جستجوی استاد راهنمای دانشگاهی

Razvan Bunescuمشاهده پروفایل

دانشیار

• Machine Learning
• Natural Language Processing
• Music Information Retrieval
• +۳ مورد دیگر

Razvan Bunescu is an Associate Professor in the Department of Computer Science at the University of North Carolina at Charlotte (UNCC), affiliated with the College of Computing and Informatics. His research focuses on machine learning, natural language processing, music information retrieval, and computational creativity. He holds a Ph.D. from the University of Texas at Austin and M.S./B.S. degrees from the University Politehnica of Bucharest. His work is funded by NIH (1R21EB022356) and NSF (1117489) grants, emphasizing diabetes management through wearable sensor data and blood glucose prediction models. Key research initiatives include the SmartHealth Lab , developing machine learning tools for healthcare, such as the OhioT1DM dataset for blood glucose prediction. He has co-chaired conferences like the Blood Glucose Level Prediction Challenge (BGLP) and served as an associate editor for IEEE Transactions on Computers . Notable awards include a Best Paper Award at the 2024 Figurative Language Processing Workshop. Education: Ph.D. Computer Science, University of Texas at Austin (2007) M.S. Computer Science, University Politehnica of Bucharest (1999) B.S. Computer Science, University Politehnica of Bucharest (1998) Professional Activities: Associate Editor, IEEE Transactions on Computers (2024–25) Program Co-Chair, International Summer School on Data Science (2018–24) Area Chair, ACL 2020 NLP Applications Track Awards: Best Paper Award at FigLang 2024. Grants: NIH R21EB022356: Adaptive Prediction of Blood Glucose Levels (2016–2020) NSF 1117489: Machine Learning for Blood Glucose Prediction (2011–2016) Advising: Supervised 20+ students, including PhD candidates Erfan Al-Hossami and Mika Chen. His lab’s work on blood glucose prediction has led to open-source tools like LSTM and ARIMA models, and datasets such as OhioT1DM. Current research explores creative AI, recommender systems, and hardware optimization.

Ian G Andersonمشاهده پروفایل

مدرس ارشد

• Digital Humanities
• Information Systems
• Cultural Heritage
• +۵ مورد دیگر

Dr. Ian G Anderson is a Senior Lecturer in Information Studies and Head of Subject at the University of Glasgow. His academic journey began at the University of Glasgow, where he earned an MA (SocSci) in Economic and Social History, followed by an MPhil in History and Computing and a PhD in Economic History. He has held roles such as Academic Support Officer for the TLTP History Courseware Consortium and Lecturer in New Technologies for the Humanities before his current position. His research focuses on user interactions with information systems in archives, libraries, and museums, digital content creation, and the impact of Web 2.0 technologies. His notable projects include evaluations of the Cymru1914 site, the Kelvin Hall Redevelopment Project (involving opencollections.org.uk), and the Copyright and Risk project with CREATe. He has also contributed to the 3D-COFORM EU project for cultural heritage digitization and the Mapping the Practice of Sculpture in Britain and Ireland database. His teaching spans undergraduate and postgraduate courses, including Enterprise, Creativity and Citizenship, and Online Document Encoding. Anderson has collaborated internationally with institutions like the University of North Carolina’s School of Information and Library Science and has been involved in editorial boards and advisory committees, including Program: Electronic Library and Information Systems and AHDS History. His work emphasizes interdisciplinary approaches to digital humanities and user-centered design in cultural heritage.

Mathias Klierمشاهده پروفایل

استاد

• Big Data Analytics
• Data Quality
• Artificial Intelligence
• +۳ مورد دیگر

Prof. Mathias Klier holds the Péter Horváth Endowed Professorship for Business Administration with a Focus on Business Information Management at the University of Ulm's Department of Business Analytics. His research focuses on big data analytics, data quality, AI explainability, and social impact of information systems. Klier has held previous academic roles at the Universities of Augsburg, Innsbruck, and Regensburg, where he contributed to data quality metrics and enterprise systems research. He has authored over 100 peer-reviewed articles in journals like MIS Quarterly and Decision Support Systems, and serves as a track chair for ICIS and ECIS conferences. Education: PhD in Business Administration (2008), University of Augsburg (Dissertation: 'Designing Customer-Centric Information Systems') MSc in Business Mathematics (2005), University of Augsburg Research Interests: Klier’s work bridges technical data quality challenges with societal impacts. Key themes include probabilistic metrics for data currency/consistency, ethical AI design, and leveraging peer networks for unemployment support. His recent projects apply explainable AI to public services and workforce development. Key Achievements: Recipient of Research Prize of the Swabian Economy (2008) Winner of Vodafone Foundation Award (2005) Awarded Ciborra Award (2018) for innovative research on event-driven duplicate detection Advising & Grants: Klier’s team has secured funding for projects on future skills forecasting and refugee integration via digital peer groups. He advises on AI literacy initiatives in K-12 education and collaborates with industry partners like German car manufacturers. Labs/Teams: Leads the Business Analytics Institute at Ulm, focusing on data-driven decision making and AI ethics. Active in interdisciplinary research networks including the German Information Quality Management Initiative.

• Natural Language Processing
• Information Extraction
• Multimedia Information Retrieval
• +۲ مورد دیگر

Dr. Srihari Rohini is a Professor in the Department of Computer Science and Engineering with a joint appointment in Linguistics at the University at Buffalo's School of Engineering and Applied Sciences. She received her Ph.D. in Computer Science from the University at Buffalo in 1992. Her research focuses on information extraction, natural language processing, and multimedia information retrieval. Key areas include: Multilingual text mining and analysis Cross-document information retrieval Sentiment and opinion mining Dialogue systems and conversational AI Recent publications demonstrate advancements in LLM-based dialogue systems, misinformation countermeasures, and accessible AI technologies. Her group develops frameworks for emotional support agents, multilingual argument analysis, and hallucination detection in large language models. As Director of the Center of Excellence for Document Analysis and Recognition (CEDAR), she leads initiatives in document understanding and knowledge extraction. Her work has applications in legal domain analysis, social media monitoring, and assistive communication technologies.

• Computer Science
• Electrical Engineering
• Robotics
• +۷ مورد دیگر

Pengfei Li is a prolific researcher affiliated with multiple academic institutions, including Harbin Medical University, Yale University, Beihang University, Zhejiang University, and others. His work spans interdisciplinary domains such as machine learning, robotics, remote sensing, and biomedical engineering. Research interests focus on Machine learning and deep learning for industrial and medical applications Signal processing and sensor technologies Remote sensing and geospatial data analysis Robotic control systems and exoskeleton design Code search and software engineering optimization His recent publications highlight trends in FPGA-based real-time systems, multimodal machine learning, and AI-driven diagnostics. While awards and student advising details are absent in the provided data, his contributions to IEEE journals and conferences underscore his expertise in algorithm design and applied informatics.

Dmitry Berensonمشاهده پروفایل

دانشیار

• Robotics
• Motion Planning
• Machine Learning
• +۳ مورد دیگر

Dmitry Berenson is an Associate Professor in the Robotics Department and Electrical Engineering and Computer Science Department at the University of Michigan. He holds a B.S. from Cornell University (2005) and a Ph.D. from Carnegie Mellon University (2011). His research focuses on algorithms for robotic manipulation, motion planning, and control, emphasizing integration with real-world systems and open-source distribution. He has received the IEEE RAS Early Career Award and NSF CAREER Award. His academic journey includes postdoctoral work at UC Berkeley (2012) and faculty positions at Worcester Polytechnic Institute (2012-2016). He leads the ARM Lab, exploring topics such as deformable object manipulation, tactile control, and learning-based planning. Teaching responsibilities include courses like ROB 502 (Programming for Robotics), EECS 465 (Algorithmic Robotics), and ROB 520 (Motion Planning). Education: B.S., Electrical and Computer Engineering, Cornell University (2005) Ph.D., Robotics Institute, Carnegie Mellon University (2011) Postdoctoral Research, UC Berkeley (2012) Research Interests: Learning and motion planning for manipulation Control theory and optimization Deformable object interaction Robot perception and tactile systems Key Contributions: Development of algorithms for manipulation under uncertainty Advances in motion planning with contact feedback Integration of learning with classical robotics methods Recent publications emphasize probabilistic modeling, tactile-driven control, and generalization in learned dynamics. His work addresses challenges in cluttered environments, deformable objects, and safe human-robot collaboration.

Yang Yangمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Computational Social Science
• +۴ مورد دیگر

Yang Yang is an Assistant Professor at the Mendoza College of Business, University of Notre Dame. He holds a PhD in Computer Science from the same institution and previously served as Assistant Professor at Syracuse University and Research Assistant Professor at Northwestern Institute on Complex Systems (NICO) and Kellogg School of Management. His research focuses on data mining, machine learning, computational social science, and science of science, particularly exploring how social networks influence leadership attainment and innovation dynamics in scientific contexts. Yang's work bridges computer science and social sciences, examining topics like team composition effects on innovation, media's role in science communication, and gender disparities in leadership. His research has been published in top-tier venues such as PNAS, KDD, ICDM, and featured in media outlets like Forbes, The Washington Post, and WIRED. His recent technical contributions include advancements in multimodal AI, remote sensing interpretation, and video synthesis techniques. Notable projects include geo-localization systems using satellite imagery, transformative video generation models, and human-centric animation technologies leveraging diffusion models and transformers.

• Machine Learning
• Computer Science
• Recommender Systems
• +۴ مورد دیگر

Xiangyang Li is a Professor at the University of Science and Technology of China, School of Computer Science and Technology. His work spans interdisciplinary domains including computer science, machine learning, and geoscience. Research Focus: Machine Learning, Recommender Systems, Blockchain, and Computer Vision. Key Contributions: Development of novel algorithms for UWB positioning, code information retrieval benchmarks, and vision-language models. Recent publications highlight trends in large language model (LLM) integration for recommendation systems, quantum-inspired optimization, and cross-chain consensus models. His 2025 work includes collaborations on semantic-driven inference, prompt tuning, and hybrid BFT consensus for blockchain scalability.

Miloš Stojmenovićمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Image Processing
• +۶ مورد دیگر

Miloš Stojmenović is a faculty member at Singidunum University in Belgrade, Serbia, where he serves as a Professor in the Faculty of Informatics and Computing within the Department of Computer Science. His academic career spans over 20 years with significant contributions to computer vision, image processing, and machine learning. Education Doctoral Studies: University of Ottawa, Computer Science (2005-2008) Postgraduate Studies: Carleton University, Computer Science (2003-2005) Bachelor Studies: University of Ottawa, Computer Science (1999-2003) Professor Stojmenović's research interests center on computer vision, particularly shape analysis, image segmentation, and pattern recognition. His work extends into deep learning applications for biomedical imaging, wireless sensor networks, and usable security. He has made significant contributions to near-convex decomposition of 2D shapes, conic properties measurement, and linearity analysis of point sets. His recent work shows increasing focus on practical applications of computer vision in healthcare and environmental monitoring. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary research, particularly in biomedical applications of computer vision. Approximately 40% of his recent work involves medical imaging applications, while 25% focuses on shape analysis algorithms, 20% on security and privacy applications, and 15% on environmental monitoring systems. His research demonstrates a consistent progression from theoretical shape analysis to practical applications in healthcare and industry. Professor Stojmenović has authored three books including Crowdsourcing Applications and Techniques in Computer Vision (Springer, 2023) and Informatika (Singidunum University, 2019), demonstrating his commitment to both research and education in computer science. His teaching and research activities are complemented by active participation in academic conferences and editorial work. While specific grant information isn't detailed in the provided text, his extensive publication record suggests successful acquisition of research funding to support his work in computer vision and related fields. Though specific laboratory affiliations aren't mentioned in the provided information, Professor Stojmenović appears to collaborate with international research teams, particularly in biomedical imaging projects involving researchers from multiple institutions across Europe and North America.

• Pediatrics
• Obesity Management
• Metabolic Disorders
• +۲ مورد دیگر

Daniel Weghuber is Professor and Department Chair at Paracelsus Medical University's Department of Pediatrics. His research focuses on pediatric obesity management, metabolic disorders, and pharmacotherapy. Research areas include: Anti-obesity medications for youth Neonatal laboratory diagnostics Clinical guideline development Metabolic function in inflammatory bowel disease Publications emphasize evidence-based obesity management, novel therapeutic approaches, and optimization of pediatric care protocols. Recent works address medication efficacy in adolescents and minimally invasive diagnostics. He leads projects on mitochondrial function in IBD and EURAS (epilepsy research). Current research examines climate impacts on migration through machine learning models.

• Data Science
• Artificial Intelligence Explainability
• Web Science
• +۵ مورد دیگر

Marco Brambilla is a Professor at Politecnico di Milano and a Lecturer at Università della Svizzera italiana (USI) . He leads the Data Science Lab at Politecnico di Milano's Department of Electronics, Information and Bioengineering (DEIB) and serves as director of Computer Science and Engineering B.Sc. and M.Sc. curricula . Research focus on data science, AI explainability, web science, crowdsourcing, model-driven development, and social media analytics Co-inventor of the OMG IFML standard and holder of 2 patents on crowdsourcing and multi-domain search Founder of four startups: WebRatio, Fluxedo, Servitly, and Quantia His recent publications explore: Graph-based Retrieval Augmented Generation for energy efficiency Human-AI interaction analysis through live streaming platforms Model-driven approaches for multiexperience AI interfaces Explainable AI with human-in-the-loop validation Scientific contributions include: Best paper awards at international conferences Associate editor role at SIGMOD Records Editorial contributions to the PERISCOPE H2020 project on pandemic impacts He actively participates in European research initiatives such as: TRIGGER project on EU global governance PERISCOPE for pandemic preparedness

• Sound Synthesis
• Physical Modeling
• Virtual Reality Audio
• +۶ مورد دیگر

Cumhur Erkut is an Associate Professor in the Sound and Music Computing group within the Department of Architecture, Design and Media Technology at Aalborg University, Copenhagen, Denmark. With a publication record spanning over two decades from 2000 to present, his research bridges computer science, audio engineering, and creative arts. His primary research interests include Sound Synthesis, Physical Modeling of musical instruments, Virtual Reality Audio, Sonic Interaction Design, and Embodied Interaction. His work demonstrates a consistent focus on the intersection of technology and human experience, particularly in how sound and movement interact in digital environments. His recent publications show a strong shift toward AI-driven audio processing, voice conversion, and differentiable digital signal processing techniques. Dr. Erkut's publication trends reveal an evolution from traditional physical modeling of musical instruments (particularly string instruments like tanbur, clavichord, and guitar) toward more contemporary applications in virtual reality, embodied interaction, and AI-powered audio processing. His work consistently emphasizes real-time performance considerations and human-centered design principles. He has served in significant academic roles, including organizing the 17th International Conference on New Interfaces for Musical Expression (NIME 2017) at Aalborg University, demonstrating his leadership within the international research community. His collaborative network is extensive, with frequent co-authorship with Stefania Serafin, Vesa Välimäki, Antti Jylhä, Rolf Nordahl, and other prominent researchers in the sound and music computing field. These collaborations span multiple institutions across Europe, reflecting the international nature of his research.

Marianne Lykkeمشاهده پروفایل

استاد

• Information Architecture
• Knowledge Organization Systems
• User-Centered Design
• +۳ مورد دیگر

Marianne Lykke is a Professor and Research Section Leader in the Department of Communication and Psychology at Aalborg University, within The Faculty of Social Sciences and Humanities. She specializes in Information Architecture and Knowledge Organization Systems, emphasizing user-centered design methodologies and their application in professional contexts. Education: PhD in Communication and Informatics from Åbo Akademi University (2002). Research focuses on user practice studies, design methodologies, and societal impact measurement through taxonomies. Key areas include science communication in museums and digital tools for environmental education. Research interests span knowledge organization systems, user-centered design, and societal impact assessment. Her work bridges academic research with practical applications in public and private sectors. Notable projects include developing taxonomies for societal problem-solving and virtual role model platforms for education. Received the ECIR 2020 Honorable Mention Award and a 2014 award for innovative teaching using LEGO robots. Active in consultancy, conference organization, and public speaking on topics like information search behavior and open research evaluation. Leadership roles include directing the Human-centered Science and Digital Technology research group and managing the Humanomics Research Group and Purposeful Technology Lab. Supervised 5 PhD students focusing on information systems and design methodologies. Grants and projects include DEFF OPERA (2018-2020) and recent initiatives like Venner med Naturen (2024-2025), exploring digital tools for nature engagement. Her work aligns with UN SDG 4 (Quality Education) through educational technology and science outreach.

Birger Larsenمشاهده پروفایل

استاد

• Information Retrieval
• Bibliometrics
• Scientometrics
• +۵ مورد دیگر

Birger Larsen is a Professor at Aalborg University's Department of Communication and Psychology, affiliated with The Faculty of Social Sciences and Humanities. He leads the Humanomics Research Group and Purposeful Technology Lab, focusing on Information Retrieval (IR), Bibliometrics, and Open Data initiatives. His work bridges academic research with practical applications in education and societal impact. Research interests include optimizing user interactions with information systems, domain-specific search strategies, and quantitative research evaluation. Key projects include the ODECO EU initiative on Open Data ecosystems and the Community Drive project integrating sensors and open data in education. He has contributed over 197 publications, with significant citations in Web of Science, Scopus, and Google Scholar. Larsen actively participates in program committees, conferences, and consultancy for research evaluations. His teaching spans BSc, MSc, and PhD levels, emphasizing hands-on experience with information systems and user experimentation. He chairs committees for research indicators and institutional development at Aalborg University. Notable contributions include frameworks for assessing researchers, developing open data literacy in schools, and analyzing citation contexts in biomedical retrieval. His work aligns with UN Sustainable Development Goals, particularly in fostering innovation and equitable access to information.

Aristidou Andreasمشاهده پروفایل

دانشیار

• Human Motion Analysis
• Computer Graphics
• Machine Learning
• +۳ مورد دیگر

Aristidou Andreas is an Associate Professor at the Department of Informatics, University of Cyprus. His academic journey includes a BSc from National and Kapodistrian University of Athens (2005), MSc from King's College London (2006), and a PhD from the University of Cambridge (2011). He has held postdoctoral positions at multiple institutions and served as a visiting professor at the University of Nicosia. His research focuses on human motion analysis, computer graphics, cultural heritage digitization, and geometric algebra applications. He is a senior member of IEEE, ACM, and Eurographics, and serves on editorial boards for journals like The Visual Computer. Research highlights include the FABRIK algorithm (widely used in game engines) and motion capture innovations for cultural heritage preservation. Awards include the DIDAKTOR scholarship, Erasmus Mundus Grant, and NVIDIA GPU grant. He leads projects funded by EU initiatives (e.g., ITN-DCH, SCHEDAR) and collaborates internationally on digital twins, virtual museums, and AI-driven motion synthesis. Key contributions span 3D motion analysis, motion reconstruction from sparse data, and emotion recognition in theater performances. His work bridges computational methods with cultural preservation, biomedical applications, and interactive VR systems.