جستجوی استاد راهنمای دانشگاهی

Laura Haasمشاهده پروفایل

استاد

• Data Management
• Database Systems
• Information Integration
• +۴ مورد دیگر

Laura Haas serves as the Donna M and Robert J Manning Dean at the University of Massachusetts Amherst's College of Information and Computer Sciences. Prior to this, she spent 36 years at IBM, where she rose to the level of IBM Fellow and held leadership roles such as Director of the Accelerated Discovery Lab and Director of Computer Science at IBM's Almaden Research Center. Her research focuses on data integration, database systems, and accelerating discovery through data-driven approaches. She pioneered systems like Starburst, Garlic, and Clio, and founded IBM's Accelerated Discovery Lab to enhance data analysis tools for domain experts. Haas holds a PhD from the University of Texas at Austin (1981) and an AB from Harvard University (1978). She has held sabbaticals at the University of Wisconsin-Madison and ETH Zurich. Her contributions earned her awards like the ACM SIGMOD Codd Innovation Award and Anita Borg Institute Technical Leadership Award. She is a Fellow of the ACM and member of the National Academy of Engineering. Her research spans federated data systems, schema mapping, and big data challenges. She advocates for data science education reforms and chairs key initiatives like the National Academies Computer Science and Telecommunications Board. The DREAM Lab under her leadership explores data systems for exploration and analytics. Education: PhD, University of Texas at Austin, 1981 AB, Harvard University, 1978 Awards: ACM SIGMOD Codd Innovation Award Anita Borg Institute Technical Leadership Award IBM Fellow Member, National Academy of Engineering Labs/Teams: DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) Her work bridges theoretical foundations and practical systems, emphasizing tools that empower domain experts over technical complexities. Current initiatives focus on democratizing data science through education and tooling advancements.

Ramesh Jainمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Multimedia Computing
• +۵ مورد دیگر

Ramesh Jain is a distinguished Professor and Founding Director of the UCI Institute for Future Health at the University of California, Irvine. He holds leadership roles across multiple institutions, including founding directorships of the Artificial Intelligence Laboratory at the University of Michigan (1987), Visual Computing Lab at UC San Diego (1995), and editorships of IEEE MultiMedia and Machine Vision and Applications journal. His research focuses on AI, computer vision, and healthcare innovation, particularly in data-driven health navigation systems and experiential computing. Education: Ph.D. in Electronics & Electrical Communications Engineering from IIT Kharagpur (1975). Research Interests: Building computing frameworks for personalized healthcare AI applications in nutrition and disease prevention Experiential computing and multimedia systems Multimodal data fusion for health monitoring Scientific Awards: ACM Distinguished Service Award (2023), ACM SIGMM Technical Achievement Award (2010), and Fellowships from ACM, IEEE, AAAS, IAPR, AAAI, and SPIE. Professional Contributions: Founded SIGMultiMedia, pioneered multimedia computing concepts like Folk Computing and Experiential Computing, and advised over 15 companies in early stages. Current projects include the Open Source Personal Health Navigator at Peng Cheng Lab, Shenzhen. Lab/Team: Leads the UCI Institute for Future Health, focusing on integrating AI and health informatics for preventive care.

Olivier Lartillotمشاهده پروفایل

پژوهشگر

• Computational Music Analysis
• Artificial Intelligence
• Music Information Retrieval
• +۶ مورد دیگر

Olivier Lartillot is a Researcher at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion at the University of Oslo. His work focuses on computational music and sound analysis, artificial intelligence, and music information retrieval. He developed the MIRtoolbox and MiningSuite frameworks, tools widely used in music analysis. Previously, he held roles including Academy of Finland Research Fellow (2009–2014) and postdoctoral positions at the Finnish Centre of Excellence in Interdisciplinary Music Research. Education: PhD in Music and Acoustics from IRCAM/UPMC Paris (2000–2004); Master’s in Musicology from Sorbonne University (1996–1999). Research Projects: MIRAGE (AI-based music analysis), SoundTracer (Norwegian folk music transcription), HIGH-M (music and health interaction). His research interests span computational models of music structure, cross-cultural music analysis, and applications in music therapy and digital heritage. He has authored over 80 articles (3,000+ citations) and contributed to initiatives like the Norwegian Folk Music Archive digitization. He is a member of the editorial board for Transactions of the International Society for Music Information Retrieval and an expert evaluator for EU Horizon 2020 programs. His work bridges technical innovation with musicological theory, addressing challenges in automated transcription, segmentation, and expressive performance analysis.

• human-centered ai
• psychology of technology
• machine learning
• +۵ مورد دیگر

Chao Zhang is an Assistant Professor in Human-Centered AI at the Human-Technology Interaction group, Eindhoven University of Technology. His research bridges psychology and technology through data-driven methods and psychological theories, focusing on behavior change, AI-human dynamics, and trustworthy systems. MSc in Human-Technology Interaction (TU/e) PhD in Intelligent Systems (TU/e, 2019) Postdoc at Utrecht University Research interests span Human-Centered AI , Explainable AI , and Behavioral Data Science . Key contributions include: Integrating psychological principles with machine learning Modeling longitudinal behavioral data Developing interpretable AI systems for lifestyle interventions Investigating social characteristics in AI-driven environments Current work includes the ENFIELD project on Trustworthy Green AI. Scientific contributions appear in CHI Conference , arXiv , and International Journal of Social Robotics . Recognized with a TU/e PhD Thesis Award nomination in 2021.

Dieter De Witteمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Science
• FAIR Data
• +۳ مورد دیگر

Prof. Dieter De Witte serves as a Professor at Ghent University, dedicating 50% of his time to the Internet Technology and Data Science Lab (IDLab) while simultaneously contributing 50% to the Royal Museums of Fine Arts Belgium (RMFAB) in Brussels through a FED-tWIN mandate from Belspo. At RMFAB, he spearheads the strategic overhaul of digital infrastructure toward FAIR-compliant and data-driven systems, while at Ghent University he collaborates with Prof. Steven Verstockt on applied AI projects across heritage, mental healthcare, and education domains. His academic foundation includes a Master's in Engineering Physics from Ghent University (2008) followed by doctoral research on Big Data technologies and FAIR data for life sciences. Prior to returning to academia in 2021, he gained industry experience as an AI consultant and team lead at Telenet and Ordina. De Witte's research centers on AI-driven transformation of cultural heritage through FAIR data publication , collection enrichment , and intuitive querying interfaces . His technical expertise spans multimodal algorithms, image segmentation, pose estimation, large language models (LLMs), and semantic technologies including SPARQL and IIIF. Current projects focus on human-in-the-loop AI systems that combine diverse AI building blocks for practical heritage applications. Analysis of his 15 most recent publications reveals a clear trajectory from early bioinformatics work (2007-2018) on genomic motif discovery and life sciences data infrastructure toward contemporary cultural heritage applications (2023-2024). Recent outputs demonstrate innovative fusion of pose estimation, linked data frameworks, and multimodal AI for museum contexts, highlighting increasing specialization in AI enrichment of digital collections while maintaining core expertise in FAIR data principles. His FED-tWIN grant enables critical knowledge transfer between academic research and cultural heritage institutions, supporting development of next-generation digital infrastructure at RMFAB. Current projects involve creating AI tools for intuitive collection exploration and systematic enrichment of heritage assets through advanced computational methods. De Witte operates within Ghent University's Internet Technology and Data Science Lab (IDLab), participating in interdisciplinary teams developing applied AI solutions. His work bridges technical innovation with practical implementation in cultural institutions, focusing on sustainable, interoperable systems that enhance public access to digital heritage collections through cutting-edge AI interaction paradigms.

Patrick Lambrixمشاهده پروفایل

استاد

• Semantic Web
• Databases
• Knowledge Engineering
• +۳ مورد دیگر

Patrick Lambrix is a Professor and Head of the Division for Database and Information Techniques at Linköping University's Department of Computer and Information Science (IDA). He holds a MSc in Mathematics and Computer Science from KU Leuven and a PhD from Linköping University. His expertise spans semantic web technologies, ontology engineering, and sports analytics. He leads the Database and Web Information Systems group and co-founded the Sports Analytics research group at IDA. Research interests include knowledge engineering applications in materials science and life sciences, ontology alignment, and performance metrics in sports. He coordinates the Master in Computer Science program and serves as Director of Studies. Notable contributions include award-winning systems in ontology debugging and completion, and organizing the Linköping Hockey Analytics Conference (LINHAC). Education: MSc (Mathematics & Computer Science, KU Leuven, 1988/1990), PhD (Linköping University, 1996) Leadership Roles: Head of ADIT Division, Director of Studies, WASP program participant Research Groups: Database & Web Info Systems, Semantic Web, Sports Analytics Publications focus on ontology engineering advancements and sports analytics innovations, with recent work on hockey performance metrics and ontology alignment methodologies. His teams collaborate with industry and sports organizations to apply semantic technologies in real-world scenarios.

Zoe Klemfussمشاهده پروفایل

دانشیار

• Developmental Psychology
• Children’s Memory
• Eyewitness Testimony
• +۴ مورد دیگر

Dr. Zoe Klemfuss is an Associate Professor of Psychological Science at the University of California, Irvine (UCI), specializing in developmental and legal psychology. She holds a Ph.D. from Cornell University and directs the Child Narratives Lab (childnarrativeslab.com). Her research focuses on children’s memory accuracy, narrative development, and sociocontextual influences, with applications to legal contexts such as child sexual abuse testimony. She has published in top journals like Developmental Psychology and Annual Review of Clinical Psychology , supported by NIH grants. Key research areas include: Narrative development and eyewitness abilities in children, Sociocontextual factors shaping memory and well-being, Legal implications of children’s testimony accuracy, Parent-child communication strategies in stress/health contexts. Recent publications highlight her work on pandemic-related mental health, misinformation resistance, and juror decision-making influenced by emotional testimony. Her lab emphasizes translational research bridging cognitive development and societal applications. She has advised multiple students and collaborates on grants addressing child welfare and forensic psychology.

• Bioinformatics
• Health Informatics
• Affective Computing
• +۳ مورد دیگر

Dr. Stamos Katsigiannis is an Associate Professor in the Department of Computer Science at Durham University. His research focuses on bioinformatics, health informatics, affective computing, and GPU computing. He holds a BSc, MSc, and PhD in Computer Science from the University of Athens and the Athens University of Economics and Business. Before Durham, he was a Postdoctoral Research Fellow and Lecturer at the University of the West of Scotland (2016–2020). His work spans biometric identification via EEG signals, medical image analysis, and deep learning applications in healthcare. Education: BSc (Hons.) in Informatics and Telecommunications from National and Kapodistrian University of Athens MSc in Computer Science from Athens University of Economics and Business PhD in Computer Science from National and Kapodistrian University of Athens Research interests include bioinformatics (e.g., microarray analysis), health informatics (e.g., ECG-based emotion modeling), and GPU computing for real-time applications. He has pioneered studies on EEG-based biometrics and affect recognition in human-horse interaction. Recent articles explore chest X-ray classification using deep learning , AI-generated content detection , and trajectory prediction with graph networks . His research emphasizes interdisciplinary applications of machine learning in healthcare and education. He advises six postgraduate students and collaborates on UK parliamentary AI governance reports. Notable contributions include the BED EEG dataset and the DREAMER emotion recognition database.

• Bioinformatics
• Health Informatics
• Affective Computing
• +۴ مورد دیگر

Dr. Stamos Katsigiannis is an Associate Professor in the Department of Computer Science at Durham University. His research focuses on bioinformatics, health informatics, affective computing, machine learning, and GPU computing applications. He holds a PhD in Computer Science from the National and Kapodistrian University of Athens (Greece), with prior roles including Postdoctoral Research Fellow and Lecturer at the University of the West of Scotland (2016-2020). Education: BSc (Hons) Informatics and Telecommunications, National and Kapodistrian University of Athens (Greece) MSc Computer Science, Athens University of Economics and Business (Greece) PhD Computer Science, National and Kapodistrian University of Athens (Greece) Research Interests: Bioinformatics, health informatics, affective computing (e.g., emotion recognition using EEG/ECG signals), machine learning applications in medical imaging and video quality, GPU-accelerated algorithms for biomedical data analysis, and biometric identification systems. His work bridges computational methods with healthcare, education technologies, and human-computer interaction. Advising & Students: Supervising postgraduate students in AI-driven healthcare, computer vision, and affective computing. Recent collaborations include projects on AI-generated content detection (De-Factify 4.0), chest X-ray image analysis (CLN network), and trajectory prediction using graph neural networks. Labs/Teams: Active in Durham's AI research groups, contributing to interdisciplinary projects in medical imaging, cybersecurity, and educational technology. Collaborates internationally in EU-funded initiatives and UK parliamentary AI governance consultations (AGENCY project).

• Multimedia Computing
• Computer Vision
• Computer Graphics
• +۷ مورد دیگر

Professor Zhiyong Wang is a faculty member at the School of Computer Science, University of Sydney, serving as Deputy Director of Sydney Informatics Hub and Director of the Multimedia Computing Laboratory. His research focuses on multimedia computing, including information retrieval, computer vision, AI, and applications in agriculture, healthcare, and environmental monitoring. He holds a B.Eng., M.Eng. from South China University of Technology, and a Ph.D. from The Hong Kong Polytechnic University. His work bridges theoretical advancements and real-world applications, with over 200 peer-reviewed publications. He is a member of IEEE and former President of Australia Pattern Recognition Society (APRS). Research Interests: Enabling computers to understand and create multimedia content, with emphasis on human-centric computing, remote sensing, and interdisciplinary applications. Notable areas include action recognition, medical image analysis, and environmental monitoring. Current Roles: Leads the Multimedia Computing Lab, collaborates with Sydney Institute of Agriculture and Brain and Mind Centre. Teaches COMP5216 (Mobile Computing), COMP5405 (Digital Media Computing), and COMP5425 (Multimedia Retrieval). Students: Supervises 9 Ph.D. students focusing on topics like precision agriculture, biomedical engineering, and AI-driven video analytics. Key projects include soil moisture forecasting, biomechanical risk analysis for knee osteoarthritis, and explainable machine learning.

Smaranda Muresanمشاهده پروفایل

دانشیار

• Artificial Intelligence (AI)
• Natural Language Processing (NLP)
• Computational Linguistics
• +۷ مورد دیگر

Smaranda Muresan is an Associate Professor in the Department of Computer Science at Barnard College, with affiliations at Columbia University. Her research focuses on artificial intelligence, natural language processing, and computational linguistics, emphasizing ethical AI applications, educational technology, and stylistic analysis. Academic Affiliation: Associate Professor at Barnard College Email: smuresan@barnard.edu Her work spans multiple subfields including fact-checking systems, argument mining, sentiment analysis, and figurative language processing. She explores abstract reasoning in LLMs through novel benchmarks like the New York Times Connections game and investigates AI's role in creativity, education, and social good initiatives. Recent publications highlight her contributions to global segmenting algorithms, explainable authorship attribution, and multimodal figurative language analysis. While no explicit student advisees are listed, her research intersects with interdisciplinary teams at Barnard and Columbia. She actively participates in academic workshops and conferences such as the ACL Annual Meeting and FigLang, shaping standards for computational analysis of nuanced language phenomena.

Orland Hoeberمشاهده پروفایل

استاد

• Interactive Information Retrieval
• Information Visualization
• Human-Computer Interaction
• +۳ مورد دیگر

Dr. Orland Hoeber is a Professor and Head of the Department of Computer Science at the University of Regina. He holds a Ph.D. in Computer Science from the University of Regina, and his research focuses on developing visual and interactive tools for information exploration in digital libraries and mobile search environments. His work bridges information retrieval, human-computer interaction, and data visualization to support complex search tasks. Education: Ph.D. Computer Science, University of Regina (2007) M.Sc. Computer Science, University of Saskatchewan B.Sc. Mathematics, University of Saskatchewan Dr. Hoeber's research examines how people interact with information systems, with particular emphasis on visual interfaces that support exploratory search behaviors. His lab investigates novel approaches to representing search results, understanding mobile search interruptions, and analyzing social media data for sport analytics. Recent work explores cross-device search patterns in academic contexts and interface designs that facilitate serendipitous discovery. His publications demonstrate a consistent focus on human-centered approaches to information system design, with recent work examining multimodal evaluation techniques and search behavior in digital humanities. Article keywords frequently include information retrieval, visualization, and interactive systems, reflecting his interdisciplinary approach. Dr. Hoeber actively mentors graduate students and postdoctoral fellows in his research group, supervising projects on exploratory search behavior, information visualization, and user interface evaluation. He has served as Associate Head (Graduate) and currently leads the Department of Computer Science.

Dalia Suliemanمشاهده پروفایل

دانشیار

• Search Algorithms
• Machine Learning
• Knowledge Graphs
• +۲ مورد دیگر

Dalia Sulieman is an Associate Professor in the Department of Mathematics, Computer Science, and Data Science at John Carroll University , where she joined in Fall 2022. Previously, she served as an Assistant Professor at Metropolitan State University in Saint Paul, Minnesota, for seven years. Her academic focus is on teaching computer science, curriculum development, and research in search algorithms, machine learning, and knowledge graphs applied to social network analysis and recommender systems. Ph.D. in Computer Science, Cergy-Pontoise University, France (2014) M.S. in Computer Science, Lille 1 University of Science and Technology, Lille, France (2009) B.S. in Computer Engineering, Aleppo University, Syria (2007) Her research explores the intersection of machine learning , knowledge graphs , and social network analysis , aiming to enhance recommender systems through semantic-social methodologies. She has contributed to curriculum design and educational innovation, including the publication of a SIGCSE case study on data science pedagogy. Dalia has served as Vice-Chair of the Twin Cities ACM professional chapter since 2019, fostering computing events and collaborations in the Upper Midwest. Her work bridges theoretical advancements in graph searching algorithms with practical applications in student-centric AI tools, such as virtual assistants for academic support.

Georgios Siolasمشاهده پروفایل

پژوهشگر

• machine learning
• text mining
• information retrieval
• +۶ مورد دیگر

Georgios Siolas is a Senior Researcher at the Artificial Intelligence and Learning Systems Laboratory (NTUA-ISLab), part of the School of Electrical & Computer Engineering at the National Technical University of Athens (NTUA). He holds a BSc in Electrical & Computer Engineering from NTUA (1998), an MSc in Cognitive Science from Sorbonne Université (1999), and a PhD in Computer Science (2003). His research focuses on machine learning, text mining, information retrieval, and semantic web technologies, with applications in computer vision, recommender systems, and social network analysis. He develops intelligent systems for cultural heritage management, smart tourism platforms, and sign language recognition. Key contributions include innovations in neural architecture search, deep learning for hyperspectral imagery, and plagiarism detection in imbalanced datasets. His work integrates semantic technologies with pervasive computing for smart home environments.

• Perinatal Mental Health
• Women's Healthcare
• Mental Health Services
• +۳ مورد دیگر

Emma Moran is a Lecturer in the Department of Nursing at the Faculty of Science and Health. Her research focuses on perinatal mental health, women's healthcare experiences, and qualitative research methods. She has published two peer-reviewed works: a 2025 conference poster analyzing women's experiences in Irish specialist perinatal mental health services, and a 2023 review article synthesizing qualitative evidence on women’s interactions with specialist perinatal care. Her work emphasizes patient empowerment, healthcare delivery, and evidence-based nursing practices. Collaborators include Dr. M. Noonan, Dr. M. Mohamad, and Prof. P. O’Reilly. Research outputs highlight her contribution to understanding mental health service efficacy in maternal healthcare contexts. No scientific awards are explicitly listed, though her work has garnered citations and reader engagement (e.g., 49 Mendeley readers for the 2023 paper). Advising and grants details are not provided in the text. She is affiliated with nursing and health professions research areas, with a focus on interdisciplinary topics like critical appraisal and information retrieval in healthcare contexts.