جست‌وجوی استاد و پژوهشگر

Olaf Parczykمشاهده پروفایل

استاد

• Probabilistic Combinatorics
• Extremal Combinatorics
• Ramsey Theory
• +۳ مورد دیگر

Dr. Olaf Parczyk is a research assistant at the Zuse Institute Berlin (ZIB) in the Interactive Optimization and Learning Laboratory under Sebastian Pokutta. In the winter term 2024/25, he served as a substitute professor at Freie Universität Berlin (FU Berlin) in the Department of Mathematics and Computer Science. Previously, he was a Math+ Postdoc at FU Berlin, a visiting fellow at the London School of Economics funded by a DFG fellowship (Grant PA 3513/1-1), and a postdoctoral researcher at TU Ilmenau. He earned his Ph.D. at Goethe University Frankfurt, supervised by Yury Person. Education: Bachelor's in Mathematics, Freie Universität Berlin (2013) Master's in Mathematics, Freie Universität Berlin (2014) Ph.D. in Mathematics, Goethe University Frankfurt (2017) Olaf's research lies at the intersection of probabilistic and extremal combinatorics, Ramsey theory, and machine learning. He focuses on embedding problems for graphs and hypergraphs, particularly those involving randomness. His work includes advancements in Ramsey-type problems, Hamiltonian cycles, and universality in sparse graphs. Recent publications highlight his contributions to Dirac-type theorems for graphs of bounded bandwidth, Ramsey multiplicity bounds, and resilience properties in hypergraphs. He has also explored applications in group testing and positional games under random perturbations. Key Awards: DFG Fellowship (Grant PA 3513/1-1) Olaf has supervised multiple theses, including ongoing Master's students Eva Schinzel and Niall Smith, and Bachelor's student Pascal Weihnhart (2025). He has taught courses in discrete mathematics, probability theory, and seminars on random graphs and extremal combinatorics at FU Berlin and TU Ilmenau.

• MPSoC Architecture
• Memory Hierarchies
• Hardware Prefetching
• +۳ مورد دیگر

Oliver Lenke is a Scientific Assistant at the Chair of Integrated Systems , Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology . He completed his Bachelor’s (2015-2018) and Master’s (2018-2020) in Electrical Engineering at TUM and has been a PhD student since 2020 . His research focuses on MPSoC architectures , memory hierarchies , hardware preloading mechanisms , and FPGA-based system prototyping . His recent publications address topics like near-memory computing , cache prefetching , and runtime adaptive MPSoCs . He supervises students in projects involving VHDL coding , C programming , and MPSoC optimization , collaborating with industry partners such as Infineon AG , BMW AG , and Huawei . His work includes developing non-intrusive performance monitoring frameworks and dynamic memory preloading solutions .

Itay Henمشاهده پروفایل

دانشیار

• Quantum Computing
• Computational Physics
• Quantum Simulation
• +۴ مورد دیگر

Itay Hen is an Associate Professor of Research in the Department of Physics and Astronomy and Principal Scientist at the Information Sciences Institute (ISI), University of Southern California. He has held research faculty positions at USC since 2013, progressing from Assistant Professor (2016-2020) to his current Associate Professor role since 2020, while leading ISI's quantum computing initiatives. His educational background includes dual bachelor's degrees in Physics and Psychology from Tel Aviv University, followed by a Ph.D. in Physics from the same institution in 2009. Postdoctoral training included theoretical condensed matter research at Georgetown University and UC Santa Cruz, plus a senior scientist role at NASA Ames Research Center within the Quantum Artificial Intelligence Laboratory—a NASA/Google/USRA collaboration. Dr. Hen's research centers on Quantum Computing and Computational Physics , with specific expertise in gate-based quantum simulation algorithms, quantum annealer limitations, and methods for studying equilibrium/non-equilibrium properties of strongly correlated quantum systems. His work bridges theoretical frameworks with practical quantum hardware applications. Analysis of his 15 most recent publications (2017-2025) reveals consistent focus on quantum algorithms and Monte Carlo techniques, with accelerating output in 2024. Key themes include Feynman path integrals, spin/Bose-Hubbard model simulations, and quantum spectrum estimation, primarily published in Physical Review journals and Quantum. He leads the Hen Lab at USC's ISI, which operates within the Quantum Artificial Intelligence Laboratory framework. His research has been supported through NASA/Google/USRA collaborations focused on quantum optimization for complex computational problems, though specific grant details and student mentorship records aren't provided in the source material.

Mark Wittekمشاهده پروفایل

استادیار

• Computational Social Science
• Social Network Research
• Sociological Theory
• +۷ مورد دیگر

Mark Wittek is an Assistant Professor at the Department of Network and Data Science (Central European University, Vienna). His research bridges Computational Social Science , Sociological Theory , and Network Analysis to explore how institutional conditions shape relational processes in schools, academic fields, and cultural industries. Key Themes : Social stratification in networks, resource inequality in science, status hierarchies, cultural consumption, and data-driven policy modeling Methods : Statistical analysis, network science, natural language processing, and computational modeling Recent work includes studies on biomedical conventionality , elite networks in governance , and migration under climate change . His collaborative projects span institutions like University of Cologne, University of Zurich, and Heidelberg Academy of Sciences. Awards : None explicitly stated Teaching : Courses like "Multivariate Data Analysis with R" and data visualization workshops

David Bommesمشاهده پروفایل

استاد

• Computer Graphics
• Geometry Processing
• Mesh Generation
• +۴ مورد دیگر

Prof. Dr. David Bommes is a leading researcher in computer graphics and geometry processing, currently a Professor at the University of Bern . His expertise lies in mesh generation, particularly quadrilateral and hexahedral meshing, numerical optimization, and automatic differentiation techniques. His research focuses on developing robust algorithms for generating high-quality meshes from complex geometries, with applications in CAD, architecture, and simulation. He has made significant contributions to the fields of surface and volume parametrization, directional field synthesis, and geometry processing optimization. Prof. Bommes has received notable recognition, including the Best Paper Award (1st place) at SGP 2022 and the Graphics Replicability Stamp for his work on TinyAD, a lightweight automatic differentiation library for geometry processing. His publications span top-tier venues such as SIGGRAPH, Eurographics, and ACM Transactions on Graphics, covering topics from automatic differentiation and geodesic computation to advanced meshing techniques. He actively collaborates with leading institutions and researchers worldwide.

Patrick Schmidtمشاهده پروفایل

پژوهشگر

• Geometry Processing
• Computer Graphics
• Mesh Processing
• +۳ مورد دیگر

Patrick Schmidt is a researcher in the Department of Computer Science at RWTH Aachen University, Germany, specializing in geometry processing and computer graphics. His work focuses on developing robust algorithms for surface mapping, mesh processing, and automatic differentiation, with significant contributions to both theoretical foundations and practical applications. He actively collaborates with the Computer Graphics group led by Prof. Leif Kobbelt, producing high-impact research published in premier venues including SIGGRAPH and Eurographics. His research spans Geometry Processing, Computer Graphics, and Computational Biology, with core interests in distortion-minimizing surface maps, mesh parametrization, and biomedical applications. Schmidt's innovative approaches include adaptive triangulation methods for bijective surface correspondence and lightweight automatic differentiation frameworks like TinyAD that simplify non-linear optimization in geometry processing. His interdisciplinary work on heart tube formation demonstrates how geometric techniques can advance biological understanding of mammalian organ development. Analysis of Schmidt's publication trajectory reveals a consistent focus on improving the robustness and efficiency of surface mapping techniques. His work progressively addresses challenges in distortion control, topological consistency, and computational performance, culminating in award-winning contributions like adaptive triangulation methods and constant-curvature metric representations. The recurring theme across his research is the unification of geometric theory with practical implementation, emphasizing reproducibility through open-source tools and Graphics Replicability Stamps. Scientific recognition includes: Günter Enderle Best Paper Award at Eurographics 2023 for pioneering adaptive triangulation techniques Consecutive Best Paper Awards at SGP 2022 and 2021 for TinyAD library and homology inference methods Honorable mention for Eurographics 2021 work on layout embedding optimization While specific student advising records aren't documented in public sources, Schmidt's role involves mentoring through collaborative research within RWTH Aachen's Computer Graphics group. His projects frequently secure institutional support for high-impact work, evidenced by consistent publication in top-tier venues and development of widely adopted tools like TinyAD. The group maintains strong industry and academic partnerships focused on advancing geometry processing fundamentals. As a core member of RWTH Aachen's Computer Graphics research team, Schmidt contributes to a vibrant ecosystem centered around Prof. Leif Kobbelt's lab. The team emphasizes both theoretical innovation and practical software development, with recent work bridging computer graphics and biomedical modeling. Current efforts focus on intrinsic optimization frameworks and expanding automatic differentiation applications, positioning Schmidt at the forefront of next-generation geometry processing techniques.

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.

• Nonlinear Optimization
• Numerical Optimization
• Optimization and Control with PDEs
• +۱۲ مورد دیگر

Prof. Dr. Nicolas R. Gauger is Full Professor and Chairholder for Scientific Computing at the University of Kaiserslautern-Landau (RPTU), holding dual appointments in the Department of Mathematics and Department of Computer Science. He directs the university's Computing Center (RHRZ) and leads the SciComp research team, with prior roles at DLR Braunschweig, Humboldt University Berlin, RWTH Aachen University, and MIT. His academic background includes a Master in Mathematics (Dipl.-Math.) from Leibniz University Hannover (1998) and a Ph.D. in Applied Mathematics (Dr.rer.nat.) from Braunschweig University of Technology (2003). 1998-2010: Research Scientist, Numerical Methods for Aerodynamics at DLR Braunschweig 2005-2010: Assistant Professor (W1), Department of Mathematics, Humboldt University Berlin 2010-2014: Associate Professor (W2), RWTH Aachen University 2014: Visiting Professor, Massachusetts Institute of Technology Prof. Gauger's research centers on optimization under uncertainty for complex physical systems. His work integrates Algorithmic Differentiation with Machine Learning to advance Computational Fluid Dynamics, Aeroacoustics, and Structural Mechanics. Recent projects focus on medical applications like proton computed tomography (pCT) for cancer treatment through SIVERT and AI Care initiatives, alongside aerodynamic design for noise reduction and flow control. His 2025 publications reveal a dominant trend in end-to-end differentiable programming for physics-based optimization, particularly in fundamental particle physics experiments and medical imaging. Key subfields include diffusion models for detector design, reinforcement learning for particle tracking, and robust optimization frameworks applied to turbulence modeling and proton therapy. Scientific recognition includes: Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA), 2018 Teaching Award from RPTU, June 2025 He has advised doctoral students including award-winning researcher Max Aehle (2025 Freundeskreis RPTU Outstanding Dissertation Award). Major grants include leadership of the Excellence Initiative-funded AICES Graduate School (2010-2019), the Center for Mathematical and Computational Modelling (CM) 2 (2014-2019), and the MathApp (2019-2024) and MSO (2024-present) research initiatives. Current projects like SIVERT and AI Care apply AI to cancer therapy. Prof. Gauger co-leads the Fraunhofer Performance Center's R&D Lab for Data Analysis and AI, serves on the Managing Board of ERCOFTAC (European Research Community on Flow, Turbulence and Combustion), and chairs the Steering Committee of ERCOFTAC's Special Interest Group on Design Optimization. His team develops critical AD tools including CoDiPack and OpDiLib within the NHR South-West high-performance computing consortium.

• Data Management
• Database Systems
• Information Retrieval
• +۵ مورد دیگر

Slava Novgorodov is an Associate Professor in the Department of Computer Science at Tel Aviv University's Faculty of Engineering. His research focuses on data management systems with applications in e-commerce, information retrieval, and machine learning. He leads research projects at the intersection of database systems and artificial intelligence, particularly exploring how large language models can enhance traditional data management tasks. Novgorodov's research interests center around data management, database systems, information retrieval, and e-commerce applications. His work addresses practical challenges in data cleaning, category tree construction, and product information extraction. Recent research has increasingly focused on leveraging large language models for cost-effective data processing, with applications in visual search, review analysis, and personalized recommendation systems. His approach combines theoretical algorithm design with practical system implementation, often addressing resource constraints in real-world applications. His publication record shows a clear progression from foundational data management research to increasingly sophisticated applications of machine learning and LLMs. Early work focused on data cleaning, fraud detection, and collaborative filtering, while more recent publications demonstrate a strategic shift toward LLM applications in e-commerce contexts. This evolution reflects broader trends in the field while maintaining his core expertise in database systems and information management. Novgorodov has established productive collaborations with researchers including Tova Milo, Ido Guy, and Kira Radinsky, resulting in numerous publications at premier venues like SIGMOD, VLDB, and WWW. His research has practical applications in e-commerce systems, particularly in product information management, review analysis, and visual search capabilities.

• Semantic Web
• RDF Data Management
• Knowledge Graphs
• +۵ مورد دیگر

Olivier Curé is a faculty member affiliated with Université Paris Est in France, specializing in semantic web technologies and knowledge engineering. His research focuses on distributed RDF processing, knowledge graph management, and edge computing adaptations of semantic technologies. Research Focus: Dr. Curé leads significant work in RDF stream processing systems (Strider engine), knowledge graph compression (LiteMat encoding), and privacy-preserving semantic data management. His recent work explores edge computing adaptations of knowledge graphs, real-time stream reasoning, and distributed SPARQL query processing. Key innovations include privacy frameworks for RDF anonymization and resource-efficient knowledge representation for IoT environments. Publication Trends: His 15 most recent publications (2020-2025) demonstrate a strong focus on: 1) Scalable RDF processing architectures, 2) Knowledge graph optimization for edge devices, 3) Privacy-enhancing techniques for semantic data, and 4) Distributed reasoning systems. This reflects an evolution from foundational RDF storage research toward adaptive, privacy-aware semantic systems for decentralized computing. Research Infrastructure: Dr. Curé contributes to multiple open-source projects including Strider (distributed RDF processing) and ZodiacEdge (Datalog engine), indicating leadership in semantic technology tooling development.

• Distributed Stream Processing
• Data Stream Analytics
• Database Optimization
• +۳ مورد دیگر

Dr. Boris Novikov is a prominent Russian computer scientist affiliated with St Petersburg University and the Higher School of Economics . His research focuses on distributed stream processing, temporal databases, and XML query optimization. Key collaborators include Artem Trofimov , Elena Mikhailova , and Anna Yarygina . Research Areas : Distributed Systems (stream processing, substream management) Database Optimization (vertical partitioning, XML caching) Temporal Data Management Machine Learning (concept drift detection) His recent work on distributed stream processing (2023-2022) explores substream bounding, deterministic models, and resource allocation strategies. Earlier contributions (2012-2008) focused on XML query performance, similarity indexing, and access control frameworks. Technical Trends : Event time alignment in distributed architectures Dynamic residual projection for cybersecurity Bi-objective optimization in database systems Speculative execution with conflict resolution Incremental Lagrangian dual solutions Matrix clustering for partitioning problems

• Computer Science Education
• Artificial Intelligence Education
• K-12 Computer Science
• +۵ مورد دیگر

Franz Jetzinger is a researcher at the Professorship for Computer Science Education (Lehrstuhl für Informatikdidaktik) at the Technical University of Munich (TUM), working under the direction of Prof. Dr. Tilman Michaeli. He is based at Marsstr. 20-22(2907)/IV, 80335 Munich, with his office in room 2907.04.446. Jetzinger teaches courses in the Computer Science Education program, including "digi4all - Kompetenzen für das Unterrichten in einer digitalen Welt" and "Übung zur Didaktik der Informatik" for both winter and summer semesters of 2024/25 and 2025. His research focuses on several key areas within computer science education, with particular emphasis on Artificial Intelligence and Machine Learning education for K-12 settings. He has developed and delivered professional development programs for computer science teachers, especially regarding AI integration into compulsory computer science classrooms. His work spans teacher training, curriculum development, and practical implementation strategies for bringing AI education to secondary school levels. Jetzinger's publications reveal a strong focus on AI education in K-12 settings, with multiple publications in 2023-2025 addressing scalable professional development for teachers and evidence-based approaches to teaching AI. His work also includes substantial contributions to German computer science textbooks for Bavarian high schools, covering topics from object-oriented modeling to AI education. Developed professional development programs for AI education Contributed to multiple computer science textbooks for Bavarian high schools Conducted research on debugging processes in learning environments Participated in the TRR419 SHARP research project Worked on the "Von Bits zu QuBits" quantum computing education initiative Through his work with the Bavarian Ministry of Education and various workshops, Jetzinger has been instrumental in implementing AI education across Bavarian schools, delivering numerous training sessions throughout 2024 and 2025 on both basic and advanced AI topics for teachers.

Mira Meziniمشاهده پروفایل

استاد

• Programming Languages
• Software Engineering
• Aspect-Oriented Programming
• +۷ مورد دیگر

Mira Mezini is a Professor of Computer Science at Technical University of Darmstadt, where she leads the Software Technology Group. She holds a LOEWE Professorship and serves as founding co-director of hessian.AI, the Hessian Center for Artificial Intelligence, and as a board member of ATHENE, the national research center for applied cybersecurity. Previously, she served as Dean of Computer Science (2013-2014) and in Vice President roles (2014-2019) at TU Darmstadt. She has held visiting professorships at Lancaster University (UK) and Università della Svizzera Italiana (Switzerland). Her research focuses on three central areas: programming systems for distributed software and AI, automated software analysis, and fundamental code models. With over 200 peer-reviewed publications in top venues, Mezini is particularly renowned for her pioneering work on aspect-oriented programming and breakthrough contributions to automated code completion. Her work has evolved from early innovations in modularization and software product lines to cutting-edge research integrating AI with programming systems. Her recent publications reveal a strong trend toward integrating artificial intelligence with traditional programming systems, with significant work on code language models, static analysis techniques, and programming models for distributed applications. The research spans foundational programming language concepts while addressing practical challenges in software development, particularly in the context of AI-assisted programming tools that have become increasingly important in the industry. IBM Eclipse Innovation Award (2005, 2006) Google Research Award (2017) Horst Görtz Foundation's IT Security Award (2nd prize, 2014) ERC Advanced Grant (2012) ACM Fellow (2024) Dahl-Nygaard Prize (2025) Mezini has chaired major conferences in software engineering and programming languages (ECOOP, OOPSLA, FSE, and ICSE) and has served on numerous influential committees, including the Computer Science Panel of the German Research Foundation (DFG) and the Computer Science Consolidator Grant Panel of the European Research Council (ERC). She is currently a member of the ERC Scientific Council's selection committee and the DFG Senate. As co-spokesperson of the Excellence Cluster 'Reasonable Artificial Intelligence,' she leads significant research initiatives at the intersection of AI and software engineering. Her Software Technology Group at TU Darmstadt functions as a hub for innovative research in programming languages and software engineering, with strong connections to both theoretical foundations and practical applications. The group maintains active collaborations with industry partners and other academic institutions, particularly through hessian.AI and ATHENE, focusing on translating research into real-world impact in areas like secure software development and AI-assisted programming tools.

• Conic Linear Programming
• Nonlinear Conic Programming
• Geometry of Convex Sets
• +۲ مورد دیگر

Bruno F. Lourenço serves as Associate Professor at The Institute of Statistical Mathematics (ISM) and SOKENDAI (The Graduate University for Advanced Studies), holding dual appointments in the Department of Fundamental Statistical Mathematics and Department of Statistical Science. He concurrently holds a Visiting Associate Professor position at RIMS-Kyoto University through March 2026. His research centers on conic optimization theory, with specialized focus on conic linear programming (including regularization techniques and ill-posedness treatment), nonlinear conic programming (algorithm development and optimality conditions), and the geometric properties of convex sets. His work consistently addresses error bounds in optimization frameworks and extends into nonsmooth optimization methodologies, contributing to both theoretical foundations and computational applications in mathematical programming. Recent publications (2024-2025) reveal concentrated research on specialized cone structures including hyperbolic, copositive, and homogeneous cones. Key thematic trends encompass facial geometry analysis, duality gap resolution in semidefinite programming, constraint qualification-free error bounds, and projection methods for hyperbolicity cones. His work demonstrates strong integration of algebraic geometry with optimization theory, particularly through polynomial representations and symmetry properties of cones. Scientific Awards: No scientific awards were documented in the provided materials. Advising and Grants: The source documentation contains no explicit references to graduate students supervised, research grants administered, or external funding sources. His active publication record and leadership of the Statistical Decision-Making Group suggest ongoing research activity, but specific mentorship or grant details remain unreported in this context. Labs and Teams: Dr. Lourenço leads the Statistical Decision-Making Group at ISM, which focuses on developing optimization frameworks for statistical inference problems. The group's recent output indicates strong emphasis on conic programming applications to statistical modeling, with particular attention to computational tractability and theoretical guarantees in high-dimensional settings.

Joachim Wehlerمشاهده پروفایل

مدرس

• Algebraic Geometry
• Complex Analysis
• Riemann Surfaces
• +۵ مورد دیگر

Privatdozent Dr. Joachim Wehler is affiliated with the Department of Mathematics at Ludwig-Maximilians-Universität München (LMU Munich). He has been a lecturer at LMU since 1985, teaching advanced topics in mathematics and computer science. Academic Rank: Lecturer (Privatdozent) Departments: Mathematics (LMU), with a focus on complex analysis, algebraic geometry, and Lie theory Research Interests span diverse areas of mathematics and computer science: Algebraic Geometry (K3 surfaces, moduli spaces, deformation theory) Complex Analysis (Riemann surfaces, Stein manifolds, modular forms) Lie Algebras (structure theory, classification, applications in physics) Computer Science (Petri nets, event-driven process chains, concurrency theory) Publications reflect his interdisciplinary work, including topics in modular forms , elliptic curves , and free-choice Petri nets . His research bridges abstract mathematics with computational models and business process analysis. Software and Teaching : He emphasizes computational tools like PARI/GP in mathematics education and has developed lectures on computational algebraic geometry and visual behavioral modeling in software systems.