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Aleksandr PopovView profile

Researcher

• Computational Geometry
• Algorithms
• Trajectory Analysis
• +2 more

Aleksandr Popov is a researcher in computational geometry currently affiliated with the ALGO cluster at Eindhoven University of Technology. He completed his PhD in 2023 focusing on algorithms for uncertain trajectories and continues active research in this domain. Education: PhD in Algorithms for Imprecise Trajectories, Eindhoven University of Technology, 2023 Master's in Similarity of Uncertain Trajectories, Eindhoven University of Technology, 2019 Dr. Popov's research centers on computational geometry with emphasis on trajectory analysis under uncertainty . His work develops algorithms to quantify trajectory similarity using Fréchet distance, simplify complex paths while preserving essential features, and handle imprecise spatial data. His research bridges theoretical computer science with practical applications in movement pattern analysis and spatial databases, addressing fundamental challenges in how uncertainty affects geometric computations. Analysis of his publication record reveals a systematic progression from theoretical foundations of uncertain curve analysis to more applied problems like map-matching and trajectory clustering. His work consistently explores how measurement uncertainty impacts geometric algorithms, developing specialized methods that maintain computational efficiency while accounting for data imprecision. A distinctive feature of his research is the adaptation of classical geometric measures like Fréchet distance to probabilistic and uncertain settings. Dr. Popov has substantial teaching experience, having assisted with bachelor courses on data structures and algorithms (2021-2023), supported master seminars on algorithms (2019-2021), and served as a student assistant for various courses during his bachelor and master studies (2014-2019). He also contributed as an editorial assistant for Dagstuhl Seminar reports on computational geometry, demonstrating engagement with the broader academic community. As an active member of the ALGO cluster at Eindhoven University of Technology, Dr. Popov collaborates with leading researchers including Kevin Buchin, Maarten Löffler, and Marcel Roeloffzen, contributing to a vibrant research environment focused on theoretical and applied aspects of algorithm design for geometric problems.

Fabio PapacchiniView profile

Senior Lecturer

• Non-classical Logics
• Automated Theorem Proving
• Knowledge Representation and Reasoning
• +12 more

Dr. Fabio Papacchini is a Senior Lecturer in Computer Science at Lancaster University Leipzig, affiliated with the School of Computing and Communications and research groups Security Lancaster (Systems and Software Security). He holds a PhD from the University of Manchester, where he was awarded a Doctoral Prize Fellowship, and previously conducted research at the University of Liverpool on projects involving robotics, artificial intelligence, and software systems for nuclear and offshore energy applications. His research centers on non-classical logics in computer science, with key interests in model theory, automated theorem proving, knowledge representation and reasoning, ontological reasoning, and formal verification. He has made significant contributions to modal logic, spatial logics, and ontology-mediated querying, particularly through reduction techniques and finite model analysis. The recent publications highlight a strong trend in logical foundations for AI and formal methods, focusing on efficient translations in modal logic, runtime verification of multi-agent systems, and integration of formal techniques with robotics and autonomous systems. His work bridges theoretical logic with practical applications in safety-critical domains. Doctoral Prize Fellowship, University of Manchester Dr. Papacchini has collaborated extensively with researchers such as Ullrich Hustadt, Clare Dixon, Cláudia Nalon, Michael Fisher, and Alessandro Ferrando. While no direct mention of student advising or grants is found, his active publication record and leadership in research projects suggest significant involvement in mentoring and collaborative research. He is associated with formal verification of autonomous systems and has explored applications in robot inspection and battery health management. He has contributed to the development of declarative languages for agent platforms and has participated in the Multi-Agent Programming Contest, showcasing applied work in automated planning and agent coordination.

• Data Management
• Computational Logic
• Database Theory
• +10 more

Michael Benedikt is Professor of Computer Science at the Department of Computer Science, University of Oxford, and a Fellow of University College, Oxford. His research lies at the intersection of computational logic, database theory, and theoretical computer science, with a focus on query answering, logic-based data management, and formal methods for data integration. Institution: University of Oxford, Department of Computer Science Position: Professor of Computer Science Email: michael.benedikt@cs.ox.ac.uk Office: 355 Wolfson Building, Parks Road, Oxford OX1 3QD, UK His primary research interests include data management, computational logic, model theory, query reformulation, integrity constraints, existential rules, and fixpoint logics. He investigates theoretical foundations of querying Web and social network data, ontology-based data integration, and finite model reasoning. His recent publications span top venues such as VLDB, PODS, LICS, ICALP, and IJCAI, covering topics like scalable querying of nested data, interpolation in fixpoint logics, finite open-world query answering, and rewriting recursive queries. A unifying theme across his work is the application of logical methods to ensure correctness, decidability, and efficiency in data access and transformation. Best Paper Award, ICALP 2017 (Track B) EPSRC Established Career Fellowship (2015–2020) He has supervised numerous PhD students and postdoctoral researchers, including Jaclyn Smith, Djordje Zivanovic, Antonia Kormpa, and Benjamin Spencer. His research group has been supported by grants from EPSRC and Microsoft Research. He actively contributes to the academic community through service on program committees (e.g., LICS, PODS, IJCAI, VLDB) and editorial boards (e.g., Journal of Computer and System Sciences). He co-authored a book on interpolation-based query reformulation published by Morgan Claypool.

• Medical and Scientific Imaging
• Algorithm Design and Analysis
• Computational Complexity
• +1 more

Jim Cox is a Professor in the CIS Department at Brooklyn College. His research spans Medical and Scientific Imaging , Algorithm Design and Analysis , Computational Complexity , and Constraint Programming and Operations Research . Medical Imaging : Focused on anatomical modeling and volume data analysis. Algorithm Design : Contributions to digital Morse theory and surface reconstruction. Computational Complexity : Theoretical work on logic programming languages and complexity hierarchies. His publications highlight advancements in 3D image segmentation , digital geometry , and constraint programming , with key applications in biomedical computing and robotics. Awards and student advising details are not publicly listed in the provided materials.

Maikel Lázaro PÉREZ GORTView profile

Researcher

• Computer Science
• Data Security
• Database Systems
• +3 more

Maikel Lázaro PÉREZ GORT is a Researcher at the Department of Environmental Sciences, Computer Science and Statistics (DAIS) at Ca' Foscari University of Venice. He is affiliated with the Research Institute for Complexity Communications and teaches advanced courses on relational data watermarking in doctoral programs. His primary research focuses on database security, specifically developing robust watermarking techniques for relational data integrity protection against attacks in untrusted environments. Teaching Activities: Doctoral Course (DM226/2021): 'Relational Data Watermarking: Fundamentals' and 'Robust Schemes' Past courses include 'Programming and Laboratory-2' for undergraduate Computer Science students and 'Lab of Human-Centered Design' for Digital Management programs. Research Interests: His work addresses challenges in data watermarking resilience, including topology optimization for watermark sources, query impact analysis, and prevention of additive attacks. Recent efforts emphasize earth observation data integrity and quantile-based distortion minimization techniques. Publications: Over 13 peer-reviewed articles from 2017-2025, focusing on watermarking methodologies for relational databases, earth observation systems, and forensic image analysis. Key themes include brute-force attack resilience, semantic-driven marking, and high-quality key generation schemes. Awards/Grants: No specific awards or grants mentioned in the provided materials. Labs/Teams: Affiliated with the DAIS department's research groups working on information systems security and complexity science.

Panagiotis G. IpeirotisView profile

Professor

• Crowdsourcing
• Human-AI Collaboration
• Data Science
• +11 more

Panagiotis G. Ipeirotis is a Professor and George A. Kellner Faculty Fellow at the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University. He is also affiliated with the Center for Data Science and the Computer Science department at NYU. His work bridges computer science, data science, and business analytics, with a focus on integrating human and machine intelligence. PhD in Computer Science, Columbia University (2004) MSc in Computer Science, Columbia University (2001) BSc in Computer Engineering & Informatics, University of Patras, Greece (1999) His research centers on crowdsourcing, human-AI collaboration, online labor markets, and social media analytics . He is widely recognized as a pioneer in human computation and has developed foundational techniques for ensuring data quality in crowd-sourced environments. His interdisciplinary work combines insights from computer science, economics, and social psychology to model user behavior and improve decision-making systems. His recent publications explore algorithmic fairness in hiring, theoretical voting models in crowdsourcing, and the economic impact of user-generated content. These works reflect a consistent trend toward building robust, ethical, and scalable human-machine systems that enhance data quality and decision accuracy across domains. Notable scientific awards include: Lagrange Prize in Complex Systems (2015) SIGKDD Test of Time Award (2020) NSF CAREER Award Best Paper Award at WWW 2011 Best Paper at SIGMOD 2006 Multiple best paper recognitions in Management Science and KDD Professor Ipeirotis has led significant research initiatives, including a $1.5 million Google Research Grant to integrate crowdsourcing with machine learning for visual search. He advises graduate students and contributes to academic leadership through editorial and collaborative roles. His work has been featured in prominent media outlets such as Forbes, WIRED, and Bloomberg Businessweek, highlighting its real-world impact. He is actively involved in research labs and initiatives at NYU, particularly those focused on data science, behavioral research, and computing. His affiliations with the Center for Research Computing and the Center for Behavioral Research underscore his interdisciplinary approach to solving complex problems at the intersection of technology and human behavior.

Aleksandr PerevalovView profile

Researcher

• Question Answering over Knowledge Graphs
• Multilingual Accessibility of Web Systems
• Quantitative Text Analysis
• +3 more

Aleksandr Perevalov is a researcher and PhD student at Anhalt University of Applied Sciences, affiliated with the Department of Computer Science and Languages. His work focuses on question answering systems over knowledge graphs, multilingual accessibility, and quantitative text analysis. Education: BS in Informatics (Perm National Research Polytechnic University), MS in Informatics (Perm), and MS in Data Science (Anhalt University of Applied Sciences, double degree) Employment: Research staff at Hochschule Anhalt since 2020 Research interests center on knowledge graph question answering systems, including automated semantics-driven service composition, entity-aware machine translation, and linguistic analysis of human-computer dialogues. He also explores conversational AI implementation strategies and language model evolution. Recent publications demonstrate expertise in semantic computing, multilingual system design, and benchmark creation. His work bridges academic research with practical applications in web engineering and language technology. 2018: 1st prize at Perm University conference 2018: TOP10 in Russian AI forum Machine Learning competition 2019: TOP9 in Data Mining Cup 2020: Bronze medal on Kaggle toxic comment classification Currently contributing to academic discourse through both peer-reviewed conference papers and industry-focused Medium publications.

Fabio PersiaView profile

Associate Professor

• Complex Event Processing
• Event Detection
• Analysis in Large Scale Databases
• +4 more

Fabio Persia is an Associate Professor at the Department of Information Engineering, Computer Science and Mathematics (DISIM) of the University of L'Aquila , Italy. Previously, he held positions as Tenure-Track Assistant Professor (RTD/B) and Assistant Professor (RTD/A) at the same department and the Free University of Bozen-Bolzano , respectively. He earned his MSc and PhD in Computer Engineering from the University of Naples Federico II (2009 and 2013), with a six-month visiting scholar stint at the University of Maryland, College Park (2012). Education : MSc, PhD in Computer Engineering (University of Naples Federico II, 2009/2013) Visiting Scholar (University of Maryland, 2012) His research focuses on event detection and analysis in large-scale databases , applied to multimedia, semantic analysis, and security . This spans video surveillance systems, social network analysis, and probabilistic itinerary planning. He co-developed ISEQL (Interval-based Surveillance Event Query Language) with advanced temporal operators and efficient algorithms for interval joins, extending its use to social networks and PostgreSQL integration. Notable collaborations include work with Prof. Sven Helmer and Dr. Paolo Bolzoni. Recent research trends include robotic platforms for dynamic surveillance (SARRI robot), multi-camera configurations , and social sensing for tourist applications . He has secured over €45,000 in research funding and published in top venues like IEEE TKDE, VLDBJ, IJCAI, and IEEE ICSC.

• Recommender Systems
• Information Retrieval
• E-Government
• +5 more

Iván Cantador is a Professor at the Autonomous University of Madrid, Spain, specializing in Recommender Systems, Information Retrieval, and E-Government applications. His research bridges computer science with social applications, particularly focusing on how technology can enhance citizen participation and government services. His research interests include: Recommender Systems and Personalization Cross-Domain Recommendation Argument Mining and Analysis E-Government and Digital Citizen Participation Social Media Analysis for Public Services Open Data Applications Cantador's recent work demonstrates a strong focus on the intersection of recommendation technologies with civic engagement. His publications show how recommender systems can be adapted for e-participation platforms, helping citizens navigate complex government information and participate more effectively in democratic processes. He has also made significant contributions to argument mining, developing tools and frameworks to analyze citizen input in government consultations. His research on misinformation highlights how recommendation algorithms can inadvertently amplify false information, and he has proposed methods to mitigate these effects while maintaining system effectiveness. Scientific contributions: Developed innovative approaches to cross-domain recommendation systems Created tools for argument annotation and analysis in citizen participation contexts Advanced methods for integrating sentiment analysis into recommendation frameworks Contributed to understanding how recommendation algorithms affect information diversity and potential misinformation spread Designed chatbots and conversational agents for government services Cantador has been actively involved in numerous research projects related to e-government and citizen participation, often collaborating with interdisciplinary teams that include computer scientists, political scientists, and public administration experts. His work has practical applications in improving how citizens interact with government services and participate in democratic processes through digital channels.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +5 more

Henry A. Kautz is a Professor at the University of Rochester, New York, USA, with a distinguished career in artificial intelligence, machine learning, and social media analysis. His research spans interdisciplinary applications in healthcare informatics, urban mobility, and computational modeling of human behavior. Research Focus : Integrates AI with public health surveillance (e.g., mental health monitoring via search engine logs), social media analysis for disease transmission modeling, and multimodal data interpretation for activity recognition. Key Contributions : Pioneered nEmesis for foodborne illness detection via social media, developed Markov logic frameworks for complex event recognition, and advanced privacy-preserving systems for cognitive disabilities support. His recent work explores hardware-accelerated SAT solving, explainable AI for deep learning, and longitudinal behavioral analysis during the COVID-19 pandemic. Publications emphasize practical AI applications in health, urban planning, and computational epidemiology.

• Computational Geometry
• Algorithms
• Graph Theory
• +3 more

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

Przemysław GordinowiczView profile

Assistant Professor

• Graph Theory
• Combinatorics
• Discrete Mathematics
• +2 more

Dr. Przemysław Gordinowicz serves as an Assistant Professor at Lodz University of Technology within the Department of Applications of Contemporary Mathematical Analysis. Based in room 161 with contact details (+48) 42 631-36-33 and pgordin@p.lodz.pl, he maintains an active research profile in discrete mathematics. His research centers on Graph Theory and Combinatorics, specializing in graph coloring extendability problems for planar and outerplanar graphs, pursuit-evasion games (cops and robbers, localization games), and polynomial method applications. He also investigates random graph models, discrete Laplacian problems, and extremal graph theory, employing combinatorial and algebraic techniques to solve complex structural problems. Analysis of his 15 most recent publications (2015-2025) reveals consistent focus on three interconnected themes: (1) polynomial method applications to list coloring extendability, (2) theoretical advances in pursuit-evasion games across diverse graph classes, and (3) firefighter problem variants including surviving rate calculations. His work bridges theoretical combinatorics with algorithmic applications, frequently appearing in top-tier journals like Journal of Graph Theory and Electronic Journal of Combinatorics. He has supervised PhD student Paweł Twardowski, whose 2023 dissertation addressed graph polynomial applications to list coloring extendability in planar graphs. No information about research grants or collaborative teams is available in the provided materials.

Scott AaronsonView profile

Professor

• Quantum Computing
• Computational Complexity Theory
• Quantum Information Science
• +1 more

Scott Aaronson is the Schlumberger Centennial Chair of Computer Science at The University of Texas at Austin and director of its Quantum Information Center. He was on leave during the 2022-2023 and 2023-2024 academic years to work at OpenAI on the theoretical foundations of AI safety. A leading researcher in quantum computing and computational complexity theory, Aaronson's work bridges computer science, physics, and philosophy. His research focuses on the fundamental capabilities and limitations of quantum computers, exploring questions about quantum complexity classes, quantum algorithms, and the relationship between quantum and classical computation. His influential book Quantum Computing Since Democritus connects quantum computing to broader intellectual questions spanning mathematics, philosophy, and theoretical physics. Aaronson's scholarly output reveals a consistent focus on quantum information science and computational complexity, with his work evolving from foundational theoretical questions to practical applications and connections with physics phenomena like black holes. His publications demonstrate how quantum complexity theory provides insights into diverse areas from quantum money to the AdS/CFT correspondence in theoretical physics. Aaronson has mentored numerous successful PhD students, many of whom have gone on to become professors at prestigious institutions including MIT, Stanford, University of Chicago, and University of Waterloo. His supervision extends across both computer science and physics departments, reflecting the interdisciplinary nature of his work. He directs the Quantum Information Center at UT Austin, which serves as a hub for research at the intersection of quantum computing, theoretical computer science, and physics. The center fosters collaboration between computer scientists, physicists, and mathematicians working on fundamental questions in quantum information.

Amr El AbbadiView profile

Professor

• Databases
• Distributed Systems
• Cloud Computing
• +4 more

Amr El Abbadi is a Professor in the Department of Computer Science at the University of California, Santa Barbara. He earned his B.Eng. from Alexandria University, Egypt, and Ph.D. from Cornell University. His work spans databases, distributed systems, and cloud computing, focusing on fault-tolerant data management, transactional consistency, and scalability in multi-datacenter environments. Education B.Eng. in Computer Science from Alexandria University Ph.D. in Computer Science from Cornell University El Abbadi has published over 300 articles and supervised more than 30 PhD students. His research emphasizes reconciling strong consistency with scalability in cloud systems, including advancements in geo-replicated data management and transactional guarantees for complex queries. He has received prestigious accolades such as the UCSB Senate Outstanding Mentorship Award (2007) and has been recognized through fellowships by the ACM , AAAS , and IEEE . Professional Leadership Chair of the Computer Science Department at UCSB (2007–2011) Executive Committee, IEEE Technical Committee on Data Engineering (TCDE) Board Member, VLDB Endowment (2002–2008) Editorial Roles Editor, The VLDB Journal Editor, IEEE Transactions on Computers Editor, The Computer Journal Conference Leadership Program Chair, SIGSPATIAL GIS 2010 Program Chair, ACM Symposium on Cloud Computing (SoCC) 2011 Program Chair, COMAD (India) 2012 Program Chair, First ACM Conference on Social Networks (COSN)

• Mathematical Foundations of Machine Learning
• Explainable Artificial Intelligence
• Online Convex Optimization
• +4 more

Tim van Erven is an Associate Professor of Machine Learning at the Korteweg-de Vries Institute for Mathematics within the Faculty of Science at the University of Amsterdam. His research focuses on the mathematical foundations of machine learning, with particular expertise in online convex optimization, statistical learning theory, and explainable AI. He leads a research group dedicated to developing mathematically rigorous machine learning methods that work effectively without manual fine-tuning. His research interests span the mathematical foundations of machine learning, with emphasis on explainable machine learning, adaptive methods in online convex optimization, faster-than-minimax rates for 'easy data' in statistical learning, PAC-Bayesian concentration inequalities, and statistical learning theory with frequentist analysis of Bayesian methods. His work bridges theoretical guarantees with practical applications, recently shifting toward formal mathematical analysis of explainability methods for black-box AI systems. His recent publications reveal a clear evolution from foundational work in online learning and statistical theory toward explainable AI, with increasing focus on theoretical guarantees for concept learning and algorithmic recourse. The publications demonstrate strong methodological rigor while addressing practical challenges in interpretability and robustness of machine learning systems. Scientific Awards: VICI grant by the Dutch Research Council (2025) VIDI grant by the Dutch Research Council (2019) TOP grant by the Dutch Research Council (2016) NIPS 2014 outstanding reviewer award Rubicon grant by the Dutch Research Council (2011) Van Erven serves in significant academic leadership roles including as a member of the board of directors for COLT, co-chair for the AI & Mathematics initiative, and organizer of the thematic seminar on machine learning. He has successfully secured multiple competitive research grants and leads a research group working on the Mathematical Foundations for Explainable AI project, with several PhD and Postdoc positions currently open.