جست‌وجوی استاد و پژوهشگر

Michael Krivelevichمشاهده پروفایل

استاد

• Probabilistic Methods in Combinatorics
• Random Structures
• Hypergraphs
• +۶ مورد دیگر

Prof. Michael Krivelevich is the Baumritter Chair in Combinatorics at the School of Mathematical Sciences , Tel Aviv University , Israel. His research focuses on probabilistic and extremal combinatorics, random graphs, positional games, and theoretical computer science. He has authored foundational work on Hamiltonicity, Ramsey theory, and pseudo-random graphs. Email: krivelev@tauex.tau.ac.il Phone: +972 3 6405366 Office: Schreiber, 301, Tel Aviv University Research Interests Prof. Krivelevich's work bridges probabilistic methods with deterministic graph properties, particularly in Hamilton cycles , extremal combinatorics , and position games . His research explores the structural properties of random and pseudo-random graphs , including connectivity, coloring, and subgraph packing. Recent studies focus on percolation thresholds, cycle distributions, and hereditary graph properties in random settings. Article Trends His recent publications emphasize Hamiltonian cycles in perturbed graphs, site percolation on hypercubes, and Turán-type problems for random subgraphs. Key themes include expanders , random graph processes , and game-theoretic graph resilience . Collaborations with leading researchers in combinatorics and algorithms underpin his interdisciplinary approach. Scientific Awards Baumritter Chair in Combinatorics, Tel Aviv University Teaching & Collaborations He has taught courses including Random Graphs , Positional Games , and Graph Theory since 2002. Co-authored books and seminal papers with researchers like Noga Alon, Benny Sudakov, and Dan Hefetz have shaped modern combinatorics.

Thomas Hacklمشاهده پروفایل

پژوهشگر

• NMR Spectroscopy
• Biological Chemistry
• Metabolomics
• +۳ مورد دیگر

Dr. Thomas Hackl is a senior NMR spectroscopy specialist at the Department of Chemistry , University of Hamburg, MIN Faculty. He leads the Food-NMR working group since 2014 and serves as Academic Council member since 2009, focusing on solution-state NMR applications for food authentication and metabolomics research.

Manfred Madritschمشاهده پروفایل

پژوهشگر

• Number Theory
• Dynamical Systems
• Digital Expansions
• +۲ مورد دیگر

Manfred Madritsch is a Senior Researcher at Montanuniversität Leoben , Austria. Previously, he served as an Associate Professor at Université de Lorraine, France (2012–2025) and an Assistant Professor at TU Graz, Austria (2009–2012). His research focuses on number theory and dynamical systems , particularly digit systems, normal numbers, and pseudorandom sequences. Research Interests: His work bridges analytical number theory, digital expansions, and uniform distribution of sequences. He explores connections between number systems, ergodic theory, and computational methods, often applying probabilistic and dynamical approaches to arithmetic problems. Recent Publications emphasize limit theorems for integer partitions, pseudorandom binary sequences, and sum-of-digits functions in canonical number systems. These articles reflect interdisciplinary efforts combining number theory, statistics, and algorithmic analysis. Students: He has mentored several PhD students, including Renan Laureti (2019–2025, with Yann Bugeaud) Hichem Zouari (2021–2024, with Mohamed Hbaib) Youssef Sadrati (2019–2023, with Youness Lamzuri) Slim Jmal (2020–2021) Stefan Planitzer (2015) Teaching: Madritsch has lectured on algebra, analysis, differential equations, and statistical inference, developing course materials in French and English for subjects like Calculs et Mathématiques , Théorie Ergodique , and Statistical Inference .

Danny Huckeمشاهده پروفایل

پژوهشگر

• Data Compression
• Grammar-Based Compression
• Empirical Entropy
• +۳ مورد دیگر

Danny Hucke is a researcher affiliated with the University of Siegen, Department of Electrical Engineering and Computer Science. His work focuses on advanced data compression techniques, algorithmic complexity, and formal verification of streaming systems. Education: PhD in Grammar-based compression for strings and trees (University of Siegen, 2019) Research Interests: Driven by challenges in grammar-based compression, empirical entropy metrics, and formal language processing in streaming environments, his research bridges theoretical computer science and practical algorithm design. Key areas include: Data Compression for Trees and Strings Sliding-Window Algorithms Circuit Complexity Algorithmic Entropy Analysis Scientific Contributions: His work has been recognized with a Best Paper Award at SPIRE 2016. Publications span top-tier venues like IEEE Transactions on Information Theory , ACM TOCT , and conferences including ICALP, STACS, and LATIN. Contact: Department of Electrical Engineering and Computer Science, University of Siegen, Hölderlinstrasse 3, D-57076 Siegen. Email: hucke@eti.uni-siegen.de . Phone: +49-271-740-3415. Office: Room H-A 7104. Collaborations: Conducted research within the group of Prof. Markus Lohrey, collaborating extensively with Moses Ganardi, Louisa Seelbach, and Eric Nöth.

Louisa Seelbach Benknerمشاهده پروفایل

پژوهشگر

• Data Compression
• Grammar-based Compression
• Succinct Data Structures
• +۳ مورد دیگر

Louisa Seelbach Benkner is a postdoctoral researcher at the University of Siegen , affiliated with the Faculty of Electrical Engineering and Computer Science. Her research focuses on data compression, particularly grammar-based compression, succinct data structures, universal source coding, and analytic combinatorics. She has contributed to theoretical advancements in tree compression and entropy analysis. Education: PhD in Computer Science from University of Siegen (2023). Research interests center on optimizing data compression techniques for tree structures, analyzing entropy bounds, and applying combinatorial methods to algorithm design. Her work bridges theoretical computer science and practical data encoding solutions. Selected publications highlight her expertise in hypersuccinct tree compression, empirical entropy comparisons, and fringe subtree analysis. Key collaborations include researchers like Markus Lohrey, Stephan Wagner, and Travis Gagie. Scientific awards: Capocelli Prize at DCC 2019 Best Student Paper Award at SPIRE 2020 Teaching roles include formal languages, complexity theory, logic, and algorithmics courses. She has also contributed to mathematics instruction in analysis and linear algebra.

• Discrete Mathematics
• Probabilistic Combinatorics
• Random Discrete Structures
• +۳ مورد دیگر

Dr. Annika Heckel is a Researcher at the Department of Stochastics and Financial Mathematics, Ludwig-Maximilians-Universität München (LMU Munich). Her research focuses on discrete mathematics, probabilistic combinatorics, and random discrete structures, particularly random graphs. She has published extensively on topics like chromatic numbers of random graphs and their probabilistic behavior. Dr. Heckel completed her PhD at the University of Oxford in 2017 under the supervision of Professor Oliver Riordan. Her work bridges theoretical combinatorics with stochastic processes, addressing fundamental questions in graph theory. Notable contributions include studies on non-concentration phenomena in chromatic numbers and rainbow connection thresholds in random graphs. Her publications span high-impact journals such as the Journal of the American Mathematical Society and Random Structures and Algorithms. While no scientific awards are explicitly mentioned, her research indicates significant contributions to probabilistic combinatorics. She is affiliated with LMU Munich's Mathematical Institute and actively contributes to the academic community through peer-reviewed articles and collaborative research.

• Remote Sensing
• Geoinformatics
• Landscape Planning
• +۵ مورد دیگر

Professor Damian Bargiel serves as Head of Institute and holds the Professorship for Applied Geoinformatics in Landscape Planning within the Department of Landscape Planning & Nature Conservation at Geisenheim University. His work bridges advanced geospatial technologies with practical applications in agricultural monitoring and landscape planning. Professor Bargiel's research focuses on applied geoinformatics with particular expertise in remote sensing technologies , especially Synthetic Aperture Radar (SAR) satellite data. His work spans crop classification, agricultural land monitoring, habitat structure detection, and the integration of time series analysis with crop phenology information. He has developed innovative classification methods that significantly improve accuracy over conventional approaches by leveraging the temporal dimension of radar imagery. His publication record demonstrates consistent contributions to the field since 2011, with recent work expanding into satellite-based disease detection (2025) and small satellite Earth observation systems (2024). The research shows a clear progression from foundational work on TerraSAR-X applications to more integrated approaches combining multiple data sources and advanced machine learning techniques. Professor Bargiel is currently leading significant research initiatives including the AGRECO4CAST project (2025-2028), which aims to develop a transdisciplinary assessment framework for agroecological transitions in European agroforestry landscapes. This EU and German government-funded project reflects his expanding research scope into sustainable agricultural systems and policy-relevant applications of geospatial technologies. His work provides valuable methodological advances for agricultural monitoring systems and contributes to practical applications in precision agriculture, landscape planning, and environmental conservation. The integration of technical remote sensing expertise with real-world agricultural and landscape planning challenges represents the distinctive contribution of Professor Bargiel's research program.

Steven Rugglesمشاهده پروفایل

استاد

• Historical Demography
• Population Studies
• Family Demography
• +۴ مورد دیگر

Steven Ruggles is Regents Professor of History and Population Studies at the University of Minnesota and Director of the Institute for Social Research and Data Innovation. He is best-known as the creator of IPUMS, the world's largest population database providing over two billion observations describing people residing in 107 countries between 1703 and 2022, including every respondent to the surviving U.S. censuses of 1790 to 1940. His work bridges historical demography, data science, and population studies. Ruggles has published extensively on historical demography, with research focusing on long-run changes in multigenerational families, single parenthood, divorce, and marriage patterns, as well as methodological innovations for population history. His work integrates computational methods with historical analysis, pioneering the use of large-scale population databases for demographic research. He has made significant contributions to understanding census technology evolution, privacy protection in demographic data, and the development of historical population infrastructure. His publications reveal a strong focus on the intersection of data science and historical demography, with particular attention to privacy protection methodologies, census data quality, and the development of large-scale population databases. Recent work examines the political dimensions of census technology, the challenges of differential privacy implementation, and the historical evolution of demographic data collection. King of Quant (Wired Magazine, 1995) Wonkblog-Certified Data Wizard (Washington Post Wonkblog, 2014) MacArthur Fellowship President of the Population Association of America (2015) President of the Association of Population Centers (2017-2018) President of the Social Science History Association (2018-2019) Ruggles has led major data infrastructure projects including IPUMS, which has transformed access to historical population data for researchers worldwide. His work has secured substantial funding for developing and maintaining population databases, enabling new research across multiple disciplines. As Director of the Institute for Social Research and Data Innovation, he oversees a major research center focused on data-driven social science. Through IPUMS and related projects, Ruggles has established one of the most significant population data infrastructure systems in the world, supporting research across history, sociology, economics, and public health. His team has developed innovative methods for harmonizing census microdata across time and space, creating unprecedented opportunities for comparative historical demographic research.

Manuel Bodirskyمشاهده پروفایل

استاد

• Constraint Satisfaction Problems (CSP)
• Algebraic Methods in Computer Science
• Computational Logic
• +۵ مورد دیگر

Prof. Manuel Bodirsky is a Professor of Algebra and Discrete Structures at Technische Universität Dresden since August 2014. He leads the Algebra and Discrete Structures group within the Faculty of Computer Science and is affiliated with the International Center for Computational Logic (ICCL). His research focuses on constraint satisfaction problems (CSP), algebraic methods in computer science, computational logic, Ramsey theory, model theory, and discrete mathematics. Notable projects include exploring the algebraic tractability of CSPs and applying universal algebra to classify computational complexity. His work frequently intersects with combinatorics, graph theory, and theoretical computer science. Recent publications address advanced topics like temporal CSPs, spectrahedral shadows, and resilience problems using valued CSP frameworks. He maintains active collaborations in computational algebra and logic, contributing to both theoretical foundations and algorithmic applications. Education: Not explicitly listed in provided texts but inferred to include advanced studies in mathematics and computer science. Research interests span foundational areas such as: Constraint satisfaction problem complexity classification Applications of universal algebra to computational problems Model theory and finite structures Combinatorial properties of graphs and tournaments Algorithmic approaches to algebraic and logical systems His recent articles emphasize methodological innovations, including reductions to semidefinite programming, Ramsey-theoretic techniques, and gadget-based transformations. Despite no listed academic awards in the provided data, his prolific publication record reflects significant contributions to theoretical computer science and discrete mathematics. No student advisees or grant details were explicitly mentioned, though his group likely engages in funded research projects given the institutional context.

Yann Strozeckiمشاهده پروفایل

دانشیار

• Enumeration Complexity
• Algorithmic Game Theory
• Simple Stochastic Games
• +۷ مورد دیگر

Yann Strozecki is an Associate Professor (Maître de Conférences HDR) at the University of Versailles Saint-Quentin, where he is based in the DAVID Laboratory and leads the ALMOST research team focused on algorithms and stochastic models. He is currently on a part-time assignment at LIGM, Gustave Eiffel University, and has previously held positions at LIP6 (RO team), Paris-Sud University (ALGO team), and completed a postdoctoral fellowship at the University of Toronto's Theory Group. He earned his PhD from Paris Diderot (Paris 7) under Arnaud Durand. His research lies at the intersection of theoretical computer science and discrete mathematics, with core interests in: Enumeration complexity, especially delay and space constraints Algorithmic game theory, particularly simple stochastic games (SSGs) Graph and matroid algorithms Cheminformatics and molecular structure generation Sparse polynomials and algebraic complexity Analysis of his recent publications reveals a strong trend in developing efficient enumeration algorithms with provable delay and space bounds, advancing the theoretical foundations of output-sensitive computation. He also contributes to practical algorithms for Cloud RAN scheduling and cheminformatics, often combining theoretical rigor with real-world applications. His work on geometric amortization and strategy improvement in SSGs demonstrates innovation in algorithm design. Notable scientific contributions include: Generic strategy improvement methods for SSGs Polynomial-delay enumeration via closure operations Efficient deterministic scheduling for low-latency networks Tools for molecular cage generation in chemistry Yann Strozecki actively supervises PhD and master’s students, including Noé Demange, Maël Guiraud, and Xavier Badin de Montjoye. He co-organizes the ALMOST team seminar and has advised numerous interns in algorithmics and game theory. His research has been supported through collaborations with Nokia Bell Labs (CIFRE thesis) and interdisciplinary projects in cheminformatics and networking.

• Artificial Intelligence
• Automated Planning
• Symbolic Reasoning
• +۶ مورد دیگر

Dr. Peter Kissmann is a researcher in the Computer Science Department at Universität des Saarlandes, Germany, affiliated with the Foundations of Artificial Intelligence (FAI) Group. His work focuses on automated planning systems, particularly in optimal and net-benefit planning domains. Research Interests: His primary research areas include Artificial Intelligence, Automated Planning, Symbolic Reasoning, Heuristic Search, and Knowledge Representation. He specializes in BDD-based planning, variable ordering, and symbolic search algorithms for optimal planning. The 15 most recent publications reflect his sustained contributions to the International Planning Competitions (IPC) from 2008 to 2014, showcasing evolution in planner design, including support for conditional effects, bidirectional search, improved variable ordering, and integration of modern BDD libraries like CUDD. These works demonstrate deep technical innovation in planner efficiency, robustness, and scalability. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no explicit information about students or grant funding in the provided material. However, his leadership in developing and releasing competitive planning systems suggests involvement in research projects, possibly with student contributors or collaboration within the FAI group. Labs and Teams: He is a member of the Foundations of Artificial Intelligence (FAI) Group at Universität des Saarlandes, which develops advanced planning systems and participates in international competitions like the IPC.

• Invasion Ecology
• Forest Ecology
• Computational Modelling
• +۴ مورد دیگر

Samuel M. Fischer is a Research Fellow at the Helmholtz Centre for Environmental Research (UFZ) in the Department of Ecological Modelling, where he has been conducting postdoctoral research since 2023. He holds a PhD in Applied Mathematics from the University of Alberta and a Bachelor of Science in Applied Systems Science from Osnabrück University. His research integrates mathematical modeling, algorithm design, and statistics to address ecological challenges. Primary interests include: Modeling invasive species spread using mobile app data and network analysis Mechanistic forest modeling for productivity assessment and stress detection via remote sensing Developing computational methods for ecological epidemiology and carbon sequestration Advancing standardization in ecological modeling through the Open Modelling Foundation Fischer's publications demonstrate a strong focus on integrating computational methods with ecological applications. Recent work emphasizes forest carbon dynamics, species persistence modeling, and optimization algorithms for environmental management. Methodological innovations in statistical computing and cross-disciplinary model integration are recurring themes across his research portfolio. He actively mentors students in computational ecology and promotes open science standards. Current projects involve linking hyperspectral satellite data with forest models to monitor ecosystem health in German forests.

Duncan Adamsonمشاهده پروفایل

مدرس

• Combinatorics on Words
• Algorithms for Combinatorial Objects
• Crystal Structure Prediction
• +۷ مورد دیگر

Duncan Adamson is a Lecturer in the School of Computer Science at the University of St Andrews, United Kingdom. He has held prior research positions at the Leverhulme Research Centre for Functional Materials Design, the University of Göttingen, Reykjavik University, and the University of Liverpool. His academic foundation includes a PhD from the University of Liverpool supervised by Prof. Igor Potapov and undergraduate studies at the University of Glasgow. His research lies at the intersection of theoretical computer science and discrete mathematics, with a focus on combinatorics on words , algorithm design , crystal structure prediction , and temporal graphs . He explores symmetry in multidimensional words, develops algorithms for combinatorial enumeration, and investigates computational hardness in materials science. His work often bridges abstract theory with real-world applications in chemistry and robotics. The recent publications show a strong trend in combinatorial algorithms , particularly in enumeration, ranking, and optimization over structured words, graphs, and sequences. A significant portion of his work involves the k-centre problem for implicitly defined combinatorial classes and temporal graph colouring , with increasing emphasis on algorithmic complexity and practical implementation. His 2024 paper on harmonious colourings in temporal matchings was awarded best paper at SAND 2024, highlighting the impact of his research. Scientific Awards: Best Paper Award, SAND 2024 – 'Harmonious Colourings of Temporal Matchings' Duncan Adamson has been supported by the Leverhulme Research Centre for Functional Materials Design during his PhD and postdoctoral work. While no formal advisees are listed, his supervision by leading researchers and collaborative projects suggest active mentorship and team integration. He teaches core computer science modules at St Andrews, including CS2001 and CS3302, and maintains a busy research schedule with weekly meetings and supervisory responsibilities. His research is conducted within the Algorithms and Complexity group at St Andrews, continuing collaborations with international teams in Iceland, Germany, and beyond. Projects often involve interdisciplinary efforts, especially in applying computational techniques to materials science and chemistry.

Jan Vogesمشاهده پروفایل

پژوهشگر

• Genomic Data Compression
• DNA Data Storage
• Bioinformatics
• +۳ مورد دیگر

Dr. Jan Voges is a Research Group Leader at the Institute for Information Processing and the L3S Research Center at Leibniz University Hannover, where he leads the BioAI Lab. He has held previous research leadership positions at the same institution (2022-2024), CIMA University of Navarra (2024-2025), and maintains a visiting position at the Peter L. Reichertz Institute for Medical Informatics. Dr. Voges' research spans the intersection of computer science, genomics, and medical informatics, with a focus on: Developing AI methods for multi-omics data analysis DNA sequencing data compression and storage 3D genome reconstruction Single-cell RNA sequencing data modeling International standardization of genomic data His recent work demonstrates a strategic shift toward integrating artificial intelligence with genomic data analysis, particularly for medical applications. The development of Genie, the first open-source ISO/IEC encoder for genomic data, represents a significant contribution to genomic data standards. His research shows strong translational potential, bridging computational methods with clinical applications as evidenced by his P4D Dashboard project for depression cohort monitoring. Dr. Voges has received recognition for his work, most notably earning his Dr.-Ing. degree with distinction in 2022. His active participation in ISO/IEC standardization committees and collaborations with international institutions demonstrates the impact of his work beyond traditional academic boundaries. As a supervisor, Dr. Voges maintains a focused lab structure with 3 PhD students, emphasizing quality mentorship and opportunities for students to contribute to high-impact projects with real-world applications. His lab provides unique opportunities at the cutting edge of genomic data science, with exposure to international standardization processes and interdisciplinary collaborations.

Peter Nejjarمشاهده پروفایل

استادیار

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +۳ مورد دیگر

Peter Nejjar is a Tenure-Track Juniorprofessor for Probability Theory at the University of Potsdam since January 2023. His research addresses fundamental questions of universality in stochastic systems , investigating why identical probability distributions emerge across disparate contexts like random matrices and growth models, with connections to combinatorics, mathematical physics, and numerical analysis. His primary research domains include stochastic particle systems (TASEP/ASEP), KPZ universality class phenomena, and Markov chain mixing behavior —particularly the cutoff phenomenon. Nejjar's work reveals deep structural parallels between shock fluctuations in interacting particle systems and random matrix eigenvalue distributions, leveraging connections to algebraic combinatorics through Schur processes. Recent publications demonstrate evolving focus from shock fluctuation theory (2015-2018) toward dynamical phase transitions in KPZ systems (2020-2022) and emerging interdisciplinary applications like DNA-based molecular tagging (2025). His collaborative network prominently features Patrik Ferrari across 7 publications, reflecting sustained focus on universality in exclusion processes.