جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Dr David McLean is a Senior Lecturer in the Department of Computing and Mathematics at Manchester Metropolitan University, and Programme Leader for the Digital Technology Solutions MSc Apprenticeship Programme. He holds a BSc in Computing from the University of Leeds, a PhD in Artificial Neural Networks, a PGCE, and is a Fellow of the Higher Education Academy. His research focuses on Artificial Intelligence, Machine Learning, Data Mining, and Conversational Agents. Notable work includes neural network architectures for function approximation, semantic similarity measures in social media, and adaptive tutoring systems. He holds a patent for psychological profiling using neural networks and has collaborated with Prof. Donald Michie, a pioneer in AI. Teaching responsibilities include Postgraduate modules on Data Management and Machine Learning using tools like Weka and MATLAB. He has supervised 8 PhD completions. Awards include Fellowship of the Higher Education Academy and contributions to Research Assessment Exercises. Technical expertise includes C#, Java, Prolog, and roles as an external examiner at Sheffield Hallam University.

Nick Rahimiمشاهده پروفایل

استادیار

• Cybersecurity
• Artificial Intelligence
• Decentralized Systems
• +۳ مورد دیگر

Dr. Nick Rahimi is an Assistant Professor in Computer Science and Computer Engineering at the University of Southern Mississippi. He serves as Director of the Cyber Innovations Lab and Cybersecurity Program Coordinator. Rahimi holds a Ph.D. and master's in Computer Science from Southern Illinois University and dual bachelor's degrees in Software Engineering and Information Systems Technologies. His research focuses on: Cybersecurity and trustworthy AI systems Brain-computer interface security Decentralized network architectures Digital warfare strategies Computing education pedagogy Recent publications (2023-2025) demonstrate strong trends in AI-driven cybersecurity, neural signal processing, and decentralized IoT systems. Over 30 articles explore machine learning applications in threat detection, network optimization, and human-computer interaction. Rahimi teaches courses including: CSC 428: Machine Learning in Cybersecurity CSC 460: Python in Data Science CSC 327: Secure Software Development ITC 510: Cyber Law & Policy He directs the Cyber Innovations Lab where he mentors graduate students in cybersecurity research and develops industry partnerships.

Sharon Goldwaterمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +۹ مورد دیگر

Sharon Goldwater is a Professor in the Institute for Language, Cognition and Computation at the University of Edinburgh's School of Informatics. Her research focuses on computational models of language acquisition, particularly in unsupervised learning of linguistic structure, Bayesian and neural network models, and cross-linguistic studies. She is affiliated with the Edinburgh NLP group and the Centre for Doctoral Training in Responsible NLP (CDT). Education & Background: PhD from Brown University (2007), postdoctoral research at Stanford University, and extensive academic roles including program chair for EACL 2014 and governance roles in the field. Research Interests: Speech perception, phonetics, morphology, and the integration of computational models with cognitive science. She explores how humans and machines learn language structures from limited data, with applications to zero-resource languages and sociolinguistic analysis. Key Contributions: Pioneered Bayesian models for word segmentation, developed self-supervised speech representations, and contributed to understanding phonetic learning dynamics. Recognized with awards like the 2016 Roger Needham Award and multiple paper prizes. Grants & Awards: Includes the 2024 Outstanding Paper Award, 2021 Best Research Paper award, and leadership in academic service. Her work bridges theoretical linguistics, machine learning, and cognitive science. Labs & Teams: Active in the Edinburgh NLP group and CDT, fostering collaborative research on NLP ethics and computational linguistics. Her lab's work spans from foundational algorithms to applied sociolinguistic studies using computational methods.

• Computational Linguistics
• Low-resource NLP
• Unsupervised Learning
• +۳ مورد دیگر

Dr. C.M. Downey is an Assistant Professor in Linguistics and Data Science at the University of Rochester, specializing in natural language processing for low-resource languages. His research develops methods for unsupervised learning, multilingual modeling, and transfer learning to address data scarcity in computational linguistics. He received his PhD in Computational Linguistics from the University of Washington (2024), with dissertation work on segmental language models and cross-lingual transfer. Professional experience includes positions at Microsoft Research (cryptography and privacy) and Apple (AI/ML). Research addresses fundamental challenges in language technology for under-resourced languages through novel neural architectures, including masked segmental models, hierarchical adaptation techniques, and communication-based translation frameworks. His contributions enable NLP applications for languages lacking large text corpora. Analysis of publications shows consistent focus on multilingual embedding spaces (3 papers), segmentation/transduction models (2 papers), and low-resource adaptation techniques (2 papers), with recent emphasis on communication-inspired learning paradigms. Current projects investigate dynamic vocabulary adaptation and emergent communication protocols for translation systems. Teaching includes graduate courses in computational linguistics and deep learning for NLP.

James Landayمشاهده پروفایل

استاد

• Human-Computer Interaction (HCI)
• Mobile & Ubiquitous Computing
• Human-Centered AI
• +۴ مورد دیگر

James Landay is a Professor of Computer Science and the Anand Rajaraman and Venky Harinarayan Professor at Stanford University's School of Engineering. He serves as the Denning Co-Director of the Stanford Institute for Human-Centered AI (HAI). His expertise spans Human-Computer Interaction (HCI), Mobile Computing, and Human-Centered AI. Landay holds a BS from UC Berkeley (1990), and MS/PhD from Carnegie Mellon University (1993/1996). He has held roles at the University of Washington, Cornell Tech, and Intel Labs Seattle. His research focuses on behavior change technologies, user interface design, and cross-cultural interfaces. Education: BS, UC Berkeley, Electrical Engineering & Computer Science (1990) MS, Carnegie Mellon University, Computer Science (1993) PhD, Carnegie Mellon University, Computer Science (1996) Research Interests: Landay explores how technology can support health and sustainability through behavior change. His work includes tools like WhoIsZuki (narrative-based health apps) and Smart Primer (educational storytelling). He also studies workplace design impacts on well-being and drone-human interaction. Awards & Recognition: ACM Fellow (2016) ACM SIGCHI Academy Member (2011) ACM SIGCHI Lifetime Research Award Teaching & Advising: Landay teaches courses like Cross-Platform Mobile App Development (CS147L) and oversees over 20 advisees across doctoral, master’s, and undergraduate programs. His labs include collaborations with Tsinghua University’s World Lab. Labs & Projects: Leads initiatives such as the Hybrid Physical-Digital Spaces project and the Smart Primer educational tool. His work bridges academia, industry, and policy, with contributions to NSF advisory roles and startups like NetRaker.

• Natural Language Processing
• Clinical Informatics
• Machine Learning
• +۳ مورد دیگر

Alexander Loeser is a researcher active in natural language processing (NLP) and its applications in clinical and financial domains. He has contributed to diverse areas including clinical outcome prediction, transformer-based reinforcement learning environments, domain knowledge integration, and information extraction from text. His recent work focuses on evaluating large language models' financial literacy via domain-specific languages and addressing data drift in clinical NLP tasks. Key Research Areas: Clinical decision support systems and outcome prediction Domain knowledge injection into transformer models Biased news article detection Interactive NLP systems for entity linking Methodological Focus: Reinforcement learning and attention mechanisms Multi-task and self-supervised learning Active sampling for annotation efficiency Topic segmentation and classification Loeser has collaborated extensively with researchers like Wolfgang Nejdl, Betty van Aken, Felix Gers, and Paul Grundmann, with publications spanning from 2012 to 2025. His work emphasizes interpretability, generalization, and practical deployment of NLP models in real-world domains.

Christian Widdascheckمشاهده پروفایل

استاد

• Aesthetic Education
• Early Childhood Education
• Transcultural Studies
• +۳ مورد دیگر

Christian Widdascheck is a Professor of Elementary Aesthetic Education at Alice Salomon Hochschule Berlin, focusing on the intersection of aesthetic education, migration studies, and 21st-century pedagogical frameworks. He leads the Erziehung und Bildung in der Kindheit Bachelor's program and directs the Lernwerkstatt für Ästhetische Bildung (Learning Workshop for Aesthetic Education). University of Witten-Herdecke (Dr. rer. medic., 2012) Hochschule für Künste im Sozialen, Ottersberg (Diplom in Art Therapy, 2004) His research explores: Aesthetic education as crisis response mechanism Phenomenological embodiment in learning Transcultural dynamics of art therapy Migrant experience through artistic expression Recent publications analyze aesthetic education's role in sustainable development responses and 21st-century pedagogy, with emphasis on migratory contexts. Scientific recognition includes Deutsche Studienstiftung scholarships (2000, 2006). He has developed art therapy frameworks for asylum seekers and established cultural integration studios in Berlin. Key projects involve Kunsttherapie_Migration research and co-developing digital self-help systems for Turkish migrants. His work combines artistic practice with social integration through the Werkstatt für Ästhetische Praxis_Theorie (Workshop for Aesthetic Practice_Theory), emphasizing experiential learning environments.

Wei Zhaoمشاهده پروفایل

استادیار

• Natural Language Processing
• Evaluation of NLP Systems
• Cross-Temporal Research in NLP
• +۷ مورد دیگر

Wei Zhao is an Assistant Professor in Natural Language Processing at the School of Natural and Computing Sciences, University of Aberdeen, where he also leads research as a Principal Investigator. He is additionally an invited lecturer at Heidelberg University, Germany. His research focuses on evaluation of NLP systems, cross-temporal research, and applications of large language models in science and humanities. His research interests include: Evaluation of Large Language Models Diachronic and Cross-Temporal NLP AI for Scientific Discovery Lexicography and Lexical Semantics Machine Translation and Human-AI Collaboration Graph-based Representation Learning The recent articles highlight a strong trend in leveraging LLMs for scientific and linguistic tasks, particularly in evaluation, cross-temporal modeling, and human-AI collaboration. His work spans both theoretical foundations (e.g., graph embedding) and practical applications in translation, lexicography, and scientific content generation. Scientific awards and recognitions include: Outstanding Paper Award at NAACL Computing Grant from Google Sponsorship from Artificial Intelligence Journal Research Catalyst Funding from University of Aberdeen Computing Credits from OpenAI Research Collaboration Grant from Royal Society of Edinburgh Wei Zhao actively supervises multiple Master's and Bachelor's students, including Qianchen Luo, Gagan Bhatia, Ran Zhang, and Lydia Körber, among others. He is also co-supervising a PhD student with Steffen Eger. He has served as an Area Chair for ARR, COLING, and CoNLL 2025, and as a reviewer for EPSRC, Leverhulme Trust, and ISPF. His research is funded by Google, OpenAI, Royal Society of Edinburgh, and the Artificial Intelligence Journal. He leads a vibrant research group focusing on LLMs, cross-temporal bias, evaluation, and NLP for science, with ongoing projects in literary translation, lexicography, and multimodal scientific content.

Marta Ruiz Costa-Jussàمشاهده پروفایل

استاد

• Machine Translation
• Natural Language Processing
• Artificial Intelligence
• +۶ مورد دیگر

Marta Ruiz Costa-Jussà is a Professor at the Faculty of Informatics of Barcelona (FIB), part of the Universitat Politècnica de Catalunya · BarcelonaTech (UPC). She is affiliated with the Department of Computer Science and holds additional teaching roles at the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona (ETSETB). She is also the academic co-director of the postgraduate program Artificial Intelligence with Deep Learning at UPC School. Her research is conducted through key UPC research groups: Intelligent Data Science and Artificial Intelligence (IDEAI), the Center for Technologies and Applications of Language and Speech (TALP), and the Speech Processing Group (VEU). Her research focuses on advancing machine translation through deep learning, particularly in multilingual and low-resource settings. She investigates neural machine translation (NMT) systems that use intermediate mathematical representations (interlingua) to improve efficiency and inclusivity across majority and minority languages. A significant emphasis of her work is on ethical AI, aiming to detect and mitigate biases in translation systems. She is also pioneering research in automatic speech translation, a domain not yet fully mastered by major tech companies. Her recent publications reflect a strong trajectory in neural machine translation, multilingual representation learning, speech-to-speech translation, and ethical considerations in AI. The research trends show a consistent focus on inclusivity, efficiency, and robustness in language technologies, with applications spanning text, voice, and cross-lingual understanding. Scientific Awards: ERC Starting Grant (€1.5 million) for the LUNAR project Google Faculty Research Award (2019) Google Faculty Research Award (2020) She has secured significant research funding, notably the ERC Starting Grant, which supports her vision for lifelong universal language representation. Her advising likely includes graduate students in AI and NLP, though specific names are not listed. She has collaborated internationally with institutions such as LIMSI-CNRS (France), University of São Paulo (Brazil), Infocomm Research Institute (Singapore), National Polytechnic Institute of Mexico, and the University of Edinburgh (UK). She is a core member of several research teams: the IDEAI research center, TALP, and the VEU group. These labs focus on cutting-edge AI, data science, language technologies, and speech processing, providing a rich interdisciplinary environment for her work on inclusive and ethical machine translation systems.

• Journalism
• Media Pluralism
• Disinformation Studies
• +۴ مورد دیگر

Jaime Suau Martínez serves as an Associate Lecturer at Ramon Llull University's Blanquerna School of Communication and International Relations, where he leads the DIGILAB research group focused on media strategy and regulation. His academic profile is anchored in contemporary media challenges including AI-driven journalism, disinformation ecosystems, and European media pluralism frameworks. His research interests span Journalism , Media Pluralism , and Disinformation Studies , with particular emphasis on narrative analysis in hybrid media environments and Spanish political communication contexts. Current projects examine trust dynamics in AI news consumption, cross-national press freedom threats, and regulatory responses to digital platform dominance. Recent publications reveal a strong focus on narrative-based disinformation analysis , comparative press freedom metrics , and cross-lingual detection methods for low-resource languages. His work demonstrates increasing engagement with European Commission initiatives through projects like MC4 and DISINFTRUST, reflecting growing influence in media policy circles. As Principal Investigator for 17 research projects including the EU-funded MC4: Media Councils in the Digital Age (2023-2025) and DISINFTRUST: Disinformation & Trust (2023-2024), he directs substantial collaborative efforts involving researchers from multiple European institutions. His DIGILAB research group serves as the operational hub for these initiatives, focusing on practical regulatory solutions for contemporary media challenges.

• Computational Linguistics
• Natural Language Processing
• Bilingualism
• +۴ مورد دیگر

Yevgen Matusevych is a researcher in the field of computational linguistics, affiliated with the Faculty of Arts and the Computational Linguistics department. His work bridges theoretical and applied research in natural language processing, bilingualism, and speech modeling. Research Interests: Primary focus on computational linguistics, with emphasis on bilingual sentence processing, phonetic learning, and cognitive plausibility in language models. Publications: Active in peer-reviewed research since 2013, recent work explores bilingual language models, mutual exclusivity bias in speech models, and phonetic learning as perceptual space modeling. Collaborations: Extensive external collaborations in computational linguistics, particularly in multimodal learning and cross-linguistic studies. Key Trends: His articles highlight the intersection of natural language processing, bilingualism, and cognitive science, with sub-fields spanning speech recognition, neural architectures, and psycholinguistic theories of language acquisition.