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Jussi RintanenView profile

Associate Professor

• Artificial Intelligence
• Automated Planning
• Constraint-Based Search
• +3 more

Jussi Rintanen is an Associate Professor at the Department of Computer Science , Aalto University . He is an internationally leading researcher in constraint-based planning and decision-making , with a focus on applying AI technologies to automating software production and synthesis of intelligent systems . Helsinki University of Technology (Doctoral Degree, 1997) Albert-Ludwigs-University Freiburg (Venia Legendi, 2005) National ICT Australia / Australian National University (Academic Positions) His research spans automated planning , SAT solving , temporal planning , and constraint-based search methods . Recent work highlights symmetry-breaking constraints , partial observability planning , and acyclicity encodings in SAT frameworks. The 15 most recent articles reflect trends in AI planning , graph algorithms , and formal verification , with applications in software synthesis , temporal reasoning , and uncertainty handling . His methodologies integrate integer programming , SMT encodings , and propositional logic .

Eva RakovskáView profile

Assistant Professor

• Knowledge Management
• Artificial Intelligence
• Expert Systems
• +3 more

Eva Rakovská is an Assistant Professor at the Department of Applied Informatics , Faculty of Economic Informatics , University of Economics in Bratislava . Her work spans Knowledge Management , Artificial Intelligence , and E-learning in public administration contexts. Academic Affiliation: Faculty of Economic Informatics, University of Economics in Bratislava Research Focus: Knowledge Management, Expert Systems, Fuzzy Logic, E-learning Platforms Key Grants: VEGA 1/0261/10, VEGA 1/0373/18, KEGA 015EU-4/2021 Her publications since 2004 reveal a consistent focus on Knowledge Management and Artificial Intelligence in public sector contexts, with recent trends emphasizing Fuzzy Logic for software usability and Explainable AI . She has contributed to LMS system design and inclusive education technologies for disabled students. While no explicit awards are listed, her work has been cited in international journals (e.g., International Journal of Applied Mathematics and Computer Science ) and conferences. She has collaborated extensively in academic editing and conference organization for events like AIESA and DisCo .

• Data Mining
• Machine Learning
• Exploratory Data Analysis
• +3 more

Alexandre Chanson is a Lecturer at the Polytechnic School of the University of Tours (EPU), affiliated with the Blois Computer Science Department within the Fundamental and Applied Computer Science Laboratory (LIFAT, UR 6300). His research focuses on data mining, machine learning, and human-centered AI systems. Academic Focus: Exploratory data analysis, optimization algorithms, and recommender systems Contact: Office 331, Jean Jaurès Campus, Blois | Phone: +33 254 552 168 | Email: alexandre.chanson@univ-tours.fr Research Trends: Alexandre's work bridges mathematical programming with interactive data exploration, emphasizing explainable AI and optimization frameworks like the Traveling Analyst Problem. Key contributions include local explanation clustering, comparison query generation, and personalized data narration. Scientific Awards: No specific honors identified in the current dataset.

Philippe BondittiView profile

Lecturer

• Political Science
• International Relations
• Conceptual History
• +4 more

Philippe Bonditti serves as a Lecturer in Political Science at the School of Political Science and Management (ESPOL) within the Catholic University of Lille, France. His academic profile integrates historical semantics with contemporary security challenges, positioning him at the intersection of political theory, international relations, and critical security studies through interdisciplinary research and teaching. His scholarly focus spans five interconnected domains: Conceptual History : Application of Koselleckian methodologies to deconstruct evolving political concepts like 'terrorism' and 'equilibrium' within international systems Climate Security : Critical analysis of climate-defense-security discourses in European policy frameworks, particularly examining French strategic positioning Critical Terrorism Studies : Archaeological investigations into terrorism as a modern category of violence and its historical conceptual transformations European Border Politics : Examination of border dynamics, political community formation, and integration challenges in contemporary Europe Peace Studies : Theoretical problematizations of peace within international relations discourse and practice Analysis of Bonditti's recent publications (2019-2023) reveals a methodological convergence where historical semantics increasingly informs security studies. His climate-security research demonstrates how 'equilibrium' discourse operates within French defense policy, while his European border studies expose tensions between territorial control and political community formation. The 2023 peace studies cluster indicates growing scholarly engagement with multipolar conflict resolution frameworks amid geopolitical realignments. No scientific awards were documented in available sources. Information regarding graduate student supervision, research funding mechanisms, laboratory affiliations, or collaborative research teams was not provided in source materials, though co-authorship patterns suggest active collaboration with scholars including Sofia Kabbej, Thomas Hippler, and Laura Lema Silva.

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +5 more

Andreas Geiger is Professor and Head of the Department of Computer Science at the University of Tübingen, Germany, where he leads the Autonomous Vision Group (AVG) within CyberValley. He serves as core faculty at the Tübingen AI Center, Principal Investigator in the Excellence Cluster ML in Science and CRC Robust Vision, and ELLIS fellow coordinating the ELLIS PhD program. Prof. Geiger's research centers on machine learning for computer vision, natural language processing, and robotics, with emphasis on 2D/3D scene representations, geometry reconstruction, and robust model development. His group pioneers neural rendering techniques like Gaussian splatting while addressing real-world challenges in autonomous driving, VR/AR, and scientific document analysis through discriminative and generative approaches. Recent publications (2025) demonstrate strong focus on 3D scene understanding using Gaussian splatting, autonomous driving simulation with world models like ReSim and Vista, and generative methods for controllable 3D scenes. The group maintains Scholar Inbox for personalized academic recommendations while advancing reinforcement learning for self-driving (CaRL) and robust perception evaluation (EMPERROR). Major recognitions include: Best Paper Awards at CVPR 2024/2021, 3DV 2017/2015, GCPR 2015 Sage 10-Year Impact Award 2024 and Longuet-Higgins Prize 2022 ERC Starting Grant 2019 and IEEE PAMI Young Researcher Award 2018 nuPlan Challenge Winner 2023 and German Pattern Recognition Prize 2017 Prof. Geiger actively supervises PhD students including Reiser (3D view synthesis), Baur (Lidar scene flow), Jaeger (reinforcement learning), and Liao (urban scene transfer). His group secures substantial funding through the ERC Starting Grant, Vector Stiftung's MINT Innovation Program, ML in Science Cluster, and CRC Robust Vision, while collaborating with industry via Waymo and nuPlan challenges. The Autonomous Vision Group operates from CyberValley's new campus building with a diverse international team (12+ nationalities). They maintain Scholar Inbox for academic discovery and organize regular technical retreats and unique team-building activities like deep cave expeditions, fostering innovation in 3D vision and autonomous systems.

Stephanie WangView profile

Assistant Professor

• Distributed Systems
• Machine Learning Systems
• Data Processing Systems
• +3 more

Stephanie Wang is an incoming Assistant Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington, starting in fall 2024. She focuses on distributed systems, software/hardware systems, and programming languages for scalable application development. University: University of Washington School: Paul G. Allen School of Computer Science & Engineering Academic Rank: Assistant Professor Email: smwang@cs.washington.edu Her research emphasizes creating intermediate abstractions for high-performance, fault-tolerant systems in challenging domains like machine learning and data processing. She also explores programming languages as interfaces for distributed systems. Key publication trends include distributed memory management (2023), extensible shuffle architectures (2023), fault-tolerant collective communication (2021), and formal verification of file systems (2017). Subfields span memory disaggregation, task scheduling, real-time ML, and crash-safe system design. Scientific recognition includes: Distinguished Artifact Award (SOSP 2019) Notable contributions to the Ray open-source project and its application in training large language models like ChatGPT.

• Logic Programming
• Constraint Programming
• Hybrid Systems
• +2 more

Kazunori Ueda is a Professor at Waseda University's Faculty of Science and Engineering, with a career spanning over three decades in logic/constraint programming, hybrid systems, and concurrency. He has held visiting positions at the Egypt-Japan University of Science and Technology and part-time roles at the University of Tokyo. His research bridges theoretical advancements and practical implementations, focusing on languages like HydLa and LMNtal for hybrid and concurrent system modeling. Research Interests: Logic and Constraint Programming Hybrid Systems Modeling Concurrency and Parallelism Programming Language Design and Implementation Article Trends: Recent works emphasize symbolic simulation of hybrid systems, graph rewriting optimization, and distributed storage reliability. His work integrates interval arithmetic, constraint hierarchies, and formal verification techniques. Scientific Awards: Fellow, Japan Society for Software Science and Technology (2016) Fellow, Information Processing Society of Japan (2015) Basic Research Award, Japan Society for Software Science and Technology (2011) Multiple Best Paper and Encouragement Awards (1985–2010)

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +3 more

Ahmad Raeisi is a Guest Researcher in the Machine Learning section at the Department of Computer Science, University of Copenhagen. His work intersects theoretical and applied machine learning across diverse domains including quantum computing, neuroscience, healthcare, and sustainability. Affiliation: Department of Computer Science, University of Copenhagen Research Focus: Hybrid AI architectures, quantum algorithms, explainable AI, cross-cultural systems, and climate-aware machine learning His recent publications span optical neural networks, emotion recognition in conversational AI, and quantum-inspired models. He contributes to the SCIENCE AI Centre and collaborates with initiatives like TreeSense for remote sensing applications. Current research trends emphasize environmental sustainability in AI development, quantum-classical hybrid systems, and fairness in recommender algorithms, with applications in medical diagnostics and ecological monitoring.

• Machine Learning
• Medical Data Analysis
• Quantum Computing
• +3 more

Simon Bartels is a researcher affiliated with the Department of Computer Science at the University of Copenhagen , contributing to the Machine Learning section. His work spans interdisciplinary applications of artificial intelligence, including quantum computing, healthcare diagnostics, and environmental modeling. Research activities at the department cover both theoretical and applied machine learning, with participation in the SCIENCE AI Centre . Key domains include medical data analysis , remote sensing , sustainability , and biological data modeling . Recent publications highlight contributions to quantum-inspired neural networks , geospatial biodiversity analysis , and energy-aware AI systems . Collaborations include rare disease research (e.g., MOSAIC framework ) and quantum computing optimizations.

• Algorithms
• Machine Learning
• Computational Complexity
• +2 more

Benjamin Bogø is a Research Fellow at the Department of Computer Science, University of Copenhagen. His work bridges algorithmic complexity, machine learning, and quantum computing, with a focus on sustainable AI and cross-cultural applications. He is affiliated with the SCIENCE AI Centre and contributes to the department's compute cluster initiatives. Benjamin's research interests include: Quantum computing applications in neural networks AI explainability and ethical frameworks Algorithm optimization for complex systems Climate-aware machine learning Interdisciplinary biomedical and cultural data analysis His recent publications analyze: Quantum-classical hybrid systems (15% of articles) Large language model interpretability (20% of articles) Medical and ecological applications (30% of articles) Quantum hardware optimization (25% of articles) Algorithmic fairness in recommender systems (10% of articles)

• Filesystems
• High Performance Computing
• Distributed Storage
• +4 more

Ramnatthan Alagappan is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois. His research focuses on distributed systems, storage systems, and fault tolerance in modern datacenter environments. He leads projects investigating high-performance storage abstractions, replication strategies, and reliability engineering for distributed infrastructure. Key research areas include filesystem design, crash consistency mechanisms, and optimizing storage hierarchies for hybrid NVM environments. His work emphasizes practical implementations of theoretical models, such as the LazyLog shared log abstraction and IONIA replication framework for disk-based key-value stores. Alagappan has received the NSF CAREER Award (2024) for his research on datacenter-aware storage systems. His recent publications address challenges in disaggregated datacenters, fault tolerance for modern workloads, and automated reliability testing for cluster management systems. He collaborates extensively on projects involving distributed storage protocols, log-based systems, and performance optimization for large-scale infrastructures. His research spans theoretical contributions (e.g., consistency models) to applied systems work (e.g., implementing fault-tolerant storage stacks), with a focus on bridging gaps between hardware capabilities and software system design.

• Cyber-physical systems
• Robust Autonomous systems
• AI security in Cyber-physical systems
• +3 more

Xin Lou is an Associate Professor at the Infocomm Technology Cluster of Singapore Institute of Technology (SIT) and an Adjunct Senior Research Scientist at the University of Illinois at Urbana Champaign's Illinois Advanced Research Center Singapore (IARCS). He holds a PhD from City University of Hong Kong and a B.E. from Sichuan University. His research focuses on cyber-physical systems, AI security, autonomous systems, and sustainable smart infrastructure. Notable awards include the Best Paper Award (2025) and Best Demo Award (2024). He leads interdisciplinary projects on AI perception for autonomous vehicles and collaborates with industry partners like NVIDIA. His career includes roles as Senior Research Scientist at IARCS (2022–present) and Postdoctoral Researcher at Illinois at Singapore (2016–2018). He teaches courses in Computer Networks and Digital Signal Processing at SIT. Key grants include AI Singapore's Holistic Moving Target Defence (S$4M) and MOE-funded projects on AI perception and CO₂ reduction. His team develops datasets like the Water Supply System Dataset and explores smart grid cybersecurity through platforms like CRaaS. Professional activities include chairing ACM e-Energy 2024 and serving on TPCs for IEEE SmartGridComm and IEEE ICC. He advises EngD students under SIT-SINGA and collaborates internationally on robust autonomous systems, AI security, and sustainable energy.

Javier Pastor GalindoView profile

Researcher

• Cybersecurity
• Cyberintelligence
• Disinformation Analysis
• +7 more

Javier Pastor Galindo is a researcher at the University of Murcia's Department of Information and Communications Engineering, affiliated with the Faculty of Informatics. He holds B.Sc., M.Sc., and Ph.D. degrees in Computer Science from the same institution. His roles include FPU-MECD Predoctoral Researcher (2019–present) and later postdoctoral researcher, specializing in cybersecurity, cyberintelligence, and disinformation analysis. He leads projects like DEFENDER (cybersecurity in IoT/5G labs), CDL-TALENTUM (cybersecurity talent development), and EUCINF (EU cyber warfare tools). Education: PhD in Computer Science (in progress), University of Murcia MSc in New Technologies on Computer Science (2019), specialization in Networks and Telematics BSc in Computer Science Engineering (2018), specialization in Information Technologies Research Interests: Focus on OSINT, cyber defence, disinformation detection, and social media analysis. His work spans frameworks for bot detection (e.g., BOTTER), Tor network studies, and NLP-based threat analysis. He emphasizes practical applications, such as the INDRA Cyber Range training platform. Awards: Recipient of the prestigious FPU Predoctoral Grant (2019), Research Initiation Grant (2018), and academic distinctions for his thesis work on SDN and TLS security. His research aligns with EU and national defense initiatives, addressing critical cybersecurity challenges. Grants & Projects: Involved in high-budget projects (e.g., EUCINF: €41M, EU-GUARDIAN: €13.5M), focusing on AI-driven cyber defence automation and European cyber resilience. Collaborates with entities like INCIBE and the European Defence Fund. Labs & Teams: Part of the CyberDataLab UMU, a hub for cybersecurity and data science R&D. Engages in interdisciplinary efforts, such as the COnVIDa pandemic data dashboard.

Jim Alves-FossView profile

Professor

• Cybersecurity
• Formal Verification
• Operating Systems
• +2 more

Jim Alves-Foss is a University Distinguished Professor of Computer Science at University of Idaho's College of Engineering, specializing in high-assurance computing systems and cybersecurity. Core research domains: Formal methods for security verification Operating system security architectures Critical infrastructure protection Automated security policy enforcement Publications demonstrate consistent focus on: Rigorous verification of security properties (e.g., seL4 kernel) Development of automated analysis tools (fuzzing, SDN policy) Industrial control system protection frameworks Adversary-aware risk assessment methodologies

Lauro SnidaroView profile

Associate Professor

• Data Fusion
• Computer Vision
• Machine Learning
• +5 more

Lauro Snidaro is an Associate Professor at the Università degli Studi di Udine , Department of Mathematics and Computer Science. His research focuses on Information Fusion , Data Fusion , Autonomous Vehicles , and Context-Aware AI Systems . He co-organized the 2024 International Conference on Information Fusion (FUSION 2024) in Venice and has led NATO-funded research projects (e.g., IST-165 RTG on High-level Fusion). Research highlights include the Context-Enhanced Information Fusion book (Springer, 2016) and a widely-downloaded paper on Context-based Information Fusion . He actively collaborates with industries for autonomous vehicle data fusion and defect detection systems . Teaching includes the Intelligent Multimedia Systems course, emphasizing openFrameworks and C++ for creative programming. He advises PhD candidates and research fellows in Computer Vision and Machine Learning domains. Key contributions span multi-sensor fusion for surveillance, context-aware tracking, and AI applications in crisis computing and healthcare.