جست‌وجوی استاد و پژوهشگر

Shadi Rezapourمشاهده پروفایل

استادیار

• Computational social science
• Natural language processing
• Network analysis
• +۷ مورد دیگر

Shadi Rezapour is an Assistant Professor in the Department of Information Science at Drexel University's College of Computing & Informatics. She serves as Principal Investigator of the SocialNLP Lab and co-director of the DONUTS Lab at Drexel University, focusing on bridging computational methods with social science theories to analyze human language in social contexts. Dr. Rezapour received her educational training at the University of Illinois at Urbana-Champaign: PhD in Information Sciences, School of Information Sciences MSc in Information Management, School of Information Sciences Her research lies at the intersection of computational social science and natural language processing (NLP), with a focus on developing "socially-aware" NLP models that incorporate cultural and social contexts to better understand human behavior, attitudes, and cultures through language analysis. She investigates how language shapes public opinion and conveys complex social and cultural information, particularly in online communities and social media platforms. Her work addresses important societal issues including substance use disorder narratives, podcast analysis, controversial topics and polarized viewpoints, and the impact of information products on socio-cultural environments. Dr. Rezapour's recent publications demonstrate a strong focus on applying NLP techniques to social good, with particular emphasis on reducing stigma in substance use conversations, analyzing narratives in media coverage, and examining the societal impacts of AI technologies. Her research combines technical innovation with social relevance, often employing network analysis alongside linguistic approaches to gain deeper insights into human communication patterns. Among her professional recognitions: NCWIT/Bloomberg grant to attend the Grace Hopper Celebration (2020) Dr. Rezapour actively contributes to the academic community through service roles including ICWSM 2025 tutorial co-chair, IC2S2 2024 local co-chair, ACM FAccT proceeding co-chair, and CSCW area chair. She has successfully secured research funding for her work on computational social science and NLP applications, mentoring students and early-career researchers in her labs. As leader of the SocialNLP Lab and co-director of the DONUTS Lab, Dr. Rezapour fosters interdisciplinary research that brings together computer science, linguistics, and social sciences to address complex societal challenges through computational approaches.

Mark Steedmanمشاهده پروفایل

استاد

• Computational musical analysis
• Computational linguistics
• Combinatory logic
• +۶ مورد دیگر

Mark Steedman is a Professor of Cognitive Science at the School of Informatics , University of Edinburgh, and an Adjunct Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research bridges Artificial Intelligence , Cognitive Science , and Computational Linguistics , with a focus on Combinatory Categorial Grammar (CCG) , Prosody and Intonation , and Temporal Semantics . He has led the Institute for Language, Cognition, and Computation and contributed to interdisciplinary research at the Human Communications Research Center and Centre for Speech Technology Research . Research Interests : Steedman's work explores the intersection of formal grammar, computational models, and cognitive processes. He investigates how CCG parsing can enhance semantic inference, how prosodic features improve speech processing, and the role of temporal semantics in language understanding. His projects often integrate language models with entailment graphs for question answering and dialogue systems. Scientific Awards : Fellow of the American Association of Artificial Intelligence (1993) Fellow of the Royal Society of Edinburgh (2002) Fellow of the British Academy (2002) Member of Academia Europaea (2006) Best Paper Awards at ACL 2023 and AACL/IJCNLP 2023 Influential Paper Award (IFAAMAS 2017) Recent Trends in Publications : His recent work emphasizes language models for semantic inference , entailment graphs in multilingual settings, and incremental parsing for brain-language interfaces. Papers address challenges in hallucination , cross-lingual transfer , and prosody-text alignment .

Benoit Favreمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Multimodal Systems
• +۵ مورد دیگر

Benoit Favre is a Full Professor (PR, Section 27) at Aix-Marseille University and its engineering school Polytech Marseille since 2021. He leads the Data Science axis at the Laboratoire d'Informatique et Systèmes (LIS, CNRS UMR7020), encompassing research teams in NLP, machine learning, and multimodal processing. He completed his HDR (habilitation) in 2019 and serves as co-head of the multimodality group in the CNRS GdR on NLP. His research focuses on Natural Language Processing with machine learning, including semantic content analysis, multimodal system fusion, and technology evaluation in realistic applications. He teaches advanced machine learning for NLP at the Master IAAA program and organizes the ILCB Summer School. The 15 most recent publications highlight trends in Vision-Language Transformers , biomedical classification, historical domain adaptation, and cross-modal analysis. These works span from 2020 to 2024, with particular emphasis on multimodal representations, semantic parsing, and robust NLP systems. Scientific Awards : Best paper at TALN 2011 Ranked 1st in PERCOL consortium (REPERE 2013-2014) Ranked 1st in TAC 2008 and 2009 Benoit is also deeply engaged in community service, having organized major conferences like IEEE SLT 2010, SLAM 2013, and TALN 2014. He serves on scientific committees for selection of PhD grants and faculty positions. Office location: University library building (Luminy campus), second floor, office C207

Hari Sundaramمشاهده پروفایل

استاد

• Applied Machine Learning
• Network Science
• Human Computer Interaction
• +۳ مورد دیگر

Hari Sundaram is a Professor in the Computer Science Department at the University of Illinois at Urbana-Champaign with affiliate appointments in the Charles H. Sandage Department of Advertising, the Institute for Communication Research, and the Center for Social & Behavioral Science. His academic journey includes positions as Associate Professor at the University of Illinois (2014-2021) and Arizona State University (2002-2014), where he also served as Associate Director of the Arts, Media and Engineering program (2012-2009). Dr. Sundaram's educational background includes a Ph.D. in Electrical Engineering from Columbia University (2002), an M.S. in Electrical Engineering from Stony Brook University (1995), and a B.Tech in Electrical Engineering from the Indian Institute of Technology, Delhi (1993). His research, conducted through the Crowd Dynamics Lab, focuses on designing computational systems that empower individuals to make better decisions. His work spans Applied Machine Learning (particularly recommender systems), Network Science (studying how platform rules induce strategic behavior), Human-Computer Interaction (developing systems to elicit truthful preferences), and Mechanism Design (creating rules to incentivize pro-social behavior). His research has significant implications for understanding fairness and discrimination in online markets. Dr. Sundaram's work has been recognized with numerous awards including multiple Best Paper Awards from ACM CSCW (2023), Best Article Award from the Journal of Interactive Advertising (2020), ACM Distinguished Member (2019), IEEE Senior Member (2019), and several IBM Faculty Awards. He has also been consistently recognized for teaching excellence, receiving the "Teacher Ranked as Excellent" award multiple times. As leader of the Crowd Dynamics Lab, Dr. Sundaram oversees research that bridges computer science with social sciences, focusing on how computational systems can enhance human decision-making while addressing fairness concerns. His work has practical applications in online marketplaces, social media platforms, and educational technologies.

Michael Ryooمشاهده پروفایل

دانشیار

• Deep Learning
• Computer Vision
• Robotics
• +۳ مورد دیگر

Michael Ryoo serves as a SUNY Empire Innovation Associate Professor in the Department of Computer Science at Stony Brook University while concurrently working as a Research Scientist with Google Brain's "Robotics at Google" team. Previously, he held positions as an Assistant Professor at Indiana University Bloomington and a Staff Researcher at NASA's Jet Propulsion Laboratory. His academic background includes a Ph.D. from the University of Texas at Austin (2008) and a B.S. from Korea Advanced Institute of Science and Technology (KAIST) in 2004. Ryoo's research centers on deep learning and computer vision with specific focus on convolutional neural network (CNN) models for video semantic understanding. His work bridges visual perception and robotic action through applications in robot perception, robot learning, and human-robot interaction. Key innovations involve developing efficient architectures for processing multimodal data and translating visual understanding into robotic control systems. Analysis of his 15 most recent publications reveals strong emphasis on multimodal AI integration, particularly vision-language models applied to robotics. His 2025 work shows significant advancement in token-efficient video representation, motion-controllable diffusion models, and zero-shot learning frameworks specifically designed for robotic control systems. The research trajectory demonstrates consistent focus on making video understanding more accessible and applicable to real-world robotic scenarios. Scientific awards: No awards were mentioned in the provided source material. Regarding academic advising, the source text does not list any students or mentoring activities. Similarly, no grant funding information is provided, though his dual academic-industry role suggests substantial research support. His Google Brain affiliation likely involves industry-sponsored research initiatives. Ryoo maintains active laboratory affiliations through Stony Brook's AI Innovation Institute and Google's Robotics team. His prior work at NASA JPL indicates experience with space robotics systems, while his current Google role focuses on large-scale robot learning infrastructure. The LAM SIMULATOR project represents his current focus on advancing data generation techniques for training large action models.

Klim Zaporojetsمشاهده پروفایل

پژوهشگر ارشد

• Natural Language Processing
• Information Extraction
• Entity Linking
• +۳ مورد دیگر

Klim Zaporojets is a Marie Skłodowska-Curie Postdoctoral Fellow in the Department of Computer Science at Aarhus University, where he conducts research within the Data-Intensive Systems Group. His work bridges theoretical advancements and practical applications in natural language understanding. His research focuses on information extraction systems that connect textual content with structured knowledge bases. His methodology emphasizes leveraging external knowledge sources to enhance information extraction performance, particularly in document-level contexts where entities evolve over time. His work spans temporal relation extraction, entity linking, and biomedical text mining applications. The publication record reveals a strong focus on document-level information extraction with increasing emphasis on temporal aspects and knowledge integration. Recent work explores large language model applications for graph learning and calibration challenges in LLMs, showing evolution from traditional NLP tasks to cutting-edge foundation model research. His publications appear in top-tier venues including ACL, EMNLP, CIKM, and NeurIPS. His scientific recognition includes the prestigious Marie Skłodowska-Curie Postdoctoral Fellowship, supporting his research at Aarhus University. His work has produced several influential datasets including DWIE, TempEL, and BioDEX that have become benchmarks in document-level information extraction. Zaporojets maintains active collaborations with researchers at Ghent University (evidenced by his ugent.be email address) and has contributed to multiple interdisciplinary projects spanning computational linguistics, healthcare informatics, and knowledge representation. His technical contributions include open-source implementations of his research, demonstrating commitment to reproducible science.

• Case-Based Reasoning
• Deep Metric Learning
• Explainability
• +۳ مورد دیگر

Dr. Kyle Martin is a Lecturer in the School of Computing, Engineering & Technology at Robert Gordon University (RGU), where he works in the Artificial Intelligence & Reasoning Research Group. He completed his PhD in 2021, after having been hired as a full-time lecturer and researcher in 2019. His academic focus centers on making AI systems more transparent and explainable, with applications across multiple sectors including digital health (with partners Jiva and Walk With Path), fintech (Sticklr), and horizon scanning (Citizen Advice Scotland). Dr. Martin's primary research interests include: Case-Based Reasoning Deep Metric Learning Explainability in AI systems Applied Machine Learning across various domains His recent publications (2023-2025) demonstrate a strong focus on explainable AI, particularly in developing case-based reasoning approaches to enhance the transparency of machine learning systems. He has published extensively on counterfactual explanations, legal question-answering systems, and automated essay grading, with 48 research outputs spanning multiple publication types. His work often bridges theoretical AI concepts with practical applications in real-world domains where understanding AI decision-making is critical. Dr. Martin is actively involved in research funding and supervision: Co-investigator on the iSee project (European consortium) focused on personalized explanation experiences Available for PhD supervision in Case-Based Reasoning, Deep Learning, Explainability, and Applied Machine Learning Program committee member for ICCBR and SGAI conferences Organizer of international workshops on Case-Based Reasoning and Deep Learning As a member of the Artificial Intelligence & Reasoning Research Group at RGU, Dr. Martin contributes to advancing reasoning capabilities in AI systems and developing practical applications of these technologies. His research has resulted in multiple publications in prestigious venues including ECAI, ICCBR, and Knowledge-Based Systems, demonstrating his growing impact in the AI research community.