جست‌وجوی استاد و پژوهشگر

• Recommender Systems
• Information Retrieval
• E-Government
• +۵ مورد دیگر

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
• +۵ مورد دیگر

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
• +۳ مورد دیگر

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 Gordinowiczمشاهده پروفایل

استادیار

• Graph Theory
• Combinatorics
• Discrete Mathematics
• +۲ مورد دیگر

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 Aaronsonمشاهده پروفایل

استاد

• Quantum Computing
• Computational Complexity Theory
• Quantum Information Science
• +۱ مورد دیگر

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 Abbadiمشاهده پروفایل

استاد

• Databases
• Distributed Systems
• Cloud Computing
• +۴ مورد دیگر

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
• +۴ مورد دیگر

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.

Soumen Chakrabartiمشاهده پروفایل

استاد

• Graph Neural Networks
• Knowledge Graphs
• Natural Language Processing
• +۳ مورد دیگر

Soumen Chakrabarti is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. His work bridges graph theory, natural language processing, and knowledge representation, focusing on scalable solutions for complex information retrieval problems. He has held visiting positions at Carnegie-Mellon University and Google. Education: B.Tech from IIT Kharagpur, PhD from UC Berkeley Key research areas: Graph Neural Networks, Knowledge Graphs, Code-Switched Text Analysis, Temporal Reasoning His recent publications emphasize neural graph matching , code-switched NLP , and multi-modal QA systems , often integrating symbolic reasoning with deep learning. Awards include the WWW 1999 Best Paper Award and ECML/PKDD 2008 Best Student Paper . He leads projects like CRUSH4SQL for text-to-SQL parsing and CSAW for annotated web search. His lab collaborates with IBM and Google on graph representation learning and temporal knowledge graph completion.

Veli Mäkinenمشاهده پروفایل

استاد

• Algorithmic Bioinformatics
• Genome-scale Algorithmics
• Computer Science
• +۳ مورد دیگر

Veli Mäkinen is a Professor at the Department of Computer Science, University of Helsinki, and holds the title of Docent in the same department. He leads the Genome-scale Algorithmics research group and is affiliated with the Helsinki Institute for Information Technology (HIIT). Mäkinen serves as a supervisor for doctoral programmes in both Integrative Life Science and Computer Science at the University of Helsinki. Professor, Department of Computer Science, University of Helsinki Docent, Department of Computer Science, University of Helsinki Supervisor, Doctoral Programme in Integrative Life Science Supervisor, Doctoral Programme in Computer Science Affiliated with Helsinki Institute for Information Technology (HIIT) His research focuses on algorithmic bioinformatics, genome-scale algorithmics, and computational methods for genomic sequence analysis. Mäkinen's work encompasses data structures, string algorithms, and graph theory applied to bioinformatics challenges such as variation detection, sequence alignment, and pan-genome analysis. Recent publications highlight his advancements in graph indexing, elastic founder graphs for scalable genomic analysis, and innovative approaches to colinear chaining. These works often intersect with high-throughput sequencing and quantum computing applications in bioinformatics. Scientific awards include: Hyvä tutkija award, University of Helsinki (2007) Mäkinen has received significant funding through grants such as EU Horizon Europe's "TeamPerMed" project and multiple Finnish Academy grants for research on cancer genetics and genomic epidemiology. He is an active editor for BMC Bioinformatics and has organized key conferences like IWOCA 2016 and WABI 2017.

Beata Megyesiمشاهده پروفایل

استاد

• Computational Linguistics
• Historical Cryptology
• Artificial Intelligence in Humanities
• +۳ مورد دیگر

Beata Megyesi is a Professor of Computational Linguistics at Stockholm University , leading groundbreaking work in Historical Cryptology and Digital Humanities . Her research bridges Artificial Intelligence with Philology to decode secret historical documents through projects like DESCRYPT (2025-2032) and DECRYPT (2018-2024), funded by Riksbankens Jubileumsfond and Vetenskapsrådet . Current Chair of Swedish Research Council's Linguistics Review Group (2024-2025) Director of the international Master's Program in AI and Language Advisor to PhD candidates Micaella Bruton and Crina Tudor Research Focus includes: Automatic analysis of 17th-19th century ciphers with AI Development of Linked Open Data infrastructure for cryptology Decryption of papal and diplomatic correspondences from 1500-1965 Creation of HistCorp multilingual historical corpus collection Key Collaborations span institutions in Sweden, Norway, Spain, Germany, Hungary, and the USA , with notable projects like DECODE2LOD and Swe-CLARIN . Her lab has pioneered automated key extraction and neural network-based alignment of encrypted manuscripts to plaintext.

• Logics
• Databases
• Formal languages
• +۵ مورد دیگر

Gabriele Puppis is an Associate Professor at the Department of Mathematics, Computer Science, and Physics of Udine University since 2022. Previously, he held a tenure-track position at the same university (2019-2022) and was a tenured CNRS researcher at LaBRI, Bordeaux (2012-2019). He completed postdoctoral research at Oxford University (2009-2012) and Udine University (2006-2008). His academic journey began with a PhD in Computer Science at Udine University (2002-2006). Education: PhD in Computer Science, Udine University (2006) Research Interests: Logics and formal languages String transducers and automata theory XML schema validation and database repairability Interval temporal logic and spatial reasoning Higher-order query languages Academic Contributions: His work focuses on the decidability and complexity of problems in automata theory, particularly for finite-valued transducers and their equivalence. He has advanced understanding of bounded repairability for tree and word languages, dynamic data structures for timed automata, and resynchronizability in transducer models. His recent publications explore decomposition theorems for streaming string transducers and their equivalence to two-way models. Teaching and Leadership: He teaches courses in Distributed Systems, Algorithms and Data Structures, and Verification techniques in AI and Cybersecurity. He has organized events like the GandALF 2023 summer school and participated in workshops on unambiguity in automata theory and regular cost functions.

Stephen M. Fioreمشاهده پروفایل

استاد

• Cognitive Science
• Team Cognition
• Human-AI Interaction
• +۶ مورد دیگر

Stephen M. Fiore is a full-time Professor at the University of Central Florida , affiliated with the Department of Philosophy and the Institute for Simulation and Training . As Director of the Cognitive Sciences Laboratory , he leads multidisciplinary research integrating cognitive, social, organizational, and computational sciences. Research Focus: Complex collaborative cognition, human-AI teaming, simulation-based training, team science, and 21st-century skills assessment. Federal Engagement: DARPA ISAT Study Group, National Academies of Sciences consensus studies. International Roles: Visiting scholar at École Normale Supérieure de Lyon (2010), OECD PISA expert panel (2015). Awards: Pegasus Professor (2023), Scroll & Quill Society (2020), Luminary Award (2019), Reach for the Stars Award (2014), multiple Research Incentive Awards. Funding: $35M+ secured as PI/Co-PI, including NSF and Minerva Research Initiative grants. Scientific Contributions: Co-author of 250+ publications across human-human and human-AI team cognition, with key works on accelerating expertise, shared cognition, and adaptive training systems. His lab explores immersive VR, robotic perception scales, and ICE environment team dynamics. Leadership Roles: Co-founder and former President of the International Network for the Science of Team Science Co-founder and former President of the Interdisciplinary Network for Group Research Co-founder of the Global Alliance for Inter- and Transdisciplinary Research Key Collaborations: Santa Fe Institute (2013), Army's Robotics CTA, NSF-funded team science initiatives, and European research networks.

David Liben-Nowellمشاهده پروفایل

استاد

• Computational Social Sciences
• Social Networks
• Theoretical Computer Science
• +۵ مورد دیگر

David Liben-Nowell is a Professor in the Department of Computer Science at Carleton College, with research spanning computational social sciences, algorithmic modeling, and interdisciplinary applications of theoretical computer science. He contributes to ACM Technical Symposia on Computer Science Education (SIGCSE) and maintains active roles in academic leadership as an Associate Provost since 2022. AISTATS'25: Machine Learning & Social Choice SIGCSE'24: Student Enrollment Systems SIGCSE'22: Intro CS Pedagogy His research explores social network structures , spoken-word recognition , and computational biology through algorithmic frameworks, with applications in peer-to-peer systems and game theory. Recent work on AISTATS'25 addresses probabilistic ranking models, while SIGCSE publications focus on educational algorithms.

Sobhan Babuمشاهده پروفایل

دانشیار

• Big Data Analytics
• Graph Theory
• Algorithms
• +۴ مورد دیگر

Dr. Sobhan Babu is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad (IIT Hyderabad). His research focuses on Big Data Analytics, Graph Theory, Algorithms, and Applied Machine Learning. He obtained his Ph.D. and MTech from IIT Bombay, and his BE from the University of Madras. Education Ph.D. in Computer Science, IIT Bombay MTech in Computer Science, IIT Bombay BE in Computer Science, University of Madras Diploma in Computer Engineering, AANM&VVRSR Polytechnic Research Interests His work bridges theoretical and applied Computer Science. In Graph Theory, he investigates structural graph properties, subdivisions, and directed graph algorithms. In AI/ML, he explores optimization techniques for VLSI design and power-efficient testing. His Big Data Analytics focus includes developing algorithms for efficient data processing and modeling. Publications His publications span VLSI optimization, algorithmic game theory, and graph theory. Key themes include power minimization in scan testing, market equilibrium algorithms, and graph structural conditions. Recent accepted works extend existing research on cycles and unicyclic graphs, indicating sustained theoretical contributions. Contact Email: sobhan@iith.ac.in Office: Room CS-307, Computer Science Building, IIT Hyderabad, Telangana, India.

• Databases
• Algorithms

Sumit Ganguly is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur), where he has established himself as a leading researcher in database systems and algorithms. Education: PhD in Computer Science, University of Texas, Austin (1992) MS in Computer Science, University of Texas, Austin (1989) B.Tech in Computer Science, IIT Kanpur (1987) Professor Ganguly's research primarily focuses on database systems with special emphasis on algorithms for data streams. His work explores the theoretical foundations and practical implementations of database technologies, particularly in the context of streaming data where traditional database approaches face significant challenges. His research has contributed to developing efficient algorithms for frequency estimation, sampling techniques, and processing hybrid frequency moments in data streams, addressing critical challenges in big data analytics. His publications demonstrate a consistent focus on data stream algorithms, with significant contributions spanning from 2008 to 2012. These works explore connections between expander graphs and data stream processing, deterministic data structures, and methods for estimating various statistical properties of streaming data. His research bridges theoretical computer science with practical database applications. Professor Ganguly maintains an active research program within the Department of Computer Science and Engineering at IIT Kanpur, contributing to the institution's reputation for excellence in theoretical computer science and database systems research.