جست‌وجوی استاد و پژوهشگر

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

Chien-Sheng Wu is a prominent researcher in natural language processing and artificial intelligence, actively contributing to advancements in large language models (LLMs), dialogue systems, and information retrieval. His work focuses on enhancing factual consistency, developing frameworks for service AI agents, and exploring vision-language model limitations in arithmetic tasks. Key research areas: LLMs, knowledge grounding, dialogue summarization, and human-AI collaboration Recent publications analyze the capacity of LLM agents in CRM tasks, multihop reasoning frameworks, and content moderation tools. His collaborations span institutions like ACL, EMNLP, and NAACL. 2025 papers address workflow extraction, visual arithmetic understanding, and RAG system evaluation 2024 work includes Haystack summarization challenges and executable text-editing interfaces Scientific awards and student mentorship details are not explicitly mentioned in available data. Affiliations remain unspecified, but his contributions to NLP benchmarks and evaluation frameworks are significant.

Arsha Nagraniمشاهده پروفایل

پژوهشگر

• Machine Learning
• Multi-modal Learning
• Computer Vision
• +۵ مورد دیگر

Arsha Nagrani is a senior research scientist at Google AI Research , focusing on machine learning for video understanding. She earned her PhD at the University of Oxford under Andrew Zisserman with a Google PhD Fellowship , and completed her undergraduate studies at the University of Cambridge with mentorship from Roberto Cipolla and Richard Turner. Research Interests : Self-supervised and multi-modal machine learning Video recognition using sound and text Computer vision for wildlife conservation Cross-modal self-supervision in biometric matching Transformer-based architectures for temporal modeling Zero-shot learning with frozen models Scientific Awards : ELLIS PhD Award Google PhD Fellowship ICASSP 2021 Outstanding Paper Award INTERSPEECH 2017 Best Student Paper Award Service Contributions : Area Chair for CVPR23, ICCV23 Reviewer for top conferences (CVPR, ECCV, ICCV, BMVC, NeurIPS, ICML, AAAI) Organized workshops: Sight and Sound Workshop @ CVPR [2020-2022] VoxSRC Challenges @ INTERSPEECH Video Understanding Pentathlon @ CVPR 2020 Women in Computer Vision (WiCV) Workshops

Daisy Zhe Wangمشاهده پروفایل

استاد

• Database Systems
• Data Science
• Machine Learning
• +۴ مورد دیگر

Dr. Daisy Zhe Wang is a Professor in the Department of Computer & Information Science & Engineering at the University of Florida, affiliated with the Herbert Wertheim College of Engineering. She directs the Data Science Research (DSR) Lab, focusing on advanced data analysis systems using machine learning and probabilistic methods. Her work spans databases, data science, and informatics, with specializations in probabilistic knowledge graphs, multimodal fusion, and healthcare analytics. Education: PhD in Computer Science from the University of California, Berkeley (2011). Awards include the Arnold and Lisa Goldberg Rising Star Professorship in Computer Science (2019-2021) and the UF Term Professorship (2018). Her lab's current projects include developing systems for tree species classification using remote sensing and improving surgical risk prediction algorithms like MySurgeryRisk. Research emphasizes bridging data science with domain-specific applications, including electronic health records (EHR) analysis, environmental remote sensing, and knowledge graph-driven decision systems. She has pioneered frameworks like RAMQA for multimodal QA and M3 for multi-hop retrieval, advancing both theoretical and applied aspects of data science. Grants and collaborations include large-scale data competitions and partnerships with healthcare institutions. The DSR Lab maintains active research in AI ethics, explainable AI, and scalable data management systems, with ongoing work on neuro-symbolic architectures and multimodal learning.

Alexander Kotovمشاهده پروفایل

دانشیار

• Information Retrieval
• Natural Language Processing
• Health Informatics
• +۳ مورد دیگر

Alexander Kotov is an Associate Professor in Computer Science at Wayne State University's James and Patricia Anderson College of Engineering. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2011) and specializes in large-scale textual information analysis, particularly information retrieval, natural language processing, and health informatics. His research develops neural architectures for conversational entity retrieval (2024), clinical outcome prediction (2024), and behavioral intervention analysis. Key themes include knowledge graph integration, multimodal retrieval systems, and computational methods for health communication analysis, with applications in weight loss counseling and precision medicine. Research consistently bridges NLP/IR techniques with healthcare applications, exploring conversational AI for clinical support, knowledge graph retrieval, and behavioral pattern mining in therapeutic contexts. Recent projects include NIH-supported work on weight management chatbots.

• Natural Language Processing
• Software Engineering
• Artificial Intelligence

Vincent Ng is a Professor of Computer Science at UT Dallas' Erik Jonsson School. His research focuses on Natural Language Processing, Software Engineering, and AI. He obtained his PhD from Cornell University (2004) and BS from Carnegie Mellon University (1997). His research explores NLP techniques applied to software engineering challenges, including automated bug reporting and code analysis. Recent publications show strong emphasis on legal AI, educational technology, and multimodal systems. Major awards include: ICSE'24 ACM SIGSOFT Distinguished Paper Award (2024) AAAI-20 Blue Sky Idea Award (2020) ECS Outstanding Faculty Teaching Award (2019) He advises doctoral students including Jing Lu and Oscar Chaparro, and leads projects supported by NSF grants. Affiliated with the Center for Machine Learning and Language Processing.

Mohammed Zakiمشاهده پروفایل

استاد

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

Mohammed Zaki is a Professor and Department Head of Computer Science at Rensselaer Polytechnic Institute (RPI). He holds a Ph.D. in Computer Science from the University of Rochester (1998) and dual B.S./M.S. degrees from Angelo State University and the University of Rochester. His research focuses on data mining, machine learning, and graph-based methods, with applications in bioinformatics, financial analytics, and personal health. He has authored over 300 publications and 6 patents, including the textbook Data Mining and Machine Learning (2nd Ed., Cambridge University Press, 2020). Education: Ph.D. (1998) and M.S. (1995) from University of Rochester; B.S. (1993) in Computer Science and Mathematics from Angelo State University. Research Interests : Zaki's work spans novel data mining techniques for graph-structured/textual data, scalable graph algorithms (e.g., GRAIL, IBMGenerator), and applications in finance, healthcare, and bioinformatics. He has pioneered methods for frequent pattern mining, sequence analysis, and knowledge graph construction. Recognition : Recipient of NSF CAREER Award, DOE Early Career Award, and Fellowships from IEEE, ACM, AAAS, and SIAM. His research is supported by NSF, DARPA, NIH, DOE, and industry partners like IBM, Google, and NVIDIA. Grants & Leadership : Leads the CRAFT Center for Financial Technology research and serves on ACM SIGKDD's Board of Directors. Has chaired major conferences including SIGKDD, CIKM, and IEEE BigData. Active in open-source projects, maintaining tools like IBMGenerator for synthetic data creation and GRAIL for graph reachability queries. Labs/Teams : Directs research in RPI's Data Mining Lab, focusing on scalable algorithms and real-world applications. Collaborates with interdisciplinary teams on health informatics, financial systems analysis, and molecular graph learning.

Bo Xiaoمشاهده پروفایل

استادیار

• Construction Informatics
• Computer Vision
• Construction Robotics
• +۳ مورد دیگر

Bo Xiao is an Assistant Professor in Construction Management at Michigan Technological University, with joint appointments in Civil Engineering and the College of Business. His research develops AI and robotics solutions for construction challenges. His work focuses on: Computer vision for construction site monitoring and productivity analysis Natural language processing for automated documentation Blockchain applications in contract management Cybersecurity for construction data Robotics and automation in building processes His publications demonstrate innovations in vision-based monitoring, including nighttime equipment tracking, safety hazard detection, and automated report generation. Recent articles integrate deep learning with construction management applications. He received the 2022 Outstanding Reviewer Award from ASCE and third place in ASCE's Best Data Competition. He leads NSF-funded projects on cyber-training platforms and geodynamic modeling frameworks. He directs the Computing and Robotic Construction (CaRC) Lab, advancing automation in construction through computer vision, robotics, and AI technologies.

Leila Kosseimمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics
• +۳ مورد دیگر

Leila Kosseim is a Professor in the Department of Computer Science and Software Engineering at Concordia University, co-directing the CLaC (Computational Linguistics @Concordia) lab. She holds a PhD from the University of Montreal (1995) and has conducted postdoctoral research at Druide informatique inc. and the RALI group. Her academic leadership roles include Vice-President (2017-2019), President (2019-2021), and Past-President (2021-2023) of the Canadian AI Association (CAIAC), and current Graduate Program Director for the PhD Programs in CSSE. Her research focuses on Natural Language Processing (NLP) and Artificial Intelligence , with particular emphasis on discourse analysis, computational linguistics, and text generation. Notable projects include automated analysis of argumentative essays, clinical trial inference, and meme persuasion detection. Her work integrates deep learning, neural networks, and transformer models to address challenges in text coherence, discourse parsing, and multilingual NLP. Leila has supervised 8 PhD and over 20 Master’s students, contributing to the next generation of computational linguists. Her CLaC lab has participated in multiple international challenges (SemEval, TREC, DEFT) and produced influential tools like TIMBERT and the Concordia NLG Surface Realizer.

Nikola Nikolovمشاهده پروفایل

دانشیار

• Machine Learning
• Natural Language Processing
• Graph Drawing
• +۵ مورد دیگر

Nikola Nikolov is an Associate Professor in the Department of Computer Science & Information Systems at the University of Limerick. He holds memberships in the Centre for Research Training in Foundations of Data Science, the Data-Driven Computer Engineering Research Centre, and Lero – the Irish Research Centre for Software, reflecting his deep integration into Ireland's national research infrastructure. His research spans Machine Learning, Natural Language Processing, and Graph Drawing, with specialized expertise in deep learning architectures, matrix factorization techniques, and feature extraction methodologies. Current work focuses on multilingual NLP applications for social media analysis, convolutional neural network optimizations, and collaborative filtering systems, demonstrating consistent innovation across computational linguistics and graph theory domains. Analysis of his 2023-2025 publications reveals a dominant focus on combating online toxicity through advanced NLP systems for racism and hate speech detection across multiple languages, alongside significant contributions to computer vision efficiency and recommendation system architectures. His work bridges theoretical advancements with real-world applications in healthcare analytics and autonomous systems. Prof. Nikolov actively contributes to UN Sustainable Development Goals through data science applications while maintaining strong industry connections via research centres focused on software innovation and data-driven engineering solutions.

Jeff Cummingsمشاهده پروفایل

استاد

• Business Analytics
• Information Systems
• Cybersecurity
• +۲ مورد دیگر

Jeff Cummings is a Professor at the University of North Carolina Wilmington's Cameron School of Business, specializing in information systems and business analytics. He holds a Ph.D. in Information Systems from Indiana University, an MBA from Texas Tech University, and a BBA in Management Information Systems from Texas Tech University. His research focuses on business analytics , cybersecurity , and technology education , with emphasis on practical applications in industry and curriculum development. He teaches graduate courses including MIS 503 - Programming for Analytics, emphasizing foundational programming skills transferable across analytics domains. His scholarly work shows consistent focus on cybersecurity frameworks, technology skill assessments, and educational methodologies, with recent publications examining maritime cybersecurity, trust dynamics in security systems, and workforce skill gaps. Awards & Grants: Gordon Hurlbert Faculty Fellowship (2015-present) EDSIGCON Best Paper Award (2016) Discere Aude Teaching Award (2015) UNCW Summer Research Grants (2013-2015) His pedagogical approach emphasizes interactive learning through discussion boards, group projects, and applied conceptual understanding in online education environments.

Olga Baysalمشاهده پروفایل

دانشیار

• Mining Software Repositories
• Empirical Software Engineering
• Code Review
• +۲ مورد دیگر

Olga Baysal is Director of Carleton University Institute for Data Science and Associate Professor in the School of Computer Science. Her research applies data mining, machine learning, and NLP techniques to software engineering challenges, focusing on mining software repositories, empirical studies, and human aspects of development. She directs the Software Analytics (SWAN) research group and collaborates with industry partners including IFS Canada and Ericsson. Education includes PhD from University of Waterloo (2014), MMath from Waterloo (2006), and BSc from Vyatka State University (2001). Prior appointments include Université de Montréal and Ryerson University. Research analyzes software artifacts to synthesize knowledge from development activities. Current work examines code review practices, API evolution, and developer collaboration patterns using repository mining and qualitative methods. Recent publications explore release cadence patterns, API documentation summarization, and terminal interface innovations. Longitudinal work investigates developer behavior in Stack Overflow and GitHub ecosystems. Extensive academic service includes program co-chair for ICPC and MSR conferences. Teaching includes courses in Mining Software Repositories and Introduction to Data Science.

Kerri Johnsonمشاهده پروفایل

استاد

• Nonverbal Visual Communication
• Body Motion Analysis
• Social Categorization
• +۳ مورد دیگر

Kerri Johnson is a Professor and Associate Vice Chancellor for Faculty Development at the University of California, Los Angeles (UCLA), affiliated with the Department of Communication within the College of Letters & Science. Her research integrates advanced methods like corneal reflection eye-tracking, 3D motion analysis, and psychophysiology to study nonverbal communication processes, focusing on visual cues, body motion, and social categorization. She holds a Ph.D. in Personality and Social Psychology from Cornell University, alongside M.A. and B.S. degrees from the University of Central Oklahoma. Her work explores how perceivers utilize visual and behavioral cues to form judgments about social categories such as race, gender, and sexual orientation. Research highlights include studies on the impact of facial typicality in political perception, the role of body motion in gender judgments, and the accuracy of social network assessments from facial cues. Johnson’s methodologies emphasize cutting-edge tools to achieve precise insights into interpersonal communication dynamics. Publications span topics like sensory adaptation in voice perception, ensemble-based group evaluations, and the intersectionality of race and gender in social perception. Her contributions have advanced understanding of how visual and contextual information shape social judgments, with implications for fields ranging from healthcare to political communication. While no specific advising or grant details are provided in the text, her extensive publication record reflects sustained research engagement. She is actively involved in UCLA’s Department of Communication, contributing to academic administration and social sciences outreach initiatives.

Simone Teufelمشاهده پروفایل

استاد

• Natural Language Processing
• Discourse Analysis
• Argumentation Mining
• +۵ مورد دیگر

Simone Teufel is a Professor of Language and Information at the University of Cambridge's Department of Computer Science and Technology. She holds a Diplom in Computer Science from the University of Stuttgart (1994) and a PhD in Cognitive Science from Edinburgh University (2000). Her postdoctoral work at Columbia University focused on medical information retrieval. She joined Cambridge in 2001 as a Lecturer and was promoted to Professor in 2017. Her research centers on text understanding, including discourse structure, scientific argumentation, and metaphor modeling. Key areas include Argumentative Zoning (AZ), which analyzes sentiment in cited work and speech acts in scientific texts, and cognitive experiments to validate models in real-world applications. She develops tools like TIARA for discourse annotation and sentence reordering in student essays. Her work bridges computational linguistics with practical applications in scientific search, academic writing tools, and legal argumentation analysis. Recent publications (2022–2024) explore metaphor detection, legal NLP ethics, and semantic relation evaluation in language models. She collaborates on projects like ChainNet (metaphor/metonymy modeling) and TongueSwitcher (code-switching identification). Her research emphasizes robust processing for real-world tasks such as improving logical flow in ESL essays and analyzing legal precedents. Teufel’s contributions include advancing discourse analysis frameworks and integrating cognitive insights into NLP systems. Her work addresses challenges in knowledge graph explanations, scientific impact measurement, and cross-lingual NLP applications.

• Strategic Communication
• Narrative Analysis
• Public Health Communication
• +۳ مورد دیگر

Steven Corman is a Professor at the Hugh Downs School of Human Communication at Arizona State University (ASU). His research focuses on strategic communication, narrative analysis, and the intersection of social media with public health and political discourse. He has conducted extensive studies on topics such as anti-vaccine subgroups, climate change framing, and the impact of social media deplatforming on political communities. His work integrates computational methods with social science theory, addressing challenges in conflict resolution, organizational behavior, and public policy. Dr. Corman’s research emphasizes real-world applications, such as developing tools like the LookingGlass contextual mapping system for tracking population movements. His expertise spans interdisciplinary domains, including artificial intelligence, narrative detection, and behavioral economics. He has contributed to understanding how narratives shape extremist ideologies and how strategic communication can counter harmful messaging. Key areas of focus include: Public health communication during crises (e.g., pandemic vaccination strategies) Social media analytics and network ripple effects Organizational influences on athlete safety perceptions Computational models for detecting extremist narratives His recent work highlights trends in AI-driven question-answering systems (e.g., UnSeenTimeQA) and innovative approaches to event argument extraction. Dr. Corman’s contributions bridge academic research with practical solutions for global challenges in communication and conflict resolution.

• Natural Language Processing
• Automated Text Summarization
• Computer Assisted Language Learning
• +۶ مورد دیگر

Professor Manabu Okumura is affiliated with the Advanced Information Processing Division at Tokyo Institute of Technology, leading the Precision and Intelligence Laboratory. His research focuses on natural language processing (NLP), automated text summarization, and speech dialogue systems, emphasizing real-time, user-friendly interaction. Key areas include incremental language understanding, robustness in ill-formed input analysis, and semi-automatic lexical acquisition. His work addresses challenges like syntactic-semantic integration using case frame analysis, ellipse resolution in Japanese, and associative lexical relations extraction from corpora. Research projects include communication assistive technologies for disabled individuals and natural language-driven animation control. Publications span foundational NLP topics such as word sense disambiguation via semantic networks, zero pronoun resolution using centering theory, and incremental discourse processing. The lab collaborates on bilingual corpus analysis for lexicon development and maintains a focus on practical applications of theoretical advances.