جستجوی استاد راهنمای دانشگاهی

Irina Voineaguمشاهده پروفایل

دانشیار

• Molecular Genetics
• Neurodevelopmental Disorders
• Autism Spectrum Disorders
• +۵ مورد دیگر

Professor Irina Voineagu is an Associate Professor at the University of New South Wales (UNSW) within the School of Biotechnology and Biomolecular Sciences. Her research focuses on molecular genetic mechanisms underlying neurodevelopmental disorders using functional genomic approaches in human brain tissue and neuronal models. She leads the Functional Neurogenomics Group and maintains an active research program with consistent high-impact publications. Her research interests center on molecular genetics of brain disorders , particularly autism spectrum disorders and intellectual disability. Through transcriptome analysis, she investigates convergent molecular pathways, circular RNA biology, and genomic instability mechanisms. Her work bridges basic molecular biology with clinical neuroscience, identifying shared pathological mechanisms across neurodevelopmental conditions using post-mortem brain tissue and iPSC-derived neuronal models. Analysis of her 15 most recent publications reveals strong emphasis on transcriptomic regulation in neurodevelopment , with particular focus on circular RNAs, single-cell resolution mapping, and cross-species evolutionary comparisons. Her work consistently identifies convergent molecular pathways across diverse neurodevelopmental conditions, demonstrating how different genetic lesions lead to common pathological endpoints. Australian Academy of Science Gani Medal (2018) UNSW Scientia Fellowship (2017) Lorne Genome Women in Science Award (2017) Biological Psychiatry Australia Aubrey Lewis Award (2015) ARC Development Early Career Award (2013) Simons Foundation top 10 autism papers (2015) NARSAD Young Investigator Award (2012) Autism Speaks top 10 papers (2011) Professor Voineagu's research program is supported by major competitive grants including ARC and NHMRC funding. Her laboratory employs cutting-edge genomic technologies to dissect gene regulatory networks in the developing brain. The Functional Neurogenomics Group maintains collaborations with international consortia studying autism genetics and brain development. The Voineagu Lab operates within UNSW's neuroscience research ecosystem, utilizing advanced genomic facilities and collaborating with clinical researchers to translate molecular findings into potential therapeutic targets for neurodevelopmental disorders. Her team combines computational biology with experimental validation to characterize disease mechanisms at unprecedented resolution.

Thierry Artièresمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۶ مورد دیگر

Thierry Artières is a University Professor at Aix-Marseille University, primarily affiliated with École Centrale Marseille (ECM), where he holds multiple leadership positions including Head of the Computer Science teaching unit, Head of the IAAA course of the Computer Science Master's degree, and Head of the IAM course of the 3rd year Computer Science option. He is a key member of the QARMA (Machine Learning) research team within the LIS (Laboratoire d'Informatique et Systèmes) and collaborates with several research institutes including the READ laboratory, Institut de Neurosciences de la Timone (INT), and the ILCB (Institute of Language, Communication and the Brain). His research interests span Machine Learning, Deep Learning, and Artificial Intelligence with applications to neuroscience, medical imaging, and computational biology. His work focuses on understanding brain representations of voice and sound, optimizing MRI acquisition through deep learning, and developing novel machine learning techniques for multi-label classification and generative modeling. He has supervised numerous PhD students including Loris Berthelot, Hamed Benazha, Malek Senoussi, Swetali Nimje, and Charly Lamothe. His recent publications reveal a strong focus on applying deep learning to neuroscience problems, particularly in understanding how the brain processes sound and voice. His work combines theoretical machine learning advances with practical applications in medical imaging and cognitive neuroscience, often through collaborations between computer science and neuroscience laboratories. His research demonstrates a consistent trend toward interdisciplinary work that bridges AI with biological and medical domains. Member of QARMA Machine Learning team at LIS Collaborator with Institut de Neurosciences de la Timone Involved with ILCB Institute (Institute of Language, Communication and the Brain) Supervisor of multiple PhD students and Master's interns Regularly posts about internship opportunities in Machine Learning and AI Professor Artières actively mentors students through PhD positions, Master's internships, and engineering student projects. He has secured funding for multiple research projects, including ANR-funded collaborations with neuroscience institutes. His lab regularly offers 5-6 month internships on cutting-edge topics in machine learning, and he has facilitated numerous research opportunities for students interested in AI and data science careers. He also contributes to understanding the French job market for AI and data science professionals.

Matthieu Labeauمشاهده پروفایل

مدرس ارشد

• Natural Language Processing
• Machine Learning
• Representation Learning
• +۲ مورد دیگر

Matthieu Labeau is a Senior Lecturer at Télécom Paris, affiliated with the Department of Image, Data, Signal (IDS). He joined the institution in 2019 after completing his PhD at the University of Paris-Saclay and a postdoctoral position at the University of Edinburgh. His research primarily centers on Natural Language Processing (NLP), with specialized interests in representation learning, language modeling, and conversational AI. His work spans: Core NLP : Contextual word representations, semantic alignment, and polysemy analysis. Machine Learning : Hierarchical classification, graph prediction, and few-shot learning techniques. Applications : Emotion recognition in dialogues, persuasiveness decoding, and educational NLP tools. Labeau leads research in the Signal, Statistics and Learning (S2A) team at the Information Processing and Communication Laboratory (LTCI). His recent publications demonstrate a strong focus on improving language model interpretability and efficiency, with innovations in tokenization effects and multimodal fusion. Though no awards or grants are mentioned, his consistent output in top-tier venues (e.g., NeurIPS, ACL, AAAI) highlights significant scholarly contributions. He actively collaborates on tools like EZCAT for conversation annotation and mentors researchers in NLP projects. Current work explores LLM capabilities in persuasion assessment and optimal transport methods for graph-based learning.

Jackie Chi Kit Cheungمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Jackie Chi Kit Cheung is an Associate Professor at the School of Computer Science, McGill University , and holds the Canada CIFAR AI Chair at Mila - Quebec AI Institute. His research bridges Natural Language Processing (NLP) with insights from linguistics and psychology, focusing on system evaluation, automatic summarization , and computational semantics . Key Affiliations : Mila - Quebec AI Institute, Centre for Research on Brain, Language and Music, Centre for Intelligent Machines His lab develops state-of-the-art NLP systems while proposing challenge datasets and evaluation measures. Research emphasizes broader applications in education , health , and language revitalization . Recent work includes the ACL 2024 COSMIC framework for summarization evaluation (SAC Award). He teaches advanced courses like Formal and Neural Models of Pragmatics (Winter 2024) and Evaluation of NLP Systems (Winter 2025). His group includes 10 PhD students, 8 Master's students, and active collaborations with institutions like Mila and SRI International. Scientific Recognition : Canada CIFAR AI Chair SAC Award for ACL 2024 paper Current projects explore representational harms in LLMs , long-context modeling , and mechanistic hallucination mitigation . Students and co-supervised researchers work on fairness, robustness, and commonsense reasoning.

Janna Hastingsمشاهده پروفایل

استادیار

• Medical Knowledge Systems
• Artificial Intelligence in Healthcare
• Ontology Development
• +۴ مورد دیگر

Janna Hastings is Assistant Professor for Medical Knowledge and Decision Support at the Institute for Implementation Science in Health Care (Faculty of Medicine) at the University of Zurich since August 2022, while also serving as Vice-Director of the School of Medicine at the University of St. Gallen. Her research focuses on digitalization in clinical contexts, examining how AI-driven knowledge systems reshape clinical practice, professional identity, and doctor-patient relationships. Education: PhD in Computational Biology (University of Cambridge, 2019), part-time MSc in Computer Science (University of South Africa, 2011), MSc in Philosophy (Open University, 2012) Prior Roles: Group Coordinator at European Bioinformatics Institute (2006-2015), Postdoctoral Researcher at Otto-von-Guericke University Magdeburg (2019-2022), Co-Leader of Human Behaviour-Change Project at University College London (2017-2022) Her research spans ontology development for biomedical domains (ChEBI, Human Behaviour Ontology), AI applications in healthcare decision-making, and behavior change interventions. Key projects include: Building ChEBI molecular ontology Developing Human Behaviour-Change Project knowledge system Ontology-driven mental health frameworks LLM applications in radiation oncology Time-series modeling of metabolism in ageing She explores the capabilities and limitations of clinical AI systems, with publications in JMIR and Lancet Digital Health , covering topics like bias prevention in generative AI and proteomic biomarker discovery. Her work bridges biomedical research with implementation science and digital ethics.

Yunwen Suمشاهده پروفایل

استادیار

• Second Language Acquisition
• Second Language Pragmatics
• Interlanguage Pragmatics
• +۵ مورد دیگر

Yunwen Su serves as Assistant Professor and Director of the Chinese Language Program in the Department of East Asian Languages and Cultures at the University of Illinois Urbana-Champaign, with additional appointments in Linguistics and the Center for East Asian Languages and Cultures. Her research centers on second language acquisition, particularly pragmatic and interactional competence development in Chinese language learning. Education: Ph.D. in Second Language Studies, Indiana University Bloomington M.A. in Applied Linguistics and TESOL, Xi'an Jiaotong University B.A. in English, Xi'an Jiaotong University Dr. Su investigates acquisition, pedagogy, and assessment of L2 pragmatics, examining learners' pragmatic production through linguistic approaches. Her recent work explores nonproductive aspects of pragmatic competence focusing on prosody-pragmatics interfaces and digital-context interactional competence assessment, alongside native speaker analysis of Chinese speech events from cross-cultural perspectives. Analysis of her 2023-2025 publications reveals increasing methodological sophistication through computational analysis, acoustic measurement, and discourse-gating tasks applied to Mandarin speech acts. Key trends include prosody-pragmatics integration in refusal strategies and digital assessment tools for business Chinese contexts. Grants: 2023–2024: CIBER Business Language Research Grant for 'Web-Based Instruction of Second Language Chinese Pragmatics in Business Chinese' (with Yingling Bao) 2021–2022: CIBER Business Language Research Grant for 'Gift Giving Interactions and the Chinese Concept of Politeness' (with Yingling Bao)

• Machine Learning
• Computational Biology
• Protein Design
• +۳ مورد دیگر

Frederikke Isa Marin is a Research Fellow in the Machine Learning section at the Department of Computer Science, University of Copenhagen. Her research focuses on the intersection of machine learning and biological applications, particularly in protein engineering, DNA language modeling, and computational biology. Her primary research interests include developing machine learning frameworks for biological sequence analysis, protein stability prediction, computational protein design, and benchmarking methodologies for DNA language models. She employs generative models, deep learning, and computational simulations to address challenges in structural biology and genomics. Marin's recent publications demonstrate a strong focus on applying machine learning to biological systems. Her work spans protein engineering (including thioredoxin fold redesign and thermal stability prediction), DNA language model benchmarking (BEND), immune receptor modeling, and predictive methods for peptide functionality. Her research consistently combines computational approaches with experimental validation. She maintains collaborative relationships within the Machine Learning section and contributes to the university's computational biology initiatives. Her work utilizes departmental compute resources and aligns with the research groups focused on biological applications of AI.

• Machine Learning
• Artificial Intelligence
• Natural Language Processing
• +۴ مورد دیگر

Felix Björklund Osmark is an Lecturer at the Department of Computer Science , University of Copenhagen. His work is affiliated with the Machine Learning section and the SCIENCE AI Centre, focusing on interdisciplinary applications of artificial intelligence. Current research interests include quantum computing , natural language processing , and sustainable AI . Recent publications highlight hybrid quantum-classical systems, biomedical applications of machine learning, and ethical considerations in AI development. His collaborative efforts span domains like structural biology , climate science , and medical diagnostics , leveraging the department's compute cluster and TreeSense research center resources.

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

David Robert Shannon is an Instructor at the Department of Computer Science, University of Copenhagen. He contributes to teaching and research within the Machine Learning section, which participates in the SCIENCE AI Centre. University: University of Copenhagen Department: Department of Computer Science Section: Machine Learning His research interests span theoretical and applied machine learning, focusing on natural language processing, information retrieval, medical image analysis, computational biology, and quantum computing applications. He utilizes the department's powerful compute cluster for projects involving AI's environmental impact, quantum algorithms, and biomedical data modeling. Recent publications highlight work in quantum-inspired neural networks, sustainable AI, medical diagnostics, and cross-cultural computational frameworks. Key themes include ethical considerations in AI, hybrid quantum-classical systems, and multimodal data analysis. David collaborates with the Machine Learning section and SCIENCE AI Centre, leveraging resources like TreeSense for remote sensing and deep learning of global tree resources. The section's activities range from foundational research to applications in sustainability and biological data modeling.

• Spanish Language Pedagogy
• Hispanic Intellectual History
• Sociolinguistics
• +۳ مورد دیگر

Luis Gabriel-Stheeman is an Associate Professor of Spanish at The College of New Jersey (TCNJ), within the Department of World Languages and Cultures, School of Humanities and Social Sciences. He has been active in curriculum development, teaching a broad range of Spanish language and culture courses, and contributing to scholarly discourse on Hispanic linguistics and pedagogy. Education: Ph.D. in Spanish Literature, University of Cincinnati, 1996 Research Interests: His research spans Spanish language pedagogy , Hispanic intellectual history , and sociolinguistics , with a particular focus on how language ideologies shape educational practices and cultural identity. He explores how authentic communicative activities can enhance language learning and how historical narratives influence contemporary Hispanic discourse. His work also delves into the evolution of Spanish language ideologies from 1800 to 2000, examining the role of intellectuals in shaping linguistic norms and cultural values. Publications & Contributions: Gabriel-Stheeman has authored and co-authored several influential works. Notably, his 2012 book The Battle over Spanish between 1800 and 2000: Language & Ideologies and Hispanic Intellectuals offers a comprehensive analysis of linguistic debates in the Hispanic world. A revised edition of La Batalla del idioma was published in 2023 with new prefaces, reflecting ongoing scholarly engagement. He has also contributed to pedagogical innovation with co-authored materials like the College Intermediate Spanish Textbook Package , currently in development. Service & Leadership: He serves on multiple committees, including the WLC Curriculum Committee and the Executive Committee, and is the Spanish Elementary Sequence Coordinator from Fall 2025 to Spring 2027.

• Human-Computer Interaction
• Software Engineering
• User Interface Design
• +۳ مورد دیگر

Aaron G. Cass is an Associate Professor of Computer Science at Union College in Schenectady, NY. His research focuses on human aspects of computing, particularly software design and user interface design. Ph.D. in Computer Science (2005) from University of Massachusetts Amherst Master’s (1996) and Bachelor’s (1993) degrees from University of Virginia Three-year industry experience at Motorola Research interests center on improving software design practices through experimental studies and tool development, with collaborations in human-computer interaction (HCI) focusing on undo mechanisms and human-robot interaction (HRI) challenges. His work spans process modeling patterns, constraint-based design guidance, and empirical evaluations of HCI/UX mechanisms. Recent publications (2000-2018) demonstrate sustained contributions to software engineering (process modeling, constraint management), human-computer interaction (undo systems, task modeling), and human-robot interaction. The research consistently bridges theoretical process models with practical implementation tools for novice designers.

Adil Khanمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۷ مورد دیگر

Adil Khan is a Professor of Machine Learning and Artificial Intelligence at the University of Hull, affiliated with the School of Computer Science within the Faculty of Science and Engineering. His research focuses on advancing machine learning techniques, including deep learning, domain adaptation, adversarial robustness, and fairness in AI, with applications in remote sensing, medical image analysis, and natural language processing. He has led significant projects funded by organizations like Huawei and the Analytical Center of Artificial Intelligence of Innopolis University, addressing challenges such as improving model generalization and mitigating catastrophic forgetting in neural networks. Recent work includes developing novel attention mechanisms and Mamba models for hyperspectral image classification, as well as exploring cross-modal retrieval using large language models (LLMs). His theoretical contributions span foundational questions in ML, such as training with limited data, adversarial robustness, and ethical AI. Notable projects include the £10 million 'Fair, Robust and Life-long Machine Learning' initiative, advancing representation learning for fair and robust models. Additionally, he serves as an external examiner for the London School of Innovation's MSc in Applied AI and Machine Learning. His publications emphasize innovative solutions in computer vision, domain adaptation, and healthcare applications, with a focus on practical industrial implementations and rigorous evaluation methodologies. Key grants include the £500,000 Wolfson Foundation grant for advanced imaging equipment and the Innovate UK-funded 'AKT TRUST-LLM' project on trustworthy LLMs. His research bridges theoretical advancements with real-world applications, reflecting a commitment to both academic rigor and societal impact.

Curtis Dyresonمشاهده پروفایل

استاد

• Database Systems
• Software Systems
• XML Databases
• +۳ مورد دیگر

Curtis E. Dyreson is a Professor in the Department of Computer Science at Utah State University. His research focuses on database systems, including temporal databases, XML databases, data cubes, and proscriptive metadata. He previously held faculty positions at James Cook University, Bond University, Washington State University, and was a visiting assistant professor at Aalborg University. He earned his Ph.D. from the University of Arizona under Richard T. Snodgrass and a bachelor's degree from New College. Education: Ph.D., Computer Science, University of Arizona (Advisor: Richard T. Snodgrass) Bachelor's Degree, New College Research Interests: His work spans advanced database systems, including temporal data management, XML schema evolution, and database interoperability. He has contributed to projects like Symbiota2, an open-source biodiversity data mobilization platform, and developed tools such as XMorph for XML transformation. His research emphasizes practical applications in fields like environmental science and healthcare. Publications: Over 129 publications, with recent work on Temporal JSON, plug-and-play database systems, and AI-driven pandemic screening. His articles explore topics such as query optimization, temporal SQL translation, and database indexing techniques. Awards: 2015 SIGMOD Contributions Award Advising & Grants: Advises graduate students including Vishal Sharma (PhD), Arihant Jain (M.Sc.), and Chetna Suryavanshi (M.Sc.). His grants include collaborative research on biodiversity data platforms and carbon modeling. Labs/Teams: Active in the Symbiota2 project, an international collaboration for biodiversity data sharing and visualization.

Mark Johnsonمشاهده پروفایل

استادیار بالینی

• Oral and Maxillofacial Surgery
• Dental Anesthesiology
• Second Language Acquisition
• +۳ مورد دیگر

Dr. Mark Johnson is a part-time Clinical Assistant Professor at East Carolina University's School of Dental Medicine, Department of General Dentistry. He holds a DDS from the University of Tennessee (1983) and completed residencies in Oral and Maxillofacial Surgery at Thomas Jefferson University (1989). His dual professional roles include academia and private practice in New Bern, NC. He is board-certified in Oral and Maxillofacial Surgery and Anesthesiology, with active memberships in national and state dental organizations. Dr. Johnson's educational background includes a BS from the University of Tennessee, followed by dental and surgical training. He has held roles such as President of the NC Dental Society's 5th District Board of Trustees and Chair of the NC Caring Dental Professionals Board of Directors. His research interests span both clinical dentistry and second language acquisition, focusing on cognitive task complexity, educational methodologies, and writing processes in L2 contexts. His recent publications explore task-based language teaching, cohesion in academic writing, and the interplay of planning and complexity in L2 production. While his academic contributions are primarily in linguistics and education, his clinical expertise aligns with oral surgery and anesthesiology. Dr. Johnson has no listed scientific awards. His academic advising and grant activities are not detailed in the provided text. He maintains no formal lab affiliations but contributes to clinical and educational initiatives through his academic and private practice roles.

Márton Karsaiمشاهده پروفایل

دانشیار

• Network Science
• Computational Social Science
• Data Science
• +۵ مورد دیگر

Márton Karsai is an Associate Professor and Head of the Department of Network and Data Science at Central European University (CEU). He also serves as a Research Professor at the Rényi Institute of Mathematics (Hungary) and Editor-in-Chief of Advances in Complex Systems . His work focuses on computational social science, human dynamics, and data-driven modeling of socioeconomic systems. Karsai holds advanced degrees including a DSc from the Hungarian Academy of Sciences and an HDR (Habilitation) in Computer Science from École Normale Supérieure de Lyon. His research integrates temporal networks, human mobility, and social contagion phenomena, often using large-scale datasets from digital platforms and wearable sensors. Notable projects include studies on evacuation behavior during disasters, vaccination hesitancy, and urban socioeconomic stratification. Karsai leads interdisciplinary initiatives like the DyLNet project, which examines social interactions and language development in preschool environments through sensor technology. Recent publications highlight innovations in network clustering algorithms (PASCO), epidemic modeling with generalized contact matrices, and the application of machine learning to infer socioeconomic status from satellite imagery. His work bridges computational methods with real-world challenges in public health, urban planning, and humanitarian development.