جست‌وجوی استاد و پژوهشگر

Fahad Panolanمشاهده پروفایل

مدرس

• Parameterized Algorithms and Complexity
• Graph Theory and Graph Algorithms
• Approximation Algorithms
• +۷ مورد دیگر

Fahad Panolan is a Lecturer in the Algorithms and Complexity group at the School of Computing, University of Leeds, UK, a position he has held since August 2023. Prior to this, he was an Assistant Professor in the Department of Computer Science and Engineering at IIT Hyderabad, India, from July 2019 to August 2023. He conducted postdoctoral research at the Department of Informatics, University of Bergen, Norway, between 2016 and 2019. Education: PhD in Theoretical Computer Science, The Institute of Mathematical Sciences, HBNI, Chennai, India (2012–2015) MSc in Theoretical Computer Science, The Institute of Mathematical Sciences, HBNI, Chennai, India (2010–2012) Master of Computer Applications, National Institute of Technology, Calicut, India (2006–2009) BSc in Physics, DGM MES Mampad College, University of Calicut, India (2002–2005) His research lies at the intersection of theoretical computer science and algorithm design, with core interests in Parameterized Algorithms and Complexity , Graph Theory , Approximation Algorithms , and Streaming Algorithms . He has made significant contributions to kernelization, matroid-based techniques, and the development of subexponential-time algorithms for NP-hard graph problems. His work often bridges structural graph theory with algorithmic efficiency, particularly on sparse and geometric graphs. The recent publications show a strong trend in advancing the frontiers of fixed-parameter tractability, including efficient kernelization, approximation schemes for matrix problems, and reconfiguration algorithms. His work frequently appears in top-tier venues such as STOC, SODA, ICALP, and journals like JACM and Algorithmica. Scientific Service: PC Member: WALCOM 2026, IPEC 2023, ESA 2022, IPEC 2021, AAAI 2021 Scientific Coordinator: Parameterized Complexity 201 Workshop Referee for journals including TALG, SIDMA, TCS, Algorithmica, and conferences like STOC, SODA, ICALP, ESA. Fahad Panolan advises PhD students and interns, including Shubhada Suresh Aute and Seshikanth Varma. He has secured research grants through collaborative projects and has delivered invited talks at international workshops and seminars, including Dagstuhl, DIMAP, and Parameterized Complexity workshops. He has taught courses such as Algorithms, Design and Analysis of Algorithms, and Parameterized Algorithms at both University of Leeds and IIT Hyderabad. He is actively involved in the parameterized complexity and algorithms research community, organizing workshops and contributing to the theoretical foundations of efficient computation on hard problems.

Viorica Sofronie-Stokkermansمشاهده پروفایل

استاد

• Formal Methods
• Theoretical Computer Science
• Automated Reasoning
• +۴ مورد دیگر

Prof. Dr. Viorica Sofronie-Stokkermans is a leading academic at the Institute of Computer Science , University of Koblenz , where she heads the Formal Methods and Theoretical Computer Science group in Department 4 . Her research focuses on formal methods , automated reasoning , and verification of parametric systems . Research Trends : Recent work emphasizes symbol elimination , interpolation , and verification of hybrid automata , with applications in wireless networks and spatial families . Team Affiliations : Leads a team including Research Assistant Dennis Peuter , Secretariat Alexandra Felzen , and former members like Dr. Matthias Horbach and Dr. Mauricio Martel . Key Contributions : She developed the SEH-PILoT system for property-directed symbol elimination and advanced hierarchical reasoning in local theory extensions. Her work intersects logic , computer science , and hybrid systems .

Melina Fabianمشاهده پروفایل

پژوهشگر ارشد

• Language-sensitive mathematics teaching
• Didactics of number range extension
• Support and supervision of practical phases (SPS)
• +۳ مورد دیگر

Melina Fabian is a doctoral student and research assistant at the Institute of Mathematics , University of Potsdam, specializing in Mathematics Education . Her work focuses on language-sensitive teaching strategies, conceptual understanding of number range extensions, and digital-age educational practices. Current research examines linguistic barriers in mathematics learning and practical phase supervision Participates in international collaborations like the Griffith University Summer School Publications highlight innovative approaches to negative numbers and geometric permanence Her recent publications analyze the role of language in arithmetic comprehension, visualize function graphs for rule derivation, and contextualize number systems through real-world examples. These contributions align with trends in digital education and inclusive didactic frameworks. As a research assistant and tutor, she contributes to curriculum development and student mentorship. Her work intersects mathematics instruction with linguistic inclusivity, supported by grants for international exchange programs.

Nicholas Gaoمشاهده پروفایل

پژوهشگر

• Graph Neural Networks
• Quantum Chemistry
• Machine Learning
• +۳ مورد دیگر

Nicholas Gao is a researcher at the Technical University of Munich (TUM) within the Department of Computer Science's Data Analytics and Machine Learning group (I26), working under Prof. Stephan Günnemann. His research bridges machine learning and quantum chemistry, specializing in graph neural networks for molecular simulations and ab-initio quantum computations. His academic background includes: M.Sc. in Computer Science from TUM (2017-2020), thesis: Fast and Flexible Temporal Point Processes using Triangular Maps (top 10%, high distinction) B.Sc. in Computer Science from Technical University of Brunswick (2014-2017), thesis: Visualizing the IoT with Mixed Reality (top 10%) Gao's research centers on graph-based machine learning for quantum chemistry , developing novel approaches to solve many-electron Schrödinger equations and model potential energy surfaces. His work integrates graph neural networks with neural wave functions to enable efficient ab-initio simulations, addressing fundamental challenges in computational chemistry through deep learning innovations. His 2020-2024 publications reveal a clear trajectory toward solving quantum chemistry problems using geometric deep learning, with increasing focus on many-body physics and electronic structure theory. Key contributions include neural Pfaffians for Schrödinger equations (NeurIPS 2024 oral) and sampling-free potential energy surface networks (ICLR 2023), demonstrating consistent innovation at the ML-chemistry intersection. Scientific recognition includes: Deutschlandstipendium from TUM (2019) Deutschlandstipendium from TU Brunswick (2016) Gao currently focuses on research without listed advisees or grants. As a core member of TUM's I26 research group, he contributes to projects in robust machine learning, graph analytics, and efficient ML systems for scientific computing, maintaining active collaboration within the group's quantum chemistry and molecular modeling initiatives.

Henrik Zimmerمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Geometry Processing
• 3D Modeling
• +۵ مورد دیگر

Dr. Henrik Zimmer is a researcher at RWTH Aachen University specializing in computer graphics and geometry processing. His work bridges theoretical computational geometry with practical applications in digital fabrication, medical visualization, and industrial manufacturing. Affiliated with the Department of Computer Science, he has made significant contributions to mesh processing, 3D modeling, and optimization algorithms for real-world applications. His research focuses on geometry optimization for fabrication constraints, including Zometool-based approximation of freeform surfaces and rationalization of point-folding structures. Key interests include Mesh processing and topology preservation Variational methods for planar polygonal meshing Mixed-integer optimization for quadrangulation Medical visualization techniques for diffusion fiber tracking Industrial vision systems for laser welding process control His publication record shows consistent innovation from 2006 to 2014, with recent work emphasizing digital fabrication and practical optimization for consumer-level manufacturing. Notable scientific contributions include: Efficient algorithms for shortest path-concavity computation in 3D meshes Interactive volume-based visualization for diffusion MRI data Rationalization methods reducing fabrication costs by over 90% Novel approaches to melt pool monitoring in industrial welding Zimmer's collaborative work with Leif Kobbelt demonstrates strong industry-academia connections, particularly in translating geometric algorithms to manufacturing applications. His research spans both theoretical computer graphics (SIGGRAPH, Eurographics) and industrial applications (laser welding process control), reflecting a unique interdisciplinary approach. Current work focuses on making advanced geometric modeling accessible through personal fabrication technologies.

Leon Bungertمشاهده پروفایل

استاد

• Machine Learning
• Applied Mathematics
• Partial Differential Equations
• +۵ مورد دیگر

Leon Bungert is a W2 Professor with Tenure Track to W3 Professorship for Mathematics III (Mathematics of Machine Learning) at the University of Würzburg, appointed since 2023. He leads the Mathematics of Machine Learning group within the Institute of Mathematics. His academic journey includes a Ph.D. (summa cum laude) from the University of Erlangen-Nürnberg in 2020, focusing on nonlinear spectral theory with variational methods, followed by postdoctoral research at TU Berlin, Bonn, and Erlangen-Nürnberg. Research Interests: Bungert's work bridges applied analysis and machine learning, emphasizing PDEs on graphs, adversarial robustness, inverse problems, optimization, and nonlinear eigenvalue problems. His research often employs variational methods and explores the intersection of geometry, probability, and numerical analysis. Notable areas include adversarial machine learning, Lipschitz learning on graphs, and regularization techniques for inverse problems. Professional Activities: He serves as a guest editor for the European Journal of Applied Mathematics , associate editor for Advances in Continuous and Discrete Models , and co-organizes major conferences like MIA'25 and SSVM 2025. His work has been published in top journals such as Annals of Applied Probability , Journal de Mathématiques Pures et Appliquées , and SIAM Journal on Imaging Sciences . Upcoming Engagements: Invited speaker and organizer at events including the IHP Paris conference (January 2025), Osaka workshop on machine learning and numerics (March 2025), and the BIRS workshop on adversarial machine learning (August 2025). His research team collaborates with institutions globally, including MIT, University of Cambridge, and University of Bonn. Lab/Team: The Mathematics of Machine Learning group at Würzburg focuses on theoretical and applied aspects of machine learning, with active projects in adversarial robustness, PDE-based optimization, and geometric data analysis. Bungert’s team includes researchers like Eloi Martinet and Tim Roith, advancing interdisciplinary work in mathematical foundations of AI.

Zahra Parsaeianمشاهده پروفایل

پژوهشگر

• Algorithm Design
• Theoretical Computer Science
• Graph Algorithms
• +۳ مورد دیگر

Zahra Parsaeian is a graduate student in Computer Science at Universität des Saarlandes and a researcher at the Max-Planck-Institut für Informatik. Her primary affiliation is with the Algorithms and Complexity department. She focuses on theoretical computer science, including algorithm design, distributed algorithms, computational geometry, and parameterized algorithms. Research Interests: Zahra's work emphasizes graph algorithms , distributed systems , and computational geometry . Her recent projects include optimizing diameter computation in geometric intersection graphs and exploring round-optimal consensus protocols in distributed networks. Publications: Her articles highlight advancements in distributed computing and algorithmic theory, such as deterministic ruling set algorithms and laminar matroid secretary problems. These contributions address core challenges in algorithmic efficiency and scalability. Teaching: She has served as a tutor for Parameterized Algorithms at IIT Madras and held other tutoring roles at Isfahan University of Technology. Labs/Teams: Affiliated with the Max Planck Algorithms and Complexity group, contributing to foundational research in theoretical computer science.

• Spatial Databases
• Big Spatial Data
• Location-Based Services
• +۶ مورد دیگر

Mohamed F. Mokbel is a Professor at the University of Minnesota with a distinguished career in spatial databases and big spatial data management. His research spans over two decades with more than 260 publications in top-tier venues including ICDE, SIGMOD, VLDB, and GIS conferences. He has established himself as a leading researcher in trajectory data management, mobility data science, and scalable spatial processing systems. Dr. Mokbel's research interests focus on the intersection of spatial databases, big data, and machine learning. He has pioneered work in trajectory data management systems, developing frameworks like ST-Hadoop for processing spatio-temporal data at scale. His recent research explores the application of large language models to trajectory analysis, spatial data cleaning systems with spatial awareness, and innovative approaches to mobility data science. His work addresses fundamental challenges in handling massive spatial datasets while maintaining efficiency and accuracy. His publication record reveals significant trends toward integrating machine learning with spatial data management, particularly in trajectory imputation using BERT models (KAMEL system) and applying NLP techniques to trajectory analysis. Recent work shows increasing focus on urban mobility applications, privacy considerations in location data, and the development of specialized frameworks for processing different types of spatial data at scale. Dr. Mokbel has mentored numerous students who have become productive researchers in their own right, with many publications featuring students as first authors. His collaborative network is extensive, with frequent co-authorship with researchers like Walid G. Aref, Ahmed Eldawy, and Amr Magdy, indicating strong research group leadership. He has contributed significantly to the spatial database community through systems like RASED for monitoring OpenStreetMap updates, KAMEL for trajectory imputation, and Sparcle for spatial data cleaning. His work bridges theoretical database concepts with practical applications in transportation, urban planning, and location-based services.

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

Mark de Berg is a Professor at Eindhoven University of Technology, The Netherlands, specializing in computational geometry, algorithms, and graph theory. His research focuses on geometric optimization problems, including the Traveling Salesman Problem (TSP), dominating set algorithms, motion planning, and Voronoi games. Recent publications highlight his work on stable approximation algorithms, geometric intersection graphs, and motion planning in rectilinear environments. Collaborations include researchers like Arpan Sadhukhan, Frits C. R. Spieksma, and Boris Aronov. Scientific awards: No awards explicitly mentioned in the provided data. Advising: No student names listed in the provided records.

• Algebraic Geometry
• Singularity Theory
• Hyperplane Arrangements
• +۶ مورد دیگر

Prof. Dr. Mathias Schulze is a full Professor at the Department of Mathematics, RPTU Kaiserslautern-Landau, leading the Department of Algebra, Geometry and Computer Algebra. His research spans algebraic geometry, singularity theory, hyperplane arrangements, D-modules, and computational algebra. Education & Affiliations RPTU Kaiserslautern-Landau, Professor, Department of Mathematics (current) Research Interests Prof. Schulze’s work lies at the intersection of algebraic geometry, commutative algebra, and singularity theory, with extensive contributions to the study of hyperplane arrangements, logarithmic differential forms, mixed Hodge modules, and categorical aspects of algebraic singularities. His recent focus includes triangulated categories, matrix factorizations, and the interplay between algebraic structures and geometric singularities. Publications & Trends His 2022–2025 publications demonstrate a strong emphasis on categorical and homological methods in singularity theory, exploring triangulated structures, matrix factorizations, and configuration polynomials. Earlier work delves into hypergeometric systems, logarithmic residues, and free divisors, reflecting a deep engagement with both theoretical and computational aspects. Teaching He regularly teaches foundational and advanced courses in algebra, algebraic geometry, commutative algebra, singularity theory, and computer algebra, catering to mathematics and computer science students.

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.

• Algebraic Topology
• Cohomology Theory
• K-Theory
• +۳ مورد دیگر

Prof. Dr. Björn Schuster is a Professor of Mathematics at Ruhr-Universität Bochum, affiliated with the Faculty of Mathematics and the Topology research group led by Prof. Gerd Laures. His academic position is within the Department of Mathematics, specializing in advanced algebraic topology and cohomology theory. He teaches courses such as Algebraic Topology, Topology Lectures, and Analysis III, with recent semesters (2023–2025) focusing on specialized topics like cohomology of groups and geometric graph theory. His research interests include Morava K-theory, cohomology of finite groups, chromatic homotopy theory, and algebraic structures in topology. Notable contributions include studies on string bordism spectra and Mathieu groups. He has supervised numerous graduate and undergraduate theses, covering topics from graph theory to fundamental group classifications. Prof. Schuster has published extensively in prestigious journals such as Proceedings of the American Mathematical Society , Topology , and Algebraic & Geometric Topology . His work bridges abstract algebra and geometric topology, with applications to group theory and homotopy theory. He is also associated with the Floer Center of Geometry at Ruhr-Universität Bochum, contributing to collaborative research initiatives.

Heather Newmanمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Combinatorial Optimization
• Approximation Algorithms
• +۷ مورد دیگر

Heather Newman is an Assistant Professor in the Department of Computer Science at Vassar College. She earned her PhD from the interdisciplinary Algorithms, Combinatorics, and Optimization (ACO) program at Carnegie Mellon University through the Mathematics Department, advised by Ben Moseley. She received her A.B. in Mathematics with a minor in Computer Science from Princeton University in 2019 and an M.Sc. in Mathematical Sciences from Oxford in 2020. Her research lies at the intersection of discrete mathematics, theoretical computer science, and operations research, focusing on approximation and online algorithms for combinatorial optimization problems. She is particularly interested in clustering, scheduling, and developing new beyond-worst-case models for online algorithms with pessimistic lower bounds. The recent publications reflect a strong trend in algorithmic design for optimization problems, especially in clustering (e.g., correlation clustering, k-median), scheduling , and graph-based optimization . Her work emphasizes theoretical rigor with practical implications, often involving novel algorithmic frameworks and combinatorial techniques. She frequently publishes in top-tier theoretical venues such as ICALP, APPROX, SPAA, ICML, and SIGMETRICS. Scientific Awards No awards listed in the provided text. Advising and Grants There is no explicit mention of graduate students or PhD advisees in the provided information. Heather Newman does not list any specific grants or funded projects, but her research output suggests active engagement in theoretical computer science research. She was involved with Moon Duchin’s Metric Geometry and Gerrymandering Group, which applies mathematical methods to redistricting and social justice issues, indicating interdisciplinary outreach and potential collaborative funding. Labs and Research Teams While no formal lab is mentioned, Heather Newman has been affiliated with interdisciplinary research groups, notably the Metric Geometry and Gerrymandering Group led by Moon Duchin. This group brings together mathematicians and computer scientists to analyze gerrymandering through geometric and algorithmic tools. Her primary research collaborations appear to be with leading figures in theoretical computer science, including Ben Moseley, Kirk Pruhs, and Anupam Gupta, suggesting active participation in a broader research network focused on algorithm design and analysis.

Felix Ambellanمشاهده پروفایل

پژوهشگر

• Statistical Shape Analysis
• Medical Imaging
• Knee Osteoarthritis
• +۵ مورد دیگر

Felix Ambellan is a researcher at the Zuse Institute Berlin (ZIB), working in the Visual and Data-Centric Computing department within the Mathematics of Complex Systems division. His research focuses on applying advanced mathematical and computational techniques to medical imaging problems, particularly in the context of knee osteoarthritis and neurological disorders. Dr. Ambellan completed his doctoral studies at Freie Universität Berlin, where he earned his PhD in 2022 with a thesis titled "Efficient Riemannian Statistical Shape Analysis with Applications in Disease Assessment," supervised by Christof Schütte and Christoph von Tycowicz. His primary research interests lie at the intersection of medical imaging, computational geometry, and machine learning. Ambellan specializes in statistical shape analysis using Riemannian geometry, developing novel approaches for disease assessment through anatomical shape variations. His work has significant applications in knee osteoarthritis diagnosis and Alzheimer's disease grading, where he applies graph neural networks and manifold-valued statistics to extract clinically relevant information from medical images. He has made substantial contributions to the field of statistical shape modeling, particularly through the development of the open-source Python library Morphomatics. Analysis of Ambellan's recent publications reveals a strong focus on advancing statistical shape modeling techniques within non-Euclidean spaces. His work bridges theoretical mathematics with practical medical applications, particularly in developing methods that can handle the complex geometry of anatomical structures. Many of his papers demonstrate how incorporating geometric awareness into machine learning models improves diagnostic accuracy for conditions like knee osteoarthritis. His research often involves large-scale medical image datasets, including thousands of knee MRI scans from the Osteoarthritis Initiative. Dr. Ambellan is actively involved in several research projects including "Manifold-Valued Graph Neural Networks," "Morphological Scoring of Disease States," and "Treating Osteoarthritis in Knee with Mimicked Interpositional Spacer," which reflect his commitment to translating theoretical advances into clinical applications. His work has been published in high-impact journals including Medical Image Analysis, Physics in Medicine and Biology, and BMC Medical Imaging, demonstrating both the theoretical rigor and practical relevance of his research.

Andreas Wieseمشاهده پروفایل

دانشیار

• Combinatorial optimization
• Approximation algorithms
• Operations research
• +۳ مورد دیگر

Prof. Dr. Andreas Wiese is an Associate Professor at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Mathematics. He previously held academic positions at the Vrije Universiteit Amsterdam (2021-2022), Universidad de Chile (2016-2021), and the Max-Planck-Institut für Informatik (2012-2016). Andreas Wiese earned his PhD in Mathematics from TU Berlin (2008-2011) and studied Mathematics and Computer Science at TU Berlin (2002-2008). His research focuses on combinatorial optimization , approximation algorithms , and geometric problem-solving , particularly for NP-hard problems in packing, scheduling, and network flow. His recent work includes A 2024 SODA publication on outlier-aware minimum sum of radii approximation A 2023 STOC paper improving weighted flow time minimization Foundational contributions to unsplittable flow and geometric knapsack problems with consistent appearances at top-tier conferences like STOC, FOCS, and SODA. Andreas Wiese leads the Discrete Optimization research group at TUM, supervising PhD students Alexander Armbruster, Elisa Dell'Arriva, and Sandy Heydrich. He has organized major conferences including LATIN 2024 and ADFOCS 2015 , and served on program committees for STOC, FOCS, and SODA.