 'Machine Learning: New Perspectives for Science' . He previously served as Professor (C4) at the University of Konstanz (1996-2018) and as a Senior Lecturer at the University of Bielefeld (1991-1996). His academic career spans multiple prestigious roles including Academia Europaea member (2015) Frege-Preis awardee (2015) Lakatos Award winner (2012) His research focuses on epistemology , philosophy of science , philosophical logic , ontology , and decision/game theory . He developed ranking theory as a foundational framework for belief dynamics and epistemic reasoning. His work bridges formal epistemology with practical applications in legal theory, economic rationality, and artificial intelligence. Recent publications highlight his contributions to Rationality frameworks for law and science Indexical utility theory and time preferences Ranking-theoretic approaches to conditionals and norms Epistemological foundations of induction Scientific honors include Lakatos Award (2012) Frege-Preis (2015) Heisenberg-Stipendium (1985) He has led major research initiatives such as the DFG Forschergruppe on counterfactuals (2011-2019) and the Reinhart-Koselleck project on reflexive decision theory (2020-present).

Arish Sateesanمشاهده پروفایل

استاد

• Computer Engineering
• Network Security
• Hardware Acceleration
• +۳ مورد دیگر

Dr. Arish Sateesan serves as Professor and Chair of the Institute for Networked Systems at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, located at Kackertstrasse 9 in Aachen, Germany. His research group operates from House C (Room C046) with direct contact via asa@inets.rwth-aachen.de. His primary research domains center on hardware-accelerated network security solutions, specializing in FPGA implementations for high-speed networking. Key focus areas include: Real-time network monitoring and intrusion detection systems Hardware-optimized cryptographic and non-cryptographic algorithms Machine learning integration for wireless beamforming and LiDAR processing Ultra-high-speed flow measurement architectures His work bridges theoretical computer science with practical hardware constraints, emphasizing throughput optimization for security-critical applications. Analysis of his 15 most recent publications (2021-2025) reveals a pronounced shift toward hardware-software co-design for next-generation networks. The research trajectory shows increasing integration of quantized neural networks with traditional security primitives, particularly for mm-Wave and 5G/6G applications. A consistent theme across all publications is the prioritization of hardware friendliness through algorithmic simplification and architectural innovation. As Institute Chair, he leads a research ecosystem focused on developing deployable security solutions for modern network infrastructures, with current projects targeting autonomous vehicle communication systems and infrastructure protection against distributed denial-of-service attacks.