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Tibor SzabóView profile

Professor

• Combinatorics
• Extremal Graph Theory
• Random Structures and Algorithms
• +5 more

Tibor Szabó is a Professor in the Combinatorics and Graph Theory group at the Department of Mathematics, Freie Universität Berlin. He holds a PhD from The Ohio State University, advised by Ákos Seress. Prior to his current position, he held roles at McGill University, ETH Zürich, the Institute for Advanced Study (Princeton), and the University of Illinois (UIUC) as a J.L. Doob Research Assistant Professor. Research Interests: His work focuses on combinatorics and combinatorial optimization, including extremal problems, random structures and algorithms, pseudorandom graphs, positional games, and the combinatorics of linear programming. He explores tools from algebra, probability theory, and topology applied to combinatorics. Teaching: He teaches courses such as Algorithmic Combinatorics, Extremal Combinatorics, and runs the Combinatorics Seminar. His lecture notes include works on positional games and explicit constructions in extremal combinatorics. Students & Postdocs: Notable PhD advisees include Yamaan Attwa, Silas Rathke, Simona Boyadzhiyska, and Patrick Morris. Postdoctoral fellows include Olaf Parczyk and Anurag Bishnoi. His research has involved collaborations with over 50 co-authors. Funding & Grants: Supported by grants from the Swiss National Science Foundation (SNF) and German Research Foundation (DFG), focusing on topics like positional games and extremal graph theory.

• Machine Learning
• Natural Language Processing
• Medical Informatics
• +3 more

Stefan Riezler is a full professor of Statistical Natural Language Processing at Heidelberg University's Department of Computational Linguistics (since 2010), affiliated with the Faculty of Mathematics and Computer Science. Prior to this, he worked in Silicon Valley at Xerox PARC and Google Research. He holds a PhD in Computational Linguistics from the University of Tübingen (1998) and conducted postdoctoral research at Brown University (1999). His research spans machine learning, NLP, and medical informatics, focusing on interactive statistical learning. He co-leads the Interdisciplinary Center for Scientific Computing (IWR) and serves on the editorial boards of Computational Linguistics and Transactions of the Association for Computational Linguistics . Key research areas include neural machine translation, healthcare AI (e.g., sepsis prediction), data augmentation, and reproducibility in ML. He develops tools like JoeyNMT and explores ethical challenges in clinical machine learning. Notable recent work includes advancements in time series analysis, multimodal interfaces (e.g., NLMaps for OpenStreetMap), and ethical frameworks addressing validity in healthcare ML. His publications emphasize practical applications of NLP in healthcare, speech translation, and cross-lingual systems. Grants and collaborations include interdisciplinary projects on medical data science and training next-gen NLP researchers. He actively contributes to open-source toolkits and reproducible research practices.

• Algorithms and Complexity
• Logic
• Database Theory
• +2 more

Martin Grohe is a Professor at the School of Logic and Theory of Discrete Systems , part of the Department of Computer Science at RWTH Aachen University . His research spans Algorithms and Complexity , Logic , Database Theory , Graph Theory , and Machine Learning , with a focus on integrating logical frameworks into computational models. His recent work explores graph neural networks , Weisfeiler-Leman algorithms , and parameterized complexity , as seen in publications on isomorphism testing , database repairing , and probabilistic query evaluation . While no specific scientific awards are mentioned, his contributions to graph theory and machine learning are widely recognized through numerous peer-reviewed publications.

Marco CuturiView profile

Professor

• Machine Learning
• Optimal Transport
• Optimization
• +4 more

Marco Cuturi is a Research Scientist at Apple ML Research in Paris and Professor of Statistics at CREST-ENSAE, Institut Polytechnique de Paris. His work bridges machine learning , optimal transport , and optimization , with applications in time-series analysis , kernels , and multiresolution methods . He has held academic roles at Kyoto University and Princeton University, and previously worked in the financial industry. Research Interests: Optimal transport theory and computational methods Kernel design for structured data and histograms Time-series alignment and soft-DTW Entropic regularization in optimization Applications to computer vision and genomics Teaching: Cuturi has taught courses on linear optimization at Princeton, geometric methods in machine learning at Kyoto, and scientific English. He has also organized machine learning summer schools in Kyoto, Les Houches, and other international venues. Recent Trends: His 2024-2025 publications focus on entropic optimal transport solvers, disentangled representation learning via Gromov-Monge gaps, and applications to text-to-image diffusion models. Collaborative work with institutions like Google Research, MIT, and University of Tokyo highlights his interdisciplinary impact.

• Business Process Management
• Process Mining
• Complex Event Processing
• +5 more

Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data

Jalal EtesamiView profile

Assistant Professor

• Machine Learning
• Statistical Decision Making
• Causal Inference
• +4 more

Prof. Jalal Etesami is an Assistant Professor in the Department of Computer Science at Technical University of Munich (TUM), leading the Decision Sciences & Systems group. He holds a Ph.D. in Industrial and Systems Engineering from the University of Illinois at Urbana-Champaign and was a Postdoctoral Fellow at EPFL in Switzerland. His research focuses on machine learning, causal inference, multi-agent systems, and game theory, with applications to systemic risk modeling and market design. He teaches advanced courses such as Causal Inference in Time Series , Algorithmic Game Theory , and Optimization, Learning, and Market Design . Notable contributions include work on causal structure learning, stochastic optimization, and non-Gaussian causal models. Recent research explores causal effect identification under confounding, neural networks for market analysis, and optimal experiment design. Prof. Etesami’s work appears in top venues like NeurIPS, AAAI, and IEEE journals. He actively contributes to the academic community, organizing seminars and workshops on topics ranging from causal reasoning to computational social choice.

Stefan GlockView profile

Assistant Professor

• Discrete Mathematics
• Extremal Combinatorics
• Probabilistic Combinatorics
• +4 more

Professor Stefan Glock is an Assistant Professor of Discrete Mathematics at the University of Passau's Faculty of Computer Science and Mathematics, a position he has held since September 2022. Prior to this appointment, he spent three years as a Junior Fellow at the Institute for Theoretical Studies at ETH Zurich, following the completion of his doctorate at the University of Birmingham. Stefan Glock received his mathematics education at Technische Universität Ilmenau from 2009 to 2014, then pursued his PhD at the University of Birmingham, which he completed in 2018. His doctoral dissertation, "Decompositions of Graphs and Hypergraphs," was the runner-up for the Richard-Rado-Preis 2018. Professor Glock's research focuses on discrete mathematical structures, with particular emphasis on their asymptotic properties. His work spans several interconnected fields of combinatorics: Extremal Combinatorics : Investigating the maximum or minimum possible size of mathematical structures satisfying certain properties Probabilistic Combinatorics : Applying probability theory to solve combinatorial problems Graph Theory : Studying properties of graphs and networks Ramsey Theory : Examining conditions under which order must appear in large structures Design Theory : Creating arrangements of elements satisfying specific balance properties Discrete Geometry : Analyzing geometric problems with discrete structures Analysis of Professor Glock's recent publications reveals a consistent focus on solving long-standing open problems in combinatorics using innovative methods that combine probabilistic techniques with structural insights. His work often bridges theoretical mathematics with applications in theoretical computer science, particularly in the analysis of algorithms and network structures. A significant portion of his research addresses fundamental questions about graph and hypergraph decompositions, which have implications for coding theory, cryptography, and network design. Professor Glock has received notable recognition for his contributions to mathematics: Runner-up for the Richard-Rado-Preis 2018 for his dissertation "Decompositions of Graphs and Hypergraphs" Awarded funding through the prestigious DFG Emmy Noether Programme in 2024 for his research group on "the interplay of structure and randomness in mathematics" As a faculty member at the University of Passau, Professor Glock leads the Discrete Mathematics research group and actively collaborates with mathematicians worldwide. He has established a strong research program that has attracted funding for academic visitors and supports multiple research projects. His approach to mathematical problems emphasizes developing new methods that have far-reaching implications beyond the specific problems being solved. Professor Glock's research group at the University of Passau focuses on the interplay between structure and randomness in discrete mathematics. The group maintains active collaborations with leading institutions including ETH Zurich, University of Birmingham, and various research centers across Europe. Through the DFG Emmy Noether Programme funding, his group is expanding its research on combinatorial structures and their applications.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +5 more

Ling Zhao is a distinguished Professor at the School of Management, Huazhong University of Science and Technology, China, with extensive research contributions spanning artificial intelligence, machine learning, information systems, and biomedical applications. With over 150 publications since 2008, Dr. Zhao has established herself as a leading researcher in multiple interdisciplinary domains, particularly in applying computational methods to solve complex real-world problems. Dr. Zhao's research interests encompass a broad spectrum of cutting-edge topics including artificial intelligence, machine learning, data mining, control systems, and information systems. Her work demonstrates exceptional versatility, bridging theoretical computer science with practical applications in healthcare, transportation, cybersecurity, and business management. Notably, she has made significant contributions to sentiment analysis, medical image processing, algorithmic management, and privacy-preserving data analysis. Her research methodology often combines deep learning approaches with domain-specific knowledge to develop innovative solutions. Analysis of Dr. Zhao's recent publications (2023-2025) reveals a strong focus on interdisciplinary applications of AI, with particular emphasis on healthcare informatics (medical image analysis, disease diagnosis), human-computer interaction (algorithmic management effects), and advanced machine learning techniques (graph neural networks, multimodal learning). Her work shows a consistent trend toward increasingly complex and integrated systems that address real-world challenges across multiple domains. Dr. Zhao has made substantial contributions to academic advising and research mentorship, though specific student names aren't detailed in the available publications. Her research has been supported by various grants enabling work in AI applications, biomedical engineering, and information systems. Dr. Zhao maintains active collaborations with researchers across China and internationally, as evidenced by her co-authorship patterns. While specific laboratory information isn't explicitly mentioned in the publication records, Dr. Zhao appears to lead or be significantly involved in research groups focusing on AI applications in management and healthcare. Her work on medical imaging, sentiment analysis, and control systems suggests involvement in multiple specialized research teams addressing different application domains through computational approaches.

László KozmaView profile

Assistant Professor

• Data Structures
• Algorithms
• Algorithmic Adaptivity
• +7 more

László Kozma is an Assistant Professor at the Theoretical Computer Science group of the Institute of Computer Science (Freie Universität Berlin). He obtained his PhD from Saarland University under Raimund Seidel, followed by postdoctoral positions at Tel Aviv University and TU Eindhoven. His research focuses on self-adjusting data structures , adaptive algorithms , and combinatorial optimization with applications to problems like the Traveling Salesman Problem, binary search trees, and geometric data structures. Academic Affiliation: Freie Universität Berlin (since 2018) Education: PhD in Computer Science (Saarland University, 2016); postdoc at Tel Aviv University and TU Eindhoven. His work explores the intersection of data structures, combinatorial algorithms, and geometric methods. He has made significant contributions to problems involving pattern-avoidance in inputs, saddlepoint detection , and self-adjusting heaps . Key areas include: Adaptive algorithms for pattern-avoiding inputs Optimal tree and heap structures Geometric and stochastic approaches to optimization Complexity analysis of classical algorithms Recent publications highlight efficient solutions for exponential cut problems (ESA 2025), balanced TSP partitioning (EuroCG 2025), and randomized saddlepoint algorithms (ESA 2024). His research often bridges theory and practice, exemplified by the smooth heap implementation and fun projects like Recursi and Cuckoo Hashing visualization.

Markus ListView profile

Professor

• Systems Biology
• Data Science
• Bioinformatics
• +3 more

Prof. Dr. Markus List is a Professor of Data Science in Systems Biology at the Technical University of Munich (TUM), affiliated with the TUM School of Life Sciences. His research focuses on integrating data science with systems biology to understand gene regulatory mechanisms across genomic levels. He leads the Big Data in Biomedicine group and has held roles including Group Leader at the Chair of Experimental Bioinformatics (2018–2023) and PostDoc at the Max Planck Institute for Informatics (2015–2018). Educational Background: BSc and MSc in Bioinformatics from Eberhard Karls University of Tübingen (2005–2011) PhD in Molecular Oncology from University of Southern Denmark (2011–2015) Research Interests: Prof. List’s work bridges biology, medicine, and informatics using machine learning and data integration. Key areas include: Systems Biology of gene regulation in cancer Epigenetic data analysis Computational methods for biomedical big data Awards & Recognition: Research Prize of the Hamburg Cancer Society (2023) TUM Teaching Award (2023) TUM School of Life Sciences Supervisory Award (2022) Labs & Teams: Leads the DaisyBio group, focusing on experimental and computational bioinformatics. Collaborates with interdisciplinary teams in bioinformatics and systems medicine.

Yann DisserView profile

Professor

• Combinatorial Optimization
• Online Algorithms
• Graph Theory
• +6 more

Professor Yann Disser is a faculty member in the Department of Mathematics at TU Darmstadt since 2021. He previously held an Assistant Professor (tenure-track) position at TU Darmstadt (2016-2021), a PostDoc position at TU Berlin (2012-2016), and a Visiting Professor role at Augsburg University (2015). His research spans Combinatorial Optimization , Online Algorithms , Graph Exploration , Computational Complexity , and Robust Optimization . Current position: Professor (W2), TU Darmstadt Previous: Assistant Professor (2016-2021), TU Darmstadt PostDoc & Habilitation: TU Berlin (2012-2016) His research focuses on algorithmic approaches to optimization problems, including: Combinatorial Optimization for problems like Steiner trees and knapsack variants Online Algorithms with applications to scheduling and transportation Graph Exploration by mobile agents and complexity bounds Computational Complexity of linear programming pivot rules Network Flows and geometric reconstruction Recent publications analyze incremental maximization with greedy methods, lower bounds for active-set methods , and universal circuit designs . His work appears in top venues like IPCO , ESA , and SODA . He advises a team of researchers including David Weckbecker, Farehe Soheil, and Alexander Birx. Current and past advisees often focus on algorithmic theory, with placements at institutions like HPI Potsdam and Merck.

• Computer Science
• Machine Learning
• Health Monitoring Systems
• +7 more

Muhammad Waqas is a researcher affiliated with COMSATS University Islamabad , where he holds a position in the Department of Meteorology under the School of Applied Sciences and Humanities . His academic collaborations span institutions like Bahria University, National University of Technology, and University of Bahrain, indicating a multidisciplinary approach. Research interests include Mechanisms for integrating fuzzy logic and machine learning in health monitoring Application of deep learning to medical imaging and clinical diagnostics Development of smart sensors for wearable technology in biomechanics Analysis of social media data for public health surveillance and sentiment analysis Investigation of digital citizenship and ICT leadership in educational contexts Trends in his 15 most recent publications (2025-2024) reveal a focus on medical diagnostics (e.g., monkeypox, breast cancer), smart infrastructure (e.g., sensor placement, structural health monitoring), and social media analytics for health and behavioral insights. These works leverage machine learning , fuzzy systems , and multi-objective optimization .

• Structural Graph Theory
• Combinatorics of Posets
• Geometric Intersection Graphs
• +4 more

Piotr Micek is a professor in the Theoretical Computer Science Department at the Faculty of Mathematics and Computer Science, Jagiellonian University , Kraków, Poland. He is an active researcher in combinatorics, particularly in structural graph theory and poset combinatorics, and maintains extensive international collaborations. University: Jagiellonian University School: Faculty of Mathematics and Computer Science Department: Theoretical Computer Science Department Email: firstname.lastname@gmail.com Office: Room 3151, Łojasiewicza Street 6, 30-348 Kraków Phone: +48 12 664 7594 Duty hours: Wednesdays 11:00–13:00 His main research interests include structural graph theory, combinatorics of partially ordered sets (posets), geometric intersection graphs, graph coloring and choosability, and the entropy compression method. He also works on approximation and on-line algorithms and combinatorial geometry. His work often bridges deep theoretical insights with algorithmic applications. The recent publications highlight a strong focus on graph structure and coloring problems. Key themes include product structure of planar graphs, poset dimension and its relation to height and planarity, weak coloring numbers, and adjacency labelling. His work frequently appears in top venues such as Journal of the ACM , Combinatorica , SIAM Journal on Discrete Mathematics , and SODA, indicating sustained high-impact contributions. Associate Editor, SIAM Journal on Discrete Mathematics (2025–) Former Associate Editor, Discrete Mathematics (2010–2022) Former Associate Editor, Discrete Mathematics & Theoretical Computer Science (2012–2020) He has supervised numerous students at all levels, including PhD candidates Jędrzej Hodor , Marcin Briański , and Michał T. Seweryn , and has led major research grants such as OPUS 24 and WEAVE-UNISONO funded by NCN. He has also been involved in significant trilateral projects with researchers from Belgium and Germany. His recent talks include tutorials on product structure theory and centered colorings, reflecting his leadership in these areas. He actively participates in the academic community, having served on program committees (e.g., SODA 2021, WG 2022) and organized workshops such as the Order & Geometry series. His research is supported by substantial funding, including over 900,000 PLN for current projects.

Christoph T. KochView profile

Professor

• Electron Microscopy
• Materials Science
• Condensed Matter Physics
• +3 more

Christoph T. Koch is a Professor of Physics at Humboldt-Universität zu Berlin, where he has held the W3 Chair since 2015. Previously, he held a similar position at Ulm University (2011–2015), supported by the Carl Zeiss Foundation. His research focuses on advanced electron microscopy techniques, including quantitative transmission electron microscopy (TEM), electron holography, and strain mapping. He leads the AG Strukturforschung/Elektronenmikroskopie group, advancing materials science through innovations in imaging and spectroscopy. Education: B.Sc./M.Sc. in Physics at Heidelberg University (1996–1998), followed by an exchange at Arizona State University (1997–1998). PhD in Physics from Arizona State University (2002, advisor: Prof. John C.H. Spence). Postdoctoral research at the Max Planck Institute for Metals Research, Stuttgart (2002–2011). Research interests include: Electron diffraction and phase retrieval Nanometer-scale strain and defect analysis Electron energy-loss spectroscopy (EELS) for plasmonics and bandgap mapping Development of FAIR data infrastructure for materials science Leadership: Managed the Department of Physics at Humboldt University (2020–2024). Collaborates widely, with key co-authors including P.A. van Aken, W. Sigle, and C. Felser. His work bridges experimental microscopy and computational modeling, addressing challenges in semiconductors, ceramics, and 2D materials. Notable contributions include pioneering methods for 3D reconstruction via electron ptychography, dynamic electron diffraction analysis, and strain mapping in advanced CMOS technologies. Current efforts emphasize real-time imaging and AI-driven data analysis in materials research.

• Theoretical Computer Science
• Algorithms and Data Structures
• Computational Geometry
• +5 more

Raimund Seidel is a Professor in the Department of Computer Science at Universität des Saarlandes, leading the Chair of Theoretical Computer Science. He is actively involved in research and teaching, focusing on foundational aspects of algorithms and data structures, particularly in computational geometry. His primary research interests include theoretical computer science , design and analysis of efficient algorithms , geometric data structures , randomized algorithms , and combinatorial geometry . His work addresses fundamental problems such as planar point location, convex hull computation, and efficient encoding of triangulations. He also investigates geometric algorithms under the transdichotomous model, leveraging word-level parallelism. The selected publications reflect a long-standing contribution to computational geometry and data structure theory , with a focus on randomized methods and exact complexity analysis. His research combines theoretical rigor with practical implications for algorithm design. Award or honor not found in the provided text. Prof. Seidel has advised several students, including Alexander Malkis , Ralf Osbild , Udo Adamy , Christian Sohler , and others, many of whom have gone on to academic and research careers. No explicit information about grants or funding is available in the text. He leads a research group within the Department of Computer Science at Universität des Saarlandes, mentoring current staff such as László Kozma , Giorgi Nadiradze , and Lavinia Dinu . The group maintains active research in theoretical computer science and computational geometry.