جستجوی استاد راهنمای دانشگاهی

Alexander Omelchenkoمشاهده پروفایل

استاد

• Enumerative Combinatorics
• Graph Theory
• Topology
• +۴ مورد دیگر

Alexander Omelchenko serves as Professor of Mathematics and Dean of the School of Computer Science & Engineering at Constructor University in Bremen, Germany. His academic leadership extends to prior roles including Dean at the Higher School of Economics and Vice-Rector positions at the Academic University of the Russian Academy of Sciences and St. Petersburg University of Technology and Design. His educational foundation includes: PhD in Fluid Dynamics from Baltic State Technical University "VOENMEH" (1996-1999) M.Sc. in Mathematics from St. Petersburg State University (1993-1997) M.Sc. in Mechanics from Baltic State Technical University "VOENMEH" (1990-1996) Professor Omelchenko's research spans Enumerative Combinatorics, Graph Theory, Topology, Knot Theory, and Asymptotic Methods in Mathematical Physics. His work integrates discrete mathematics with physical applications, particularly in optimal control systems and nonlinear hyperbolic equations, demonstrating rigorous theoretical frameworks with engineering relevance. Analysis of his 2017-2021 publications reveals dominant themes in combinatorial enumeration: systematic classification of graph embeddings on surfaces (torus, projective plane, Klein bottle), Hamiltonian cycle enumeration in multipartite graphs, and topological invariants for textile structures. This work establishes foundational contributions to algebraic topology and discrete mathematics through novel enumeration techniques. Scientific Awards: No scientific awards were mentioned in the provided text. Advising and Grants: Information regarding student supervision, research grants, or funding sources was not specified in available materials. Labs and Teams: The documentation does not reference specific laboratories, research groups, or collaborative teams led by Professor Omelchenko.

• Computer Science
• Distributed Systems
• Blockchain Technology
• +۳ مورد دیگر

Hans-Arno Jacobsen is a Professor at the Faculty of Computer Science (Technische Universität München, TU Munich) and affiliated with the Department of Electrical and Computer Engineering at the University of Toronto. His work spans Computer Science , Distributed Systems , and Artificial Intelligence . Research interests include Blockchain Technology , Consensus Algorithms , Graph Neural Networks , and Quantum Computing . Recent projects focus on decentralized consensus , energy-efficient databases , and federated learning in edge environments. His 15 most recent articles (2024–2025) explore topics such as dynamic resource orchestration , CRDT-based blockchains , and multimodal depression recognition . Collaborates with researchers like Ruben Mayer , Gengrui Zhang , and Shiqiang Wang on systems for federated computing , blockchain benchmarking , and distributed GNN training .

• Computer Science
• Algorithms
• Computational Geometry
• +۴ مورد دیگر

Sujoy Bhore is affiliated with the Indian Institute of Technology Bombay (Department of Computer Science & Engineering), Université libre de Bruxelles, and Technische Universität Wien. His research focuses on algorithms , computational geometry , and graph theory , with a strong emphasis on geometric optimization , dynamic data structures , and parameterized algorithms . Recent publications highlight advancements in Euclidean spanners for sparse network design online algorithms for dynamic geometric problems Steiner trees and tree covers in planar domains k-median/means approximation using coresets His collaborative work spans institutions, with frequent joint research on geometric intersection graphs , map labeling , and planar graph embeddings . Co-authors include prominent researchers like Csaba D. Tóth, Martin Nöllenburg, and Timothy M. Chan. While no formal awards are documented here, his contributions to algorithmic complexity and geometric networks remain significant.

• Ontology Merging
• Knowledge Graph Management
• Semantic Web
• +۲ مورد دیگر

Samira Babalou is an active researcher affiliated with the University of Jena, Germany. Her work primarily focuses on ontology merging, knowledge graph management, and semantic data integration in the biodiversity and biomedical domains. She has developed tools like CoMerger and SimBio for ontology integration and similarity assessment. Education: PhD in Computer Science from University of Jena (2021). Key Contributions: Developed methods for handling inconsistencies during ontology merging, partitioning-based approaches for ontology aggregation, and tools for semantic similarity computation. Collaborations: Works with Birgitta König-Ries, Erik Kleinsteuber, and other researchers in semantic web and knowledge graph projects. Her recent publications (2020-2024) demonstrate expertise in provenance management, domain-specific knowledge graphs, and semantic annotation frameworks. She contributes to open science through systematic literature reviews and reproducible workflows for biodiversity data.

Zafeirakis Zafeirakopoulosمشاهده پروفایل

پژوهشگر

• Symbolic Computation
• Discrete Mathematics
• Combinatorics
• +۴ مورد دیگر

Zafeirakis Zafeirakopoulos is a researcher at the National and Kapodistrian University of Athens (Greece) in the ELIDEK project led by Prof. Maria Chlouveraki. His academic career includes roles as an assistant professor at Gebze Technical University (2016-2022) and postdoctoral research at University of Athens (Greece), Galatasaray University (Turkey), and University of Geneva (Switzerland) under the Eccellenza project of Prof. Jehanne Dousse. PhD in RISC - Research Institute for Symbolic Computation (supervised by Prof. Peter Paule and Prof. Matthias Beck) Current affiliations: Mathematics department of National and Kapodistrian University of Athens Service roles: Information Director of ACM SIGSAM, Associate Editor of ACM CCA His research focuses on symbolic computation, discrete mathematics, and computational geometry. He has developed algorithms for parametric curve topology (PTOPO) and linear Diophantine systems (Polyhedral Omega), emphasizing efficiency and geometric interpretations. Recent work involves Julia/Maple implementations for practical applications. Publication trends highlight interdisciplinary work in symbolic algorithms, polyhedral geometry, and combinatorial optimization. He actively contributes to international conferences like ACA 2025 (co-organizer) and SCALE 2022.

Niv Buchbinderمشاهده پروفایل

استاد

• Combinatorial Optimization
• Online Algorithms
• Algorithmic Game Theory
• +۲ مورد دیگر

Prof. Niv Buchbinder is a faculty member in the Department of Statistics and Operations Research at the School of Mathematical Sciences, Tel Aviv University. His research centers on algorithmic solutions for combinatorial optimization in offline and online contexts, with significant contributions to primal-dual methodologies and algorithmic game theory. His academic background includes a Ph.D. in Computer Science from the Technion (2008) under Prof. Seffi Naor and an M.Sc. in Computer Science from the Technion (2003) under Prof. Erez Petrank. Key research areas encompass Combinatorial Optimization, Online Algorithms, Algorithmic Game Theory, Primal-Dual Methods, and Submodular Optimization, focusing on competitive analysis for problems like set cover, ad-auctions, and caching. Recent publications (2012-2015) reveal a concentrated effort in submodular optimization and online decision-making, with applications in advertising, resource allocation, and machine learning. These works consistently employ primal-dual frameworks to achieve strong competitive ratios in adversarial settings. Scientific recognition includes: Best Paper Award at ESA 2007 for “Online Primal-Dual Algorithms for Maximizing Ad-Auctions Revenue” Best Paper Award at FOCS 2011 for “A Polylogarithmic Competitive Algorithm for the k-Server Problem” No information is available regarding student advising or research grants. Similarly, details about laboratory facilities, research teams, or future projects are not provided in the source materials.

Matthias Löweمشاهده پروفایل

استاد

• Probability Theory
• Statistical Physics
• Random Matrix Theory
• +۳ مورد دیگر

Matthias Löwe is a Professor at the Institute of Stochastics within the Faculty of Mathematics and Computer Science at the University of Münster. His academic career spans several decades with continuous contributions to probability theory and statistical physics. His research focuses on deep theoretical investigations of complex stochastic systems, particularly in the context of disordered systems and random structures. Löwe's research interests center on Probability Theory , Statistical Physics , and Random Matrix Theory . His work examines phase transitions in spin systems, fluctuation phenomena in random graphs, and the mathematical foundations of neural network models. His research program consistently bridges theoretical probability with applications in statistical physics, particularly in understanding the behavior of complex interacting particle systems at critical points. His publication record shows consistent output in top probability journals including Annals of Probability , Electronic Journal of Probability , and Annales de l'Institut Henri Poincaré . His most recent work (2020-2023) focuses on propagation of chaos in mean-field models, fluctuations in Ising models on random graphs, and exact recovery problems in block spin systems. Löwe frequently collaborates with researchers including Zakhar Kabluchko, Kristina Schubert, and Jonas Jalowy. Löwe has supervised multiple doctoral students including Raphael Meiners, Mirko Ebbers, Jens Ameskamp, and Sarah Behrens. His research group has included postdoctoral researchers and doctoral candidates working on various aspects of probability theory and its applications.

• Computational Complexity
• Algorithmic Game Theory
• Mechanism Design
• +۳ مورد دیگر

Argyrios Deligkas is a Senior Lecturer (equivalent to Associate Professor) at the Royal Holloway University of London. He obtained his PhD from the University of Liverpool under the supervision of Rahul Savani and held postdoctoral positions at the University of Liverpool and the Technion (Industrial Engineering and Management department). His research focuses on computational aspects of game theory, optimization, and complexity. Key interests include: Algorithmic Game Theory and Mechanism Design Computational Complexity (PPAD/PPA-hardness) Fixed-parameter algorithms and combinatorial optimization Fair division and resource allocation Equilibrium computation in multi-agent systems Recent publications (2023-2025) demonstrate a strong emphasis on computational hardness in fair division (e.g., PPA-hardness proofs), mechanism design with real-world constraints, and parameterized algorithms for pathfinding/graph problems. Collaborative work with European researchers dominates his output, with frequent appearances at AAAI, IJCAI, and AAMAS. He actively presents at conferences (e.g., ALGA2025, WINE 2024) and collaborates internationally, including visits to universities in Vienna, Prague, Glasgow, and Edinburgh.

Zhen Dongمشاهده پروفایل

دانشیار

• Software Reliability
• Software Security
• Mobile Computing
• +۴ مورد دیگر

Zhen Dong is an Associate Professor at Fudan University, China, specializing in software engineering with a focus on software reliability and security, particularly in mobile computing. Previously, he was a PostDoc and Senior Research Fellow at the National University of Singapore under the guidance of Abhik Roychoudhury. His educational background includes: PhD in Computer Science from Heidelberg University (2017), advised by Prof. Artur Andrzejak Dr. Dong's research centers on developing techniques and tools for improving software reliability and security. His work spans mobile application testing, Android security, flaky test detection, and vulnerability localization. He has made significant contributions to the field of software testing and analysis, with a particular emphasis on practical applications for mobile systems. His research bridges theoretical foundations with real-world software engineering challenges. Analysis of Dr. Dong's recent publications reveals a strong focus on leveraging AI/ML techniques for software engineering tasks, particularly using LLMs for test automation and program analysis. His work consistently addresses critical challenges in mobile computing, especially Android application reliability and security. There's a clear trajectory toward more sophisticated analysis techniques, from traditional testing methods to AI-driven approaches. His notable scientific achievements include: ACM Distinguished Paper Award at ICSE'20 Best Paper Award at AsiaCCS'21 (1/370 submissions) ASE'22 Distinguished Reviewer Award Dr. Dong serves on the Board of Distinguished Reviewers for ACM Transactions on Software Engineering and Methodology and has been an active member of numerous program committees for top software engineering conferences including ICSE, ASE, and ISSTA. His service to the academic community extends to reviewing for prestigious journals such as IEEE Transactions on Software Engineering and Methodology and ACM Transactions on Software Engineering and Methodology. His research has been supported through various academic channels, enabling him to maintain an active lab focused on software testing and analysis, particularly for mobile platforms. The lab has produced numerous tools and techniques that have influenced both academic research and industrial practice in software reliability.

Stefan Wirtensohnمشاهده پروفایل

پژوهشگر

• Control Engineering
• Mechatronic Modeling
• Parameter Identification
• +۴ مورد دیگر

Stefan Wirtensohn is a Researcher at the Institute of System Dynamics, Konstanz University of Applied Sciences, where he has served as a research associate since 2012. His work bridges theoretical control engineering with practical marine robotics applications, focusing on autonomous surface vehicle development and advanced control systems. His academic credentials include: Bachelor of Engineering in Mechanical Engineering, HTWG Konstanz (2010) Master of Engineering in Mechatronics, HTWG Konstanz (2012) Wirtensohn's research centers on Control Engineering, Mechatronic Modeling, and Nonlinear Control systems for marine environments. He specializes in parameter identification, motion estimation, and trajectory optimization for autonomous vessels, with significant contributions to docking control, wave filtering, and extended object tracking. His methodology integrates unscented Kalman filters, model predictive control, and sensor fusion techniques to address real-world navigation challenges. Analysis of his 15 most recent publications (2013-2022) reveals consistent focus on marine robotics control systems. Key trends include the application of random matrices for lidar-based target tracking, model predictive path integral control for vessel stabilization, and advanced disturbance estimation techniques. His work demonstrates strong emphasis on experimental validation using autonomous surface vehicles across diverse maritime scenarios. He actively contributes to teaching through Control Engineering 1 exercises and laboratory supervision, emphasizing hands-on implementation of control algorithms. Current projects under his involvement include: Integrated autonomous measuring system for maritime surveying tasks Automated parking systems for truck trailers Collision prevention procedures for inland waterway vessels Wirtensohn operates within the Institute of System Dynamics research team specializing in signal processing, control engineering, and robotics, maintaining close collaboration with Johannes Reuter and other institute members on marine automation challenges.

Simon Apersمشاهده پروفایل

پژوهشگر

• Quantum Algorithms
• Theoretical Computer Science
• Random Walks
• +۴ مورد دیگر

Simon Apers is a CNRS researcher at IRIF (Institut de Recherche en Informatique Fondamentale) at Université Paris Cité. He focuses on quantum algorithms with broader interests in theoretical computer science, including random walks, graph theory, and combinatorial optimization. His primary research interests span quantum computing, theoretical computer science, and algorithms. Apers has made significant contributions to quantum walks, quantum property testing, graph algorithms, and computational complexity. His work often bridges quantum information with classical theoretical computer science, exploring how quantum techniques can improve classical algorithms and solve problems more efficiently. He has developed quantum speedups for various computational tasks including sampling, optimization, and graph problems. Simon Apers serves as a program committee member for prestigious conferences including TQC'25, QIP'25, TQC'23, SODA'23, and ESA '21. He also serves as an editor for the journal Quantum. His teaching activities include courses at Sorbonne University (2022-present) on Advanced Quantum Algorithms, MPRI (2021-present) on Quantum Algorithms and Complexity, and Bad Honnef (2022). His publication record shows a strong trend toward quantum algorithms for graph problems, quantum walks, and connections between quantum computing and classical theoretical computer science. He frequently collaborates with researchers from various institutions across Europe and has published in top venues including FOCS, STACS, ESA, PRL, and JMLR. Simon Apers actively mentors students and has open positions for PhD students and postdocs in quantum algorithms at IRIF. His research group is involved in cutting-edge work at the intersection of quantum computing and theoretical computer science, with significant contributions to quantum walks, property testing, and computational complexity.

• Process Systems Engineering
• Chemical Engineering
• Optimization
• +۶ مورد دیگر

Alexander Mitsos is a Professor at Forschungszentrum Jülich in Germany, where he leads research at the intersection of process systems engineering, chemical engineering, and computational methods. His work spans optimization theory, machine learning applications, and energy systems, with a focus on developing novel methodologies for complex engineering problems across multiple domains. Dr. Mitsos's research interests center on the application of advanced optimization techniques to chemical engineering problems. His primary areas of focus include: Process systems engineering and optimization Machine learning applications in chemical engineering Energy systems and hydrogen technologies Bioprocess engineering and control systems Ammonia energy storage and carbon capture His recent publications reveal a strong trend toward integrating machine learning with traditional chemical engineering approaches. He has pioneered work on graph neural networks for molecular property prediction, reinforcement learning for control systems, and bilevel optimization for energy systems. His research demonstrates a consistent focus on developing computationally efficient methods that bridge theoretical advances with practical engineering applications, particularly in sustainability-focused domains like hydrogen technologies and carbon emission reduction. The analysis of his 15 most recent publications shows a balanced portfolio between theoretical method development (e.g., optimization algorithms) and practical applications (e.g., cement production, hydrogen compression). Dr. Mitsos has mentored numerous graduate students and postdoctoral researchers, as evidenced by his extensive publication record with junior authors. His research has been supported by various grants focused on energy transition, process optimization, and sustainable chemical engineering solutions, with significant collaborations across European institutions. The funding landscape for his work appears to emphasize sustainability transitions and industrial decarbonization, particularly in energy-intensive sectors. His work appears to be conducted within a research group focused on process systems engineering, with strong connections to both computational mathematics and practical chemical engineering applications. The group maintains laboratories for experimental validation of computational models, particularly in bioprocess engineering and hydrogen technologies, while maintaining strong theoretical foundations in optimization and control theory.