جست‌وجوی استاد و پژوهشگر

Aniket Kitturمشاهده پروفایل

استاد

• AI-Augmented Cognition
• Collective Intelligence
• Social Computing
• +۷ مورد دیگر

Aniket (Niki) Kittur is a Professor in the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University's School of Computer Science. His research focuses on AI-augmented cognition, exploring how humans and computational systems can partner to accelerate knowledge acquisition and innovation. With over 100 peer-reviewed publications and 17 best paper awards or honorable mentions, Dr. Kittur has established himself as a leader in human-computer interaction and social computing. Dr. Kittur's research spans multiple interconnected areas focused on enhancing human cognition through technology. His work on crowd-augmented cognition examines how crowds and computation can collectively enhance human intellect. In social computing, he investigates how sociotechnical architectures can motivate and coordinate large groups for complex tasks. His research in data visualization and sensemaking explores how people collect, organize, and make decisions with online information. Most recently, his work has shifted toward LLM-augmented cognition, investigating how humans and large language models can work together in partnership to achieve better decisions and greater creativity than either could alone. Dr. Kittur's publications reveal a consistent trajectory toward increasingly sophisticated human-AI collaboration systems. Early work focused on crowdsourcing complex tasks (CrowdForge, CrowdSynthesis), then evolved to knowledge acceleration systems (Knowledge Accelerator, Fuse), and has recently centered on LLM-integrated cognition (Selenite, InkSpire, BioSpark). His research consistently demonstrates practical impact, with systems influencing products used by millions and receiving coverage in major media outlets including Nature News, The Economist, and The Wall Street Journal. Kavli fellow NSF CAREER award recipient Allen Newell Award for Research Excellence Inductee into the CHI Academy 17 best paper awards or honorable mentions across his publications Dr. Kittur has advised numerous students, including Andrew Kuznetsov, and has secured major research funding from NSF, NIH, Google, Microsoft, Bosch, Toyota, and other organizations. His Skeema browser extension project, which addresses tab overload through innovative project-based organization, has shown remarkable user retention and represents a practical application of his research on knowledge acceleration. His work with industry partners has influenced products used by millions, including Semantic Reader, Google Shopping, and Wikipedia features.

Tim Althoffمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Human-Centered Computing
• Data Science
• +۵ مورد دیگر

Tim Althoff is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington, specializing in Artificial Intelligence and Human-Centered Computing. His research focuses on behavioral data science, combining Data Science Natural Language Processing Social Computing Human-Centered AI Ethics & Fairness to extract insights about health and well-being. Recent publications highlight advancements in: Mental health support through AI Wearable sensor health monitoring Online community analysis Reproducibility in data science Public health interventions with notable papers in ACL, Nature Machine Intelligence, and NeurIPS. Scientific recognition includes: ACL 2023 Outstanding Paper Award WWW 2021 Best Paper Award Double ICWSM 2021 Best Paper Awards SIGKDD Dissertation Award 2019 Fulbright Scholarship German National Merit Foundation Actively mentoring postdoctoral researchers and seeking PhD students in areas like neural representation learning, NLP applications to psychology, and mobile health technologies through his Behavioral Data Science Group .

• Learning Analytics
• Educational Technology
• Virtual Reality Education
• +۵ مورد دیگر

Ulrik Schroeder is a Universitätsprofessor (Full Professor) at RWTH Aachen University, leading the Chair of Learning Technologies within the Faculty of Computer Science. His research focuses on the intersection of educational technology, learning analytics, and immersive technologies with particular emphasis on practical implementations in higher education settings. Professor Schroeder's research spans multiple interconnected domains in educational technology. His primary interests include Learning Analytics implementation (particularly using xAPI standards), Virtual Reality applications for education, Open Educational Resources development and conversion, and gamification approaches for programming education. He has developed several notable tools including convOERter for OER conversion, WebWriter for creating explorable explanations, and various xAPI-based learning analytics infrastructures. His work consistently bridges theoretical frameworks with practical educational applications, often focusing on computer science education contexts. Analysis of his recent publications reveals a strong trend toward integrating Learning Analytics with immersive technologies, particularly Virtual Reality environments. His research demonstrates a systematic approach to educational technology development, with emphasis on scalability, interoperability through standards like xAPI, and practical implementation in real educational settings. The work increasingly focuses on personalized learning paths, quality assurance for educational resources, and privacy-conscious data collection. Co-editor of 21. Fachtagung Bildungstechnologien (DELFI) (2023) Co-editor of Hochschuldidaktik der Informatik HDI 2018 Co-editor of DeLFI 2018 conference proceedings Professor Schroeder has supervised numerous doctoral and postdoctoral researchers who frequently appear as co-authors on his publications, indicating an active research group. His projects often involve interdisciplinary collaborations across computer science, education, and psychology. Current major initiatives include the AIStudyBuddy project for study path analysis and the development of VR classroom simulations for teacher training. His research group, the Learning Technologies Innovation Lab, develops open research tools that support various aspects of educational technology research and implementation.

Jia Zhangمشاهده پروفایل

استاد

• Machine Learning
• Natural Language Processing
• Information Retrieval
• +۱۰ مورد دیگر

Dr. Jia Zhang is the Inaugural Robert H. Dedman Jr. Endowed Department Chair and Professor of Computer Science at Southern Methodist University (SMU Lyle School of Engineering). She holds the Cruse C. and Marjorie F. Calahan Centennial Chair in Engineering and has a courtesy appointment in the Department of Operations Research and Engineering Management. Her research focuses on applying machine learning, natural language processing, and information retrieval to data science infrastructure, particularly scientific workflows, provenance mining, software discovery, knowledge graphs, cloud computing, immune AI, and applications in earth science and healthcare. Education: Ph.D. in Computer Science, University of Illinois at Chicago M.S. in Computer Science, Nanjing University B.S. in Computer Science, Nanjing University Dr. Zhang's work emphasizes data science infrastructure and machine learning for scientific workflows and knowledge graphs. Her recent publications highlight deep learning , graph neural networks , and optimization algorithms in cloud computing, cybersecurity, and environmental applications. Key trends include spatiotemporal modeling , hybrid neural architectures , and AI-driven service ecosystems . Scientific Awards: Best Paper Awards IEEE SCC (2011, 2017) Best Student Paper Awards IEEE ICWS (2014, 2018), IEEE ICCC (2018) Distinguished Paper Award ICSOC (2023) First Outstanding Service Award IEEE Technical Committee on Services Computing (2016) She has secured over $5 million in federal grants (as PI) and $11 million as PI/Co-PI from NSF, NASA, NIH, UTSW, Ericsson, SAP, and Google. Her lab (Caruth Hall 308) actively recruits research assistants. She previously served as a faculty member at Carnegie Mellon University, Northern Illinois University, and Nanjing University, and worked in industry as a software architect.

• Open and Distance Learning
• Educational Technology
• Distance Education Design
• +۳ مورد دیگر

Can Güler is an Assistant Professor in the Department of Lifelong Learning and Adult Education at the Faculty of Education, Anadolu University, Turkey. Previously, from 2002 to 2023, he served as a Lecturer in the Department of Distance Education at the Faculty of Open Education, Anadolu University. His academic career spans over two decades with continuous contributions to open and distance education systems. His educational background includes: Bachelor's degree in Computer and Instructional Technologies Education, Anadolu University (2002) Master's degree in Distance Education, Institute of Social Sciences, Anadolu University (2007) Ph.D. in Distance Education, Institute of Social Sciences, Anadolu University (2022) Güler's research centers on open and distance learning methodologies, educational technology integration, and instructional material development. He specializes in video-based learning systems, interactive media design, and gamification strategies for enhancing learner engagement. His work addresses practical challenges in digital content creation and accessibility for diverse learner demographics, particularly adult populations. Analysis of his publication trajectory reveals consistent innovation in multimedia applications for distance education, with recent emphasis on generative AI awareness among educators and interactive video transformation techniques. His research frequently employs design-based methodologies and institutional case studies from Anadolu University's open education infrastructure. Scientific Awards: None mentioned in available sources. Advising and Grants: No information provided regarding student supervision or research funding in current documentation.

Muhammad Asaduzzamanمشاهده پروفایل

استادیار

• Software Engineering
• Mining Software Repositories
• Empirical Software Engineering
• +۲ مورد دیگر

Muhammad Asaduzzaman is an Assistant Professor in the School of Computer Science within the Faculty of Science at the University of Windsor. His research focuses on software engineering, particularly software maintenance, mining software repositories, and recommendation systems for developers. Research interests span empirical studies of software artifacts, API usage analysis, and improving developer productivity through tools like COSTER for API element identification. Recent work examines dependency management in Maven ecosystems and AI-assisted code completion. Publications show consistent focus on analyzing developer activities through platforms like Stack Overflow and GitHub. Current investigations include LLM applications for code synthesis and technical debt impact analysis.

Felipe Thomazمشاهده پروفایل

دانشیار

• Marketing Strategy
• Digital Marketing
• AI Ethics in Business
• +۶ مورد دیگر

Felipe Thomaz is an Associate Professor of Marketing at Saïd Business School, University of Oxford, and Deputy Director of the Oxford Future of Marketing Initiative. He holds a PhD in Marketing from the University of Pittsburgh and previously taught at the University of South Carolina. His research focuses on marketing strategy, AI ethics, illicit markets, and ESG integration, with notable contributions to frameworks like Ad Net Zero for net-zero advertising emissions. He collaborates with UN agencies, NGOs, and tech companies to address global sustainability goals and wildlife trafficking networks. Education: PhD in Marketing (University of Pittsburgh), MSc in Marketing & Finance (University of Pittsburgh), BSc in Animal Sciences (University of Florida). Research interests include digital marketing channels, brand performance via social networks, AI-driven marketing strategies, and conservation science linked to wildlife trade. His work bridges academia and industry, resulting in spinouts and IP transfers from Saïd Business School. Key projects include: Ad Net Zero: Global standard for reducing advertising emissions UN collaboration on wildlife trafficking through dark web analysis UNESCO partnerships on eliminating stereotypes in advertising His interdisciplinary approach spans marketing, mathematics, and conservation science, with publications in top journals like Journal of Marketing and Conservation Science and Practice .

Ngoc Thanh Nguyenمشاهده پروفایل

استاد

• Computational Collective Intelligence
• Knowledge Integration
• Data Mining
• +۶ مورد دیگر

Ngoc Thanh Nguyen is a Full Professor at Wroclaw University of Science and Technology where he serves as Head of the Department of Applied Informatics. He holds the prestigious title of Professor granted by the President of Poland and has been recognized as a Distinguished Scientist of ACM since 2009. He serves as Editor-in-Chief of both the Journal of Information and Telecommunication (JIT) and the Vietnam Journal of Computer Science (VJCS), and chairs the IEEE SMC Technical Committee on Computational Collective Intelligence. His research spans computational collective intelligence, knowledge integration, data mining, social media analysis, and sentiment analysis. Professor Nguyen has pioneered significant methodologies in spatial data clustering within network space, inter-sequence pattern mining, and graph neural network applications. His work bridges theoretical computer science with practical applications in intelligent information systems, demonstrating particular expertise in handling complex spatial and sequential data structures. His research has evolved from foundational pattern mining techniques to sophisticated neural network approaches for geospatial and social data analysis. The analysis of his recent publications reveals a strong focus on spatial data analysis in network environments, with significant contributions to clustering algorithms, graph neural networks, and pattern mining. His work consistently addresses efficiency challenges in data processing while expanding into emerging areas like Vietnamese language processing and topological data analysis. The research demonstrates a clear trajectory from traditional data mining techniques toward more sophisticated AI-driven approaches that incorporate spatial relationships and network topologies. Distinguished Scientist of ACM (2009) ACM Distinguished Speaker (2009-2013) IEEE Distinguished Visitor (2009-2013) Title of Professor granted by the President of Poland Professor Nguyen has supervised over 20 PhD students to completion and currently mentors several ongoing doctoral candidates. His academic leadership extends to founding two major conference series: the Asian Conference on Intelligent Information and Database Systems (ACIIDS) and the International Conference on Computational Collective Intelligence (ICCCI), which have become significant venues in their respective fields. His collaborative network spans multiple institutions, particularly with Yeungnam University as evidenced by several co-supervised PhD projects. As founder and chair of the IEEE SMC Technical Committee on Computational Collective Intelligence, he leads an international community of researchers advancing this specialized field. His departmental leadership at Wroclaw University of Science and Technology positions him at the center of applied informatics research and education in Poland, with particular emphasis on computational intelligence applications.

Christos Makrisمشاهده پروفایل

دانشیار

• Data Structures
• Information Retrieval
• Data Mining
• +۵ مورد دیگر

Christos Makris is an Associate Professor in the Department of Computer Engineering and Informatics at the University of Patras, Greece. His academic career spans over two decades with significant contributions to computer science, particularly in data structures, algorithms, and information systems. He maintains active research collaborations and supervises graduate students in his areas of expertise. Dr. Makris's research spans several key areas in computer science with a strong focus on efficient data organization and processing. His work encompasses Data Structures , Information Retrieval , Data Mining , String Management and Processing Algorithms , Computational Geometry , Internet Technologies , Bioinformatics , and Multimedia Databases . His interdisciplinary approach bridges theoretical computer science with practical applications across various domains including web technologies, bioinformatics, and emergency response systems. Analysis of Dr. Makris's publication record reveals a consistent research trajectory focused on efficient algorithms for information management. His work demonstrates evolution from foundational data structure research in the 1990s to more applied work in web technologies, social media analysis, and machine learning applications in recent years. A notable pattern is his ability to adapt core algorithmic techniques to emerging application domains while maintaining theoretical rigor. Dr. Makris maintains an impressive scholarly record with over 3,000 citations, an h-index of 29, and an i10-index of 71 according to Google Scholar metrics. These indicators reflect the significant impact of his research within the computer science community. As an active faculty member, Dr. Makris maintains regular office hours on Tuesdays from 18:00-20:00 and Thursdays from 12:00-14:00. He is accessible via email at makri@ceid.upatras.gr or makri@upatras.gr for academic inquiries and student supervision.

Xin Pengمشاهده پروفایل

استاد

• Software Analytics
• Intelligent Software Development
• Microservice
• +۴ مورد دیگر

Xin Peng is a Professor and Deputy Dean at the School of Computer Science, Fudan University, China. He leads the CodeWisdom research team focusing on intelligent software engineering techniques for development, maintenance, and operation of software systems. His educational background includes a PhD in Computer Science (2001-2006) and Bachelor's degree in Computer Science (1997-2001), both from Fudan University. He progressed through the academic ranks from Assistant Professor (2006-2010) to Associate Professor (2010-2015) and finally to Professor (2015-present). Professor Peng's research interests span Software Analytics, Intelligent Software Development, Microservice systems, and AIOps. His work leverages AI technologies including deep learning and knowledge graphs to develop intelligent software engineering techniques. A significant portion of his recent work focuses on applying Large Language Models to various software engineering tasks, including vulnerability detection, API usage analysis, and test automation. His publication record shows a clear trend toward increasingly sophisticated applications of AI in software engineering, with recent work heavily featuring LLMs for tasks ranging from vulnerability patch porting to resource leak detection. The research spans multiple domains including microservice systems, automotive software, and Web of Things security. Best Paper Award of ICSM 2011 ACM SIGSOFT Distinguished Paper Award of ASE 2018 and 2021 IEEE TCSE Distinguished Paper Award of ICSME 2018, 2019, and 2020 IEEE Transactions on Software Engineering Best Paper award for 2018 Professor Peng serves in numerous leadership roles including Deputy Director of CCF Technical Committee on Software Engineering, Co-Editor-in-Chief of Journal of Software: Evolution and Process, and Associate Editor for ACM Transactions on Software Engineering and Methodology. He has been actively involved in program committees for major software engineering conferences including ICSE, ASE, ESEC/FSE, and ICSME. He leads the CodeWisdom research team at Fudan University, which has developed several benchmark systems including TrainTicket for microservice research. The team's work bridges academic research with industrial applications, particularly in microservice systems analysis and intelligent software development tools.

• Game Theory
• Regulatory Economics
• Health Economics
• +۳ مورد دیگر

Dr Jonathan Cave serves as Associate Professor (Reader) in the Department of Economics at the University of Warwick, with additional affiliation at The Alan Turing Institute. His career spans applied game theory, regulatory economics, and health policy, informed by extensive government advisory roles including the UK Regulatory Policy Committee (2015-2024). His educational background includes: BSc in Chemistry & Economics from Yale University (1969-1973) MA in Economics from University of Cambridge (1974-1975) PhD in Economics from Stanford University (1975-1979) Cave's research integrates economic theory with real-world policy challenges, particularly in digital health systems and AI governance. Recent work analyzes patient experience data through NLP techniques, remote healthcare delivery in low-resource settings, and transatlantic ICT policy frameworks. His scholarship bridges theoretical game theory with practical applications in health economics, emphasizing ethical data usage during crises like the COVID-19 pandemic. He maintains strong industry connections through GNKS Consulting, applying academic insights to regulatory practice. Trends in his 2020-2024 publications reveal three dominant strands: 1) Methodological innovations in health data analysis using corpus linguistics; 2) Implementation science for telemedicine in African healthcare systems; 3) Policy frameworks for EU-US digital collaboration. These intersect with Sustainable Development Goals 3 (Health), 10 (Inequality), 11 (Cities), and 13 (Climate), reflecting his commitment to socially impactful research. His grant portfolio demonstrates sustained funding success, including: UKRI's GCRF_NF151 REaCH project (2020-2022) on remote consulting safety in Africa MRC-funded mobile health initiative (2019-2020) for marginalized African communities ESRC grant on livestock disease management (2007-2010) examining epidemiology-economics-politics interfaces Cave collaborates extensively with Professor Frances Griffiths (Warwick) and African researchers on digital health projects, while maintaining advisory capacity for government bodies. His current work focuses on scaling remote consulting solutions validated in the REaCH trials across LMIC urban-rural divides.

• Natural Language Processing
• Text Simplification
• Emoji Analysis
• +۹ مورد دیگر

Dr Matthew Shardlow is a Reader in the Department of Computing and Mathematics at Manchester Metropolitan University . His career spans teaching and research, with a focus on Natural Language Processing (NLP) and its applications in Text Mining across disciplines like Neuroscience , Chemistry , Finance , and Journalism . He leads the Natural Language Processing Lab , which explores the use of Large Language Models (LLMs) in text-based applications. Key research areas include: Text Simplification : Developing methods to make complex texts accessible. Emoji Analysis : Understanding emoji usage in language and machine interpretation. Text Mining : Applying NLP to diverse fields like healthcare and education. Matthew’s recent publications highlight advancements in LLMs , lexical simplification , biomedical text processing , and AI ethics . He actively mentors BSc , Master’s , and PhD students aligned with his interests and maintains collaborations through the OpenMinTeD project and National Centre for Text Mining . Contact: m.shardlow@mmu.ac.uk .

Filippo Menczerمشاهده پروفایل

استاد

• Web Science
• Social Media
• Social Networks
• +۵ مورد دیگر

Filippo Menczer is a Professor of Informatics and Computer Science and Director of the Center for Complex Networks and Systems Research at Indiana University School of Informatics and Computing. He maintains courtesy appointments in Cognitive Science and Physics, and is affiliated with the Center for Data and Search Informatics and the Biocomplexity Institute. Additionally, he holds a Fellowship at the ISI Foundation in Torino, Italy. His research spans computational analysis of digital ecosystems with emphasis on: Web Science: structural and behavioral analysis of internet-scale systems Social Media Dynamics: information diffusion, meme competition, and attention economy modeling Complex Networks: traffic pattern analysis, popularity dynamics, and social link prediction Publications from 2009-2012 reveal consistent focus on social network analytics and information diffusion mechanisms. Key trends include modeling attention-limited meme competition, bursty popularity patterns in social media, and social link prediction through metadata analysis. His work integrates network science, computational social science, and data mining to decode online behavior. His scientific recognition includes: Fellow of ISI Foundation (2013) He leads the NaN research group within the Center for Complex Networks and Systems Research, focusing on interdisciplinary approaches to complex information networks and social media analytics.

Paul Brindleyمشاهده پروفایل

مدرس ارشد

• Digital Representations of Landscape
• GIS Mapping
• Health Inequality
• +۵ مورد دیگر

Dr. Paul Brindley is a Senior Lecturer at the School of Architecture and Landscape , University of Sheffield, specializing in geospatial analysis of human-landscape interactions. His work combines GIS, statistics, and digital data to address urban greenspace equity, rural-urban classification, and health-environment linkages. Senior Lecturer in GIS and Spatial Analysis Key researcher in the NERC-funded Improving Wellbeing through Urban Nature (IWUN) project Co-author of official Rural-Urban Classification for England and Wales Research focuses on: Urban Greenspace Accessibility using GPS and social media data Health Inequality Mapping through epidemiological GIS studies Vague Geographic Objects modeling of subjective spatial boundaries Computational Neighborhood Mapping via web data mining Project leadership includes: 2016-2019: IWUN Work Package 1 (Sheffield health-green space analysis) 2013-2014: Rural-Urban Classification updates for 2011 Census 2015: Real-Time Bus Data Mapping system development Teaching modules: LSC119: The Changing Landscape LSC336: Landscape Planning Toolkits LSC5020: Rural Landscape Planning GIS Workshops Professional contributions: Co-developer of Crime Map Analyst toolkit for non-GIS experts Joint Director of Learning and Teaching Links Member of Creative Spatial Practices initiative

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.