جست‌وجوی استاد و پژوهشگر

• Big Data Analytics
• Text Mining
• Machine Learning
• +۶ مورد دیگر

Dr. Wei Zhang is a Senior Lecturer and Associate Head of People and Culture at the University of Adelaide's School of Computer and Mathematical Sciences, with a secondary appointment as Honorary Lecturer at Macquarie University. She holds a PhD in Computer Science from the University of Adelaide (2017) and leads research in big data analytics, text mining, machine learning, and IoT. Her work has resulted in over 100 peer-reviewed publications, including top-tier journals like ACM Computing Surveys and IEEE Communications Surveys. Dr. Zhang currently holds an ARC Early Career Industry Fellowship (IE230100119) and leads an ARC Discovery Project (DP240103070), with total funding exceeding AUD $3M from ARC, Google, and DST over five years. Her research focuses on text summarization, AIoT systems, adversarial attacks on neural networks, and medical information extraction. Professional roles include Program Coordinator, Course Coordinator, and outreach contact at her school. She actively contributes to academic governance as Associate Head of People & Culture and serves on conference committees (ACL, KDD, WWW) and journal editorial boards. Industrial collaborations span six years with roles in project leadership and technical advisory roles across multiple sectors. Dr. Zhang is a member of ACM, IEEE, and ACS, and her work has been recognized by prestigious awards such as top 100 global researchers in Network Security (SciVal). Her lab develops AI-driven solutions for IoT-enabled agriculture (e.g., mushroom cultivation monitoring) and smart construction systems, with applications in energy optimization, healthcare, and environmental monitoring.

Mitch Marcusمشاهده پروفایل

استاد

• Statistical natural language processing
• Cognitively plausible models
• Linguistic structure acquisition

Mitch Marcus is a Professor Emeritus in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He earned his Ph.D. from MIT in 1978 and joined Penn in 1987 after working at AT&T Bell Laboratories. His research focuses on statistical natural language processing and cognitively plausible models for automatic acquisition of linguistic structure. Dr. Marcus created and led the Penn Treebank Project, which revolutionized natural language parsing accuracy. He has served as PI on multiple major projects including DARPA LORELEI and GAILA AIX, focusing on unsupervised morphology acquisition and child language acquisition simulations. Awards: Fellow of American Association for Artificial Intelligence (1992) Founding Fellow of Association for Computational Linguistics (2011) Advising: His former PhD students hold positions at institutions including MIT, Johns Hopkins, Google, and Microsoft Research. Research groups: Directed the Penn Treebank Project and co-led the OntoNotes Project. Currently collaborates on natural language understanding for human-robot interaction.

• Legal Technology
• Natural Language Processing (NLP) for Legal Texts
• Computational Law
• +۵ مورد دیگر

Prof. Dr. Matthias Grabmair is a Professor of Legal Tech at the TUM School of Computation, Information and Technology, part of the Technische Universität München (TUM). His research focuses on advancing computational methods for legal analysis, particularly leveraging natural language processing (NLP) to enhance legal document summarization, judgment prediction, and AI-driven legal systems. He leads the JUSMOD organization and has contributed to datasets like ECtHR-PCR for precedent understanding. Key research areas include automated legal reporting (e.g., LexGenie), cross-jurisdictional analysis, and ethical AI applications in privacy policies (PrivaT5). His work emphasizes improving judicial decision-making transparency through models like Hiculr for rhetorical role labeling and Chronoslex for temporal generalization. Prof. Grabmair also explores adversarial robustness in legal AI systems and transfer learning across legal domains. Publications span 2005–2025, with recent emphasis on generative AI for legal texts, explainable AI in judgment prediction, and curriculum learning for legal document analysis. His research bridges computational methods with legal practice, addressing challenges in fairness, reliability, and scalability of AI applications in law.

François Jacquenetمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Natural Language Processing
• +۵ مورد دیگر

François Jacquenet is a Professor of Computer Science at the University of Saint-Etienne, where he is a member of the Machine Learning Team at the Hubert-Curien Laboratory. His research focuses on machine learning and data mining applications for natural language processing, with significant contributions to privacy-preserving systems including Hippocratic Multi-Agent Systems and Automata-Based Sequence Mining. His research interests span Machine Learning , Data Mining , Natural Language Processing , and Privacy-Preserving Systems . Jacquenet has led multiple research projects including the PASCAL II Network of Excellence (2008-2012), the Bingo2 project (2008-2010), and the Web Intelligence project (2006-2008), where he focused on ethical web design and privacy protection techniques. His work bridges theoretical foundations with practical applications in areas like fraud detection, meeting summarization, and video tag correction. Analysis of his recent publications reveals a consistent research trajectory in deep learning applications , privacy-preserving techniques , and cross-modal learning . His work shows a progression from foundational research in grammatical inference and automata theory to contemporary applications in neural networks and self-organizing systems. The publications demonstrate strong interdisciplinary connections between computer science, physics, and security applications. Best AI Paper Award at Conference (2006) Professor Jacquenet has supervised numerous PhD students including Maria Galvan, Hoang-Tung Tran, Ludivine Crépin, and Stéphanie Jacquemont. His research has been supported by significant grants including the French Research Agency (ANR), the Rhône-Alpes region, and international networks like PASCAL. He has organized multiple conferences including Privacy on the Web at the ACM Symposium on Applied Computing and the PASCAL Workshop on Teaching Machine Learning. He is actively involved with the Machine Learning Team at Hubert-Curien Laboratory , contributing to the PASCAL Network of Excellence and the REWERSE Network. His research group focuses on developing practical applications of machine learning while addressing fundamental theoretical questions in pattern mining and language learning.

• Natural Language Processing
• Multilingual Language Models
• Knowledge Representation
• +۵ مورد دیگر

Constanza Catalina Fierro Mella is a PhD Fellow and Postdoc in the Natural Language Processing section at the Department of Computer Science, University of Copenhagen's Faculty of Science. Her research bridges computational linguistics with philosophical inquiry into knowledge representation in language models. Her primary research interests include Multilingual Language Models , Knowledge Representation , Mechanistic Interpretability , and Cross-cultural NLP . Fierro's work examines how language models remember and represent facts across languages, with particular attention to the epistemological foundations of artificial intelligence. Her publication record shows a strong focus on understanding the inner workings of language models, with recent papers exploring how multilingual models remember facts, the intersection of philosophy and mechanistic interpretability, and knowledge representation in large language models. Her research often involves collaboration with Anders Søgaard and other members of Copenhagen's NLP group. Fierro has received significant attention for her work, with papers like 'Challenges and Strategies in Cross-Cultural NLP' garnering over 100 citations. Her research spans both theoretical foundations and practical applications of natural language processing. She has contributed to diverse areas including multimodal learning (investigating connections between vision and language models), healthcare applications (predicting hospital readmissions), and historical document analysis (date recognition in parish records).

• 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.

Wen Huaمشاهده پروفایل

پژوهشگر ارشد

• Data Science
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Dr. Wen Hua is an Honorary Senior Research Fellow at the University of Queensland's School of Electrical Engineering & Computer Science. His research focuses on Data Science, Machine Learning, and Knowledge Graphs, with significant contributions to privacy-preserving techniques, neural network architectures, and trajectory analysis. He has co-authored over 80 peer-reviewed publications, including influential works on entity linking, meta-learning, and spatiotemporal data privacy. His work bridges theoretical advancements with practical applications in areas like recommendation systems, sentiment analysis, and information resilience. Key research interests include: Machine Learning and Deep Learning Privacy-Preserving Data Publishing Knowledge Graph Construction Text Mining and Natural Language Processing Spatiotemporal Trajectory Analysis Information Retrieval Systems Recent publications highlight trends in: Neural architectures for meta-learning and transformer-based systems Efficient algorithms for relation extraction and entity disambiguation Temporal and spatial data anonymization techniques Integration of knowledge graphs with text corpus analysis

• Natural Language Processing
• Sentiment Analysis
• Machine Learning
• +۶ مورد دیگر

Nikolai Rusnachenko is a Post-Doctoral Research Fellow at Bournemouth University's CfACTs PLUS and NLP-MMI research groups, affiliated with the Talbot Campus. His work focuses on advancing natural language processing through interdisciplinary collaborations, particularly in targeted sentiment analysis, multi-document summarization, and biomedical search interfaces. His research spans multiple domains, including: Developing deep learning models for sentiment attitude extraction in Russian and Vietnamese Creating context sampling frameworks for relation extraction in large texts Enhancing dialogue systems with literary personality traits Designing biomedical search interfaces He has published extensively in venues like SemEval, IEEE WIC, and ACM conferences, working with collaborators across Russia, Vietnam, and the UK. Recent publications highlight his expertise in: Applying LongT5 for Vietnamese media summarization Implementing chain-of-thought reasoning in emotion detection Advancing attention-based models for legal document analysis Optimizing biomedical information retrieval

Goran Nenadicمشاهده پروفایل

استاد

• Natural Language Processing
• Text Mining
• Biomedical Information Extraction
• +۳ مورد دیگر

Goran Nenadic is a Professor in the Department of Computer Science at the University of Manchester and a Turing Fellow at the Alan Turing Institute. His research focuses on natural language processing (NLP), text mining, and semi-automated knowledge curation from unstructured data, particularly in biomedical and healthcare domains. He leads the UK Healthcare Text Analytics Network (Healtex) and has extensive experience in clinical text mining, including projects with healthcare providers and industrial partners. Education: PhD in Computer Science (2003, School of Sciences, University of Salford) MSc in Computer Science (1997, Faculty of Mathematics, University of Belgrade) BSc in Mathematics and Computer Science (1993, Faculty of Mathematics, University of Belgrade) Research Interests: Nenadic's work emphasizes large-scale extraction of clinical/epidemiological findings from EHRs, healthcare social media, and biomedical literature. His team addresses challenges like medication prescription extraction, patient-generated data analysis, and clinical decision support systems. Projects include semi-automated anonymization of clinical narratives and mental health symptom identification in social media. Key Projects: Integrating hospital outpatient letters into the healthcare data space (2021–2025) Centre for Digital Trust and Society (2021–present) SC-SVI: Social Care Strengths and Vulnerability Index (2022–2024) Assembling the Data Jigsaw for MSK Disease Research (2020–2024) Labs/Teams: Leads the Clinical Knowledge Management group and collaborates with the Health eResearch Centre (HeRC). His group's website is gnteam.cs.manchester.ac.uk . His research contributes to UN Sustainable Development Goals related to health and well-being through innovations in clinical text analytics and healthcare data utilization.

• Natural Language Processing
• Computational Linguistics
• Machine Translation
• +۷ مورد دیگر

Eneko Agirre is a Professor in the Department of Computer Science and Artificial Intelligence at the Faculty of Informatics, University of the Basque Country. He is a leading researcher in Natural Language Processing with a strong focus on multilingual systems, particularly for the Basque language, and has published extensively in top NLP conferences including ACL, EMNLP, and NAACL. His research spans multiple areas of computational linguistics including word sense disambiguation, machine translation, cross-lingual learning, and information extraction. Agirre has pioneered work on zero-shot learning approaches, data contamination issues in LLM evaluation, and low-resource language processing. His recent work includes developing the Latxa family of Basque language models and creating novel methods like WiCkeD for more challenging benchmarks and GUIDEX for zero-shot information extraction. Analysis of his recent publications reveals a strong trend toward addressing fundamental challenges in large language model adaptation, evaluation reliability, and cross-lingual transfer learning. His work often combines theoretical insights with practical applications, particularly for under-resourced languages like Basque. His research demonstrates how linguistic typology impacts cross-lingual performance and how to overcome data limitations through innovative methodology. Agirre has mentored numerous researchers who have become active contributors to the NLP field, including Oscar Sainz, Jon Ander Campos, and Iker García-Ferrero. His collaborative work spans institutions across Spain and internationally, reflecting his standing in the global NLP community.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۸ مورد دیگر

Rohini Srihari is a Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York, where she also serves as Associate Chair. Additionally, she holds an Adjunct Professor position in the Department of Linguistics. Her academic journey includes a PhD in Computer Science from the University at Buffalo (1992) and a B. Math degree from the University of Waterloo (Canada). Dr. Srihari's research spans multiple cutting-edge areas in artificial intelligence, with a strong focus on Natural Language Processing, Information Retrieval, and Text Mining. Her work encompasses both theoretical foundations and practical applications, particularly in Conversational AI systems, disinformation detection, and social unrest prediction. She has pioneered approaches in multilingual text mining for less commonly taught languages and developed innovative methods for social media analysis. Her recent research emphasizes AI for Social Good, addressing critical societal challenges through technology. Her publication record shows a clear evolution from foundational work in multimedia information retrieval and document analysis toward increasingly socially impactful applications. The recent publications demonstrate a strong focus on using AI to address misinformation, predict civil unrest, and develop empathetic conversational agents. Her work increasingly integrates multiple data sources and leverages state-of-the-art deep learning techniques while maintaining a focus on real-world applicability and social impact. Awards & Recognition: Bronze prize in the Alexa Prize Socialbot Grand Challenge 4 (2021) Two US patents in text mining and image analysis Extensive publication record with over 100 papers and an h-index of 40 Dr. Srihari has secured significant research funding from major agencies including the National Science Foundation (NSF), DARPA, and IARPA. She has directed multiple research projects focused on AI applications for social good, including an NSF-funded project on Purposeful Conversational Agents based on Hierarchical Knowledge Graphs. She supervises students across both Computer Science and Linguistics departments, fostering interdisciplinary research. Her research group, focused on Conversational AI for Social Good, addresses critical issues like combating disinformation and assisting the disabled through trustworthy socialbots. She also directs the Center of Excellence for Document Analysis and Recognition (CEDAR) and has led interdisciplinary student teams in major competitions. Her industry experience includes founding and directing technology startups focused on big data analytics, two of which were acquired by larger media analytics companies.

• Artificial Intelligence
• Reinforcement Learning
• Generative AI
• +۱۰ مورد دیگر

Álvaro Jesús López López is a researcher at the Instituto de Investigación Tecnológica (IIT) and a professor at the Higher Technical School of Engineering (ICAI), both part of Comillas Pontifical University. He holds the position of Associate Research Fellow and serves as a principal investigator at the Chair of Smart Industry, where he leads applied research on digital technologies such as generative AI, computer vision, and synthetic data generation. He also directs executive programs in generative artificial intelligence for professionals at Comillas Onexed. His research interests include Artificial Intelligence, Reinforcement Learning, Generative AI, Smart Industry, and Railway Systems . His work focuses on applying advanced AI techniques to industrial and transportation challenges, particularly in energy efficiency and automation. He has published extensively in high-impact journals such as Engineering Applications of Artificial Intelligence , Applied Intelligence , and Transportation Research Part C . The recent articles highlight a strong trend toward applied AI in industrial and robotic systems , with a focus on sim-to-real transfer, keyphrase extraction, and reinforcement learning enhanced with semantic knowledge. His work bridges the gap between theoretical AI and real-world industrial applications. Scientific Awards: Honorable Mention from the Industry 4.0 Observatory (2021) Honorable Mention at the 6th Connected Industry Promotion Award Erasmus Teaching/Research Staff Training Grant (2014) He has advised multiple PhD students and leads several industry-funded research projects with organizations such as Ferrovial, Endesa, Metro de Madrid, and the European Commission. He has also organized scientific events and delivered numerous invited talks on AI and digitalization. His research has led to practical applications, including a patent on utilizing regenerative braking energy in railways. He completed a research stay at the Royal Institute of Technology (KTH) in Stockholm and has contributed to books and policy reports on digitalization in industry.

Krishnamurthy Dvijothamمشاهده پروفایل

پژوهشگر

• AI Safety
• Robustness in AI
• Differential Privacy
• +۵ مورد دیگر

Krishnamurthy Dvijotham (Dj) is a research scientist with a focus on developing safe, reliable, and secure AI systems. His current role as a Research Lead at ServiceNow Research (2024-2025) builds on his extensive experience at Google DeepMind (Researcher, 2017-2024), Pacific Northwest National Laboratory (Researcher, 2016-2017), and a postdoctoral fellowship at Caltech's Center for Mathematics of Information. Research Interests: Mathematical optimization, control theory, AI robustness, differential privacy, neural network verification, power systems optimization. Scientific Awards: Best Paper at ICML 2024, Best Paper at UAI 2018, Best Paper at Constraints 2016, Best Student Paper at UAI 2014. Dvijotham's work emphasizes the application of rigorous mathematical frameworks to AI systems. His publications span certified robustness , adversarial defense mechanisms , privacy-preserving learning , and power grid optimization , reflecting interdisciplinary contributions to both theoretical and applied domains. He has mentored numerous PhD students and postdoctoral fellows, many of whom now hold academic or research positions at institutions like UC Berkeley, Google DeepMind, and Microsoft Research. His recent articles highlight advancements in diffusion models , selective deferral systems , and formal verification techniques , with a strong focus on security and reliability in AI deployment.

Giuseppe Careniniمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• Human-Computer Interaction
• +۴ مورد دیگر

Giuseppe Carenini is a Professor in the Department of Computer Science at the University of British Columbia (UBC), holding the role of Director of the UBC Master of Data Science program. He is affiliated with the Faculty of Science and leads multiple research groups including the UBC NLP Group, CANARY, IUI Group, and CAIDA. His research focuses on intersections between Artificial Intelligence, Computational Linguistics, and Human-Computer Interaction, with notable contributions to discourse analysis, summarization, and interactive visualization. He has been recognized with awards such as the SIGDIAL Best Paper Award and ACM TiiS Best Paper Award. His work spans academic leadership, course instruction (e.g., CPSC courses on AI, NLP), and collaborations with industry and sustainability initiatives (e.g., IRES, Data Science Institute). His projects often integrate multimodal data analysis, decision support systems, and adaptive interfaces. Research Interests: - Artificial Intelligence: User Modeling, Preference Elicitation, Decision Theory. - Computational Linguistics: Discourse Analysis, Summarization, Argumentation. - Human-Computer Interaction: Intelligent Interfaces, Information Visualization. Publications highlight advancements in neural discourse parsing, topic segmentation, and LLM applications in healthcare and operations research. His work bridges theoretical contributions with practical tools like CONVisIT and T³-Vis for data exploration and model interpretation.

Javed Mostafaمشاهده پروفایل

استاد

• Multimedia Information Retrieval
• Personalization and User Modeling
• Cyberinfrastructure for Research and Learning
• +۷ مورد دیگر

Javed Mostafa is a Professor and Dean of the Faculty of Information at the University of Toronto since September 2023. He previously served as Professor and founding Director of UNC's Carolina Health Informatics Program (2011-2023), Deputy Director for Education & Training at UNC's Biomedical Informatics unit (2010-2023), and as Victor H. Yngve Endowed Professor and Associate Dean at Indiana University (2000s). Education : PhD in Information Science (1994) from The University of Texas at Austin, MA from The Ohio State University, BSc from Northwestern Oklahoma State University Companies : Co-founder of KeonaHealth (2010-present) and Cymantix (2018-present) His research focuses on multimedia information retrieval , personalization/user modeling , and cyberinfrastructure for research . Key contributions include: Development of neurophysiologically-informed information retrieval systems Creation of the Health Data Exchange platform for expert identification Advancements in SNOMED CT-based clinical cohort identification Editorial leadership as Editor-in-Chief of the Journal of the Association for Information Science and Technology Establishment of the Laboratory of Applied Informatics Research (LAIR) at U of T Mostafa's scientific awards include: Victor H. Yngve Endowed Professorship McColl Term Professorship His work spans interdisciplinary collaborations with grants like the Sponsored Research Agreement (2025-2026) for the Society 2025 project. He has authored over 105 peer-reviewed publications and contributed to policy development for health data systems in India, Malawi, and Bangladesh.