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Henry EhlersView profile

Researcher

• Network Visualization
• Graph Visualization
• Biological Network Visualization
• +5 more

Henry Ehlers is a PreDoc Researcher and PhD Candidate at the Vienna University of Technology (TU Wien), working within the Faculty of Informatics, Institute of Visual Computing & Human-Centered Technology, specifically in the Computer Graphics Group (E193-02). He holds the position of University Assistant (Univ.Ass.) and is actively pursuing his doctoral studies under the supervision of Renata Raidou since 2021, with expected completion in 2024. His research focuses on advanced visualization techniques, particularly in the areas of network and graph visualization. Ehlers specializes in biological network visualization, compound graphs, uncertainty visualization, and data physicalization. His work bridges theoretical computer science with practical applications in visual analytics, creating innovative methods for representing complex network structures and making them accessible to domain experts. Analysis of his publication record reveals a strong emphasis on improving network visualization techniques, with particular focus on biological applications, uncertainty representation, and physical data representations. His research trajectory shows increasing sophistication in addressing visualization challenges across multiple domains including biology, environmental science, and social networks. Recent publications demonstrate his leadership in developing novel interaction techniques and visualization metaphors for complex data structures. Ehlers is actively involved in multiple research projects including ArtVis (2022-2027), SANE (2024-2027), and SMGV-Esprit (2024-2027), which support his research in visualization techniques. His collaborative approach is evident in his extensive co-authorship network across international institutions and research groups at TU Wien. As a member of the Visualization Group led by Renata Raidou, Ehlers contributes to the team's mission of developing innovative visualization solutions for complex data challenges. His work particularly focuses on creating more intuitive and effective ways to represent network structures through both digital and physical means, pushing the boundaries of how we interact with complex relational data.

• Distributed Systems
• Consensus Algorithms
• Dynamic Networks
• +1 more

Stephan Felber is a PreDoc Researcher at the Embedded Computing Systems group (Institute E191-02) within the Faculty of Informatics at Vienna University of Technology, holding a Bachelor of Science and Diplom-Ingenieur degree. His research expertise spans: Distributed Systems Consensus Algorithms Dynamic Networks Fault Tolerance His recent publications demonstrate deep theoretical contributions to distributed computing, particularly analyzing consensus impossibility in lossy snapshot models and characterizing message adversaries in directed dynamic networks, establishing foundational limits for fault-tolerant distributed systems. At TU Wien, he teaches core computer engineering courses including Advanced Multiprocessor Programming (191.022, VU), Computer Engineering Practical (191.005, PR), and multiple project-based courses (191.006-191.009, PR). As an integral member of the Embedded Computing Systems research group, he advances theoretical frameworks for reliable distributed computation in challenging network environments.

• Theoretical Computer Science
• Algorithms
• Complexity Theory
• +2 more

Alexander Firbas serves as a Researcher at the Institute of Logic and Computation within Vienna University of Technology's Faculty of Informatics, specializing in theoretical computer science with emphasis on graph algorithms and computational complexity. His academic background includes a Diploma Engineer (Dipl.-Ing.) and Bachelor of Science (BSc), with his 2023 Diploma Thesis establishing foundational work on vertex splitting for hereditary graph properties. Current doctoral research extends into parameterized complexity and geometric graph representations. Firbas investigates structural graph properties through computational lenses, focusing on tractability boundaries for problems involving vertex splitting, cluster modifications, and geometric thickness. His methodology integrates fine-grained complexity analysis with algorithmic design for NP-hard graph problems. Recent publications demonstrate consistent exploration of graph modification paradigms, evolving from hereditary property establishment (2023) to cluster vertex splitting (2024) and geometric thickness tractability (2025), reflecting deepening specialization in parameterized approaches to graph-theoretic challenges. He contributes to the multi-year 'Parameterized Graph Drawing' project (2023–2027) while teaching Algorithmic Geometry and Graph Algorithms seminars. His advising activities include supervising master's theses within the Algorithms and Complexity research framework. As an active member of the Algorithms and Complexity research group, Firbas collaborates on theoretical projects examining computational boundaries in graph theory, with ongoing work targeting degree-constrained spanning trees and geometric embedding problems.

Pantelis FrangoudisView profile

Researcher

• Mobile/Wireless Networking
• Edge Computing
• Network Softwarization
• +4 more

Pantelis Frangoudis is a PostDoctoral Researcher at the Distributed Systems Group of the Vienna University of Technology since 2019. His academic journey includes a Ph.D. in Computer Science (2012), M.Sc. (2005), and B.Sc. (2003) from the Department of Informatics, Athens University of Economics and Business (AUEB) , Greece. His research spans Edge Computing , IoT , Network Softwarization , Federated Learning , and Wireless Networking , focusing on adaptive systems and security. Education B.Sc. in Computer Science (2003), AUEB M.Sc. in Computer Science (2005), AUEB Ph.D. in Computer Science (2012), AUEB His research explores Edge Computing Continuum systems, with emphasis on QoS-aware orchestration , serverless edge deployment , and self-adaptive IoT services . Recent work addresses federated learning for 6G , WebAssembly for edge serverless , and dynamic resource allocation in heterogeneous environments. Key projects include AloTwin (2023–2025), INTEND (2024–2026), and EDENSPACE (2019–2022). His publications (2025–2020) cover machine learning for 5G/6G , MEC application orchestration , and IoT security , with a focus on distributed systems and edge-cloud collaboration . Scientific Awards ERCIM Alain Bensoussan Post-Doctoral Fellowship He has supervised multiple diploma theses at TU Vienna, including topics like serverless edge computing , traffic light AI optimization , and vertical farming IoT systems . His collaborations with institutions like EURECOM (France) and IRISA/INRIA (France) reflect a global academic footprint.

• Virtual Reality
• Augmented Reality
• Haptics
• +3 more

Mohammad Ghazanfari is a PreDoc Researcher at Vienna University of Technology (Vienna UT), affiliated with the School of Informatics. His work focuses on virtual and augmented reality systems, particularly encounter-type haptics, collaborative architectural design, and robotic interfaces. Current projects include the ACD (2020–2028) initiative, developing AR safety monitoring and stiffness simulation with origami-based haptic devices. Research intersects virtual reality, robotics, and human-centered design, with applications in architectural prototyping and immersive user experiences. His publications demonstrate expertise in haptics, reinforcement learning, and multi-agent systems. Recent articles emphasize walkable VR collaboration, tactile feedback optimization, and real-time safety protocols in mixed reality environments.

Sabrina HerbstView profile

Researcher

• Quantum Computing
• Machine Learning
• High-Performance Computing
• +3 more

Sabrina Herbst is a PreDoc Researcher at the Department of Computational Sustainability within the Faculty of Informatics at Technische Universität Wien . She works on Quantum Computing , focusing on Machine Learning integration and High-Performance Computing (HPC) augmentation for quantum algorithms. Education : Dipl.-Ing. (2023) in Informatics from TU Wien, with a thesis on quantum machine learning. Her research explores scalable Quantum Machine Learning (QML) algorithms, emphasizing theoretical foundations and robust numerical implementations. Key areas include Quantum Neural Networks (QNNs) , hyperparameter optimization, and the intersection of quantum computing with HPC and edge computing. Recent work investigates channel distinguishability in QNNs and hybrid quantum-classical systems for IoT data processing. Projects she contributes to include HPQC (High Performance integrated Quantum Computing), Themis FWF (Trustworthy and Sustainable Code Offloading), and TRITON FWF (Transprecise Edge Computing). Awards include the Andreas Dieberger–Peter Skalicky Scholarship (2025), Siemens Award for Excellence (2023), and a conference scholarship from TU Wien (2024). Scientific Awards Andreas Dieberger–Peter Skalicky Scholarship (2025) Siemens Awards for Excellence (2023) Conference Scholarship for female PhD students (2024)

• Parameterized Complexity
• Graph Theory
• Computational Complexity
• +3 more

Liana Khazaliya is a PreDoc Researcher in the Department of Algorithms and Complexity at Vienna University of Technology , specializing in parameterized complexity and graph theory. Her work intersects computational complexity, graph drawing, and structural decomposition techniques. Research Focus: Double-exponential lower bounds for NP problems, crossing number analysis, planar graph extensions, and metric dimension parameterizations Key Collaborations: Works with teams in computational geometry, graph drawing, and algorithmic research Her recent publications analyze algorithmic hardness through treewidth and vertex cover parameterizations, contributing to the understanding of complexity thresholds in graph problems. She actively participates in international conferences like SoCG, GD, and IPEC, advancing fixed-parameter tractability frameworks.

• Computer Science
• Network Security
• Malware Detection
• +4 more

Engin Kirda is a researcher at the Department of Distributed Systems , Vienna University of Technology . His work focuses on Network Security , Malware Detection , and Web Security , with a particular emphasis on Malware Analysis , XSS Prevention , and Botnet Detection . He has led and contributed to projects such as WOMBAT , SECoverer , and SysSec , funded by entities like the Austrian Research Promotion Agency (FFG) and the European Commission . Research Areas : Network Security, Malware Analysis, Web Application Vulnerabilities Key Projects : WOMBAT (2008–2011), SECoverer (2008–2011), SysSec (2010–2014) His publications highlight advancements in automated malware analysis , phishing detection , and web spam mitigation . He has mentored students in topics including Android security , CSV Attack Prevention , and Malware Threat Landscapes .

Benedikt LorchView profile

Researcher

• Steganography
• Multimedia Forensics
• Machine Learning
• +3 more

Dr. Benedikt Lorch is a researcher currently affiliated with the Security and Privacy Lab at the University of Innsbruck, Austria. His professional journey includes roles as a Postdoctoral Fellow (2023–2024) and Research Assistant (2022–2023) at the same institution, preceded by research and academic roles at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany, including a Ph.D. in Computer Science (2018–2023) focused on cybercrime and forensic computing. He has also held visiting researcher positions at Dartmouth College (USA) and Imperial College London (UK). His research interests center on steganography, steganalysis, multimedia forensics, and machine learning applications in digital security. Notable work includes analyzing JPEG image directionality for security implications, improving steganalysis techniques using leaked cover thumbnails, and developing reliable machine learning models for forensic image analysis. His contributions address challenges in digital forensics, such as detecting image tampering, authenticating multimedia content, and enhancing degraded license plate recognition. Key academic awards include the ASQF award for outstanding studies (2019) and a DAAD scholarship for international research (2017). His work spans projects like UNCOVER and emphasizes compliance with regulations like the Artificial Intelligence Act in forensic analysis. Beyond technical contributions, Lorch has explored medical imaging applications, including motion artifact detection in MRI and heart rate estimation using wearable sensors. His research trends reflect a focus on leveraging probabilistic methods and deep learning to enhance forensic reliability, with a particular emphasis on JPEG-based steganalysis and digital image security. Collaborations with institutions like the Pattern Recognition Lab and Biomedical Image Analysis Group highlight interdisciplinary engagement in computer science and medical imaging.

Ricardo Baeza-YatesView profile

Professor

• Algorithms
• Responsible AI
• Web Search and Mining
• +7 more

Ricardo Baeza-Yates serves as Director of Research at the Institute for Experiential AI at Northeastern University's Silicon Valley campus. He also maintains part-time professor positions at Universitat Pompeu Fabra in Barcelona and Universidad de Chile in Santiago. Previously, he was VP of Research at Yahoo Labs (2006-2016) and CTO of NTENT, a semantic search technology company. His academic career spans over three decades with significant contributions to both industry research and academia. Dr. Baeza-Yates earned his Ph.D. in Computer Science from the University of Waterloo, Canada, in 1989. His academic journey includes professorships at Universidad de Chile (1989-2004), ICREA Professor at Universitat Pompeu Fabra (2004-2005), and ongoing part-time appointments at both institutions since 2004 and 2006 respectively. His research spans algorithms, information retrieval, web search and mining, data science, with increasing emphasis on responsible AI and ethical considerations in technology. He is particularly renowned for his work on bias in search systems, algorithmic fairness, and the societal impacts of AI. His expertise uniquely bridges technical aspects of search and data mining with ethical and societal implications, making him a leading voice in the responsible AI movement globally. His recent scholarly output demonstrates a strong focus on bias detection, fairness metrics, responsible technology development, and the societal impacts of algorithms. The work spans technical machine learning and information retrieval while increasingly addressing ethical and social dimensions across diverse application areas from finance to healthcare to social media. Dr. Baeza-Yates has received numerous prestigious awards throughout his distinguished career: 2019 Corresponding Member of the Brazilian Academy of Sciences 2019 Award Salvà i Campillo to the Person of the Year 2018 National Research Award for applied research and technology transfer 2012 ASIS&T Best Information Science Book Award for Modern Information Retrieval 2011 IEEE Fellow for contributions to computer science development 2009 ACM Fellow for contributions to information retrieval algorithms 2002 Corresponding member Chilean Academy of Sciences As an educator and mentor, Dr. Baeza-Yates has advised numerous students throughout his academic career at multiple institutions. His textbook 'Modern Information Retrieval' has become a standard reference in the field, influencing generations of computer scientists. He has secured significant research funding through his leadership roles at Yahoo Labs and academic grants, demonstrating his ability to bridge theoretical research with practical applications. Dr. Baeza-Yates is actively involved with the DATA Lab at Northeastern University's Khoury College of Computer Sciences and previously founded the Web Science and Social Computing Research Group at Universitat Pompeu Fabra. He serves on multiple high-impact advisory boards including the Global AI Ethics Consortium, Global Partnership on AI, and IADB's fAIr LAC Initiative for Latin America and the Caribbean, reflecting his global influence in shaping responsible AI practices.

Petter E. BjørstadView profile

Professor

• Numerical analysis
• Domain decomposition
• Elliptic PDE
• +4 more

Petter E. Bjørstad is a Professor of Computer Science and Mathematics at the University of Bergen, Norway, where he has served as Head of the Department of Informatics since 2010. Previously, he was Director of the Bergen Center for Computational Science (2000-2010) and Professor in both the Department of Mathematics (2006-2010) and Department of Informatics (1985-2005). He also held a position as Professor of Mathematics at the University of Minnesota during 1996-1997 on unpaid leave from Bergen. Dr. Bjørstad earned his PhD in Computer Science from Stanford University in 1980, following which he completed a postdoctoral fellowship at the Courant Institute of Mathematical Sciences at New York University. Before entering academia, he worked as a Principal Engineer at Det Norske Veritas from 1981-1985. Dr. Bjørstad's research focuses on numerical analysis and high-performance computing, with particular expertise in domain decomposition methods, parallel algorithms for elliptic partial differential equations, and scientific computing. His work bridges theoretical mathematics with practical computational challenges, especially in industrial applications. He has led significant research projects including 'Multiscale Domain Decomposition: Algorithms and Analysis' (2010-2014), funded by the Research Council of Norway and the University of Bergen. His publication record demonstrates consistent contributions to the field of numerical methods and parallel computing over several decades. The research trends show a strong focus on developing efficient algorithms for solving partial differential equations through domain decomposition techniques, with applications spanning structural analysis, reservoir simulation, semiconductor device modeling, and other industrial contexts. His work increasingly emphasizes practical implementation on modern parallel architectures including SIMD and MIMD systems. Large number of research grants from Norway and the European Union Dr. Bjørstad has been actively involved in mentoring students and collaborating with industry through projects like the Europort effort, where industrial codes were ported to parallel computing platforms. His laboratory for parallel computing (Parallab) has been instrumental in advancing practical applications of high-performance computing since its establishment in 1985 with Europe's first 64-processor Intel hypercube. The lab has evolved to include multiple MIMD machines including an Intel Paragon, Parsytec GC/Power-Plus, and DEC α-cluster. As Head of the Department of Informatics, Dr. Bjørstad leads one of Norway's premier computing research units with expertise spanning theoretical computer science, numerical methods, and practical applications in various scientific domains. His leadership has helped establish Bergen as a significant center for computational science in Europe.

Austin BurtView profile

Professor

• Evolutionary genetics
• Gene drive
• Genetic control of pests
• +2 more

Austin Burt is a Professor of Evolutionary Genetics at Imperial College London , specializing in genetic mechanisms with applications in pest control and disease vectors. His career at Imperial spans since 2005, preceded by roles as Reader (2000–2005) and Governors' Lecturer (1995–2000). Research focuses on evolutionary genetics, gene drive systems, and population modeling for public health applications. Principal Investigator of Target Malaria , an international consortium developing genetically modified mosquitoes to eradicate malaria. Honors and Awards : President’s Medal for Excellence in Societal Engagement (2017) Cozzarelli Prize from PNAS (2012) Wolfson Research Merit Award (2011–2016) Institute of Zoology Science Medal (2000) His 2012 PNAS paper on music evolution demonstrates interdisciplinary work combining genetics and social sciences. Current work emphasizes self-sustaining gene drive technologies for mosquito population control.

Regino CriadoView profile

Professor

• Complex Networks
• Discrete Mathematics
• Cybersecurity
• +4 more

Dr. Regino Criado is Full Professor of Applied Mathematics at Rey Juan Carlos University (URJC), where he has held academic positions since June 2000. He currently serves as Academic Director of the Data, Complex Networks and Cybersecurity Sciences Institute (since March 2017) and was elected to the Academia Europaea in May 2023. His career spans European research projects (ESPRIT II/III) and interdisciplinary collaborations at the intersection of mathematics, computer science, and cybersecurity. His research focuses on Complex Networks , including hyper-networks, multiplex networks, and mesoscale structures, with applications to cybersecurity, intentional risk management, and synchronization phenomena. Key innovations include: A game theory-based intentional risk model integrating accessibility, anonymity, and value metrics Hybrid network mining algorithms for credit card fraud detection PageRank extensions to multiplex networks Framework for discrete resilience analysis in technological systems His work on the 2014 Physics Reports monograph The structure and dynamics of multilayer networks (3,500+ citations) established foundational concepts in multilayer network theory. Current research explores linguistic pattern analysis through multilayer hypergraphs for automatic text summarization. Member of Academia Europaea (2023) Chaos Journal Best Paper Award (2011) Academic Entrepeneurs Prize (2002) EUROPA-1992 Prize (1992) As director of the DCNC Institute (2017-present) and BBVA-URJC Chair (2017-2018), he has led major academic-industry collaborations. His 180+ publications (6,500+ citations) demonstrate sustained impact across network science, applied mathematics, and cybersecurity.

Cem ErsoyView profile

Professor

• Computer networks
• Pervasive computing
• Edge computing
• +1 more

Cem Ersoy is a Professor at Bogazici University 's Department of Computer Engineering, with a career spanning over 30 years. He has held visiting positions at the University of Trento, Italy, and previously served as Department Head at Bogazici University multiple times. Education : Ph.D. in Electrical Engineering (Polytechnic University, 1992), M.S. in Electrical Engineering (Bogazici University, 1986), B.S. in Electrical Engineering (Bogazici University, 1984) His research focuses on computer networks , pervasive computing , edge computing , and wireless networks . Recent work highlights AI integration in air computing, stress monitoring with wearables, and edge network optimization. Articles span applications of multi-agent DRL, probabilistic tracking, and network slicing. Scientific awards : Elected to the Academy of Sciences of Turkey (2022) Bogazici University Research Excellence Awards (Junior 2007, Senior 2014) He has supervised 24 PhD and 58 MS theses. As principal investigator, he led 35 funded research projects by agencies such as the EU, TUBITAK, and Bogazici University Research Fund. His Google Scholar profile shows 11,856 citations, h-index 43, and i10-index 130.

Esparza JavierView profile

Professor

• Algorithms and tools for the design and verification of reactive and distributed systems
• Verification of systems with infinitely many states
• Software Model Checking
• +7 more

Javier Esparza is a Professor at the Technische Universität München, holding the Chair of Foundations of Software Reliability and Theoretical Computer Science since 2007. Prior to this, he held chairs at the University of Stuttgart (2003-2007) and the University of Edinburgh (2001-2003), along with academic positions dating back to 1990. Education: M.S. in Theoretical Physics (1987, University of Zaragoza), Ph.D. in Computer Science (1990, University of Zaragoza), and Habilitation in Computer Science (1994, University of Hildesheim). His research focuses on formal verification, distributed systems, and software reliability, particularly through algorithms for software model checking, logic and automata theory, and analysis of probabilistic systems. His work has been supported by numerous grants from the German Research Council (DFG), British Engineering and Physical Sciences Research Council (EPSRC), and the Humboldt Foundation. Scientific Awards: Doctor honoris causa from Masaryk University (2009), TeachInf Awards for best Bachelor and Master courses (2009, 2010), TU München Diploma for excellent teaching (2011), Member of Academia Europaea (2011), and Dissertation Prize from the University of Zaragoza (1990). Esparza has supervised over 15 PhD students, including Dr. Stefan Schwoon, Dr. Claus Schröter, and Dr. Michael Luttenberger, and collaborated on research projects with institutions across Europe and the UK.