جستجوی استاد راهنمای دانشگاهی

Laxman Dhulipalaمشاهده پروفایل

استادیار

• Parallel Algorithms
• Graph Processing
• Parallel Graph Algorithms
• +۷ مورد دیگر

Laxman Dhulipala serves as an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park, while also working as a research scientist at Google Research with the Graph Mining team. Dr. Dhulipala earned his Ph.D. from Carnegie Mellon University under Guy Blelloch's supervision and completed a postdoctoral fellowship at MIT with Julian Shun. His research centers on efficient parallel algorithms, particularly for parallel clustering and graph processing, along with developing computational models for emerging hardware technologies. His scholarly output demonstrates significant expertise across parallel computing domains, with particular emphasis on scalable graph algorithms, dynamic data structures, and computational geometry. Dr. Dhulipala's work bridges theoretical computer science with practical systems implementation, producing algorithms that achieve both theoretical optimality and real-world performance. His research group has made substantial contributions to benchmarking frameworks including the Graph Based Benchmark Suite (GBBS) and ParClusterers Benchmark Suite, establishing standardized evaluation methods for graph processing systems. The collective work shows progression from theoretical foundations to practical implementations that handle massive-scale datasets. Best Paper Award at SPAA 2022 Best Paper Runner Up at VLDB 2022 Distinguished Paper Award at PLDI 2019 Memorable Paper Award Finalist at NVMW'20 CMU's SCS Dissertation Award Honorable Mention As an educator, Dr. Dhulipala mentors numerous graduate students while teaching advanced courses in algorithm design and parallel computing. His research collaborations span multiple institutions including Carnegie Mellon University, MIT, and Google Research, reflecting his position at the intersection of academia and industry research.

• Legal Informatics
• Artificial Intelligence and Law
• Data Protection and Privacy
• +۵ مورد دیگر

Erich Schweighofer serves as Associate Professor at the University of Vienna within the Institute for European, International and Comparative Law, specifically affiliated with the Department of International Law and International Relations. His research activities are centered at the Juridicum building (Schottenbastei 10-16, 1010 Vienna), where he maintains an active office presence with scheduled consultation hours. His scholarly focus spans Legal Informatics , Artificial Intelligence and Law , Data Protection , and Legal Knowledge Representation , with particular emphasis on explainable AI systems for legal contexts and formal methodologies for translating legal norms into computational frameworks. This interdisciplinary work bridges jurisprudence and computer science through projects examining biometric regulation, autonomous vehicle governance, and natural language processing applications in legal domains. Analysis of his 2021-2024 publications reveals consistent thematic progression toward operationalizing legal principles in AI systems, with increasing focus on transparency mechanisms, temporal logic for dynamic regulations, and cross-jurisdictional compliance challenges. His work predominantly appears in the International Legal Informatics Symposium (IRIS) proceedings and JURIX conferences, reflecting deep engagement with the legal informatics community. Professor Schweighofer leads a dedicated research team including project assistants Mag. Jessica Fleisch, Mag. Jonas Pfister, Felix Schmautzer, and Mag. Jakob Zanol, while actively participating in the University of Vienna's Working Group on Legal Informatics (Arbeitsgruppe Rechtsinformatik). His collaborative approach extends to organizing the biennial IRIS symposium, which has established itself as a cornerstone event for European legal informatics scholarship since 1998.

Santiago Ontañónمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Game AI
• +۱۰ مورد دیگر

Santiago Ontañón is an Associate Professor in the Department of Computer Science at Drexel University's College of Computing and Informatics. He is also a Senior Research Scientist at Google DeepMind, reflecting a strong dual affiliation in both academic and industrial AI research. His work bridges theoretical AI with practical applications in gaming and machine learning. PhD in Computer Science (Artificial Intelligence), cum laude, Autonomous University of Barcelona Postdoctoral Researcher, Georgia Institute of Technology Researcher, Artificial Intelligence Research Institute (IIIA), Barcelona, Spain Dr. Ontañón's research focuses on artificial intelligence, machine learning, and robotics, with a particular emphasis on game AI. His interests span case-based reasoning, reinforcement learning, Monte Carlo tree search, player modeling, and procedural content generation. He has made significant contributions to AI in real-time strategy games and explainable AI systems. His recent publications reflect a consistent trend in AI for games, hierarchical planning, and learning from demonstration. The articles span topics such as reproducible deep reinforcement learning, adaptive player modeling, and integrating domain knowledge into search algorithms, indicating a mature and impactful research trajectory in AI and game technologies. Senior Research Scientist, Google DeepMind Organizer, microRTS AI Competition Advising multiple PhD students in AI and game-related topics He has advised numerous PhD students, many of whom have completed their theses on advanced AI topics in games and reasoning. His research is supported by access to substantial computational resources and collaborative networks in both academia and industry. He actively promotes open science by releasing software, data, and teaching materials. He leads research efforts in AI for games and maintains an active lab focused on game AI, with projects like microRTS, FTL, and Darmok. His team develops systems for reinforcement learning, planning, and natural language understanding in game environments.

Alper Yilmazمشاهده پروفایل

استاد

• Deep Learning
• Reinforcement Learning
• Computer Vision
• +۱۱ مورد دیگر

Alper Yilmaz is Professor with appointments in Civil Environmental and Geodetic Engineering and Computer Science and Engineering (courtesy) Departments at The Ohio State University. He serves as Director of PCVLab and is currently interim president for the ISPRS Technical Commission II. Dr. Yilmaz has been inducted to the U.S. National Academy of Inventors in 2020 and is a Fellow of the American Society for Photogrammetry and Remote Sensing (ASPRS) and senior member of IEEE. Dr. Yilmaz's research focuses on biomimetic navigation systems for unmanned systems, mining anomalies in multi-physics and multi-dimensional data for surveillance, and learning geospatial information for scene understanding. His expertise spans Deep Learning, Reinforcement Learning, Computer Vision, Photogrammetry, Data-centric Surveillance, Collaborative Swarm Navigation, and Indoor Positioning Systems. His recent work on biomimetic navigation systems has resulted in UbiHere Inc., an Ohio State University spin-off founded in 2018. Dr. Yilmaz has served as Editor-In-Chief for the Photogrammetric Engineering and Remote Sensing Journal (PE&RS) between 2016-2024, during which the journal's impact factor increased to its highest since 1934. He previously served as Associate Editor for Computer Vision and Image Understanding Journal (2014-2016) and Machine Vision and Applications Journal (2006-2011). Outstanding Service Award (2022, ASPRS) Innovator of Year (2020, OSU) Presidential citation (2019, ASPRS) Masao Horiba Award honorable mention (2016, Japan) Lumley Interdisciplinary Research Award (2015, OSU) Lumley Research Award (2012, OSU) Top 1% most cited researcher in Artificial Intelligence & Image Processing and Geological & Geomatics Engineering (2024) Dr. Yilmaz has advised 29 Ph.D. and 15 M.Sc. students to completion on topics ranging from photogrammetry, machine learning, and computer vision. His research has received over $13M in extramural funding from NASA, NSF, DOD, DOE, NIH, and industry partners including Ford Motor Company, Trimble Inc., and UbiHere Inc. PCVLab, located in Bolz Hall Suite 233, features a sensor calibration room, state-of-the-art workstations with GPUs for deep learning studies, and small robotic systems including ground and aerial units.

• Software Engineering
• Deep Learning
• Human-Computer Interaction
• +۳ مورد دیگر

Prof. Dr. Chunyang Chen is a Full Professor at the Department of Computer Science, Technical University of Munich (TUM), Heilbronn, Germany. He holds the Chair of Software Engineering & AI, serves as a core member of the Munich Data Science Institute, board member of the Heilbronn Data Science Center, and Fellow at Fortiss. He also maintains an Adjunct Professor role at Monash University, Australia. Research Focus: His work bridges Software Engineering, Deep Learning, and Human-Computer Interaction (HCI), specializing in AI/ML, NLP, and program analysis for mobile app development, testing, and security. Key areas include LLM-assisted app development, robustness of deep learning models, and accessibility testing. Scientific Awards: Best Paper Honorable Mention in CHI 2024 Discovery Early Career Researcher Award (DECRA), Australian Research Council ACM SIGSOFT Early Career Researcher Award Facebook Research Award in Probability and Programming Dean's Award for Research Impact at Monash University Academic Leadership: He actively mentors PhD students, supervises postdocs, and leads research teams focusing on software security, automated testing, and LLM applications. His recent work explores the intersection of software security and large language models, with a special issue call for EMSE journal.

Amin Mesmoudiمشاهده پروفایل

دانشیار

• Data Engineering
• Machine Learning
• Anomaly Detection
• +۵ مورد دیگر

Amin Mesmoudi serves as Associate Professor in Data Engineering at the University of Poitiers' IUT (Institut Universitaire de Technologie), with dual laboratory affiliations at LIAS-ENSIP (Poitiers campus) and LIAS-ISAE-ENSMA (Chasseneuil campus). His research bridges theoretical database systems with practical large-scale data engineering challenges, particularly in semantic web technologies and machine learning applications. The laboratory maintains physical presences at both ENSIP's Bâtiment B25 in Poitiers and ISAE-ENSMA's Téléport 2 facility in Chasseneuil, facilitating cross-institutional collaboration. Mesmoudi's research program centers on scalable data management systems, with three interconnected pillars: (1) RDF and graph-based query optimization techniques for billion-triple datasets, (2) machine learning integration for spatial query performance and anomaly detection, and (3) explainability frameworks for complex black-box models. His work demonstrates consistent evolution from foundational database systems (2011-2016) toward contemporary AI-driven data engineering, particularly evident in his 2023-2025 publications on temporal dependency preservation and co-selection explainability. The Data Engineering team within LIAS laboratory provides the primary research context for these investigations. Publication analysis reveals strong methodological continuity in addressing scalability bottlenecks across database paradigms. Early work focused on SQL-on-MapReduce benchmarking for astronomy databases (2015-2016), transitioning to specialized RDF processing frameworks (2019-2021), and culminating in current hybrid approaches combining temporal modeling with machine learning (2023-2025). Key technical themes include fragmentation strategies for distributed data, optimizer feedback mechanisms, and graph-based query acceleration - all targeting real-world performance constraints in big data environments. As a core member of LIAS laboratory's Data Engineering team, Mesmoudi contributes to France's national research infrastructure in computer science and automation systems. The laboratory's dual-university structure enables unique cross-pollination between University of Poitiers' academic programs and ISAE-ENSMA's engineering specialization, with Mesmoudi's work exemplifying this synergy through applications spanning astronomy databases to wireless sensor networks.

Gabriele Bavotaمشاهده پروفایل

دانشیار

• Software Quality
• Empirical Software Engineering
• Mining Software Repositories
• +۳ مورد دیگر

Gabriele Bavota is an Associate Professor at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. He leads the SEART (Software Engineering Advanced Research Team) group and serves as Principal Investigator for the DEVINTA ERC starting grant focused on developer intelligence through mining software artifacts. Dr. Bavota's research spans Software Quality, Empirical Software Engineering, and Mining Software Repositories. His work has evolved from foundational studies on code smells and technical debt to cutting-edge research at the intersection of artificial intelligence and software development. He has made significant contributions to understanding API usage patterns, software quality metrics, and developer behavior through empirical studies of large software repositories. His recent publications reveal a strong focus on AI-assisted software development, with extensive research examining code generation, code summarization, and code review automation using large language models. He has also expanded his research to include quality assurance in game development (detecting game stuttering and low engagement events) and voice user interface testing. His work consistently bridges theoretical insights with practical applications for software developers. ACM SIGSOFT Distinguished Paper Award for API compatibility research (MSR 2019) ACM SIGSOFT Distinguished Paper Award for Hugging Face model documentation study (ICPC 2024) ACM SIGSOFT Distinguished Artifact Award for deep learning fault taxonomy (ICSE 2020) As an active member of the software engineering research community, Dr. Bavota serves on program committees for major conferences including ICSE, ASE, FSE, and MSR. He has held leadership roles such as Program Co-Chair for ICSME 2023 and Vision/Reflection Track Co-Chair for ICSE. His SEART research group develops practical tools like the SEART Data Hub that streamline large-scale source code mining and preprocessing for empirical software engineering research.

Tse-Hsun Chenمشاهده پروفایل

دانشیار

• Software Engineering
• Performance Engineering
• DevOps & AIOps
• +۳ مورد دیگر

Tse-Hsun (Peter) Chen is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. He serves as Director of the SPEAR lab (Software Performance, Analysis, and Reliability lab), which focuses on improving the quality of large-scale software systems through research in log analysis and AIOps, software performance analysis, software testing, and mining software repositories. His research group maintains extensive collaborations with industry partners including ERA Environmental, Ericsson, Microsoft, and BlackBerry. Dr. Chen received his PhD and MSc in Computer Science from Queen's University and his BSc in Computer Science from the University of British Columbia. Dr. Chen's research addresses critical challenges in modern software engineering, including leveraging Large Language Models to assist developers with development, debugging, and maintenance; helping developers debug production systems by utilizing rich software data; providing optimization suggestions by analyzing user usage data; improving software quality assurances in DevOps environments; and mining software development history for useful developer suggestions. His work spans Software Engineering, Performance Engineering, DevOps & AIOps, Software Testing, and Mining Software Repositories, with a strong emphasis on practical applications that bridge academic research and industrial practice. His recent publications (2024-2025) demonstrate a pronounced shift toward integrating Large Language Models into various aspects of the software engineering lifecycle, particularly in log analysis, fault localization, code generation, and performance testing. This trend reflects the growing importance of AI in software engineering research and practice. Gina Cody Research award (2022) Ranked as one of the most active software engineering researchers worldwide by an independent study published in JSS Dr. Chen has successfully advised numerous PhD and Master's students, many of whom have secured prestigious academic positions. Several of his graduated PhD students now hold tenure-track assistant professor positions at institutions including York University, University of Alberta, DePaul University, and IIT Gandhinagar. His SPEAR lab has developed research tools that have been integrated into industrial practice for ensuring the quality of large-scale enterprise systems. The SPEAR lab, under Dr. Chen's leadership, has established itself as a leading research group in software engineering, with particular expertise in software performance analysis, log analysis, and AI applications for software engineering. The lab maintains strong industry connections and has produced numerous high-impact publications in top-tier software engineering venues including ICSE, FSE, ASE, and TSE.

Tien N. Nguyenمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۹ مورد دیگر

Tien N. Nguyen is a Professor in the Computer Science Department at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He has been actively contributing to the software engineering research community since 2005, with significant publications and service to major conferences including ASE, ICSE, and ESEC/FSE. His extensive research portfolio spans multiple areas at the intersection of artificial intelligence and software engineering. Dr. Nguyen's research focuses on AI/ML4Code, encompassing Machine Learning, Natural Language Processing for Software Engineering and Software Security. His work specifically addresses Program Analysis, Software Evolution and Mining, Software Security, Software Maintenance, Mining Software Repositories, Version and Configuration Management, and Web Code Analysis and Security. His research has been consistently funded by multiple NSF grants including NSA NCAE-C-002-2021, CNS-2120386, CCF-1723215, CCF-1723432, CNS-1723198, and others dating back to CCLI-0737029. His recent publications demonstrate a strong trend toward leveraging large language models for various software engineering tasks including program analysis, bug detection, code completion, and automated program repair. The research spans both theoretical foundations and practical applications, with numerous papers accepted at top-tier conferences across multiple years. His scientific contributions have been recognized with several prestigious awards: ACM SIGSOFT Distinguished Paper Award at FSE 2024 IEEE Computer Society TCSE Distinguished Paper Award at SANER 2022 ACM SIGSOFT Distinguished Paper and ASE Best Paper Award at ASE 2014 ACM SIGSOFT Distinguished Paper Award at ASE 2012 ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2009 Dr. Nguyen has served in numerous leadership roles including Program Co-Chair for ICSE 2020 Demonstrations, Doctoral Symposium Co-Chair for ESEC/FSE 2021, NIER Track Chair for ASE 2020, and Tutorials Co-Chair for ASE 2024. He has received multiple NSF grants supporting his research in software analysis, mining, and security. His work with the Boa infrastructure for ultra-large-scale code mining has established significant infrastructure for the research community. His laboratory focuses on AI for software engineering, with particular emphasis on program analysis, software security, and mining software repositories. The research group develops techniques that bridge the gap between artificial intelligence and practical software engineering challenges, creating tools that are both theoretically sound and practically applicable to real-world software development.

• Marketing
• Digital Transformation
• Sustainability
• +۳ مورد دیگر

Dr. Alessandro Inversini is an Associate Professor at the EHL Hospitality Business School, part of HES-SO University of Applied Sciences Western Switzerland. His primary role involves teaching and researching in the Department of Global Hospitality Business, focusing on marketing, digital transformation, and sustainability within the hospitality and tourism sectors. Educational Background: No explicit educational details provided in the text. Research Interests: Explores the transformative impact of emerging technologies (e.g., AI, ChatGPT) on hospitality operations and customer experience. Advocates for regenerative and net-positive approaches to sustainability in the hospitality industry. Investigates CSR communication strategies on social media and cross-cultural dynamics in tourism reviews. Analyzes digital media’s role in B2B relationships and customer-centric frameworks. Article Trends: Recent works emphasize AI-driven changes in tourism, ethical digital transformation, and CSR practices. Key themes include regenerative hospitality, cultural review differences, and leveraging social media for transformative learning. Awards: No scientific awards explicitly listed. Advising & Grants: Guided research on regenerative hospitality in Lebanon and transformative homestay experiences in Malaysia. Contributed to international conferences like ENTER2016 and EuroCHRIE 2024. Labs/Teams: Collaborates with interdisciplinary teams on projects involving digital tools for rural development and ethical technology integration in hospitality.

Ninghao Liuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Explainable AI (XAI)
• +۶ مورد دیگر

Dr. Ninghao Liu is an Assistant Professor of Computer Science in the School of Computing at the University of Georgia, part of the Franklin College of Arts & Sciences - Division of Physical & Mathematical Sciences. He holds a Ph.D. in Computer Science from Texas A&M University (2021) and an M.S. in Electrical and Computer Engineering from Georgia Institute of Technology (2015). His research focuses on Explainable AI (XAI), Graph Mining, Model Fairness, Recommender Systems, and Outlier Detection, with notable contributions to foundational AI techniques and their applications in education, healthcare, and environmental sciences. Dr. Liu has secured significant funding, including a three-year NSF grant (2022–2025) for 'Graph-Oriented Usable Interpretation' and a five-year $10 million grant from the U.S. Department of Education (2024–2029) for the GenAI Empowered National Initiative for STEM+C Education. He has also been honored with the Outstanding Paper Award at ICML 2022, Best Paper Award Shortlist at WWW 2019, and other distinctions. His work emphasizes interpretable machine learning, graph neural networks, and addressing algorithmic bias. He collaborates across disciplines, contributing to radiology AI, climate-smart forestry, and pandemic prediction through knowledge-enhanced deep learning. His lab is based at the Boyd Research and Education Center, where he advances research in trustworthy AI systems and data-centric solutions.

• Management
• Sustainable Supply Chain Management
• Digital Transformation
• +۳ مورد دیگر

Andrea Appolloni is an Associate Professor at the Department of Management and Law, University of Rome Tor Vergata. His academic career focuses on Management with emphasis on Sustainable Supply Chain Management , Digital Transformation , and Circular Economy . His research explores the intersection of technological innovation and sustainability, particularly through topics like AI in Logistics , Green Procurement , and Policy Optimization . Publications span both theoretical frameworks and empirical studies in China, Italy, and Malaysia, with a strong focus on environmental impact and organizational performance. Recent work includes digital twin applications for human-AI collaboration, blockchain integration in sustainable supply chains, and analyzing barriers to circular economy adoption. His 15 most recent articles (2025-2022) demonstrate a trend toward combining Artificial Intelligence , Operations Management , and Environmental Governance .

Yannis Theodoridisمشاهده پروفایل

استاد

• Moving Objects Databases
• Location-Based Services
• Data Mining for Spatial Objects
• +۳ مورد دیگر

Yannis Theodoridis is a Professor at the Department of Informatics, University of Piraeus, leading the Information Systems Laboratory (InfoLab). He specializes in spatiotemporal databases, mobility analytics, and maritime data science. His research focuses on trajectory analysis, location-based services, and big data frameworks for transportation and maritime surveillance. Key projects include the MOD (Moving Objects Databases) initiative, the ARGOS framework for real-time trajectory prediction, and contributions to the HERMES trajectory database engine. He has advised 7 PhD students and co-authored numerous papers in IEEE and ACM venues. His work addresses challenges like vessel collision risk assessment, urban mobility optimization, and maritime event detection. He serves on the editorial board of the International Journal of Data Warehousing and Mining and contributes to conferences like PCI and ECML PKDD. His labs emphasize interdisciplinary approaches to mobility data science, integrating machine learning with domain-specific analytics.

Nathanial Wigginsمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Mixed Reality
• Complex Dynamical Systems
• +۶ مورد دیگر

Dr. Nathanial Wiggins is a Senior Lecturer at the University of Houston in the Department of Industrial and Systems Engineering, with additional roles as Distinguished Professor of Engineering and Mathematics at San Jacinto College and Adjunct Professor of Mathematics at University of Houston - Clear Lake. He is affiliated with the Institute of Electrical and Electronics Engineers (IEEE) and the American Society for Engineering Education (ASEE). Ph.D. in Systems and Engineering Management, Texas Tech University M.S. in Mathematical Science, University of Houston – Clear Lake B.S. in Mathematics and Russian Studies, University of Colorado, Boulder Dr. Wiggins' research focuses on artificial intelligence, mixed reality, and complex dynamical systems. He develops systems that scaffold knowledge from repetition to utilization, particularly in neural networks and intelligent infrastructure. His work intersects with aerospace, oil and gas, and defense industries, emphasizing student-driven projects and collaborative learning frameworks. His publications span mixed reality applications in education, swarm robotics for Mars exploration, neural network methodologies, and aerospace systems like microgravity flame suppression and battery cooling. Keywords include AI, computer science, and engineering education, with sub-fields like neural networks and systems integration. SJC Gold Level Honoraria, Institutional Impact Award, 2019 IEEE CCWC Best Speaker Award, Las Vegas, 2019 Roueche Excellence Award, League for Innovations, 2013 NISOD Award, Community College Organization, 2013 Edu-STEM Award, Texas Space Grant Consortium, 2012 NASA Microgravity University recognition, 2012 Dr. Wiggins leads grants including NSF Bridges to STEM Careers and NASA collaborations (Swarmathon, Microgravity University). He founded the Artificial Intelligence Technology Labs, integrating peer mentoring and hands-on research to enhance STEM education.

Shawki M. Areibiمشاهده پروفایل

استاد

• VLSI Physical Design Automation
• Reconfigurable Computing Systems
• Hardware/Software Co-Design for Embedded Systems
• +۷ مورد دیگر

Shawki M. Areibi is a Professor and Area Head of Engineering Systems and Computing in the School of Engineering at the University of Guelph. His research focuses on VLSI Physical Design Automation, Reconfigurable Computing Systems, and Hardware/Software Co-design for Embedded Systems. He leads efforts in developing advanced algorithms for CAD tools, FPGA design, and machine learning applications. His work addresses challenges in VLSI layout optimization, parallel processing, and embedded systems design. Affiliations: AI Affiliated Faculty, Area Heads, Computer Engineering, Engineering Systems and Computing Research. Research Interests: VLSI Circuit Layout, Reconfigurable Computing, Machine Learning, and FPGA-based Accelerators. His research integrates meta-heuristics like Genetic Algorithms and Tabu Search to solve complex optimization problems. He has contributed to hardware acceleration frameworks for machine learning algorithms and embedded systems, with applications in domains like signal processing and data mining. His recent work includes congestion-estimation models for modern FPGAs and analytic placement tools for ultra-scale architectures. Publications span VLSI design, reconfigurable computing, and machine learning, emphasizing algorithmic innovation and hardware-software co-design. His students have explored topics ranging from FPGA placement to domain adaptation in remote sensing. Grants and Advising: Advises graduate and undergraduate students on projects involving FPGA acceleration, machine learning, and embedded systems. His labs focus on developing next-generation CAD tools and hardware accelerators.