جست‌وجوی استاد و پژوهشگر

Lukasz Grabowskiمشاهده پروفایل

استاد

• Group Theory
• Combinatorics
• Algorithms
• +۴ مورد دیگر

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.

• Bioinformatics
• Computational Biology
• Genomics
• +۳ مورد دیگر

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

• Computational Geometry
• Algorithms
• Trajectory Analysis
• +۳ مورد دیگر

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.

Klaus Steinمشاهده پروفایل

مدرس پاره‌وقت

• Digital Heritage Technologies
• Heritage Conservation
• Geographic Information Systems (GIS)
• +۶ مورد دیگر

Klaus Stein is a part-time lecturer at the Chair of Heritage Conservation , Otto-Friedrich University of Bamberg. His work bridges digital technologies , spatial modeling , and cultural heritage through interdisciplinary research projects. Co-leader of the UrbanMetaMapping project (BMBF-funded) analyzing WWII war damage maps Developed KTF (Wiki-based terminology framework) for SMEs Contributed to COM (Communication-Oriented Modeling) DFG research network Active in EMN-MOVES project for age-friendly mobility solutions His research focuses on digital heritage , social network analysis , and collaborative GIS , with over 20 years of publications in spatial cognition and information systems . While his primary role involves teaching in the Master's program in Digital Heritage Technologies , he also contributes to open-source GIS and web mining methodologies. Key projects include: Find Diversity (BioDiv2Go): Promoting biodiversity awareness via location-based games Virtual Spaces : Digital reconstruction of historical church color schemes

Alexander Eckerمشاهده پروفایل

استاد

• Machine Learning
• Computational Neuroscience
• Visual Perception
• +۳ مورد دیگر

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.

Alexander Wolffمشاهده پروفایل

استاد

• Graph Drawing
• Computational Geometry
• Algorithms
• +۳ مورد دیگر

Alexander Wolff is a Professor at the Chair of Algorithms and Complexity within the Institute of Computer Science at the University of Würzburg. His work focuses on graph drawing, computational geometry, and algorithmic complexity, with applications in geographic information systems and network visualization. Chair of Algorithms and Complexity, Institute of Computer Science, University of Würzburg (since 2009) Managing Director, Institute of Computer Science (2011–2013, 2015–2017) Editorial roles in journals like JoCG and JGAA Conference leadership in Graph Drawing (GD) and SOFSEM His research explores geometric graph representations, obstacle numbers, and parameterized complexity. Recent publications address level planarity, polyhedral surface adjacency, and metro map visualization. Collaborative projects include algorithmic quality assurance and interactive industrial network visualization. Wolff’s work bridges theoretical graph algorithms with practical applications, such as optimizing public transport schematics and enhancing data accessibility. He has supervised numerous PhD students and co-authored over 100 publications, with editorial and organizational roles in major computational geometry and graph drawing conferences.

Bin Gaoمشاهده پروفایل

دانشیار

• Riemannian Optimization
• Tensor Computation
• Parallel/Distributed Computing
• +۵ مورد دیگر

Bin Gao is an Associate Professor at the Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences. He holds a Ph.D. in Applied Mathematics (2019, University of Chinese Academy of Sciences) and a B.Sc. in Mathematics (2014, Sichuan University). His postdoctoral experience includes positions at UCLouvain (2019-2021) and the University of Münster (2021-2022). Research Interests: Riemannian optimization, tensor computation, parallel/distributed algorithms for orthogonality constraints, machine learning applications. Key Contributions: Development of retraction-free methods on Stiefel manifolds, preconditioned Riemannian algorithms, and geometric frameworks for symplectic eigenvalue problems. Article Trends: Recent work focuses on overcoming the curse of dimensionality via manifold-based optimization, including distributed algorithms for Stiefel manifolds, graph-regularized tensor completion, and second-order methods for symplectic structures. Keywords span numerical analysis, quantum information, and machine learning. Scientific Awards: 2021 Zhong Jiaqing Mathematics Award 2018 Best Student Paper Award (CSIAM) 2018 CAS Special President Scholarship 2017 National Scholarship for Doctoral Students (China) 2016 Honor Student Award (International Workshop on Modern Optimization and Application) Advising & Collaborations: Collaborates with researchers from UCLouvain, University of Münster, and AMSS. Mentors students in Riemannian optimization and tensor computation. Leads the popman research group.

• Knowledge Representation and Reasoning
• Geometry Processing
• Machine Learning
• +۱ مورد دیگر

Dr. Stavros Nousias is a researcher at the Chair of Computing in Civil and Building Engineering at the Technical University of Munich , focusing on applications of Artificial Intelligence in the Built Environment . His work bridges Knowledge Representation and Reasoning , Geometry Processing , and Machine Learning to advance construction informatics and digital twinning. Research Interests: AI for building evacuation prediction, technical drawing segmentation, BIM optimization, and respiratory disease modeling. Publications: 15+ peer-reviewed articles on topics including graph neural networks for construction simulations, pulmonary airflow analysis, and heritage site monitoring. Supervised Theses: Guided projects on AI-based BIM command prediction and robotized construction simulation . Labs: Active in the BIM-Lab and Robotic Fabrication Lab . Teaching: Co-instructor for courses like Artificial Intelligence in Engineering and Computation in Engineering 1 .

Mahdi Vasighiمشاهده پروفایل

استادیار

• Bioinformatics
• Structural Classification of Proteins
• Protein-Protein Interaction
• +۹ مورد دیگر

Mahdi Vasighi is currently serving as an Assistant Professor at the Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS) in Zanjan, Iran, a position he has held since February 2012. Prior to this, he was a Post-doc Researcher at the same institution from February 2011 to February 2012. He has also served as a Visiting Researcher at the Milano Chemometrics and QSAR Research Group, University of Milano - Bicocca, Milan, Italy from September to October 2009, and as a Guest Lecturer at the Pasteur Institute, Tehran, Iran since September 2016. Dr. Vasighi earned his educational qualifications from the Institute for Advanced Studies in Basic Sciences (IASBS) in Zanjan, Iran, where he completed his Ph.D. in Chemometrics in May 2010 and his M.Sc. in Analytical Chemistry between 2002 and 2005. His undergraduate education was in Pure Chemistry at Imam Khomeini International University, Qazvin, Iran, from 1998 to 2002. Dr. Vasighi's primary research interests lie at the intersection of bioinformatics, machine learning, and data analysis. His work focuses on structural bioinformatics, particularly on modeling relationships between biological sequences and their corresponding structure or function. He has made significant contributions to the field of self-organizing maps with dynamic structure, developing innovative approaches like the Directed Batch Growing Self-Organizing Map (DBGSOM) that enhance topology preservation and visualization of high-dimensional data. His research spans multiple domains including protein structural classification, cancer diagnostics using fluorescence spectroscopy, and drug discovery for diseases like COVID-19. Dr. Vasighi's publication record demonstrates a strong trajectory in applying machine learning techniques to solve complex problems in bioinformatics and medical diagnostics. His recent work shows an increasing focus on applying computational approaches to healthcare challenges, including cancer detection, protein analysis, and drug discovery for viral diseases. He has successfully bridged the gap between theoretical machine learning advancements and practical applications in biology and medicine, with a particular emphasis on developing interpretable models that can be used by domain experts. Dr. Vasighi has actively contributed to the academic community through teaching and conference organization. He has served as Local Chair for the International Conference on Contemporary Issues in Data Science 2019 (CiDaS 19) and as Scientific Committee Member and Organizing Chair for previous CICIS conferences. His teaching portfolio includes graduate courses in Artificial Neural Networks, Computational Data Mining, Bioinformatics, Statistical Pattern Recognition, and Multimedia Systems. Dr. Vasighi has supervised numerous MSc students, with over twenty graduated students and nine current students listed in his profile. His research has been supported through collaborations with institutions like the Pasteur Institute, where he worked on projects related to nuclear magnetic resonance-based screening of thalassemia and determination of coronary heart disease risk using NMR spectra of plasma lipoproteins. Through his Directed Batch Growing Self-Organizing Map (DBGSOM) package and other software contributions, Dr. Vasighi has made his research tools accessible to the broader scientific community. His work continues to push the boundaries of how machine learning can be applied to solve challenging problems in bioinformatics and medical diagnostics.

• Knowledge Graphs
• Semantic Web
• Machine Learning
• +۴ مورد دیگر

Prof. Dr. Axel Cyrille Ngonga Ngomo is a Professor at the University of Paderborn, affiliated with the Faculty of Electrical Engineering, Computer Science and Mathematics. He serves as the leader of the Data Science group at the Heinz Nixdorf Institute and the Informatik Rechnerbetrieb (IRB) unit. His primary research focuses on Knowledge Graphs, Semantic Web technologies, and Machine Learning applications in data science. University of Paderborn Faculty of Electrical Engineering, Computer Science and Mathematics Data Science / Heinz Nixdorf Institute Informatik Rechnerbetrieb (IRB) His research spans automated knowledge extraction, description logic learning, and explainable AI systems through dynamic data federation. Recent work explores convolutional embeddings for complex knowledge graphs and adaptive retrieval augmented generation architectures. Current projects include SAIL (Sustainable Life Cycle of Intelligent Sociotechnical Systems), TRR 318 (Constructing Explainability), Colide (Co-training and co-regulation for industrial data), 3DFed (Dynamic Data Distribution and Federation), and SFB 901 (On-The-Fly Computing). Contact details include offices at Fürstenallee 11 (Room F1.225) and Technologiepark 6 (Room TP6.3.106) in Paderborn, Germany. Consultation hours are available by appointment.

• Theoretical Computer Science
• Algorithm Design and Analysis
• Approximation Algorithms
• +۳ مورد دیگر

Michael Lampis is a Maître de conférences HDR (Assistant Professor) at LAMSADE , Universite Paris Dauphine. His research focuses on theoretical computer science , particularly in approximation algorithms , parameterized complexity , and graph algorithm design . He has held post-doctoral positions at Kyoto University and KTH, Stockholm, and earned his PhD from the Graduate Center of CUNY under Amotz Bar-Noy. Research Interests include: Structural Graph Parameters (treewidth, pathwidth, clique-width) Algorithmic Meta-Theorems Approximation Schemes Combinatorial Optimization Computational Complexity Recent Research Trends highlight his work on parameterized approximation algorithms for graph problems (e.g., feedback vertex set, matching) and complexity analysis of games/puzzles. His projects S-EX-AP-PE-AL (ANR JCJC), COAL-GAS (CNRS-PSL), and collaborations with Japanese institutions (PARAGA, GRAPA) emphasize cross-border innovation. Scientific Awards include: Best Student Paper Award at WG 2025 Best Paper Award at SOFSEM 2024 Advising involves PhD students Ioannis Katsikarelis, Louis Dublois, and Manolis Vasilakis, alongside Master's advisees like Edouard Nemery and Alban Guerbois. He actively participates in peer review for conferences (ICALP, ESA, STACS) and journals (Algorithmica, JCSS, DAM).

• dynamical systems
• symbolic dynamics
• subshifts
• +۲۴ مورد دیگر

Ville Salo is an Associate Professor at the University of Turku's Department of Mathematics and Statistics. His research spans dynamical systems (especially symbolic dynamics, subshifts, and cellular automata), group theory (automorphism groups, geometric group theory), automata theory, and topology (including algebraic and general topology). He maintains a comprehensive list of open problems in symbolic dynamics, reflecting his deep engagement with foundational questions in these areas. Teaching Activity (2017–2025): Logiikka (Logic), spring 2025 & 2023 Lebesguen mitta ja integraali (Lebesgue measure and integral), fall 2023 Topologian perusteet (Basic topology), fall 2022 & 2019 Cellular automata, spring 2022 & 2018 Symbolic dynamics II, spring 2019 Johdatus automaattien teoriaan (Introduction to automata theory), spring 2018 & 2017 Algoritminen matematiikka (Algorithmic mathematics), spring 2017 He is fluent in Finnish and English, with proficiency in several other languages. Outside academia, he maintains math blogs, creates programming projects (including esoteric languages), and runs competitively.

• Bioinformatics
• Computational Biology
• Phylogenetics
• +۳ مورد دیگر

Daniel Huson is a Professor of Algorithms in Bioinformatics at the University of Tübingen , affiliated with the Faculty of Science and actively contributing to the Computer Science Department . He has held this position since 2002 and previously served as Senior Staff Scientist at Celera Genomics (1999-2002) and Post-Doc at Princeton University and the University of Pennsylvania (1997-99). Education: PhD in Mathematics, Bielefeld University (1990, summa cum laude) Habilitation in Mathematics, Bielefeld University (1997) Studied Mathematics and Physics, University of Washington (1980-86) Research Interests: Designing algorithms for bioinformatics and computational biology Metagenomic data analysis using tools like MEGAN and SplitsTree Phylogenetic networks and evolutionary modeling Microbiome dynamics and industrial bioproduction optimization Development of interactive software for biological data visualization Exploring autocatalytic reaction networks in early biochemistry Scientific Contributions: Developed MEGAN, a widely used metagenome analysis tool Created SplitsTree for phylogenetic network analysis Published extensively on microbiome analysis, metagenomic binning, and evolutionary relationships Contributed to power-to-gas technology and bioelectrochemical systems Awards & Grants: Royal Society of New Zealand International Leader Fellowship (2020-22) PLOS Computational Biology Research Prize (2017) Technology Transfer Prize of the IHK Reutlingen (2016) Co-organizer of major conferences (GCB, RECOMB, ISMB, etc.) Leadership Roles: Head of Computer Science Department (2011-14) Founding member of Computomics (since 2012) Faculty member of IMPRS 'From Molecules to Organisms' (since 2011)

Peter Florian Stadlerمشاهده پروفایل

استاد

• Bioinformatics
• Computational Biology
• RNA Biology
• +۴ مورد دیگر

Peter Florian Stadler is a Professor of Bioinformatics at the University of Leipzig since 2002 and Director of the Interdisciplinary Center for Bioinformatics (IZBI) since 2023. He holds affiliations with the Max Planck Institute for Mathematics in the Sciences (scientific member since 2009), the Austrian Academy of Sciences (corresponding member since 2010), and the Santa Fe Institute (external member since 1994). Doctorate in Chemistry (University of Vienna, 1990) Habilitation in Theoretical Chemistry (University of Vienna, 1994) His research focuses on Bioinformatics , Computational Biology , and Evolutionary Dynamics , with specific interests in RNA structure prediction , non-coding RNA , and genome-wide data analysis . Recent work involves developing ViennaRNA Package tools and studying translational control mechanisms in melanoma. Professional milestones include: Co-development of Vienna RNA Package , FRANz (phylogenetic tree reconstruction), code2aln (alignment tools) Key contributions to RNA gene finding algorithms and ncRNA family characterization Honors: Corresponding member, Austrian Academy of Sciences (2010) Honorary professor, Faculty of Science, Bogota (2018)

Fuyuki Ishikawaمشاهده پروفایل

دانشیار

• Software Engineering
• Testing
• Formal Methods
• +۴ مورد دیگر

Fuyuki Ishikawa is an Associate Professor at the Information Systems Architecture Science Research Division of the National Institute of Informatics (NII) in Tokyo, Japan, where he also serves as Director of the GRACE Center. Additionally, he holds positions as Associate Professor at Sokendai (The Graduate University for Advanced Studies) and Visiting Associate Professor at The University of Electro-Communications. His research program, which he describes as 'Trustworthy & Smart Software Engineering,' focuses on dependability in Cyber-Physical Systems and Machine Learning Systems through techniques of formal methods and automated test generation. He investigates verification, reasoning, optimization, automated test generation, and self-adaptation by utilizing various models for requirements, specifications, and designs. His group promotes international and industry-academia collaborations with members from different organizations. Ishikawa's recent work has increasingly concentrated on quantum program testing, autonomous driving systems verification, and quality assurance frameworks for AI systems. His publications demonstrate significant contributions to software engineering, particularly in applying formal methods to emerging domains like quantum computing and machine learning. His research often bridges theoretical foundations with practical applications in safety-critical contexts, particularly for autonomous systems. Awards for Science and Technology (Research Category) from the Minister of Education, Culture, Sports, Science and Technology (April 2024) IPSJ/IEEE Computer Society Young Researcher Award for 'Research on Intelligence-driven Engineering of Dependable Smart Systems' (March 2020) Multiple Best Paper Awards at conferences including ICFEM 2020, SEKE 2020, ICECCS 2019, and ISSRE 2019 Industry Track Best Artifact Award at ISSTA 2015 Ishikawa actively supervises research projects and interns through the ERATO-MMSD Project and MIRAI-eAI Project. He has served on numerous program committees for major software engineering conferences including ASE, ICSE, and ICST, and has organized workshops on machine learning systems engineering (SIG-MLSE) and quality assurance for AI systems (QA4AI). His laboratory provides two main research directions: generative AI for trustworthy software engineering and testing/trust exploration for AI systems, with specific focus on autonomous driving and deep learning applications.