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Mark BurkhardtView profile

Researcher

• Control Systems
• Mechanical Engineering
• Automation and Robotics
• +3 more

Mark Burkhardt is a doctoral researcher and research associate at the Institute for System Dynamics within the Cluster of Excellence IntCDC at the University of Stuttgart. His work focuses on control systems and automation technologies for construction machinery, particularly tower cranes. Current affiliation: Institute for System Dynamics, University of Stuttgart Academic rank: Researcher Specializations: Crane control systems, cyber-physical construction platforms, automation of timber structures Research Interests: Burkhardt's research spans control systems engineering, automation technologies, and cyber-physical systems. His work addresses critical challenges in: Load sway damping for top-slewing cranes Path planning and absolute positioning of tower cranes Modeling and control of collaborative crane systems Development of novel grab systems for construction automation Georeferenced payload tracking Multi-agent crane coordination Teaching: He has taught courses on system dynamics, control theory, and programming since 2019, including: "Flache Systeme" (Flat Systems) "Systemdynamische Grundlagen der Regelungstechnik" (System Dynamics Fundamentals of Control Engineering) "Einführung in die Programmiersprache C" (Introduction to C Programming) "Praktikum Automatisierungstechnik/Systemdynamik" (Automation/System Dynamics Practical) Education: He holds an M.Sc. in Technical Cybernetics from the University of Stuttgart (2016-2019) and a B.Sc. in the same field (2013-2016).

Fabian LenzenView profile

Researcher

• Algebraic Topology
• Computational Geometry
• Topological Data Analysis
• +3 more

Fabian Lenzen is a postdoctoral researcher at Technische Universität Berlin in the Discrete Mathematics/Geometry group under Prof. Michael Joswig. Previously, he completed his PhD under Prof. Ulrich Bauer at TU Munich. His work bridges theoretical mathematics and computational applications. Research Focus: Algebraic topology, computational geometry, and their applications in data analysis Key Concepts: Topological data analysis, persistent homology, combinatorics, mathematical software development His publications explore: Algebraic shifting techniques (2025, 2024) Two-parameter persistence algorithms (2023, 2024) Clifford-symmetric polynomials (2023) Categorical structures in representation theory (2021) Software contributions include 2pac for two-parameter persistent cohomology and a TU Berlin Beamer theme aligning with university design standards. He actively develops LaTeX packages and visualization tools for mathematical education.

• Information Theory
• Machine Learning
• Fairness in AI
• +6 more

Changho Suh is a Professor in the Department of Electrical Engineering at Korea Advanced Institute of Science and Technology (KAIST), College of Engineering. His research spans information theory, machine learning, and data science with significant contributions to matrix completion, fairness in AI, and network communications. Dr. Suh's research interests focus on the theoretical foundations of information processing and machine learning. He has pioneered work in matrix completion with graph side information, developing efficient algorithms that leverage hierarchical structures and similarity graphs. His recent work emphasizes fairness in machine learning systems, addressing correlation shifts and developing methods for fair training and generative modeling. He has also made significant contributions to information theory, particularly in interference channels, network coding, and quantum key distribution. Analysis of his recent publications reveals a strong trend toward addressing fairness challenges in AI systems while maintaining theoretical rigor. His work bridges information theory with practical machine learning applications, particularly in recommender systems and community detection. Suh's research demonstrates how graph structures can enhance data recovery and how theoretical insights from information theory can improve modern machine learning systems. Dr. Suh has received recognition for his scholarly contributions through numerous publications in top-tier venues including IEEE Transactions on Information Theory, NeurIPS, ICML, and AAAI. His work has influenced both theoretical understanding and practical implementations in data science. As an academic advisor, Suh has mentored numerous graduate students who have gone on to publish significant research in their own right. His collaborative approach is evident in the diverse range of co-authors across his publications, indicating strong research partnerships both within KAIST and internationally. His laboratory work appears to focus on information-theoretic approaches to machine learning problems, with particular emphasis on structured data analysis, fairness considerations, and efficient algorithm design for large-scale data processing tasks.

• Computer Science
• Algorithms
• Computational Geometry
• +4 more

Sujoy Bhore is affiliated with the Indian Institute of Technology Bombay (Department of Computer Science & Engineering), Université libre de Bruxelles, and Technische Universität Wien. His research focuses on algorithms , computational geometry , and graph theory , with a strong emphasis on geometric optimization , dynamic data structures , and parameterized algorithms . Recent publications highlight advancements in Euclidean spanners for sparse network design online algorithms for dynamic geometric problems Steiner trees and tree covers in planar domains k-median/means approximation using coresets His collaborative work spans institutions, with frequent joint research on geometric intersection graphs , map labeling , and planar graph embeddings . Co-authors include prominent researchers like Csaba D. Tóth, Martin Nöllenburg, and Timothy M. Chan. While no formal awards are documented here, his contributions to algorithmic complexity and geometric networks remain significant.

• Linear Algebra
• Matrix Theory
• Graph Theory
• +3 more

Louis A. Deaett is a Professor of Mathematics at the College of Arts & Sciences, Quinnipiac University. He holds a Ph.D. in Mathematics from the University of Wisconsin-Madison (2009) and previously served as a postdoctoral fellow at the University of Victoria. Education: BS, University of Rochester BA, University of Rochester PhD, University of Wisconsin-Madison Deaett's research focuses on the intersection of Linear Algebra , Graph Theory , and Discrete Mathematics , particularly in understanding matrix patterns and their sparsity structures. His work addresses computational and theoretical challenges in minimum rank problems. Recent publications highlight his contributions to matroid theory , spectrally arbitrary patterns , and graph-based matrix computations , reflecting interdisciplinary trends in mathematics and computer science.

Karsten BorgwardtView profile

Professor

• Machine learning
• Systems biology
• Bioinformatics
• +3 more

Prof. Dr. Karsten Borgwardt is Director of the Research Department of Machine Learning and Systems Biology at the Max Planck Institute of Biochemistry in Martinsried, Germany. A leading figure in the intersection of machine learning, bioinformatics, and systems biology, he heads a multidisciplinary team that develops novel computational methods to extract knowledge from large biomedical data sets. Research Mission: The Borgwardt lab converges big data analytics and biomedical research . Two overarching goals drive their work: (1) Automatically generating new biological and medical knowledge from massive data via state-of-the-art machine-learning algorithms. (2) Understanding the molecular underpinnings of biological system function, with emphasis on personalized medicine and biomarker discovery. Their methodological toolbox spans graph neural networks, kernel methods, conformal prediction, deep learning on sequences and structures, and topological data analysis . Application domains include antimicrobial resistance prediction, protease engineering, acute-kidney-injury forecasting, coronary-artery-disease diagnostics, single-cell spatial proteomics, and Long-COVID immune profiling. Recent Publication Landscape (2023-2025): The group’s latest articles demonstrate a clear trend toward translationally relevant machine learning . High-impact venues such as Nature Communications , Science , ICLR , and RECOMB feature their work on: Data-driven protein engineering using DNA-recorded deep mutational scanning. Guaranteed antimicrobial resistance detection from MALDI-TOF spectra via conformal prediction. Graph-based biomarker discovery with theoretical guarantees. Deep phenotyping of human iPSC-derived neuronal networks to study disease mutations. Multi-modal learning that fuses genomics, proteomics, and clinical data for patient stratification. These contributions collectively advance both the theoretical foundations and real-world deployment of machine learning in medicine. Scientific Awards & Honors: While no explicit award list is provided, the breadth and impact of publications, invited book chapters, and keynote-level conference presentations (ICLR, RECOMB, ISMB/ECCB) testify to sustained international recognition. Laboratory & Collaboration Ecosystem: The Borgwardt lab operates at the Max Planck Institute of Biochemistry —a world-leading biomedical research campus. Collaborations span multiple Max Planck centers, university hospitals across Europe, and international consortia such as the EyeConic study on optogenetics therapy. The lab’s open-source footprint includes the Multi-SConES R package for multi-task network-regularized feature selection, fostering reproducible science across the community.

• Formal Concept Analysis
• Machine Learning
• Semantic Web
• +3 more

Professor Dr. Tom Hanika is affiliated with the University of Hildesheim , working in the Intelligent Information Systems (IIS) division within the Institute of Computer Science. His research bridges formal concept analysis , machine learning , and knowledge representation , focusing on geometric interpretations of data and explainable AI systems. Research Themes: Intrinsic dimensionality, lattice structures, and hybrid human-AI collaboration Teaching: Offers courses in databases, C++ programming, and semantic technologies Contact: Office (SC.C. 2.03), Phone +49 5121 883-40312, Email via contact form Recent publications highlight his work on geometric data analysis and formal context manipulation , including applications in graph neural networks, ordinal pattern recognition, and conceptual lattice visualization. His Collaborative Hybrid Human AI Learning framework demonstrates practical implementations of these theories. Current projects explore dimensionality resilience in machine learning models and topic flow visualization in academic networks, reflecting his dual focus on theoretical foundations and applied knowledge systems.

Lukasz GrabowskiView profile

Professor

• Group Theory
• Combinatorics
• Algorithms
• +4 more

Lukasz Grabowski is Professor for Theoretical Mathematics at the Institute of Mathematics, Leipzig University, actively engaged in research, teaching, and academic outreach. His institutional affiliation places him within Germany's prominent research-focused university system. His research centers on advanced mathematical structures with three core emphases: Group Theory (discrete groups, group rings, l2-invariants, and finite approximations), Measured and Borel Combinatorics (expansion properties, Kazhdan property, Aldous-Lyons conjecture, and equidecompositions), and Algorithms/Complexity Theory for graphs and groups (including Lovasz Local Lemma applications). These interconnected fields address fundamental questions in theoretical mathematics with implications for computational theory. Professor Grabowski currently supervises PhD students Jardon Hector Sanchez (Aldous-Lyons conjecture and Kazhdan property in groupoids) and Onur Bilge (Borel and measurable combinatorics), building on mentorship of former postdocs Joan Claramunt and Tomasz Ciesla. He actively promotes mathematical talent through the Mathe-Zirkel program for secondary students and delivers specialized lectures internationally, as evidenced by his 2024 Bonn talk on unimodular random graphs and 2018 Madrid lecture notes on L2-invariants. He leads a dynamic research group within Leipzig University's Institute of Mathematics, fostering collaboration through seminar presentations and academic exchanges while maintaining strong institutional ties through departmental teaching responsibilities including Algebraic Topology courses.

• Quantum Computing
• Quantum Foundations
• Diagrammatic Reasoning
• +3 more

John van de Wetering is an Assistant Professor at the Theoretical Computer Science group of the Informatics Institute at the University of Amsterdam . He works with the QuSoft research center and co-develops the PyZX open-source quantum compiler. His research spans quantum computation, diagrammatic reasoning, and quantum foundations. Education: PhD in Computer Science (2022, Radboud University) Research: Focuses on ZX-calculus for quantum circuit optimization, verification, and simulation. Explores quantum foundations through algebraic and compositional methods. Recent work includes quantum circuit optimization with AlphaTensor, completeness proofs for ZH-calculus, and scalable spider nets for transversal non-Clifford gates. He directs the new Master's program in Quantum Computer Science at UvA and co-authored the book Picturing Quantum Software . Notable collaborations include PyZX development and EU Gender Equality Working Group participation. Supervision includes current PhD students Lia Yeh (Oxford), Sarah Li (UvA), and Marc Farreras (Leiden). Former students include Boldizsár Poór (Quantinuum), Julien Codsi (Princeton), and Yanbin Chen (TUM). Tools & Outreach: Maintains the ZX-calculus Wikipedia page, co-lectured courses like Quantum Processes and Computation , and develops the ZX-calculus educational website. Organized QPL2026 conference and contributed to open-access journal Quantum .

• Bioinformatics
• Computational Biology
• Genomics
• +3 more

Mr. Leonard Bohnenkämper, M.Sc., is a researcher affiliated with the Faculty of Engineering at Bielefeld University . He is actively involved with the Genome Informatics Group and the Center for Biotechnology (CeBiTec) , focusing on computational genomics and genome rearrangement analysis. His work spans algorithm development for genomic distance metrics and natural genome halving. His research interests include: Computational modeling of genome rearrangements Development of algorithms for natural genome analysis Genomic distance metrics and epidemiology applications Bioinformatics tool creation for large-scale genomic datasets Mr. Bohnenkämper contributes to academic infrastructure through his membership in the Institute for Bioinformatics Infrastructure (BIBI) board and management. His work aligns with Bielefeld University's strategic focus on socio-technical systems and interdisciplinary research at the intersection of computer science, biology, and technology. He is involved in: IT support for research groups Academic infrastructure development Training and education in bioinformatics

Fred HamprechtView profile

Professor

• Machine Learning
• Image Analysis
• Quantum Chemistry
• +4 more

Fred Hamprecht is a Professor whose work bridges machine learning algorithms and complex problems with spatial structure . He has spent two decades advancing image analysis before pivoting to quantum chemistry , focusing on learning kinetic energy functionals via density functional theory and geometric machine learning . His research is grounded in rigorous mathematical frameworks like combinatorial optimization and algebraic graph theory , with a commitment to practical applications. His lab comprises exceptionally talented individuals, and he takes pride in mentoring students who have transitioned into prominent roles, including: Anna Kreshuk - PI at EMBL Bernhard Renard - Professor at HPI Bjoern Andres - Professor at TU Dresden Bjoern Menze - Professor at Zurich University Melih Kandemir - Associate Professor at University of Southern Denmark

Niv BuchbinderView profile

Professor

• Combinatorial Optimization
• Online Algorithms
• Algorithmic Game Theory
• +2 more

Prof. Niv Buchbinder is a faculty member in the Department of Statistics and Operations Research at the School of Mathematical Sciences, Tel Aviv University. His research centers on algorithmic solutions for combinatorial optimization in offline and online contexts, with significant contributions to primal-dual methodologies and algorithmic game theory. His academic background includes a Ph.D. in Computer Science from the Technion (2008) under Prof. Seffi Naor and an M.Sc. in Computer Science from the Technion (2003) under Prof. Erez Petrank. Key research areas encompass Combinatorial Optimization, Online Algorithms, Algorithmic Game Theory, Primal-Dual Methods, and Submodular Optimization, focusing on competitive analysis for problems like set cover, ad-auctions, and caching. Recent publications (2012-2015) reveal a concentrated effort in submodular optimization and online decision-making, with applications in advertising, resource allocation, and machine learning. These works consistently employ primal-dual frameworks to achieve strong competitive ratios in adversarial settings. Scientific recognition includes: Best Paper Award at ESA 2007 for “Online Primal-Dual Algorithms for Maximizing Ad-Auctions Revenue” Best Paper Award at FOCS 2011 for “A Polylogarithmic Competitive Algorithm for the k-Server Problem” No information is available regarding student advising or research grants. Similarly, details about laboratory facilities, research teams, or future projects are not provided in the source materials.

• Computational Geometry
• Algorithms
• Trajectory Analysis
• +3 more

Maike Buchin is a Professor of Theoretical Computer Science / Algorithmics at the Faculty of Computer Science, Ruhr-University Bochum, where she has been serving since 2019. She also holds the position of Studiendekanin Informatik (Dean of Studies for Computer Science). Prior to her current position, she was a Visiting Professor at Technical University Dortmund (2017-2019) and a Juniorprofessor at Ruhr University Bochum (2013-2017). Dr. Buchin's research focuses on computational geometry, algorithms, and trajectory analysis. Her work particularly emphasizes Frechet distance computations, curve matching, and geometric algorithms. She has made significant contributions to understanding the computational complexity of geometric problems and developing efficient algorithms for trajectory data analysis. Her research has applications in geographic information systems, movement pattern analysis, and shape comparison. Analysis of her recent publications reveals a strong focus on clustering algorithms for polygonal curves, Frechet distance computations, and trajectory analysis. Her work bridges theoretical computer science with practical applications in GIS and movement data analysis. She has developed approximation algorithms, coreset constructions, and efficient query processing techniques for geometric problems. Her research has been published in top-tier venues including ACM Transactions on Algorithms, Computational Geometry: Theory and Applications, and proceedings of major conferences like Symposium on Computational Geometry (SoCG) and European Symposium on Algorithms (ESA). Dr. Buchin has supervised numerous students and taught courses including Algorithm Paradigms, Computer Science 2 - Algorithms and Data Structures, Computer Science 3 - Theoretical Computer Science, Geometric Algorithms, Data Structures, and seminars on Cryptology and Theoretical Computer Science. She leads research in the Theoretical Computer Science / Algorithmics group at Ruhr-University Bochum, collaborating with researchers worldwide on computational geometry problems and their applications.

Alexander EckerView profile

Professor

• Machine Learning
• Computational Neuroscience
• Visual Perception
• +3 more

Prof. Dr. Alexander Ecker is Professor of Data Science at the Institute of Computer Science, University of Göttingen, and concurrently holds the prestigious Max Planck Fellow position at the Max Planck Institute for Dynamics and Self-Organization. Since 2020 he also serves on the Executive Board of the Campus Institute Data Science in Göttingen. He leads the Neural Data Science research group, comprising 14 PhD students and 2 postdoctoral researchers, focusing on the interface of machine learning and computational neuroscience. His educational background includes a Dr. rer. nat. in Neuroscience (2014) from the Graduate School of Neural and Behavioral Sciences/IMPRS, University of Tübingen, followed by post-doctoral and group-leader positions at the University of Tübingen and the Max Planck Institute for Biological Cybernetics. Research Interests Machine Learning & Deep Learning: developing novel algorithms for representation learning and generative modeling. Computational Neuroscience: large-scale data-driven modeling of visual cortical circuits. Visual Perception: bridging biological vision and computer vision via biologically inspired architectures. His work has produced a steady stream of influential publications (2019-2025) in leading journals such as Nature Communications , Nature , Nature Methods , PLOS Computational Biology , ICLR , NeurIPS , and CVPR . The publications trend toward integrating high-resolution neural recordings with state-of-the-art machine-learning models to uncover principles of sensory processing, neuron-type classification, and behavior. Scientific Awards & Honors Max Planck Fellow, Max Planck Institute for Dynamics and Self-Organization (ongoing) Executive Board Member, Campus Institute Data Science, Göttingen (since 2020) Teaching, Advising & Grants Regularly teaches advanced courses: “Deep Learning for Image Synthesis”, “Current Topics in Deep Learning”, and “Graph Machine Learning”. Supervises 14 current PhD students and 2 postdocs within the Neural Data Science Group. Offers numerous Bachelor’s and Master’s thesis projects, with topics ranging from neuronal morphology clustering to primate vocalization analysis. Leads or co-leads large collaborative consortia with labs in Göttingen, Tübingen, Baylor College of Medicine, and other institutions across the US and Germany. Labs & Teams The Neural Data Science Group operates at the Institute of Computer Science, University of Göttingen, and is tightly integrated with the Max Planck Institute for Dynamics and Self-Organization. The group maintains active collaborations with over a dozen partner laboratories, including groups led by Fabian Sinz, Andreas Tolias, Thomas Euler, Tim Gollisch, and Viola Priesemann, fostering an interdisciplinary environment that spans computer science, physics, biology, and psychology.

• Temporal Databases
• Time Series Analysis
• Database Systems
• +4 more

Anton Dignös is a professor at the Free University of Bozen-Bolzano , specializing in temporal databases , time series analysis , and database systems . His research focuses on efficient query processing for interval data, temporal joins, and schema design, with significant contributions to in-memory and time series databases. Key research areas include: Temporal Data Management : Advanced techniques for interval and duration queries. Time Series Analytics : Machine learning integration and pattern detection. Schema Optimization : Automated design and tuning of database schemas. Visual Analytics : Tools for period data comparison and correlation analysis. His work spans collaborations with researchers like Johann Gamper and Michael H. Böhlen , addressing challenges in healthcare systems, industrial applications, and financial data analytics. Notable contributions include algorithms for temporal anti-joins , range-duration queries , and machine learning-based anomaly detection .