Find Academic Supervisors

• Algebraic Combinatorics
• Non-crossing Partitions
• Subword Complexes
• +5 more

Prof. Dr. Christian Stump holds the W2-Professorship for Algebraic Combinatorics at the Faculty of Mathematics, Ruhr-Universität Bochum . He coordinates the DFG Priority Program Combinatorial Synergies and actively engages in interdisciplinary research at the intersection of combinatorics, algebra, and geometry. His team includes Jun.-Prof. Dr. Marie Brandenburg PhD students Elena Hoster, Nupur Jain, and Federico Lazzeri Former members Research Interests span algebraic combinatorics , cluster algebras , non-crossing partitions , subword complexes , and machine learning applications in mathematics . His work explores the deep connections between combinatorial structures and algebraic geometry, particularly through reflection groups and Catalan theory. Scientific Trends from his recent articles include Non-crossing and m-divisible partition analysis Hyperplane arrangement topology Matroid theory and lattice paths AI-assisted mathematical discovery Combinatorial geometry of polyhedra and complexes Scientific Awards include DFG Heisenberg Fellowship (2017-18) CRM-ISM Fellowship (2009-11) Grants & Projects highlight PI for DFG Priority Program Combinatorial Synergies (€473,360) Heisenberg Fellowship project on Noncrossing phenomena (€585,300) DFG Research Grant for Coxeter-Catalan Combinatorics (€324,900) Labs & Collaborations include the Combinatorial Synergies program, the FindStat.org combinatorial statistics database, and collaborations with institutions in Frankfurt, Barcelona, and Berlin.

• Software Engineering
• Value-Based Software Engineering
• Software Quality Assurance
• +7 more

LiGuo Huang is an accomplished researcher and academic in the field of software engineering with a publication record spanning over two decades from 2003 to 2025. With 95 publications documented in the dblp database, Huang has established a significant presence in both traditional software engineering domains and emerging areas where machine learning intersects with software development practices. Huang's research has evolved from foundational work in value-based software engineering to cutting-edge applications of artificial intelligence in software analysis and maintenance. Huang's research interests encompass a broad spectrum of software engineering topics, with particular emphasis on value-based software engineering, software quality assurance, defect classification, and software process modeling. More recently, Huang has focused on applying machine learning and deep learning techniques to software engineering problems, including code summarization, vulnerability detection, and software maintenance. This evolution reflects the broader shift in the field toward data-driven approaches for software development and analysis. The publication trends reveal a consistent research trajectory with increasing publication rates in recent years, particularly in the application of machine learning to software engineering problems. Huang's work shows a strategic progression from theoretical foundations in software quality to practical applications of AI in software development. The research spans empirical studies, systematic literature reviews, and novel technical approaches to longstanding software engineering challenges, demonstrating both theoretical depth and practical relevance. Huang has collaborated extensively with researchers across multiple institutions, forming particularly strong partnerships with Jidong Ge, Bin Luo, Chuanyi Li, and Barry W. Boehm. The collaboration with Boehm in early career publications suggests mentorship that evolved into peer collaboration, while more recent work shows Huang mentoring newer researchers who now serve as primary authors on joint publications. Huang's research has practical implications for software development practices, particularly in improving software quality, enhancing developer productivity through AI-assisted tools, and providing empirical evidence for software engineering decision-making. The work bridges theoretical computer science with practical software engineering concerns, making significant contributions to both academic research and industry practice.

Kai-Uwe BletzingerView profile

Professor

• Computational Mechanics
• Structural Engineering
• Finite Element Analysis
• +5 more

Kai-Uwe Bletzinger is a Professor and Chair of Statics and Dynamics at the Technical University of Munich's School of Engineering, Department of Civil, Geo and Environmental Engineering. With an extensive publication record spanning from 1991 to 2025 totaling 246 publications, he maintains an active research program in computational structural engineering. His research productivity shows consistent output with significant activity in recent years, particularly in 2022-2025. Professor Bletzinger's research focuses on computational mechanics with specializations in Finite Element Analysis , Shape Optimization , Form Finding , Computer-Aided Design , Membrane Structures , and Fluid-Structure Interaction . His work bridges theoretical computational methods with practical engineering applications, particularly in structural optimization and analysis. The fingerprint of his research shows strong emphasis on numerical methods for structural engineering problems. Analysis of his recent publications (2022-2025) reveals a consistent focus on advancing computational methods for structural optimization, particularly through the Vertex Morphing technique. His work increasingly integrates modern computational approaches including machine learning applications in structural dynamics and digital twin technologies for construction. The research spans fundamental numerical method development to practical applications such as the tensegrity tower for the German Museum in Munich, demonstrating both theoretical depth and practical relevance. While specific awards are not mentioned in the provided text, his extensive publication record in high-impact engineering journals suggests recognition within the computational mechanics community. His research appears to be well-funded given the consistent output and collaborative nature of many publications. Professor Bletzinger's work demonstrates leadership in computational structural engineering, with particular expertise in developing and applying advanced numerical methods to complex structural problems. His research group appears to be active in both fundamental method development and practical engineering applications, contributing significantly to the field of computational mechanics and structural engineering.

Gunther DirrView profile

Senior Lecturer

• Nonlinear control theory
• Bilinear systems
• Quantum mechanical systems control
• +3 more

Dr. Gunther Dirr is a Senior Lecturer at the Chair of Mathematics II (Harmonic Analysis) within the Faculty of Mathematics and Computer Science at the University of Würzburg. His research focuses on nonlinear control theory, bilinear systems, control of quantum mechanical systems, and ensemble controllability. His recent work spans control theory, quantum mechanics, and geometric optimization. Specific areas include bilinear interconnection systems, quantum spin dynamics, Lyapunov function design for monotone systems, and Riemannian optimization on Grassmann manifolds. He collaborates on quantum information and Lie-theoretic approaches to control. Dr. Dirr teaches courses such as Introduction to Geometric Analysis and Linear Algebra for the Summer Semester 2025, with lectures and exercises held across Hubland Nord and Informatics buildings. Email: gunther.dirr@uni-wuerzburg.de Office: Building 40 (Mathematics East), Room 01.009, Hubland Nord, Würzburg

• Deep Learning
• Brain-Computer Interfacing
• Neurotechnology
• +3 more

Robin Tibor Schirrmeister is a researcher at the University of Freiburg's Faculty of Engineering, specializing in deep learning applications for brain-computer interfacing and neurotechnology. His work bridges engineering and medical domains through affiliations with BrainLinks-BrainTools, the Neurorobotics Lab, and the Department of Presurgical Epilepsy Diagnostics. His doctoral research (2024) focused on EEG signal decoding under advisors Frank Hutter and Tonio Ball. Key research areas include: Deep learning for EEG pathology classification Real-time brain-signal decoding systems Human-robot interaction via BCI Medical diagnostics using neural networks Publication trends since 2018 reveal consistent advancement in EEG decoding methodologies, with recent work (2024-2025) focusing on brain-age dynamics, small-data prediction models, and Riemannian geometry applications. His research demonstrates strong clinical translation potential, particularly in epilepsy diagnostics and assistive robotics. Award: 2018 Poster Prize for µECoG research Collaborative work spans multiple labs including BrainLinks-BrainTools and the Epilepsy Center, with significant contributions to clinical EEG dataset development and intracranial signal processing. Current projects focus on scaling behavior in EEG classification and brain-age modeling. Research infrastructure includes the Neurorobotics Lab for BCI-robot integration and the Epilepsy Center for clinical validation, enabling translational work from algorithm development to medical applications.

• Data Mining
• Machine Learning
• Multidimensional Data Analysis
• +4 more

Dr. Gintautas Dzemyda is a leading research professor in data mining, machine learning, and multidimensional data visualization. With over 30 years of academic contributions, he has authored/co-authored 49+ publications and participated in 20+ conferences, including WorldCIST and Baltic DB&IS. His work spans medical imaging, financial security, and maritime decision support systems. Research Focus: Multidimensional Scaling (MDS), Neural Networks, Medical Image Analysis Key Collaborations: Olga Kurasova, Viktor Medvedev, Martynas Sabaliauskas Research Interests include: Advanced Data Visualization Techniques Machine Learning for Medical Diagnostics Fraud Detection in Financial Systems Decision Support Systems for Maritime Navigation Optimization of Neural Network Architectures Dimensionality Reduction Methods Scientific Awards (none explicitly mentioned).

Alexander EfflandView profile

Professor

• Mathematical Image Processing
• Computer Vision
• Deep Learning
• +3 more

Prof. Dr. Alexander Effland is a faculty member at the Institute for Applied Mathematics within the University of Bonn . His work bridges mathematical image processing , computer vision , and deep learning , with applications in immunology , cardiology , and radiology . Research Interests: He specializes in variational methods , PDE-based approaches , shape analysis , and discrete Riemannian geometry . His group develops AI-driven techniques for medical imaging, including optimal transport-based clustering and uncertainty quantification in diagnostic tools. Recent Publications focus on deep learning for brain tumor detection , retinal disease monitoring , and cardiac electrophysiology modeling . Key trends include image reconstruction , medical signal amplification , and biomarker discovery via AI. Email: effland@iam.uni-bonn.de

Hannes DänschelView profile

Researcher

• Numerical Mathematics
• Mathematical Modeling
• Algorithms
• +3 more

Hannes Dänschel is a Researcher at the Institute of Mathematics, Faculty II - Mathematics and Natural Sciences, Technical University of Berlin. He has been affiliated with the university since 2024 as a WiMi (Research Assistant) in the Numerical Mathematics research group, with prior roles in DFG and BMBF-funded projects since 2019. Education: M.Sc. in Physikalische Ingenieurwissenschaften (2021-2024), B.Eng. in Physikalische Technik (2009-2013), and Erasmus studies at Loughborough University (2012). His research focuses on numerical mathematics, mathematical modeling of physical effects/systems, algorithms, and algorithmic geometry. Recent work examines artifacts in Harmonic Balance solutions for nonlinear dynamical systems like the Duffing oscillator. He contributes to collaborative projects such as the CRC Transregio 154 and has served as a tutor for Numerical Linear Algebra courses. His career spans roles at institutions including Rolls-Royce Deutschland and the Max Planck Institute for Plasma Physics.

Andreas EberlView profile

Senior Lecturer

• Mathematics Education
• Didactics of Mathematics
• Educational Technology
• +5 more

Andreas Eberl is a Senior Lecturer in the Department of Mathematics Education at the University of Regensburg, Faculty of Mathematics. Since 2012, he has focused on teacher training, curriculum development, and bridging university mathematics with secondary education. 1998–2004: Studied Mathematics at the University of Regensburg with studies abroad at Brandeis and Harvard Universities. 2004: Completed a diploma thesis on "The real Schubert calculus and applications to line configurations" . 2005–2007: Completed the Second State Examination in mathematics/physics. Eberl’s research centers on mathematics education , particularly addressing the transition from university to school-level teaching. His work explores curricular coherence , educational technologies (e.g., GeoGebra, CAS calculators), and interdisciplinary approaches (e.g., music and mathematics). Key themes include correcting student misconceptions, problem-based learning, and tactile methods like origami-based geometry. His publications and workshops highlight practical strategies for integrating university-level concepts into secondary classrooms, with notable contributions on stochastic teaching, sequence and series, and graph theory. Articles often analyze the "second discontinuity" in teacher education, emphasizing alignment between academic and pedagogical knowledge. While no scientific awards are documented, Eberl actively supports teacher training through workshops , bridging courses , and collaborative initiatives with schools. He has led seminars on lesson planning for Gymnasium, Realschule, and Mittelschule levels.

Philipp Alexander GrzywaczykView profile

Research Fellow

• Algebra
• Model Theory
• Computational Complexity
• +3 more

Philipp Alexander Grzywaczyk is a Research Fellow and PhD student at the Institute of Algebra, Dresden University of Technology, working under Prof. Dr. Manuel Bodirsky in the POCOCOP project (ERC Synergy Grant). His research focuses on algebraic and model-theoretic methods for analyzing the complexity of constraint satisfaction problems. He earned a Bachelor's degree at the University of Bonn (2017-2021) and a Master's in Computational Mathematics at Johannes Kepler University Linz, where his thesis under Dr. Arne Winterhof explored primitive elements of finite fields outside affine hyperplanes. Research Interests : Algebraic structures, model theory, computational complexity, and constraint satisfaction problems. His work bridges abstract algebra with theoretical computer science, emphasizing the interplay between finite field properties and algorithmic complexity. Publications : His recent work on primitive elements in finite fields investigates foundational algebraic problems with implications for cryptography and coding theory. The 2024 publication in Finite Fields and Their Applications represents a key contribution to this area. Contact : Office located at Willersbau, Room B117, Zellescher Weg 12-14, 01069 Dresden, Germany. Phone: +49 (0) 351 463 - 32451.

• Symmetric Functions
• Polynomial Optimization
• Real Algebraic Geometry
• +3 more

Sebastian Debus is an active Researcher at Technische Universität Chemnitz, regularly contributing to academic discourse through presentations at specialized seminars including the Oberseminar Diskrete Mathematik, Geometrie und Optimierung—a collaborative series between Goethe-Universität Frankfurt and TU Chemnitz. His work bridges theoretical mathematics and computational applications with concrete industry relevance. His research centers on symmetric function inequalities and superdominance order, specifically analyzing homogeneous symmetric function inequalities across variable counts. He investigates cones of symmetric sums of squares versus nonnegative polynomials, leveraging tropicalization to uncover combinatorial structures. Key contributions include identifying symmetric quartics that are nonnegative but not sums of squares in non-trivial variable counts, with applications in polynomial optimization where set-theoretic differences between these cones are critical. His methodology combines algebraic geometry with discrete combinatorial frameworks to solve foundational problems in optimization theory. Dr. Debus collaborates internationally, as evidenced by joint work with researchers from diverse institutions including J. Acevedo, G. Blekherman, and C. Riener. His current projects focus on extending tropicalization techniques to higher-degree polynomials and exploring the superdominance order's implications for power-sum inequalities on nonnegative orthants, potentially impacting computational algebra systems and optimization algorithms.

• Probability Theory
• Machine Learning
• Stochastic Optimization
• +4 more

Stephan Eckstein is a junior professor in the Department of Mathematics at the University of Tübingen and a member of the university's machine learning cluster. His research bridges probability theory and machine learning with particular focus on stochastic optimization and numerical approximation. Research interests include: Optimal transport theory and its computational aspects Regularization techniques for high-dimensional problems Causal models and probabilistic structures Graphical models in machine learning Graph neural networks Recent publications analyze dimensional stability in optimal transport, exponential convergence rates for Sinkhorn algorithms, and causal modeling in financial time series generation. Contact: stephan.eckstein@uni-tuebingen.de

Ulrich BrennerView profile

Researcher

• Combinatorial Optimization
• VLSI Placement
• Logic Optimization
• +3 more

Dr. Ulrich Brenner is a researcher at the Research Institute for Discrete Mathematics at the University of Bonn. His work focuses on combinatorial optimization and its applications in VLSI design, particularly in placement and logic optimization algorithms. University of Bonn Research Institute for Discrete Mathematics Research interests: Combinatorial Optimization VLSI Placement Logic Optimization Publications span two decades, with significant contributions in delay optimization , soft rectangle packing , and transportation algorithms . Recent work includes advancements in AND-OR path restructuring (2022) and soft packing ratios (2024). Teaching materials include courses on linear optimization, combinatorics, and C++ programming. He has developed frameworks like BonnPlace and BonnLogic for industrial VLSI design applications.

• Chemical Engineering
• Catalysis
• Combustion Technology
• +3 more

Hendrik Goßler is a Group Leader (Digitalization) and Senior Scientist at the Karlsruhe Institute of Technology (KIT), leading the 'Digitalization' group within the Institute for Chemical Technology and Polymer Chemistry. His work focuses on digitalization in research, including data management systems, automation of numerical simulations, and validation against experimental data. Goßler holds a Dr. rer. nat. (PhD) from KIT (2019), with a thesis on syngas production via partial oxidation in engines, and a Dipl.-Chem. (Master’s) in Chemical Engineering from KIT (2014). Research interests span catalysis, combustion technology, and computational modeling. His group develops tools like Adacta for traceable research data management and CaRMeN for reaction mechanism analysis. He has held visiting researcher positions at the Colorado School of Mines (2015, 2017), funded by DAAD stipends, collaborating with Prof. Robert J. Kee on fluid dynamics and engine-related projects. No scientific awards are listed, but his contributions include over 10 peer-reviewed publications since 2015. His work bridges experimental and computational methods, emphasizing automation and data-driven approaches in chemical engineering.

• Mathematical Biology
• Numerical Simulation
• Fluid-Structure Interaction
• +4 more

Prof. Elfriede Friedmann is a Professor at the University of Kassel's Department of Numerics and Mathematical Modelling. Her research focuses on interdisciplinary applications of numerical methods in biomedical engineering, particularly in ophthalmology and systems biology. She specializes in fluid-structure interaction, pharmacokinetics modeling, and developing computational tools for medical diagnostics and treatment optimization. Education: Advanced degrees in mathematics and applied sciences (details not explicitly provided) Key Research Areas: Mathematical modeling of ocular systems, drug delivery mechanisms, and biological signaling pathways Her work emphasizes translating complex biological systems into mathematical frameworks, such as the 'Virtual Eye' project simulating human eye dynamics. Recent studies investigate retinal neurogenesis, intraocular lens stability, and viscoelastic fluid dynamics in ocular environments. Prof. Friedmann collaborates with clinical partners to validate numerical models against experimental data. Notable contributions include: Development of coupled PDE/ODE systems for intercellular signaling Optimization algorithms for medical device performance High-resolution MRI-based anatomical modeling Numerical methods for drug diffusion analysis Her lab integrates computational fluid dynamics, finite element analysis, and systems biology to address unmet clinical needs in ophthalmology. Current projects explore personalized treatment simulations for retinal diseases and glaucoma.