جست‌وجوی استاد و پژوهشگر

Pedro Pablo Lucas Bravoمشاهده پروفایل

پژوهشگر ارشد

• Music Technology
• Robotics
• Artificial Intelligence
• +۵ مورد دیگر

Pedro Pablo Lucas Bravo is a Doctoral Research Fellow at the University of Oslo's Department of Informatics (IFI), affiliated with the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion within the Faculty of Mathematics and Natural Sciences. His research focuses on developing interactive music systems using autonomous agents and swarm intelligence, with applications in Extended Reality (XR), Spatial Audio, and robotics. His Ph.D. project explores automatic tempo synchronization in human-machine systems through swarms of autonomous agents, aiming to create emergent musical behaviors in virtual, physical-virtual, and physical platforms. Key technologies include XR, spatial audio synthesis, motion capture, and robotic platforms. Bravo has contributed to projects like the XR Human-Swarm Interactive Music System and the MusicLab Copenhagen Dataset. His work bridges computer science, music technology, and artificial intelligence, with publications in venues such as IEEE ACSOS, NIME, and SMC. He holds a focus on interdisciplinary collaboration, leveraging swarmalator systems for self-organizing compositions and exploring cross-disciplinary applications of embodied oscillators in music performance.

Waqar Aliمشاهده پروفایل

مدرس

• Pattern Recognition
• Graph Neural Networks
• Deep Learning
• +۳ مورد دیگر

Mr. Waqar Ali is a Lecturer and Research Assistant at the ZHAW School of Engineering (Zurich University of Applied Sciences), affiliated with the Machine Perception & Cognition Group. His academic journey includes a Bachelor's (2016) and Master's (2019) in Computer Science from Comsats University. He has held teaching roles at Lahore Garrison University (2021) and Abasyn University (2019–2021). His research focuses on Pattern Recognition, Graph Neural Networks, Deep Learning, and Natural Language Processing. Awards include the Shahbaz Sharif Merit Scholarship (2017) and a Bachelor Fellowship from HEC Pakistan (2012). Notable projects include an Evidence-Based Diagnostic Assistance for Echocardiography initiative. His publications span advanced graph learning techniques, sentiment analysis models, and energy optimization systems. He has contributed to peer-reviewed journals and conferences such as the Joint IAPR Statistical Techniques in Pattern Recognition (2024). His work bridges theoretical machine learning with practical applications in healthcare diagnostics, smart grids, and Urdu language processing. Collaborative projects include roles at the University of Alicante (2024).

Aleksandar Dimov Dimovمشاهده پروفایل

دانشیار

• Software Reliability Engineering
• Neuro-Fuzzy Systems
• Machine Learning
• +۵ مورد دیگر

Aleksandar Dimov Dimov is an Associate Professor at the Department of Software Technologies, Faculty of Mathematics and Informatics (FMI), University of Sofia. He specializes in software reliability engineering, neuro-fuzzy systems, and cloud computing architectures. His research focuses on optimizing software systems through adaptive methodologies and addressing challenges in distributed computing environments. His work spans over two decades, with contributions to neuro-fuzzy optimization for manufacturing, machine learning applications in healthcare, and architectural patterns for scalable systems. Key areas include improving software reliability models, cloud storage performance, and privacy-preserving software design. He has published extensively on topics like microservices, service-oriented architectures, and educational technology. Dr. Dimov's publications reflect a strong emphasis on practical solutions for real-world challenges in software engineering and data systems. His research bridges theoretical models with applied implementations, particularly in domains requiring high precision and adaptability.

Belgin Ergenç Bostanoğluمشاهده پروفایل

دانشیار

• Database Systems
• Data Mining
• Privacy-Preserving Techniques
• +۳ مورد دیگر

Belgin Ergenç Bostanoğlu is an Associate Professor in the Computer Engineering Department at Izmir Institute of Technology (Turkey). Her research focuses on query optimization in distributed databases, association rule mining, privacy-preserving data mining, and graph-based algorithms. She leads the Dworld research laboratory and has held academic and industry roles since the 1980s. Education: B.Sc. in Computer Engineering, Middle East Technical University (1983) M.Sc. in Computer Engineering, Izmir Institute of Technology (2002) Ph.D. in Computer Engineering, Paul Sabatier University, France (2008) Research Interests: Dynamic frequent itemset mining and hiding under multiple support thresholds Subgraph mining in evolving graphs Federated query processing over linked data Privacy-preserving techniques in distributed databases Medical NLP applications (e.g., TurkMedNLI dataset) Her recent work emphasizes large-scale graph analysis, medical NLP dataset development, and adaptive join operators for federated SPARQL queries. She has contributed to over 30 peer-reviewed publications and led projects like the TÜBİTAK ARDEB 3501 platform for dynamic frequent itemset mining. Teaching: Courses include Advanced Database Management Systems, Knowledge Discovery, and Privacy-Preserving Data Mining. Labs & Projects: Manages Dworld lab and coordinates projects such as 'Turkish Medical NLP Model Development' (BAP-funded) and the Behavioral Next Generation Wireless Networks COST Action.

Michael A Normanمشاهده پروفایل

دانشیار

• Discovery Systems
• Transaction Log Analysis
• Web-scale Discovery Services
• +۳ مورد دیگر

Michael A Norman is an Associate Professor in the University Library at the University of Illinois Urbana-Champaign, serving as Discovery Services Librarian and Integrated Library System (ILS) Coordinator. His work focuses on advancing discovery systems, transaction log analysis, and web-scale library technologies. He has contributed to innovations like the Bento discovery framework and conducted extensive research on library resource management, user behavior, and digital preservation. Norman has collaborated on projects involving Primo, SFX, and schema.org integration, emphasizing usability and interoperability in library systems. Education: Details not explicitly stated in provided texts. Research Interests: Discovery systems architecture, user-centered design in library technology, web-scale resource management, metadata standards implementation, and historical collection analysis. His work bridges technical innovation with practical library services, addressing challenges in digital resource access and preservation. Publications: Over 13 peer-reviewed articles and datasets, focusing on discovery systems evaluation, copyright renewal studies, and collection management strategies. Recent work emphasizes Bento architecture and user interaction analysis within digital libraries. Awards: No specific awards listed in provided materials. Advising/Grants: No formal advisee list provided; contributions include collaborative research grants related to library technology projects.

• Software Engineering
• Programming Languages
• Distributed Virtual Environments
• +۳ مورد دیگر

Crista Lopes is a Professor in the Department of Computer Science at the University of California, Irvine (UCI), affiliated with the Donald Bren School of Information and Computer Sciences. She serves as Director of the Master of Software Engineering Program and previously led the Institute for Software Research. Her research focuses on software engineering, programming languages, and distributed virtual environments. Dr. Lopes holds editorial roles as Editor-in-Chief of The Art, Science, and Engineering of Programming and on the Advisory Board of PACMPL . She has received notable awards including IEEE Fellow (2019), ECOOP Test of Time Award (2017), and ACM Distinguished Scientist (2011). Her teaching spans courses like INF 212 Programming Languages II and INF 225 Information Retrieval . Recent research includes AI-assisted programming tools, code analysis frameworks like Sourcerer , and studies on climate-conscious conference practices. She actively mentors students in UCI’s Software Engineering doctoral program.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۲ مورد دیگر

Haw-Shiuan Chang is a Research Fellow in the Department of Computer Science at the University of Massachusetts Amherst, based in Room 366 of the Computer Science Building. His research focuses on advancing natural language processing and artificial intelligence through innovative modeling techniques. His primary research interests include: Natural Language Processing (core focus) Machine Learning optimization Large Language Model efficiency Text generation factuality and diversity Computational creativity measurement Recent publications (2021-2025) reveal a strong emphasis on resolving fundamental limitations in language models, particularly addressing entropy extrapolation, softmax bottlenecks, and multi-mode distribution representation. His work bridges theoretical modeling with practical applications in story generation, recommendation systems, and scientific document processing. No scientific awards or mentoring activities are documented in the provided materials. Research is conducted within the Computer Science department infrastructure, with no specific lab or team affiliations mentioned.

• Computer Vision
• Machine Learning
• Federated Learning
• +۳ مورد دیگر

Shitong Sun is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. She teaches a postgraduate module on Deep Learning and Computer Vision , focusing on emerging applications and methodologies in this field. Her research interests center around federated learning, zero-shot learning, and computer vision applications such as image retrieval and action recognition, with a particular emphasis on decentralized systems and semantic knowledge transfer. Her work explores advanced techniques in distributed machine learning frameworks, including federated learning architectures and cross-modal retrieval systems. Recent research highlights include contributions to robustness benchmarking in text-image retrieval and innovative approaches to skeleton-based action recognition using adaptive topology structures. While no scientific awards are listed, her research output demonstrates engagement with cutting-edge topics in artificial intelligence and computer vision. There is no mention of current advising roles or grants, though her teaching and research activities align with the School's focus on electronic engineering and computational sciences.

Carlos Vianna Lordeloمشاهده پروفایل

استاد مهمان

• Audio Signal Processing
• Machine Learning
• Music Information Retrieval
• +۳ مورد دیگر

Carlos Vianna Lordelo is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on Audio Signal Processing and Machine Learning, particularly in music-related applications such as source separation, instrument recognition, and deep learning architectures. He contributes actively to music information retrieval through projects like harmonic-percussive source separation and dataset development for folk music analysis. Key research interests include developing algorithms for music audio processing, adversarial domain adaptation, and CNN-based models. His work integrates theoretical advancements with practical applications in audio engineering and multimedia systems. Lordelo's GitHub repository showcases contributions to instrument prediction, source separation techniques, and open-source tools for music research. Notable projects include the 'source-separation-AHS' repository and collaborations on datasets like 'Tap & Fiddle'.

• Digital Communications
• Signal Processing
• Wireless Communications
• +۳ مورد دیگر

David Huang is a Professor at The University of Western Australia (UWA), affiliated with the School of Engineering and the Electrical, Electronic and Computer Engineering Institute of Agriculture. His roles include research, teaching, and supervision of higher degree students in areas such as digital communications and signal processing. Education: PhD in Electrical & Electronic Engineering, Hong Kong University of Science and Technology (2004) Master of Engineering in Electronic Engineering, Tsinghua University (1999) Bachelor of Engineering in Electronic Engineering, Tsinghua University (1996) Research Interests: David’s work focuses on signal processing for radar and satellite systems, underwater acoustic communications, and meteorological applications . His research leverages innovative techniques such as forward scatter radar, LEO satellite signals, and tomographic rainfall reconstruction. His contributions align with UN Sustainable Development Goals through advancements in environmental monitoring and communication technologies. Grants & Projects: David has secured significant funding, including ARC Discovery Projects (e.g., $385K for reconfigurable antennas, $520K for underwater acoustic systems) and UWA grants. Current projects include passive positioning using satellite signals and real-time NMR data acquisition for rock optimization. Teaching: He has taught courses such as Advanced Communications, Signal Processing, and Wireless Communications across UWA and Tsinghua University. Labs/Teams: His work involves collaborations with the Institute of Agriculture and global networks like WUN, focusing on radar systems, underwater networks, and meteorological signal processing.

• Bayesian Statistics
• Environmental Science
• Climate Change
• +۵ مورد دیگر

Jonathan Rougier is a Visiting Professor at Imperial College London's Faculty of Natural Sciences (Department of Mathematics) with an honorary appointment at the University of Bristol. He specializes in statistics, focusing on applications in environmental science, climate change, and geohazards. His work bridges academia and industry, emphasizing probabilistic modeling, uncertainty quantification, and decision-support systems for natural hazards. Education & Affiliations: Honorary staff at Imperial's Mathematics and Statistics departments. Collaborates with institutions on global geophysical and climate projects. Research Interests: Bayesian methods, spatio-temporal modeling, volcanic eruption prediction, climate change impacts, and risk assessment. His research addresses challenges in environmental data analysis, including unreliable historical records and complex simulation outputs. Articles Trends: Recent work focuses on volcanic hazard modeling (e.g., Aso volcano likelihoods), Antarctic ice mass evolution, and Kalman filter applications in environmental monitoring. Themes include uncertainty quantification, multi-scale computational methods, and interdisciplinary data integration. Advising & Grants: No formal advisees listed. Contributions include methodological advancements for climate projections and hazard analysis, supported by collaborative research networks. Labs/Teams: Engages in global initiatives like the VOLCORE database and Antarctic mass balance studies, emphasizing interdisciplinary teams for geophysical and environmental challenges.

Kathleen Carleyمشاهده پروفایل

استاد

• Social Network Analysis
• Computational Social Science
• Text Mining
• +۴ مورد دیگر

Kathleen Carley is a Professor at Carnegie Mellon University, holding dual appointments in the Department of Electrical and Computer Engineering (College of Engineering) and the Institute for Software Research (ISR). She directs the Center for Computational Analysis of Social and Organizational Systems (CASOS), an interdisciplinary center specializing in network analysis, computational social science, and organizational theory. Her research focuses on dynamic network analysis, text mining, and modeling complex social systems, particularly in crisis response and cybersecurity contexts. Carley’s work integrates cognitive science, computer science, and organizational behavior to develop tools like ORA (statistical network analysis toolkit) and AutoMap (text-mining system). She leads the development of agent-based models such as BioWar (disease spread simulation) and DyNet (covert network dynamics). These tools address challenges in disaster communication, misinformation diffusion, and conflict escalation analysis. Her contributions include pioneering computational methods for analyzing social media bots, cross-platform information diffusion, and cyber defense strategies. Carley serves as founding co-editor of Computational and Mathematical Organization Theory and has co-edited influential works in computational social science. Her research spans global case studies, including analysis of Russian propaganda during the Ukraine invasion and Chinese influence campaigns on social media.

Shengyao Zhuangمشاهده پروفایل

مدرس

• Information Retrieval
• Neural Networks
• Machine Learning
• +۳ مورد دیگر

Dr. Shengyao Zhuang is an Adjunct Lecturer at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on advancing information retrieval systems, particularly leveraging neural networks, large language models (LLMs), and adversarial machine learning to enhance robustness and efficiency. He specializes in dense retrievers, query processing with typos, and federated search frameworks. His recent work explores vulnerabilities in retrieval systems, cross-modal applications, and optimization of retrieval models through techniques like Matryoshka training and reinforcement learning. Dr. Zhuang is affiliated with the IELAB research group, evident in collaborative TREC track submissions. His publications span conferences such as SIGIR and ACM venues, addressing challenges in zero-shot search, pseudo relevance feedback, and multimodal document processing. Notable contributions include developing the Tevatron toolkit and investigating the impact of adversarial attacks (e.g., pixel poisoning) on retrieval systems. He has also explored environmental considerations of IR models, emphasizing sustainable computing practices.

Jerry Alan Failsمشاهده پروفایل

استاد

• Human-Computer Interaction (HCI)
• Design for Children
• Participatory Design
• +۷ مورد دیگر

Jerry Alan Fails is the Chair and Professor in the Computer Science Department at Boise State University, Idaho. He specializes in Human-Computer Interaction (HCI), focusing on designing technologies with and for children using participatory design methods. His research emphasizes ethical concerns in child-centered design, digital privacy, and inclusive technology development at global scales. He leads the intergenerational Kidsteam design group, which collaborates with children and adults to co-design technologies. Key research areas include children’s online privacy, digital resilience, and innovative design modalities for diverse populations. Fails also serves as International Steering Committee Chair for ACM Interaction Design and Children (IDC), organizing major conferences like the 2019 ACM IDC in Boise. His work integrates participatory design principles with ethical frameworks, addressing challenges in emerging technologies such as extended reality (XR) and wearable devices. He actively contributes to academic committees and editorial roles, including the IJCCI Associate Editorship. Current projects explore children’s online information-seeking behaviors, family digital ecology of fear, and scalable co-design practices. He advocates for democratizing ethical technology development through interdisciplinary collaboration and global engagement.

Annabel Lathamمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence in Education
• Ethics and Trust of AI
• Computational Intelligence
• +۸ مورد دیگر

Dr. Annabel Latham is a Senior Lecturer in Computer Science at Manchester Metropolitan University within the Faculty of Science and Engineering’s Computing and Mathematics department. She holds a PhD in Artificial Intelligence, an MSc in Computing, a Postgraduate Certificate in Academic Practice, a CIM Diploma in Marketing, and a BSc(Hons) in Computation. As a Fellow of the Higher Education Academy (FHEA) and Senior Member of IEEE (SMIEEE), she contributes extensively to AI research and education. PhD in Artificial Intelligence MSc in Computing PGC Academic Practice CIM Diploma in Marketing BSc(Hons) Computation FHEA (2015) SMIEEE (2018) Research Interests include Artificial Intelligence in Education , Ethics of AI , and Computational Intelligence . She specializes in conversational agents, intelligent tutoring systems, and public trust in AI. Her work addresses fairness, accountability, and accessibility in AI-driven education, leveraging technologies like large language models and fuzzy logic. Research Outputs span 15 years, with a focus on explainable AI, educational applications of conversational agents, and ethical frameworks. Recent work (2024-2025) explores postdigital citizen science, trustworthy AI implementation, and XAI usability for non-specialists. 2023 IEEE Region 8 Outstanding Women in Engineering Volunteer Award 2019 IEEE Region 8 Outstanding Women in Engineering Affinity Group of the Year 2018 Outstanding Peer Reviewer, Elsevier: Computers & Education Senior Member IEEE (2018) FHEA (2015) Teaching and Supervision includes undergraduate Databases, postgraduate units in Information Systems and AI Ethics. She supervises MSc and PhD students in areas like explainability-aware machine learning, data responsibility, and AI trustworthiness. Her grants involve collaborations with international funding bodies such as NAFOSTED (Vietnam) and Croatia’s National Council for Science. Labs and Groups include the Computational Intelligence Lab, Machine Intelligence research group, and the Data and AI Ethics research group, where she co-leads initiatives on ethical AI in education.