Find Academic Supervisors

• Deep Learning
• Data Mining
• Artificial Intelligence
• +7 more

Asad Abdi is a Lecturer in Computer Science at the College of Science and Engineering. His research focuses on deep learning, data mining, and artificial intelligence , with applications in traffic analysis, educational technology, and maritime logistics. He has published extensively on topics like social media-based traffic forecasting, fake news detection, and vessel arrival prediction. Abdi’s work bridges theoretical advancements in machine learning with practical challenges in domains such as transportation systems and education. He has explored hybrid approaches combining deep learning models with linguistic knowledge and knowledge graphs to address real-world problems. Notable contributions include frameworks for feedback analysis in hybrid classrooms and fusion-based prediction models for vessel arrival times. His recent articles highlight trends in leveraging large language models and multi-feature fusion techniques for tasks like opinion summarization and aspect extraction. Abdi’s research often emphasizes interdisciplinary collaboration, integrating insights from computer science, transportation engineering, and educational psychology. While no formal awards or grants are explicitly listed, his publication record demonstrates sustained contributions to applied AI and data-driven solutions across multiple sectors.

Xinya DuView profile

Assistant Professor

• Natural Language Processing (NLP)
• Large Language Models (LLMs)
• Multimodal Models (VLMs)
• +7 more

Xinya Du is an Assistant Professor in the Department of Computer Science at the University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. She holds a Ph.D. in Computer Science from Cornell University and completed a postdoctoral fellowship at the University of Illinois at Urbana-Champaign. Her research focuses on advancing trustworthy and impactful AI systems, particularly in Natural Language Processing (NLP), Large Language Models (LLMs), and Vision-Language Models (VLMs). Key research areas include Document understanding and knowledge acquisition Trustworthy reasoning and hallucination detection in LLMs Applications of NLP in scientific research and multimodal systems Alignment of AI systems with human values Dr. Du has received notable awards such as the NSF CAREER Award (2024), Amazon Research Award (2023), and recognition as a Spotlight Rising Star in Data Science. She has authored over 30 papers in top venues like ACL, EMNLP, NeurIPS, and CVPR, contributing to foundational work in multimodal reasoning, LLM evaluation, and automated scientific hypothesis generation. She teaches advanced courses including CS 6301: Special Topics in Computer Science - Deep Learning for NLP and actively mentors students in research projects. Her work has been highlighted in major media and led to impactful open-source contributions, including repositories for event extraction and LLM benchmarking.

Jiebo LuoView profile

Professor

• Computer Vision
• Natural Language Processing
• Machine Learning
• +7 more

Jiebo Luo is the Albert Arendt Hopeman Professor of Engineering and Professor of Computer Science at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD and has been affiliated with the Department of Computer Science since 2011, following a 15-year career at Kodak Research. His research spans computer vision, natural language processing, machine learning, data mining, computational social science, and digital health. Luo is an ACM Fellow, AAAI Fellow, IEEE Fellow, SPIE Fellow, and IAPR Fellow. Education: PhD (specific discipline not explicitly stated in text). Research interests include computer vision, machine learning, data mining, social media analysis, biomedical informatics, human-computer interaction, and ubiquitous computing. He co-authored the book Deep Neural Network for Medical Image Computing: Principles and Applications (Elsevier, 2022). His work has led to nearly 600 technical papers and 90+ U.S. patents. Research Trends: Recent work focuses on large language models (LLMs), multimodal systems, AI-driven social media analysis, and healthcare applications. Key areas include bias analysis in political simulations, video understanding, and benchmark development for AI-generated content evaluation. His research bridges theoretical advancements with practical applications in healthcare, social sciences, and multimedia systems. Awards: ACM SIGMM Technical Achievement Award (2021), IEEE Region 1 Technological Innovation Award (2018), Eastman Innovation Award (2004), and multiple best-paper recognitions at top conferences. Service & Leadership: Served as program co-chair for ACM Multimedia 2010, IEEE CVPR 2012, ACM ICMR 2016, and IEEE ICIP 2017. Currently Editor-in-Chief of IEEE Transactions on Multimedia (2020–2022). Editorial board roles include several IEEE Transactions journals and conferences. Labs & Teams: Leads research groups in computer vision and multimodal computing at the University of Rochester, collaborating on projects like UroSAM (kidney stone classification) and computational social science initiatives.

Hongwei WangView profile

Researcher

• Natural Language Processing
• Interconnected Systems
• Large Language Models
• +6 more

Dr. Hongwei Wang is a Senior Researcher at Tencent AI Lab Seattle , specializing in applied machine learning for Natural Language Processing and Interconnected Systems . His work bridges Knowledge Graphs , Recommender Systems , and Graph Neural Networks , with a focus on large language models and retrieval-augmented generation. Ph.D. (2018), B.E. (2014) in Computer Science from Shanghai Jiao Tong University Postdoctoral Researcher : Stanford University (2019-2021), University of Illinois Urbana-Champaign (2021-2022) Dr. Wang’s research explores integrating Knowledge Graphs with Graph Neural Networks to enhance recommendation systems, language models, and information retrieval. His work spans Retrieval-Augmented Generation , Representation Learning , and GAN-based Graph Modeling , with recent papers on State-Space Exploration for LLM agents and Semantic Watermarking . His 15 most recent publications (2022-2024) focus on Retrieval Granularity , Interactive Memory , and Agent Systems , with keywords spanning Computer Science , Machine Learning , and Knowledge Graphs . Trends highlight advancements in Token-Level Semantic Matching , Schema-Guided Event Prediction , and Multi-Document Summarization . Scientific Awards: 2020 CCF Outstanding Doctoral Dissertation Award 2018 Google Ph.D. Fellowship Dr. Wang contributes to open-source projects like DKN and RippleNet , with 11 repositories on GitHub. He actively engages in Knowledge Graph Conferences (KDD, WWW, AAAI) and studies Chinese Classical Poetry and Film Arts .

• Natural Language Processing
• Machine Learning
• Explainable AI
• +4 more

Isabelle Augenstein is a Professor at the University of Copenhagen's Department of Computer Science, where she leads the Copenhagen Natural Language Understanding (CopeNLU) research group and the Natural Language Processing section. She became Denmark's youngest female full professor in 2022 and co-leads the Danish Pioneer Centre for Artificial Intelligence's Speech and Language collaboratory. ERC Starting Grant recipient DFF Sapere Aude Research Leader fellow Karen Spärck Jones Award winner Hartmann Diploma Prize recipient Her research focuses on fair and accountable NLP systems, with specific emphasis on explainability, factuality, bias detection, and social NLP. She investigates cultural biases in language models, develops frameworks for explainable fact checking, and explores uncertainty estimation in NLP systems. Recent publications demonstrate expertise in: Mechanistic analysis of cultural bias representations Context utilization techniques for LLMs Explainability metrics and attribution methods Cross-domain label adaptation Retrieval-augmented generation Fact checking uncertainty quantification Major scientific contributions include: Numerous EMNLP and ACL publications Foundational work on stance detection Development of fact checking benchmarks Multilingual model analysis AI ethics frameworks She supervises a team of researchers working on explainable AI and fact checking systems, with current projects including the ExplainYourself ERC-funded initiative on explainable fact checking. Her group recently presented multiple papers at EMNLP 2025 on topics spanning explainable AI and social NLP.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Min Peng is a Professor at Wuhan University's School of Computer Science. His research focuses on artificial intelligence, machine learning, natural language processing, and knowledge graphs. He has collaborated extensively with institutions like Hefei University of Technology and the University of Chinese Academy of Sciences. His work bridges theoretical advancements in AI with practical applications in finance, social media analysis, and network optimization. Recent contributions include neural-symbolic reasoning frameworks, contrastive learning for knowledge graphs, and financial benchmarking with large language models. Research interests emphasize scalable machine learning models for complex reasoning tasks, explainable AI, and domain-specific applications in finance and social networks. Over 100 publications span venues like WWW, ACL, and NeurIPS, highlighting interdisciplinary impact. Notable projects include SymAgent (neural-symbolic agent frameworks), PIXIU (financial LLM benchmark), and DTC (commonsense machine comprehension). Key technical trends include integrating large language models with structured data, temporal knowledge graph reasoning, and transfer learning across domains. His work often addresses real-world challenges in data efficiency, interpretability, and cross-domain scalability. Current efforts explore financial LLMs, agent-based reasoning systems, and multimodal applications. While no specific grants or awards are listed in the provided data, his prolific publication record indicates sustained research excellence. Collaboration networks include teams in computer science, electrical engineering, and finance disciplines.

Nazli GoharianView profile

Clinical Professor

• Information Retrieval
• Text Mining
• Natural Language Processing
• +1 more

Nazli Goharian is a Clinical Professor of Computer Science at Georgetown University and Associate Director of the Information Retrieval Lab. She holds a PhD from Florida Institute of Technology and joined Georgetown in 2010 after industry experience and previous academic positions at Illinois Institute of Technology. Education: PhD Computer Science, Florida Institute of Technology (2001) MSc Computer Science, George Mason University (1995) BSc Computer Science, Dortmund University (1992) Her research spans information retrieval, text mining, and natural language processing with applications in health/medical domains. She focuses on developing computational methods for medical search, mental health analysis from social media, clinical text summarization, and adverse drug reaction detection. Her recent publications (2020-2016) predominantly focus on neural ranking models, transformer architectures for document retrieval, and clinical NLP applications. Notable trends include work on BERT-based re-ranking, zero-shot multilingual retrieval, and ontology-aware medical summarization. Awards & Honors: EMNLP 2017 Best Long Paper Award COLING 2018 Honorable Mention & Area Chair Favorite Julia Beveridge Award for Faculty (IIT, 2009) Multiple Teaching Excellence Awards (2002-2007) Research Leadership: She has supervised 6 PhD students to completion with placements at leading institutions. Secured over $500,000 in research funding from NSF, Adobe, and international partners. Founded the Semi-Annual Graduate Research Presentation Days at Georgetown and served as Program Chair for ECIR 2024. She leads the Information Retrieval Lab which focuses on developing novel algorithms for efficient document retrieval, cross-lingual search, and specialized applications in healthcare text analysis.

• Artificial Intelligence
• Computer Vision
• Natural Language Processing
• +4 more

Armin Kirchknopf serves as a Junior Researcher at the Media Computing Research Group within the Institute of Creative Media/Technologies, Department of Media and Digital Technologies at the University of Applied Sciences St. Pölten. His interdisciplinary work bridges artificial intelligence, computer vision, and social media analysis, with significant contributions to misinformation detection and disaster response systems. Based at Campus-Platz 1 in St. Pölten, Austria, he actively collaborates on EU-funded projects and publishes in top-tier AI venues. His educational journey spans humanities and technology: a Bachelor of Arts in Egyptology and Master of Arts in Classical Archaeology from the University of Vienna (including fieldwork at excavation sites across Austria, Germany, and Egypt), followed by a Bachelor of Science in Media Technology from FH St. Pölten. This unique background informs his human-centered AI research approach. Kirchknopf's research centers on explainable multimodal AI systems for real-world challenges. His recent work demonstrates expertise in transformer-based architectures for cross-lingual fake news detection, sexism identification, and flood monitoring through social media imagery. He pioneers techniques like Grad-CAM for object detection explainability and develops visualization tools for complex data interpretation, emphasizing transparency and social impact in AI deployment. Analysis of his 13 publications (2017-2022) reveals a strategic shift toward applied AI in societal contexts , particularly using social media data for disaster management and combating online toxicity. His projects consistently integrate computer vision with natural language processing, showing increasing sophistication in multilingual capabilities and model interpretability frameworks. His scientific recognition includes: Creative Business Award for co-developing the Tenjin learning quiz application No documented student advisement or grant leadership appears in current records, though he actively mentors through project-based collaborations. His work with the Media Computing Research Group drives innovation in educational technology and public safety applications. Kirchknopf contributes to the Media Computing Research Group's portfolio including Fake News Detection, SAiEX (Safe AI with explainable integrity), InfraBase (building footprint segmentation), and Ressel Center music therapy projects. His cross-disciplinary collaborations span computer scientists, archaeologists, and social scientists, reflecting the group's commitment to human-centric technological solutions .

Antoine DoucetView profile

Professor

• Information Retrieval
• Natural Language Processing
• Text Mining
• +7 more

Antoine Doucet is a Full Professor at the University of La Rochelle, where he teaches in the Computer Science department of the University Institute of Technology (IUT). He conducts his research at the Computer Science, Image and Interaction Laboratory (L3i) within the 'Images and Content' team, which he has led since 2015. He is also a member of the Franco-Vietnamese laboratory ICTLab and serves as Director of the ICT Department at the University of Science and Technology of Hanoi since 2016. His research focuses on information retrieval, natural language processing, text mining, and artificial intelligence, with emphasis on automatic analysis of text in all forms across languages. His work prioritizes generic methods that work across languages without relying on language-specific linguistic resources. This approach is particularly valuable for under-resourced languages and noisy texts from sources like social media or OCR output. As coordinator of the Horizon 2020 NewsEye project, he led efforts to improve access to European historical newspapers through semantic enrichment and advanced search capabilities. His research has practical applications in epidemic surveillance, document fraud detection, and historical content analysis. The NewsEye project involved 11 teams across Europe, including 3 national libraries and multiple research groups. Best paper award from IMIA Yearbook 2016 (among 1,272 candidates) Best paper award at HCI International with Ilona Nawrot Press coverage for ACL 2013 paper in major publications Recipient of French scientific excellence award (Prime d'Excellence Scientifique) Doucet actively supervises PhD and Master's students, with recent advisees including Chloé Artaud (Document fraud detection), Paul Martin (Photograph Time-Stamping), Ilona Nawrot (Temporal and Multilingual Text Analysis), and Gaël Lejeune (Multilingual Epidemic Surveillance). His research has been funded through multiple projects including ANR Digistory, AmeliOCR, PHC Nusantara, and USTH SWARMS. He has also coordinated significant European projects like NewsEye and Embeddia. At L3i, he leads a research group of approximately 40 persons focused on Images and Digital Content. His work bridges theoretical advances in multilingual text processing with practical applications in historical document analysis, epidemic surveillance, and document security.

• Instructional Technology
• Computer-Supported Collaborative Learning
• Technology-Enhanced Learning
• +7 more

Hannie A. Gijlers is an Associate Professor at the Digital Society Institute, affiliated with the Department of Instructional Technology in the Faculty of Behavioral, Management and Social Sciences at the University of Twente. Her work bridges technology and education, focusing on innovative learning environments and collaborative processes. Academic Rank: Associate Professor Institution: University of Twente Research Institute: Digital Society Institute Department: Instructional Technology Her research interests center on computer-supported collaborative learning (CSCL) , technology-enhanced STEM education , inquiry-based learning , and the role of emotions and feedback in learning. She investigates how digital tools and agent-facilitated environments support students and teachers in collaborative settings, often through cross-cultural comparisons. Her work integrates physiological and gaze-based data to understand implicit learner emotions, pushing the boundaries of learning analytics. The recent publications (2021–2024) reveal a strong focus on teacher guidance models , gamified STEM apps , clinical reasoning in medical education , and climate change literacy . These works span disciplines including educational psychology, computer science, and environmental education, indicating interdisciplinary collaboration. Keywords across these articles include collaborative learning, emotion detection, technology integration, and cross-cultural studies, reflecting a consistent research trajectory in digital pedagogy and learner support. While no specific awards are listed in the provided text, her sustained publication record in high-impact venues and leadership in collaborative research projects suggest recognition within the academic community. Hannie Gijlers actively supervises research, as evidenced by co-authored works with junior researchers and contributions to practitioner research models. Her involvement in datasets and conference presentations indicates ongoing grant-funded or institutionally supported research activities. She has contributed to both theoretical frameworks (e.g., four-phase mentorship model) and applied tools (e.g., Science Chaser app), demonstrating a balance between scholarship and practical innovation. She is part of a vibrant research network at the University of Twente, collaborating with scholars like T.H.S. Eysink, A.W. Lazonder, and N. Janssen. Her team explores agent-facilitated learning, gamification in STEM, and teacher development, often in international collaborations (e.g., Dutch-Finnish studies). These efforts are supported by digital infrastructure and interdisciplinary datasets, positioning her at the forefront of digital society research in education.

Chris ThomasView profile

Assistant Professor

• Computer Vision
• Cross-modal Retrieval
• Multimodal Knowledge Representation
• +3 more

Chris Thomas is an Assistant Professor in the Department of Computer Science at Virginia Tech’s College of Engineering. His research focuses on computer vision, cross-modal retrieval, and multimodal knowledge representation, with applications in information extraction, fake news detection, and AI safety. He leads the Sanghani Center for Artificial Intelligence and Data Analytics and has received grants from the Commonwealth Cyber Initiative and a Google Research Scholar award. Education: Ph.D. (2020) and B.S. (2013) in Computer Science, University of Pittsburgh. Research Interests: Developing robust cross-modal systems that bridge vision and language. Key areas include fine-grained visual entailment, multimodal inconsistency detection, and defending AI models against adversarial attacks. His work emphasizes practical applications like fact-checking and cybersecurity. Recent Contributions: Led the development of JourneyBench (a vision-language benchmark) and the Semantic Shield defense framework. Recent grants include cybersecurity for embodied agents and safer multimodal web agents. Awards: Google Research Scholar Award (2025), multiple Commonwealth Cyber Initiative grants (2024–2025). Advising & Grants: Advises students like Hani Alomari (ACL 2025) and collaborates with institutions like Columbia University and UCLA. His work integrates multimodal data to address real-world challenges in AI safety and information integrity. Labs/Teams: Active in Virginia Tech’s AI initiatives, focusing on interdisciplinary research at the intersection of vision, language, and security.

• Natural Language Processing
• Text Simplification
• Emoji Analysis
• +9 more

Dr Matthew Shardlow is a Reader in the Department of Computing and Mathematics at Manchester Metropolitan University . His career spans teaching and research, with a focus on Natural Language Processing (NLP) and its applications in Text Mining across disciplines like Neuroscience , Chemistry , Finance , and Journalism . He leads the Natural Language Processing Lab , which explores the use of Large Language Models (LLMs) in text-based applications. Key research areas include: Text Simplification : Developing methods to make complex texts accessible. Emoji Analysis : Understanding emoji usage in language and machine interpretation. Text Mining : Applying NLP to diverse fields like healthcare and education. Matthew’s recent publications highlight advancements in LLMs , lexical simplification , biomedical text processing , and AI ethics . He actively mentors BSc , Master’s , and PhD students aligned with his interests and maintains collaborations through the OpenMinTeD project and National Centre for Text Mining . Contact: m.shardlow@mmu.ac.uk .

Snigdha ChaturvediView profile

Associate Professor

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +8 more

Snigdha Chaturvedi is an Associate Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. She previously held faculty positions at the University of California, Santa Cruz, and has conducted postdoctoral research at the University of Pennsylvania and University of Illinois, Urbana-Champaign. PhD in Computer Science from University of Maryland, College Park Bachelor's degree in Computer Science and Engineering from Indian Institute of Technology (IIT) Kanpur Her research spans Natural Language Processing with emphasis on Narrative Understanding , Text Summarization , and Socially Aware Language Generation . She advances Fairness in AI through ethical NLP applications in Mental Health and Educational Technology . Recent work focuses on 2025 publications in ACL and NAACL journals, alongside 2024 contributions to EMNLP Findings and ICLR . Earlier projects include the NarraSum dataset (2022) and MOOC forum analysis (2020). Scientific recognitions include: ACM Student Research Competition First Place (2014) IBM PhD Fellowship (2014-2015, renewed in 2015) Kulkarni Summer Research Fellowship (2015) WPI STEM Faculty Launch Program Participant (2015) Her team has advised 13 PhD and Master's students with notable placements at Bloomberg, AI2, and University of Southern California. Research integrates Accessibility challenges through collaborations with Google and IBM labs.

Michihiro YasunagaView profile

Assistant Professor

• Large Language Models
• Multimodal Models
• Reasoning Systems
• +6 more

Michihiro Yasunaga is an Assistant Professor in the Department of Computer Science at Stanford University's School of Engineering. He received his PhD in Computer Science from Stanford, advised by Percy Liang, Jure Leskovec, and Chris Manning. Prior to his faculty position, he worked as a researcher at Google DeepMind and Meta. His research focuses on building LLMs and agents that assist humans in diverse tasks, with particular expertise in post-training techniques (RL, reward models, and evaluation), reasoning systems (AnalogicalReasoner), retrieval and tool use for LLMs (LinkBERT, QAGNN, DRAGON, REPLUG, HippoRAG), and multimodality (RA-CM3, Med-Flamingo, Transfusion). His work spans both theoretical foundations and practical applications of large language models. Yasunaga's publication record demonstrates significant contributions to the field of AI, with 15 recent articles (2023-2025) covering diverse aspects of language model development, evaluation, and application. His research shows a clear trajectory toward building more capable, efficient, and reliable multimodal AI systems, with particular emphasis on knowledge integration and robust evaluation frameworks. Among his notable achievements is the Best Paper Award at AAAI 2023 Deep Learning on Graphs Workshop for the DRAGON paper. He has also been deeply involved in major benchmarking efforts including HELM and HEIM, which provide comprehensive evaluation frameworks for language and vision-language models. Yasunaga actively contributes to the research community through service roles including Organizing Committee for the Workshop on Knowledge-Augmented Methods for NLP (ACL 2024), Workshop on Structured and Unstructured Knowledge Integration (NAACL 2022), and the Workshop on Scientific Document Summarization (SIGIR 2017-2020). He has also served on program committees for top conferences including NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, and ICCV from 2020-2025.

David MillardView profile

Professor

• Hypertext Systems
• Interactive Digital Narratives
• Mixed Reality Games
• +3 more

Professor David Millard is a Professor of Computer Science at the University of Southampton's Electronics and Computer Science department. He serves as Head of the ECS Education Group, leading a team of 15 Teaching Fellows and Senior Fellows to improve teaching and learning across the department. Previously, he was Director of Admissions for ECS from 2012 to 2022. David is a founding member of the Web and Internet Science research group and has been active in the international hypermedia community for over twenty-five years. His professional affiliations include: Chair of the ACM Hypertext steering committee SIGWEB Liaison for ACM Web Science Former vice-chair of ACM SIGWEB (2015-2019) Editorial board member of the New Review of Hypermedia David Millard's research focuses on the intersection of hypertext, digital narratives, and mixed reality games, with particular emphasis on locative literature. His work has helped define 'Sculptural Hypertext' - hypertext governed by rules and constraints rather than navigational links - which is highly suitable for designing narrative structures within digital games. He has been a pioneer in locative narratives, interactive digital narrative games experienced on location-aware smart devices, with work dating back to 2002 at Chawton House. His recent research focuses on understanding the locative narrative design space and proposing the 'Balance of Attention' as the primary poetic of locative narrative. His research has been recognized with multiple awards including the Engelbart Award in 2013 and 2016 for his work on location-based storytelling and sculptural hypertext. His interdisciplinary StoryPlaces project explores the poetics of location-based narratives through digital storytelling deployments in Southampton, Bournemouth, and Crystal Palace. David has authored more than 230 peer-reviewed articles across Technology Enhanced Learning, Web Science, and Hypertext and Narrative, with over 4800 citations and an h-index of 35. His work has been published in prestigious journals including Springer Multimedia Tools and Applications, Elsevier Computers in Education, IEEE Intelligent Systems, PloS one, ACM Transactions on Information Systems, and ACM Transactions on Learning Technologies. He leads a team of PhD students focused on Interactive Narratives and Mixed Reality Spaces and has graduated 34 postgraduate students. His students have pursued careers in academia at institutions like Oxford, Winchester, Bournemouth, and East Anglia, as well as in industry at major technology firms including IBM and Google. David has been instrumental in Open Educational Resources development, creating EdShare in 2010 - an OER platform based on EPrints that now hosts over 20,000 learning items. He has also developed innovative teaching approaches, including modules on Multimedia Systems (which won the Vice-Chancellor's Teaching award), Programming 1, Social Media and Network Science, and Games Design and Development.