Find Academic Supervisors

• Theoretical Computer Science
• Graph Algorithms
• Computational Complexity
• +4 more

Frank Fuhlbrück is a Researcher at the Institute of Computer Science within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin, actively contributing to the Algorithms and Complexity II research group. His position as a scientific collaborator places him at the forefront of theoretical computer science research in Berlin. His research centers on fundamental problems in graph theory and computational complexity, with deep expertise in the Weisfeiler-Leman algorithm for graph isomorphism testing. Key interests include parameterized complexity, graph invariants, spectral properties, and combinatorial methods for distinguishing graph structures. His work bridges discrete mathematics, algebra, and theoretical computer science to address foundational questions in algorithm design. Analysis of his 2020-2024 publications reveals a cohesive research trajectory focused on the expressive power of combinatorial invariants, particularly through subgraph counts and spectral hierarchies. His contributions to understanding fixing numbers, vertex individualization, and fractional packing dimensions demonstrate innovative applications of algebraic and logical frameworks to graph classification problems. As an integral member of the Algorithms and Complexity II group, he collaborates on advancing core theoretical frameworks in algorithms while maintaining connections to empirical research methodologies as evidenced by his 2022 work on R-based statistical tools.

• Theoretical Computer Science
• Algorithms
• Computational Complexity

Benjamin Hauskeller is a scientific collaborator at the Institute of Computer Science , part of the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin. His work focuses on theoretical computer science and algorithmic research. Research interests include: Theoretical foundations of computation Algorithm design and analysis Computational complexity theory Contact: hauskeller@hu-berlin.de | Personal Website

• Distributed Computing
• Population Protocols
• Graph Algorithms
• +5 more

Joel Rybicki is a Professor at the Institute of Computer Science within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin. His research focuses on distributed computing and theoretical aspects of computer science, with particular expertise in population protocols, graph algorithms, and consensus problems. Professor Rybicki's research interests span several key areas in theoretical computer science and distributed systems . His work primarily focuses on distributed computing, where he investigates fundamental problems in population protocols, consensus algorithms, and graph-based distributed systems. His research bridges theoretical computer science with practical applications in networked systems and biological computing models. He has made significant contributions to understanding the computational power and limitations of distributed systems, particularly in challenging environments with faults, asynchrony, and dynamic network topologies. His work often examines the intersection between computer science theory and biological systems, as evidenced by publications on microbial communities and competitive populations. Analysis of Professor Rybicki's publication record reveals a strong focus on population protocols and graph-based distributed algorithms. His work spans both theoretical foundations and practical applications, with research themes including consensus problems, leader election, fault tolerance, and the computational aspects of biological systems. The publications demonstrate expertise across multiple subfields of distributed computing, with particular emphasis on understanding the fundamental limits and possibilities of distributed computation in various network models. His recent work shows increasing interest in space-efficient protocols and connections to biological systems, suggesting an evolving research trajectory that continues to push the boundaries of theoretical distributed computing. Professor Rybicki is actively involved in doctoral education at Humboldt University, as evidenced by the disputation announcements for doctoral candidates in the Institute of Computer Science. His research group likely focuses on theoretical aspects of distributed systems and algorithms, contributing to the vibrant academic community within the Department of Computer Science.

• Machine Learning Theory
• Game Theory
• Information Theory
• +9 more

Wouter M. Koolen is a Professor of Mathematical Machine Learning at the University of Twente and a Senior Researcher in the Machine Learning group at Centrum Wiskunde & Informatica (CWI) in Amsterdam. Appointed to his professorship on June 1, 2022, he delivers his expertise across both institutions with offices in Enschede and Amsterdam. He is actively engaged in academic leadership through his organization of the Machine Learning Theory Research Semester Programme at CWI in Spring 2023 and serves as an ELLIS Scholar since December 2020. Dr. Koolen's research spans machine learning theory with particular focus on pure exploration in multi-armed bandit models , game tree search algorithms , and provably accelerated learning in both statistical and individual-sequence settings, which he characterizes as 'learning faster from easy data.' His work bridges theoretical foundations with practical applications, especially in safe statistical testing using e-values. He maintains active collaborations through INRIA-CWI associate teams 6PAC with Inria Lille and 4TUNE with Inria Paris and Grenoble. His recent publications reveal a strong emphasis on developing theoretically sound methods for statistical inference that maintain validity under optional stopping and continuation, representing a significant shift from traditional p-value based approaches. This work has important implications for fields requiring rigorous statistical guarantees in adaptive experimental settings. NWO VENI grant recipient QUT Vice-Chancellor's postdoctoral research fellowship awardee ELLIS Scholar (elected December 2, 2020) Member of ACM Future of Computing Academy Professor Koolen has supervised numerous PhD students to completion, including Hongwei Wen, Clément Lezane, and Tyron Lardy in 2025, and formerly Rianne de Heide who won the VVSOR Willem R. van Zwet award. He has served on program committees for major conferences including COLT, ICML, and ALT, and actively organizes workshops on cutting-edge topics in machine learning theory. His research group at CWI hosts regular reading groups and seminar series, fostering a vibrant theoretical machine learning community in the Netherlands.

• Nonparametric Statistics
• Semiparametric Models
• Credit Scoring
• +4 more

Professor Marlene Müller serves as Professor of Applied Statistics at Beuth University of Applied Sciences Berlin within the Department of Mathematics, Physics, and Chemistry in the School of Engineering. Her academic career spans over two decades with significant contributions to nonparametric and semiparametric statistical methods, particularly applied to financial data analysis and credit scoring. Her research interests center on Nonparametric Statistics , Semiparametric Models , and Statistical Computing , with a strong emphasis on practical applications in finance. She has developed numerous R implementations for statistical methods, making complex techniques accessible to practitioners. Her work bridges theoretical statistics with real-world financial applications, particularly in credit risk modeling and portfolio analysis. Professor Müller's publication record shows a consistent focus on applying advanced statistical techniques to financial problems. Her work evolved from foundational theoretical contributions in the 1990s to more applied financial modeling in the 2000s, with a particular emphasis on credit scoring systems under regulatory frameworks like Basel II. She has maintained a strong focus on developing computational tools, especially through the R programming language, to implement these statistical methods. She is co-author of the influential textbook Nonparametric and Semiparametric Models (Springer Series in Statistics, 2004) with Härdle, Sperlich, and Werwatz, which has become a standard reference in the field. Her teaching portfolio includes advanced statistics courses with a strong computational component, and she has developed extensive educational materials for teaching nonparametric methods. Professor Müller maintains an active consultation schedule, with office hours listed through the summer semester of 2025, indicating her ongoing engagement with students and academic work. Her contributions to statistical computing through R package development and teaching materials demonstrate her commitment to advancing both theoretical and applied statistics education.

• Quantum Chemistry
• Theoretical Chemistry
• Atomic Physics
• +5 more

Ruiz Ruiz serves as an Associate Professor in the Department of Chemistry and Pharmacy within the Faculty of Natural Sciences at Friedrich Alexander University Erlangen-Nuremberg (FAU). Affiliated with the Chair of Theoretical Chemistry (led by Prof. Dr. Görling), Ruiz Ruiz maintains an active research profile in quantum chemistry with numerous publications spanning several decades. As a PD Dr. (Privatdozent Doctor), Ruiz Ruiz has completed the German habilitation process, signifying advanced scholarly achievement beyond the doctoral degree. Ruiz Ruiz's research focuses on developing and applying advanced computational methods for quantum mechanical calculations of atomic systems. Primary research areas include Hylleraas-Configuration Interaction (Hy-CI) methods, Slater orbital integrals, and wave function theory for atomic and molecular systems. The research demonstrates particular expertise in high-precision calculations of atomic energy levels, electron correlations, and excited states. Recent work continues to refine computational algorithms for increasingly complex atomic systems while maintaining high accuracy in quantum mechanical predictions. Analysis of recent publications reveals a consistent trajectory in advancing computational quantum chemistry methods. Ruiz Ruiz has made significant contributions to the theoretical framework for calculating atomic properties with exceptional precision, particularly for systems involving multiple electrons. The research shows strong continuity in methodological development, with recent work building upon decades of prior contributions to Hy-CI methods and related computational approaches. Key application areas include helium-like systems, lithium ions, boron atoms, and other light elements where precise quantum mechanical treatment is essential. Ruiz Ruiz's scholarly output demonstrates sustained contribution to the field of theoretical quantum chemistry, with publications appearing consistently from the 1990s through 2025. The research program shows particular strength in developing analytical methods for quantum mechanical integrals and advancing computational techniques for atomic structure calculations. While specific grants and advising activities aren't detailed in the available information, the extensive publication record suggests active supervision of research students and potential grant-funded research activities within the theoretical chemistry group at FAU.

• Combustion
• Molecular Spectroscopy
• Laser Diagnostics
• +3 more

Thomas Häber is a Senior Scientist and Research Fellow at the Karlsruhe Institute of Technology (KIT), where he leads research in the Institute for Chemical Technology and Polymer Chemistry. He holds a PhD in Physical Chemistry from the University of Göttingen (2000) and an MS in Chemistry from the University of Paderborn (1997). His work focuses on combustion diagnostics, molecular spectroscopy, and catalysis, with expertise in laser-based techniques like PLIF and high-resolution FTIR. He has pioneered studies on flame-wall interaction, ignition mechanisms, and computational fluid dynamics modeling. Key achievements include developing tomographic multispecies visualization methods and analyzing NOx storage catalysts. His research projects include collaborations within the SFB 150 and FOR 1447 frameworks. He has received awards such as the Richard Zsigmondy Award and Humboldt Foundation fellowship. Research interests span combustion dynamics, laser diagnostics, and computational tools like machine learning. His contributions to understanding flame quenching, hot particle ignition, and catalytic oxidation have advanced both fundamental science and engineering applications. He collaborates across disciplines, integrating experimental and numerical approaches to solve complex fluid flow and reaction problems.

• Multiphase Transport Phenomena
• Computational Fluid Dynamics (CFD)
• Fluid Dynamics
• +5 more

Dr. Martin Wörner is a Senior Researcher and Head of the Multiphase Flow Group at the Karlsruhe Institute of Technology (KIT), affiliated with the Institute of Catalysis Research and Technology (IKFT). He holds a PhD in Mechanical Engineering (Dr.-Ing.) from the University of Karlsruhe (1994), with a thesis on turbulent Rayleigh-Bénard convection. His work spans over 30 years in multiphase flow dynamics, computational fluid dynamics, and reaction engineering. Education : PhD in Mechanical Engineering (Dr.-Ing.), University Karlsruhe (1994) Diploma in Mechanical Engineering (Dipl.-Ing.), University Karlsruhe (1989) Research Interests : Wörner’s research focuses on multiphase transport phenomena, computational fluid dynamics (CFD) of single and two-phase flows, residence time distribution theory, and turbulence modeling. He has pioneered numerical methods for simulating complex multiphase systems, including Taylor flow, bubble column dynamics, and droplet impact mechanics. His work integrates experimental validation with advanced numerical techniques such as phase-field methods and volume-of-fluid approaches. Teaching & Academic Contributions : He teaches advanced CFD courses on multiphase flows at KIT and co-lectures international training courses on ERCOFTAC Best Practice Guidance. Notable contributions include co-initiating the DFG Priority Programme 1506 on Transport Processes at Fluidic Interfaces (2010–2016) and serving as an International Subject Editor for Applied Mathematical Modelling . Awards & Recognition : Redtenbacher Award (1989) – Best diploma thesis in mechanical engineering Outstanding Contribution in Reviewing for Chemical Engineering Science (2018) Assigned Member of DECHEMA/VDI Section on Computational Fluid Dynamics (2020–present) Lab & Collaborations : His group collaborates internationally on projects involving CFD for dispersed multiphase flows, with applications in catalytic reactors, emission control, and microfluidics. Key tools include OpenFOAM® and custom numerical frameworks for phase-field simulations.

• Channel Coding
• Machine Learning
• Optical Communications
• +3 more

Laurent Schmalen is a full professor at the Karlsruhe Institute of Technology (KIT), co-directing the Communications Engineering Laboratory (CEL). He holds a Dipl.-Ing. and Dr.-Ing. in Electrical Engineering from RWTH Aachen University (2005 and 2011). Previously, he worked at Alcatel-Lucent Bell Labs (2011–2019) and led the Coding for Optical Communications department at Nokia Bell Labs (2016–2019). His research focuses on channel coding, machine learning, optical communications, and high-speed data transmission. Education and Professional Experience: 1999: Abitur at Lycée Classique Diekirch (Luxembourg) 2005: Diploma in Electrical Engineering and Information Technology, RWTH Aachen University 2011: PhD (summa cum laude) in Electrical Engineering, RWTH Aachen University 2011–2019: Research roles at Bell Labs 2014–2019: Visiting Lecturer at University of Stuttgart 2019–present: Full Professor at KIT Research Interests: His work spans channel coding (e.g., LDPC codes), machine learning for communications, modulation formats, and optical transmission systems. Key areas include non-linear channel analysis, probabilistic constellation shaping, and FEC optimization for fiber optics. Awards and Recognition: IEEE Fellow (2023) ERC Consolidator Grant (2023) Best Paper Awards in IEEE journals (2016, 2018) E-Plus Prize for Doctoral Thesis (2011) Editorial and Leadership Roles: Associate Editor, IEEE Transactions on Communications TPC Co-Chair, International Symposium on Topics in Coding (ISTC 2025) General Co-Chair, ITG Conference on Systems, Communications, and Coding (2025) Labs and Teams: He co-leads the Communications Engineering Lab (CEL) at KIT, focusing on cutting-edge research in optical communications and machine learning applications.

Marian Benner-WicknerView profile

Professor

• Software Engineering
• E-Learning
• Case Management
• +7 more

Prof. Marian Benner-Wickner is a Professor of Computer Engineering at IU University of Applied Sciences, where he has been a faculty member since 2018. He leads the Industrial Engineering & Management and Industry 4.0 online study programs, focusing on software engineering and gender-neutral e-learning methodologies. His professional background includes roles as an IT specialist trainer at CampusLab GmbH and a software developer at the Fraunhofer Institute for Software and Systems Technology. He holds a PhD in Software Engineering (2016) from the University of Duisburg-Essen, where he managed the “Agenda-Driven Case Management” research cluster. His research interests span software engineering, case management techniques, process flexibility, smart home technologies, and digital youth protection. Notable contributions include work on adaptive case management systems, ontology-based recommendation systems, and e-learning metadata frameworks. Prof. Benner-Wickner actively contributes to organizations like the Wikimedia Foundation, CPS.HUB NRW, and the Society for Computer Science. His publications address topics such as IT integration architectures, automated grading systems, and security frameworks in mobile networks. His recent projects emphasize integrating IT applications, enhancing educational technologies, and optimizing knowledge-intensive business processes through semantic web technologies and process mining. In advising and grants, he oversees company-specific final theses in IT and technology, emphasizing practical industry collaboration. He has developed guidelines for design science research in academic works and advocates for empirical approaches to software process improvement. His leadership in Industry 4.0 programs reflects his commitment to bridging academic research with industrial applications. Leveraging his role as department head, he fosters innovation in curriculum design, particularly in leveraging digital tools for inclusive and adaptive learning environments. His interdisciplinary work intersects computer science, education, and policy, addressing societal challenges like digital youth protection and smart home security.

Niklas MückView profile

Researcher

• Formal Methods
• Programming Languages
• Type Theory
• +3 more

Niklas Mück is a PhD student at the Max Planck Institute for Software Systems in Saarbrücken, Germany, co-advised by Derek Dreyer and Deepak Garg. His research focuses on robust compilation techniques and formal verification within the semantic multi-language framework DimSum. He holds a Bachelor's degree from Saarland University. Education: Bachelor's degree in Computer Science, Saarland University Research Interests: His work integrates formal methods and programming language theory to ensure correctness in complex systems. Key areas include: - Compilation to capability machines - Theoretical foundations of type systems (Calculus of Inductive Constructions) - Semantic analysis for property preservation. Teaching Contributions: Teaching Assistant for courses like Pearls of Functional Programming (2025), Semantics (2023/24), and Mathematics Preparatory Courses (2020–2023) Main Organizer for Introduction to Computational Logic (2023) Research Trends in Publications: His publications emphasize bridging abstract theory (e.g., Kleene-Post theorems) with practical compiler design, leveraging semantic frameworks for rigorous guarantees. Recent work explores computability and reducibility in formal systems. Labs/Teams: Active contributor to the DimSum framework development team.

• Machine Learning
• Artificial Intelligence
• Stochastic Games
• +5 more

Xian Chen is an active academic researcher with an extensive publication record spanning over 30 years from 1994 to 2025. Their research demonstrates significant contributions across multiple disciplines within computer science, engineering, and applied mathematics. The publication pattern indicates sustained scholarly activity at a high level, with numerous papers in top-tier journals and conferences. Chen's research interests span a remarkably diverse range of fields, with particular emphasis on machine learning, artificial intelligence, stochastic processes, and their applications across various domains. Their work bridges theoretical foundations with practical applications, as evidenced by publications in both theoretical journals like SIAM Journal on Control and Optimization and applied venues like IEEE Access . The research portfolio shows evolution from early work in parallel computing and wireless networks toward contemporary AI and machine learning applications. The publication record reveals significant trends in Chen's research trajectory. Early work focused on parallel computing, wireless networks, and database systems. Over time, there's a clear shift toward machine learning applications, optimization techniques, and interdisciplinary work connecting computer science with fields like biomedical engineering, renewable energy, and education technology. Recent publications (2024-2025) show strong focus on large language models, federated learning, hate speech detection, and educational AI applications, reflecting current trends in artificial intelligence research. The consistent output across multiple domains suggests a highly collaborative research approach with numerous co-authors across different institutions. Chen has established productive collaborations with researchers worldwide, as evidenced by the diverse author lists across publications. The research demonstrates both theoretical depth in areas like stochastic games and Markov decision processes, as well as practical applications in fields ranging from medical diagnostics to renewable energy systems. The interdisciplinary nature of the work indicates versatility in applying computational methods to solve domain-specific problems.

Martin SchäferView profile

Researcher

• Applied Mathematics
• Harmonic Analysis
• Approximation Theory
• +3 more

Dr. Martin Schäfer is a Researcher at the Chair of Applied Analysis within the Faculty of Mathematics at Chemnitz University of Technology. His research focuses on advanced mathematical frameworks such as α-molecules, coorbit space theory, and approximation methods in harmonic analysis. He earned his Diploma in Mathematics from the University of Bonn (2012) and completed his PhD at the Technical University of Berlin (2018) with a thesis on α-molecules. His work bridges pure and applied mathematics, addressing challenges in signal processing, image analysis, and high-dimensional approximation. Key research interests include approximation theory, Fourier analysis, function spaces, and the development of adaptive representation systems like shearlets and curvelets. Schäfer has contributed to conferences such as the International Conference on Multivariate Approximation (2016) and the GAMM Annual Meeting (2014), presenting on topics like phase retrieval and sparse recovery. His publications explore theoretical foundations of wavelet systems and their applications in optimal function recovery and sampling problems. Notable achievements include establishing new upper bounds for sampling numbers and developing subsampling techniques for finite frames. While no specific awards are listed, his prolific publication record in top journals like Applied and Computational Harmonic Analysis and Journal of Fourier Analysis and Applications highlights his academic impact. Schäfer collaborates internationally, evidenced by his participation in workshops in Germany, Canada, and the USA.

• Approximation Theory
• Functional Analysis
• Harmonic Analysis
• +3 more

Pozharska Kateryna is a researcher at the Department of Function Theory, Institute of Mathematics, National Academy of Sciences of Ukraine. Her work focuses on approximation theory, functional analysis, and harmonic analysis with applications to periodic and multivariate functions. Research Interests : Approximation theory, nonlinear approximation, widths and entropy numbers, sampling recovery algorithms, and function spaces like Nikol'skii-Besov and Lq. Scientific Awards : 2023 Joseph F. Traub Information-Based Complexity Young Researcher Award. Publications : Over 15 articles on approximation characteristics, D-optimal designs, and recovery of periodic functions (only the 15 most recent included). Contact : Email pozharska.k@imath.kiev.ua | Office Room 304 | Postal: Institute of Mathematics, 3 Tereshchenkovska St., Kyiv, Ukraine.

Sen ChengView profile

Professor

• Computational Neuroscience
• Neural Mechanisms of Learning and Memory
• Hippocampal Dynamics
• +5 more

Prof. Sen Cheng is a Professor at the Institute of Neuroinformatics (INI), part of the Faculty of Computer Science at Ruhr-Universität Bochum. His research focuses on computational neuroscience, particularly the neural mechanisms underlying learning, memory, and spatial navigation. He leads a lab investigating hippocampal dynamics, combining mathematical modeling with optogenetic and electrophysiological data analysis. Collaborations span neuroscientists in Germany and internationally. Research interests include hippocampal replay mechanisms, episodic memory functions, and the interplay between sensory inputs and neural networks. His work bridges computational models (e.g., spiking neural networks) with experimental data from rodents and humans. Recent projects explore deep reinforcement learning agents for spatial navigation and the role of grid cells in cognitive mapping. Publications span Neuron , Current Biology , and eLife , addressing topics like hippocampal network stabilizations, spatial coding efficiency, and cerebellar contributions to fear extinction. Teaching includes courses on computational neuroscience, artificial neural networks, and mathematical psychology. Lab activities involve interdisciplinary research through colloquia like Brains in Space and supervision of master’s and bachelor’s theses in areas like reinforcement learning algorithms and embodied associative networks. The INI’s mission drives his work, linking biological insights to artificial cognitive systems design.