جست‌وجوی استاد و پژوهشگر

Matthew Plumleeمشاهده پروفایل

مدرس

• Computer Science
• Human-Computer Interaction
• Data Visualization
• +۳ مورد دیگر

Matthew Plumlee is a Principal Lecturer in the Department of Computer Science at the University of New Hampshire's College of Engineering and Physical Sciences. He holds a Ph.D. and B.S. in Computer Science from the same institution. His research focuses on data visualization, human-computer interaction, and geospatial systems, with notable contributions to weather display design, marine navigation tools, and underwater behavior visualization of animals like humpback whales. Plumlee has authored numerous articles on topics ranging from hydrographic data processing to 3D visualization techniques in oceanography and robotics. His educational background includes advanced degrees from UNH, and he teaches courses such as Introductory Scientific Programming, Data Structures, and Software Engineering. His work frequently integrates interdisciplinary approaches, combining computer science with marine science and environmental modeling. Key projects include the GeoCoastPilot system for port navigation and the GeoZui3D platform for oceanographic data fusion. Plumlee's publications span over two decades, emphasizing user-centered design principles and real-time data visualization challenges. He collaborates with institutions like CCOM (Center for Coastal and Ocean Mapping) to develop innovative geospatial tools for maritime and environmental applications. His research also addresses cognitive aspects of interface design, such as comparing zooming versus multiple window techniques for visual tasks.

Ra'ed Bani Abdelrahmanمشاهده پروفایل

مدرس ارشد

• modelling critical systems
• AI
• cybersecurity
• +۱ مورد دیگر

Dr. Ra'ed Bani Abdelrahman is a Senior Lecturer in Computer Science and Software Engineering at Sheffield Hallam University's School of Computing and Digital Technologies. He holds a PhD in Software Engineering from Loughborough University (2019), an MSc in Computer Engineering from Yarmouk University (2012), and a BSc in Computer Science and IT from Yarmouk University (2008). His research focuses on: Modelling critical systems Artificial intelligence applications Cybersecurity frameworks Authentication protocols Formal verification methods Recent publications demonstrate interdisciplinary work spanning AI-driven mechanical diagnostics, formal verification of network protocols, and biomedical signal processing. His research trajectory shows increasing focus on AI applications in industrial systems and security. He has held leadership positions including Dean of Information Technology and Director of E-Learning at Ajloun National University, where he implemented distance learning initiatives.

• Spoken Language Processing
• Natural Language Processing
• Deception Detection
• +۵ مورد دیگر

Sarah Levitan is an Assistant Professor in the Department of Computer Science at Hunter College and a doctoral faculty member in the Computer Science and Linguistics PhD programs at the CUNY Graduate Center. She holds a PhD (2019) and MS from Columbia University and a BS from Brooklyn College. Her research focuses on spoken language processing, with special emphasis on paralinguistic aspects of speech and computational methods for analyzing deception, misinformation, and speaker traits. Her key research areas include acoustic-prosodic and lexical analysis of speech to identify deception cues, trustworthiness perception in conversational agents, and mental health analysis through social media data. She has developed methodologies for detecting hate speech, radicalization content, and psychiatric conditions from online text and speech. Recent work highlights include studies on cross-lingual trust perception in dialogues, multimodal deception detection systems, and computational models for analyzing speaker states. Her research has been supported by NSF grants ($298k as PI), Google Cyber NYC ($80k), and the Air Force Office of Scientific Research ($764k as Co-PI). Levitan leads the Hunter Speech Lab, mentoring 3 PhD students, 4 MS students, and 2 undergraduates. She has published over 30 peer-reviewed articles and received the 2015 Cognitive Conference Best Paper Award. Her teaching portfolio includes undergraduate/graduate courses in Natural Language Processing, Computational Linguistics, and Computer Theory. Funded projects include $20M ARNI Institute (Senior Personnel) and NSF EAGER grant exploring trust perception in AI agents. Current research trends focus on hybrid acoustic-lexical models, human-AI collaboration in speech analysis, and ethical applications of NLP in media.

Zining Zhuمشاهده پروفایل

استادیار

• Natural Language Processing
• Explainable AI
• Model Interpretability
• +۳ مورد دیگر

Zining Zhu is an Assistant Professor at the Department of Computer Science at Stevens Institute of Technology, affiliated with the Stevens Institute for Artificial Intelligence (SIAI) and the Center for Research Toward Advancing Financial Technologies (CRAFT). He leads the Explainable and Controllable AI Lab, focusing on foundational and applied research in model interpretability, natural language explanations, and safe AI deployment. His work bridges theoretical advancements and practical applications in NLP and AI systems. Education: PhD in Computer Science (2024, University of Toronto), advised by Frank Rudzicz; BS in Engineering Science (2019, University of Toronto). Prior to Stevens, he was a research intern at Amazon, Tencent, and Winterlight Labs. Research Interests : Explainable AI and Model Interpretability Natural Language Processing and Large Language Models Societal Implications and Safe AI Deployments Knowledge Editing and Model Intervention Publications : Recent work emphasizes explainability in LLMs, dataset effects, and financial AI applications. Key papers include NAACL’s Outstanding Paper Award-winning ACCORD and tutorials on LLM explanations. Awards : Top Reviewer at NeurIPS (2023), Ontario Graduate Scholarship (2022-2023), and Vector Institute grants (2020-2023). Teaching : Instructors CS 584 (Natural Language Processing) and CS 810 (Explainable NLP). Extensive TA experience at University of Toronto. Labs : Leads the Explainable and Controllable AI Lab, fostering research into trustworthy AI systems.

• Artificial Intelligence
• Computational Modelling
• Data-Driven Modelling
• +۹ مورد دیگر

Savas Konur is a Professor of Artificial Intelligence and Computational Modelling at the University of Bradford's Faculty of Engineering and Digital Technologies, serving as Head of the School of Computer Science, Artificial Intelligence and Electronics. Previously, he held roles as Lecturer, Senior Lecturer, and Reader at Bradford, and worked at the University of Sheffield and Liverpool. He earned his B.Sc. from METU (Turkey), M.Sc. from RWTH Aachen (Germany), and Ph.D. from the University of Manchester (UK). His research focuses on computational modelling, machine learning, software engineering, and high-performance computing with interdisciplinary applications in systems biology, membrane computing, and smart manufacturing. He has secured £1.5M as PI and £1.3M as Co-I in grants from EPSRC, Innovate UK, and others, fostering collaborations with industry and academia globally. He leads the Computational Modelling Research Group and serves on editorial and advisory boards including the Journal of Membrane Computing and International Membrane Computing Society. Prof Konur’s work spans cybersecurity, synthetic biology tools, and automation, evidenced by over 76 publications and 11 research projects. His research has garnered multiple awards and recognitions, reflecting his contributions to computational science and interdisciplinary innovation.

Stanisław Kacprzakمشاهده پروفایل

استادیار

• Audio and Speech Processing
• Automatic Speech Recognition
• Speaker Recognition
• +۳ مورد دیگر

Stanisław Kacprzak is an Assistant Professor at the AGH University of Science and Technology in Kraków, Poland, specializing in audio and speech processing. His research focuses on speaker and language recognition, automatic speech recognition (ASR), and audio classification. He is also an experienced software engineer with industry experience. Education: M.Sc., eng. in Computer Science (2011), Technical University of Lodz Ph.D. in Information and Communication Technology (2020), AGH University of Science and Technology Research Interests: His work emphasizes audio/speech processing advancements, including adversarial domain adaptation, speech enhancement, and dataset enrichment (e.g., HeightCeleb). He actively contributes to conferences like ICASSP and Interspeech as a reviewer. Recent Work Trends: Recent publications address ASR hallucinations in non-speech contexts (2025), causal signal-based speech enhancement (2023), and adversarial domain adaptation for acoustic scene classification (2021). Labs/Teams: He collaborates with the Signal Processing Group at AGH University, advancing technologies in audio and speech processing.

Sheela Ramannaمشاهده پروفایل

استاد

• Artificial Intelligence (AI)
• Machine Learning
• Multimodal Information Processing
• +۵ مورد دیگر

Dr. Sheela Ramanna is a Professor and Chair of the ACS Graduate Program in the Department of Applied Computer Science at the University of Winnipeg, and an Adjunct Professor at the University of Manitoba. She holds a Ph.D. in Computer Science from Kansas State University, and completed her earlier education at Osmania University, India. Her research focuses on AI, machine learning, and soft computing with applications in natural language processing, multimodal information processing, and topological data analysis. She has been recognized with numerous awards, including multiple UW Merit Awards and Senior Member status in the International Rough Set Society. Dr. Ramanna leads projects funded by NSERC and MITACS, including work on precipitation forecasting, microplastics analysis, and cannabinoid medicine studies. She supervises over 20 graduate students and has authored/co-authored over 150 publications in top journals/conferences. Key roles include editorial positions for EAAI and KES journals, and program chairs for major conferences like IJCRS and RSCTC. Education: B.S. Electrical Engineering (Osmania University), M.S. Computer Science (Osmania University), Ph.D. Computer Science (Kansas State University) Research Interests: Machine Learning, Natural Language Processing, Multimodal Deep Learning, Computational Topology, Rough Set Theory, Social Network Analysis Grants & Projects: NSERC Alliance/Engage Grants, MITACS Accelerate Projects, WeatherLogics collaborations on precipitation forecasting and road condition mapping

Vassil Vassilevمشاهده پروفایل

استاد

• Artificial Intelligence
• Cyber Security
• Machine Learning
• +۶ مورد دیگر

Prof. Vassil Vassilev is a Professor of AI and Intelligent Systems and Head of the Cyber Security Research Centre at London Metropolitan University. His work spans artificial intelligence, cyber security, and data integration with a focus on applications in urban systems, malware analysis, and cyber-physical systems. He leads initiatives in secure transaction risk assessment (CyDRA) and smart city technologies. Research interests include reinforcement learning for cyber incident response, AI policy validation frameworks, and adaptive security systems. He explores hybrid AI models, IoT-based environmental monitoring, and semantic frameworks for vulnerability detection. His work bridges theoretical AI advancements with practical implementations in cyber defense, urban data spaces, and financial security. Grants and funding support his projects on cyber security analytics, threat intelligence, and low-cost data platforms. He collaborates on initiatives like the first urban data space in Bulgaria and innovative authentication techniques using audio steganography. His leadership at the Cyber Security Research Centre drives applied research in both academic and enterprise contexts.

• Signal Processing
• Audio Technology
• Speech Processing
• +۵ مورد دیگر

Martin Hagmueller is a researcher at the Institute of Signal Processing and Speech Communication at Graz University of Technology. His work focuses on signal processing, audio technology, and biomedical applications such as vocal fatigue monitoring and speech data analysis. He has contributed to projects involving acoustic sensing, speech corpora management, and bionics. Key research areas include: Acoustic feature extraction for voice analysis Development of speech corpora version control systems Real-time intrusion detection via distributed acoustic sensing Hagmueller has received prestigious awards including the ATC Maastricht Innovation Award (2004) and the 2020 Final Pitch competition recognition. He actively engages in public outreach through events like the Graz Electrical Engineering Days (GEED) exhibitions. Current projects involve acoustic fiber sensing for gas pipelines, high-efficiency refrigeration systems, and clinical applications for dysphonic speakers. He collaborates internationally, evidenced by his involvement in Antarctic research expeditions and interdisciplinary teams.

Andreas Spaniasمشاهده پروفایل

استاد

• Adaptive Signal Processing
• Speech Processing
• Sensor Systems
• +۳ مورد دیگر

Andreas Spanias is a Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University (ASU). He directs the Sensor Signal and Information Processing (SenSIP) center and founded the SenSIP industry consortium (an NSF I/UCRC site). His research focuses on adaptive signal processing, speech processing, and sensor systems. He developed the Java-DSP simulation software, including award-winning mobile versions, and authored textbooks on audio processing and DSP. He has held editorial roles for IEEE Transactions on Signal Processing and served as General Co-chair for IEEE ICASSP-99 and Vice-President for Conferences in the IEEE Signal Processing Society. Research Interests: Dr. Spanias specializes in advanced signal processing techniques, including adaptive algorithms, speech enhancement, and sensor network applications. His work bridges theoretical foundations with practical implementations through software tools like Java-DSP, which has been widely adopted in academia and industry. He also contributes to audio coding standards and educational resources in DSP. Awards: 2002 IEEE Donald G. Fink Paper Prize Award IEEE Fellow (2003) IEEE Signal Processing Society Distinguished Lecturer (2004) Advising & Contributions: His student team pioneered the Java-DSP software. He has led industry collaborations through SenSIP and contributed to IEEE initiatives. No specific grants are listed in the provided text. Labs & Teams: Director of the SenSIP center, fostering interdisciplinary research with industry partners through the SenSIP consortium.

Gabriel Vigliensoniمشاهده پروفایل

استادیار

• Creative Artificial Intelligence
• Machine Learning
• Human-Computer Interaction
• +۷ مورد دیگر

Gabriel Vigliensoni is an Assistant Professor in Creative Artificial Intelligence within the Department of Design and Computation Arts at Concordia University. He holds a PhD in Music Technology from McGill University and maintains active research and creative practice at the intersection of artificial intelligence, music, and human-computer interaction. His work spans academic research, artistic performance, and music production. PhD in Music Technology, McGill University Assistant Professor, Department of Design and Computation Arts, Concordia University Vigliensoni's research focuses on sound and music making through machine learning, human-computer interaction, artificial intelligence, embodied musical interaction, music information retrieval, and new interfaces for musical expression. His approach merges formal musical training with extensive experience in sound recording, music production, and computational techniques. His creative work challenges traditional notions of liveness and immediacy in digital music production through procedural composition and embodied interaction. His recent publications demonstrate a strong focus on interactive machine learning for creative applications, particularly in audio and music domains. The research trends show increasing emphasis on explainable AI for the arts, ethical considerations in AI music applications, and the development of interfaces that facilitate sustained artistic practice with machine learning systems. His work spans from theoretical foundations to practical implementations in musical interfaces. 2025-2027: Co-Applicant, New Frontiers in Research Fund—Exploration 2024–2025: PI, Petro-Canada Young Innovator Award (PCYIA) 2024–2025: Collaborator, Responsible AI UK international partnerships UKRI 2023–2025: PI, Explore and Create | Research Creation (Canada Council for the Arts) 2023–2025: PI, Faculty Research Development Program (Concordia University) 2022–2023: PI, Knowledge Mobilization Grant (FRQSC) 2020–2022: PI, Postdoctoral Research Creation (FRQSC) Vigliensoni supervises graduate students in Design (MDes), Individualized Programs (MA, MSc), and Individualized Programs (PhD). His research is supported by significant grants from Canadian funding agencies as well as international collaborations. His work bridges academic research with artistic practice, creating feedback loops between theoretical exploration and creative output. His studio practice and research involve developing interactive systems for musical expression, with notable projects including Clastic Music, Telematic Awakening, and Re•col•lec•tions. These projects often combine real-time audio processing, machine learning, and gestural interfaces to create novel musical experiences that explore the relationship between human performers and AI systems.

• Human-Computer Interaction
• Digital Accessibility
• Voice Processing
• +۷ مورد دیگر

Professor Georgios Kouroupetroglou is a leading academic at the National and Kapodistrian University of Athens (NKUA), serving as Professor in the Department of Informatics and Telecommunications. He holds significant roles including President of the Association for the Advancement of Assistive Technology in Europe (AAATE) and membership in the European Academy of Sciences and Arts. His research focuses on digital accessibility, voice processing, assistive technologies for disabilities, and music computing. He has pioneered initiatives such as the 'Accessibility Unit' and the Speech and Accessibility Research Laboratory at NKUA. Education: B.Sc. in Physics and Ph.D. in Communications and Signal Processing. He has held administrative roles including Chairman of the Department of Informatics and Telecommunications, Director of Postgraduate Studies, and Director of the M.Sc. Programme in 'Language Technology'. Research interests span: Computer Accessibility & Voice User Interfaces Accessibility of Documents and Mathematics Music Computing and Ancient Instruments Gesture-Based UIs and Haptics Inclusive Education Technologies Publications highlight innovations in: Assistive tech for mobility and motor disabilities Audio-tactile interfaces for blind users Accessibility in higher education Acoustic modeling of historical instruments He has authored over 180 papers and books, reviewed numerous EU projects, and teaches courses on accessibility, signal processing, and assistive tech. His work bridges clinical science and technology, emphasizing sustainability and global inclusivity.

• Database Systems
• Virtual Reality Applications
• Digital Humanities
• +۴ مورد دیگر

Yannis Ioannidis is a Professor at the Department of Informatics and Telecommunications of the National and Kapodistrian University of Athens. His research spans interdisciplinary areas including database systems, virtual reality applications, and cultural heritage technology. He leads projects integrating technology with education, art, and social sciences, such as VR environments for urban planning (ARSINOE VR) and AI-driven scientific discovery frameworks. His work emphasizes collaborative tools for digital storytelling and participatory cultural experiences. Ioannidis has contributed to advancements in database query optimization, text exploration through SQL extensions (DETEXA), and indexing techniques for data lakes. His recent focus includes exploring the intersection of tabletop role-playing games with historical education and ethical AI-human coevolution. He actively participates in initiatives like the EBRAINS infrastructure for brain research and the MagicARTS social VR project. Notable research trends in his publications include leveraging VR for immersive learning, designing hybrid museum kits, and applying AI to support UN Sustainable Development Goals. His work bridges technical innovation with societal impact, addressing challenges in education, art preservation, and ethical technology use.

• Design Process
• Community Engagement
• Digital Storytelling
• +۴ مورد دیگر

Dr. Dylan Davis is a Lecturer and Manager of Multimedia Design at Swinburne University's School of Design and Architecture. His interdisciplinary work bridges design research, audio production, and electronic music composition. Research interests include: Design process innovation and community-centered methodologies Digital storytelling through interactive media Electronic music production, particularly acid house and techno genres AI-assisted creative workflows and generative music systems His scholarly output reflects a consistent exploration of technology-mediated creativity, with publications spanning computer music algorithms, community design practices, and electronic music history. Creative works frequently experiment with analog synthesizers (especially Roland TB-303 techniques) and explore the intersection of coding and composition.

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Wang Zhaoxia is an Associate Professor of Computer Science (Practice) at the School of Computing and Information Systems, Singapore Management University. Her expertise lies in Artificial Intelligence, Data Science, and Machine Learning with a focus on Natural Language Processing (NLP), Sentiment Analysis, and Knowledge Graphs. She holds a PhD from Nankai University (2004). Research Interests: Her work spans AI-driven solutions for sentiment analysis, maritime risk management, audio-to-music conversion, and social media analytics. Recent projects include leveraging Large Language Models (LLMs) for tasks like logical reasoning, translation optimization, and risk assessment. Publications: Recent work highlights include developing metrics for LLM performance (RAP), emotion analysis in images, and automated music transcription. Her research demonstrates cross-disciplinary applications of AI in fields like finance (ICO success prediction) and health (disease surveillance). Awards: No awards explicitly listed Advising: Supervises 3 PhD/Master students including HUANG Donghao and Nicole Anne Hui Ying TEO Contact: zxwang@smu.edu.sg | +65 68261347