Dr. Dunwei Wen is Associate Professor and Chair at the School of Computing and Information Systems within Athabasca University's Faculty of Science and Technology. With academic credentials from Ph.D. in Pattern Recognition and Intelligent Systems (Central South University) M.Sc. in Computer Science (Tianjin University) B.Eng. in Electrical Engineering (Hunan University) , he bridges theoretical AI research with practical implementations in information systems. His research program focuses on statistical learning and deep learning for Natural language processing Sequential data analysis Multimodal content understanding with applications spanning education, healthcare, and industrial domains. Publication trends show increasing emphasis on deep learning architectures for medical image analysis (SRTNet 2024), contextual topic modeling in education (2013-2015), and multimodal systems combining text/image analysis (2015-2018). Recent projects (2021-2022) center on self-supervised learning for natural language understanding. Professional engagements include Member, AAAI (Association for the Advancement of Artificial Intelligence) Member, ACM and ACM SIGAI Senior Member, IEEE Former CAAI Board Member (2001-2010) As academic advisor, he has supervised over 25 graduate students and interns, including Co-supervised 4 PhD candidates (Jilin University) Mentored 12+ Master's students (AU, Jilin University) Hosted 6 MITACS Globalink Research Interns with projects spanning from cardiac detection systems to educational data mining.
Jessy Li is an Associate Professor in the Department of Linguistics at The University of Texas at Austin, specializing in computational linguistics and natural language processing (NLP). She actively engages in interdisciplinary research across discourse processing, language generation, and NLP applications in social and code-related contexts. Education: Ph.D. in Computer and Information Science, University of Pennsylvania (2017) Jessy’s research spans four core areas: Discourse Processing (analyzing discourse structure, pragmatics, and human/machine comprehension), Natural Language Generation (improving text generation through discourse models), Language and Society (exploring how self-perception and social context influence language use), and Language and Code (applying NLP to software evolution and documentation). Her recent publications align with these themes, emphasizing discourse-level analysis, generation evaluation, and interdisciplinary applications of NLP in software engineering and sociolinguistics. She received recognition for an Outstanding Paper at EMNLP 2024. She serves on the Graduate Studies Committee in the Department of Computer Science and leads AI initiatives at the NSF-Simons AI Institute for Cosmic Origins (CosmicAI) . Her service roles include Senior Area Chair for ACL 2025, Action Editor for Transactions of the Association for Computational Linguistics (TACL) , and co-organizing the Workshop on Computational Approaches to Discourse (CODI).
Peter West is an incoming Assistant Professor at the University of British Columbia (UBC) Computer Science Department, specializing in Natural Language Processing (NLP) and AI. His research focuses on understanding the capabilities and limitations of large language models (LLMs) and generative AI systems, emphasizing their divergence from human intuition and alignment challenges. He holds a PhD from the University of Washington (2024), supervised by Yejin Choi, and a BSc (Honours Computer Science) from UBC (2017). His work has been recognized with awards including Best Method Paper at NAACL 2022 and Outstanding Paper awards at ACL 2023 and EMNLP 2023. He conducted internships at the Allen Institute for AI and Microsoft Research’s NLP group. Research interests include analyzing LLM behavior through a natural sciences lens, exploring model capabilities versus human expectations, and developing decoding algorithms to infuse models with algorithmic logic. His recent publications address generative AI paradoxes, constrained text generation, and symbolic knowledge distillation. He serves on panels for NeurIPS workshops and is beginning a postdoc at Stanford with Chris Potts. His research group at UBC seeks students interested in generative AI’s analytical frontiers.
Yue Guo is an Assistant Professor in the Department of Information Sciences at the University of Illinois. Her research focuses on natural language processing (NLP), biomedical informatics, and healthcare communication. She explores how large language models (LLMs) can improve lay language summarization of complex scientific content, particularly in healthcare contexts. Her work emphasizes personalized jargon detection, factuality evaluation of summaries, and accessibility of health information for diverse audiences. Guo has also conducted extensive studies on radiation oncology outcomes, including xerostomia (dry mouth) recovery in head and neck cancer patients and the impact of radiation dose patterns on patient-reported symptoms. Her interdisciplinary research bridges computational methods with clinical needs, aiming to enhance patient understanding and care through advanced NLP techniques. Her recent articles (2021–2025) highlight trends in LLM-driven biomedical communication, evaluation of health summaries, and data-driven approaches to cancer treatment optimization. Notable projects include APPLS (a summarization metric framework) and personalized jargon identification systems for interdisciplinary collaboration. Guo teaches IS 504: Sociotechnical Information Systems , integrating technical and human factors in information systems design.
Carlos Castillo is an ICREA Research Professor (Part-time) at Universitat Pompeu Fabra in Barcelona, where they lead the Social and Responsible Computing Research Group within the Department of Information and Communication Technologies. Dr. Castillo identifies as nonbinary and prefers they/them pronouns, and is also a Latinx migrant to Barcelona in Catalunya, Spain. Dr. Castillo received their Ph.D from the University of Chile in 2004, followed by visiting scientist positions at Universitat Pompeu Fabra (2005) and Sapienza Universitá di Roma (2006) before working as a scientist and senior scientist at Yahoo! Research (2006-2012), as a senior scientist and principal scientist at Qatar Computing Research Institute (2012-2015), and as director of research for data science at Eurecat (2016-2017). Dr. Castillo's research addresses issues of social significance through interdisciplinary computer science research, with primary focus on algorithmic fairness, crisis informatics, web content quality and credibility, and adversarial web search. Their work combines technical expertise in information retrieval with deep consideration of social implications, particularly in high-risk applications including criminal justice and recruitment. They have made significant contributions to understanding and mitigating discrimination in algorithmic systems, as evidenced by their book on Big Crisis Data and numerous influential publications. Recent publications demonstrate a strong emphasis on fairness in algorithmic decision-making, with numerous papers examining bias in hiring algorithms, recidivism prediction systems, and social media content analysis. The research shows an interdisciplinary approach that bridges computer science, social science, and policy considerations, with increasing attention to practical applications and real-world impact across domains including criminal justice, healthcare, education, and music recommendation systems. Dr. Castillo has received significant recognition for their work: Two test-of-time awards Four best paper awards Two best student paper awards ACM Distinguished Member IEEE Senior Member Accredited at the full professor level in Catalonia Dr. Castillo has served extensively in academic leadership roles, including as Program Committee or Senior PC member for major conferences (WWW, WSDM, SIGIR, KDD, CIKM), editorial committee member for ACM Transactions on the Web and ACM Transactions in Social Computing, and Executive Committee member of ACM FAccT. They were General Co-Chair of ACM FAccT (formerly FAT*) 2020, PC Co-Chair of ACM Digital Health 2016-2018, and PC Co-Chair of WSDM 2014. Dr. Castillo currently coordinates the Horizon Europe project FINDHR on detecting and mitigating discrimination in algorithmic hiring. They lead the Social and Responsible Computing Research Group, which takes an interdisciplinary approach to developing computational methods that consider social impact and ethical implications. The group works on projects involving computer scientists, social scientists, and domain experts to address societal challenges through responsible technological innovation, with current focus on algorithmic fairness in high-risk applications, crisis informatics, and understanding social dynamics through computational methods.
Chaitanya Shivade is a prominent researcher specializing in medical natural language processing with significant contributions to clinical text analysis, radiology informatics, and behavioral health documentation. His work bridges computational linguistics and healthcare applications, focusing on practical solutions for clinical documentation challenges. Shivade's research spans multiple critical areas: developing evaluation frameworks for behavioral therapy notes (TN-Eval), creating shared tasks for medical summarization (MEDIQA), advancing visual dialog systems for radiology, and pioneering synthetic clinical note generation. He has made substantial contributions to textual inference in clinical domains through the MedNLI dataset and has explored fundamental linguistic challenges like negation detection and gradable term analysis in medical text. As a workshop organizer for the NLP for Medical Conversations series, he has helped shape community standards and foster collaboration. His publication record demonstrates consistent leadership in applying NLP to real-world healthcare problems, with particular emphasis on evaluation methodologies, dataset creation, and practical clinical applications. Shivade has collaborated extensively with medical professionals and researchers across institutions to ensure clinical relevance of his technical work. Organized MEDIQA shared tasks (2019, 2021) Co-organized NLP for Medical Conversations workshops (2019, 2020) Developed TN-Eval framework for therapy note quality assessment Created MedNLI dataset for clinical textual inference Pioneered synthetic clinical note generation approaches His work consistently addresses the tension between clinical utility and technical innovation, with growing emphasis on evaluating LLM performance in healthcare contexts. The progression from foundational clinical NLP techniques to complex evaluation frameworks demonstrates his evolving research trajectory toward ensuring reliable AI deployment in medical settings.
Rocco Oliveto is a Professor in the Department of Computer Science at the University of Salerno, Italy, with a distinguished research career spanning over two decades in empirical software engineering. His work bridges theoretical software engineering principles with practical applications, with recent expansion into healthcare informatics and machine learning applications. His research interests focus on code quality assessment, software maintenance practices, developer behavior analysis, and empirical studies of software engineering phenomena. He has made significant contributions to understanding code smells, bug prediction, API compatibility issues, and more recently, container technologies and smart contract analysis. His recent work demonstrates a strategic expansion into healthcare applications, leveraging software engineering techniques for medical diagnostics and rehabilitation systems. Oliveto's publication pattern shows consistent productivity with multiple high-impact publications each year across top venues including IEEE Transactions on Software Engineering, ACM Transactions on Software Engineering and Methodology, and Empirical Software Engineering journal. His recent articles (2023-2025) reveal a growing interest in applying software engineering techniques to healthcare domains while maintaining strong contributions to core software engineering topics. The research demonstrates sophisticated methodological approaches combining empirical studies with machine learning techniques. His collaborative network includes prominent researchers such as Simone Scalabrino, Gabriele Bavota, and Andrea De Lucia, with whom he has co-authored numerous high-impact publications. This collaboration spans both traditional software engineering topics and emerging interdisciplinary applications in healthcare.
Ian Lane is an Associate Professor in the Computer Science and Engineering Department at the University of California, Santa Cruz's Baskin Engineering school, serving as Program Director for the Natural Language Processing Professional Master's Degree Program. He joined UCSC in Fall 2022 after an extensive career spanning academia and industry. His research centers on computational systems that understand spoken human language, spanning speech recognition, transcription, meaning interpretation, and contextually appropriate responses. Key research areas include: Natural Language Processing for real-world applications Conversational AI systems development Speech-to-speech translation technologies Multimodal interaction (audio-visual integration) Language technologies that learn through real-world interaction His recent publications demonstrate strong focus on hallucination detection in LLMs, tabular data understanding, explainable AI, and robust speech recognition systems. Current work emphasizes "in the wild" language technologies that adapt through user interaction. Dr. Lane has received recognition through impactful industry applications including Jibbigo (the first mobile speech translation app) and military translation systems deployed in Iraq and Afghanistan. He actively mentors students and collaborates across UCSC's Silicon Valley Campus programs including Games and Playable Media and Human-Computer Interaction. His vision includes integrating NLP with virtual environments for language learning and skill acquisition.
Michael Strube is an Honorary Professor at the Department of Computational Linguistics at Heidelberg University and leads the Natural Language Processing (NLP) Group at HITS (Heidelberg Institute for Theoretical Studies) in Germany. He has been with HITS (previously EML Research and European Media Laboratory) since 2003 and became an Honorary Professor at Heidelberg University in 2010. He is also a Fellow of the Association for Computational Linguistics (2019). Dr. Strube received his PhD from the Computational Linguistics Department at the University of Freiburg in December 1996 under the supervision of Udo Hahn. Between 1997 and 1999, he was a postdoctoral fellow at the Institute for Research in Cognitive Science at the University of Pennsylvania, Philadelphia. Michael Strube's research focuses on semantics and discourse pragmatics, graph-based methods for text representation and analysis, extraction of world knowledge from Wikipedia for computational linguistics, and development of methods to synchronize multilingual content. His work spans coreference resolution, discourse processing, text summarization, entity linking, and natural language generation. He has made significant contributions to coherence modeling, anaphora resolution, and the application of geometric deep learning in NLP. His recent publications demonstrate strong trends in discourse processing, coreference resolution, and the application of geometric approaches to NLP problems. Strube has pioneered work in hyperbolic space for entity typing and graph embeddings, while maintaining his foundational work in discourse and coherence. His research bridges theoretical linguistics with practical NLP applications across multiple languages. Dr. Strube has received several prestigious awards, including: Fellow of the Association for Computational Linguistics (2019) Best Paper Award for "Fine-grained entity typing in hyperbolic space" (2019) Honorable Mention for the IJCAI-JAIR best paper prize 2010 for "Knowledge Derived from Wikipedia for Computing Semantic Relatedness" Professor Strube has advised numerous PhD students who have gone on to successful careers in academia and industry. His current PhD students include Yi Fan, Wei Liu, Haixia Chai, Mehwish Fatima, and Sungho Jeon, working on topics such as discourse structure, discourse relations, coreference resolution, and cross-lingual summarization. His former students include Federico Lopez, Benjamin Heinzerling, Mohsen Mesgar, and Nafise Moosavi, who now hold positions at institutions like Argo AI, RIKEN, Bosch Center for AI, and the University of Sheffield. As group leader of the NLP Group at HITS, Strube oversees a team focused on advancing natural language processing through research in discourse analysis, coreference resolution, text generation, and knowledge extraction. The group has been involved in numerous collaborative projects and has made significant contributions to the field through publications, shared tasks, and community building via workshops and conferences.
Daniel Sonntag is a Research Professor at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, Germany, with extensive contributions to artificial intelligence, particularly in medical applications and human-AI interaction. His work spans multiple domains including explainable AI, medical imaging analysis, eye tracking technology, and clinical decision support systems. His research interests focus on developing interpretable AI systems for healthcare applications, with particular emphasis on ophthalmology and radiology. He investigates how to make AI systems more transparent and trustworthy through concept bottleneck models, multi-agent systems, and interactive learning approaches. His work bridges the gap between theoretical AI advancements and practical clinical applications, ensuring that AI tools are both effective and understandable for medical professionals. Sonntag's publication record demonstrates a consistent trend toward more interactive and interpretable AI systems, with recent work focusing on multi-agent RAG systems for radiology report generation, clinical decision support tools for ophthalmology, and biodiversity monitoring through soundscape analysis. His research shows a strong interdisciplinary approach, combining computer vision, natural language processing, and human-computer interaction to solve complex problems in healthcare and environmental monitoring. His work has appeared in leading venues including IUI, ICMI, KI, and various IEEE and ACM conferences, reflecting his contributions to both the theoretical foundations and practical applications of artificial intelligence.
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.
Luca Cagliero is an Associate Professor in the Department of Control and Computer Engineering at Politecnico di Torino (Polytechnic University of Turin), Italy. His research spans multiple domains within computer science, with particular expertise in data mining, machine learning, natural language processing, and multimodal analysis. He has established a prolific research career with over 150 publications spanning from 2009 to the present, demonstrating consistent scholarly productivity. Dr. Cagliero's research interests focus on the intersection of artificial intelligence and practical applications. His work addresses fundamental challenges in data mining, information retrieval, and educational technology, with recent publications showing increasing emphasis on large language models, multimodal analysis, and explainable AI. He has made significant contributions to text summarization techniques, database systems, and applying machine learning to educational contexts. His recent publications (2023-2025) demonstrate a clear research trajectory toward multimodal AI systems, with particular attention to the integration of vision and language processing. His work spans theoretical contributions in machine learning methods as well as practical applications in educational technology, social media analysis, and document understanding. The breadth of his collaborations across different application domains indicates a versatile research profile that bridges theoretical and applied computer science. Dr. Cagliero has mentored numerous researchers who have become his frequent collaborators, including Lorenzo Vaiani, Moreno La Quatra, and Davide Napolitano. His work has appeared in top-tier venues including ACL, IEEE Transactions on Knowledge and Data Engineering, and Expert Systems with Applications, reflecting the high quality and impact of his research contributions.
Assoc. Prof. Bilal Şimşek is an active faculty member at Akdeniz University's Faculty of Education, Department of Turkish Language Education, where he has served as a Research Assistant since 2015. His academic career focuses on the intersection of Turkish language education and emerging technologies, particularly augmented and virtual reality applications in educational settings. His educational background includes: Doctorate (2018-2022): Akdeniz University, Institute of Educational Sciences, Turkish and Social Sciences Education Postgraduate (2015-2018): Akdeniz University, Institute of Educational Sciences, Turkish Language Education Undergraduate (2012-2015): Anadolu University, Faculty of Open Education, Turkish Language and Literature Undergraduate (2010-2014): Necmettin Erbakan University, Faculty of Education, Turkish Teaching Şimşek's research primarily investigates how technology-enhanced learning environments impact language acquisition, reading comprehension, and writing skills. His work demonstrates particular expertise in augmented reality applications for storybooks and vocabulary development, with significant contributions to understanding how digital and traditional reading mediums affect cognitive processing. His research methodology frequently employs comparative studies examining both quantitative metrics (citation counts, H-indices across multiple databases) and qualitative classroom observations. His scholarly output shows consistent growth with 64 publications indexed in Web of Science, demonstrating strong international recognition. The publication trend reveals increasing focus on immersive technologies in language education, with recent works exploring virtual reality's impact on story retelling performance and the temporal effects of augmented reality experiences on cognitive load. Among his notable recognitions is the TUBITAK Domestic Doctoral Scholarship (2211) awarded from 2018-2022. His research has been supported through multiple funded projects including EU-supported initiatives like 'Mapping Teacher Training in Europe' and institutional projects documenting regional dialects in Antalya province. As an academic advisor, he currently supervises postgraduate research, most recently guiding B.ACAR's 2025 thesis on metacognitive writing strategies and writing anxiety. His teaching portfolio spans both undergraduate and postgraduate levels, covering courses in academic writing for Turkish education, augmented/virtual reality applications, reading education, Turkish language, and cultural geography.
Leo Leppänen is a Postdoctoral Researcher at the University of Helsinki's Department of Computer Science, currently working at the MOOC Center. His research spans automated natural language generation, educational data mining, learning analytics, and computer science education. PhD in Computer Science (2023) MSc in Computer Science (2017) BA in Language Technology (2015) His work investigates AI-generated content applications, particularly LLMs' impact on education and journalism. Recent projects include analyzing student confidence gaps, studying AI ethics in MOOCs, and developing cross-lingual embeddings for news media. Research trends show increasing focus on Large Language Models (LLMs), with studies comparing pre- and post-LLM student responses, analyzing LLM integration in classrooms, and exploring AI's role in journalism. His publications span conferences like AIED, ITiCSE, and Koli Calling. Pro Gradu Award for Exceptional Master's Thesis (2017) Researcher of the Year Award (2018) Active in academic service, Leppänen serves as Programme Committee Member for ACM conferences and peer-reviewer for Journalism Practice journal. His projects include RAPHAEL (Planetary Health Education) and Generative AI in Journalism (2025-2028).
Pradeep Murukannaiah is an Associate Professor in the Interactive Intelligence group at the Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS) at Delft University of Technology (TU Delft). He co-directs the Hippo Lab, a Delft AI lab focused on AI for fair, efficient, and interpretable analysis of climate policies. He also holds leadership roles as Use Cases Coordinator and Diversity Co-Chair in the Hybrid Intelligence center, and serves as Master Coordinator for the MSc in Data Science and Artificial Intelligence Technology (DSAIT). Dr. Murukannaiah received his PhD in Computer Science from North Carolina State University in 2016. Prior to joining TU Delft, he served as an Assistant Professor at Rochester Institute of Technology (2017-2019), completed an internship at Google, and worked as a Software Engineer at Alcatel-Lucent. His research centers on engineering socially intelligent agents through three interconnected thrusts: Natural Language Processing (focusing on argument mining, value alignment, and claim analysis), Multi-Agent Systems (exploring negotiation, social choice, and multi-objective reinforcement learning), and Hybrid Intelligence (developing frameworks for human-AI synergy in decision making). Cross-cutting these areas are his investigations into values (representing what matters to stakeholders) and norms (representing expectations between stakeholders), which together form sociotechnical systems where humans interact, make decisions, and stay accountable to each other while AI agents augment human intelligence. His recent publications demonstrate a strong focus on value-sensitive AI systems, with work spanning moral frame preservation in news summarization, multi-objective reinforcement learning for climate policy analysis, and mechanisms for responsible autonomy. His research consistently bridges theoretical advances in AI with practical applications in societal decision making, particularly around climate change and democratic processes. Dr. Murukannaiah has successfully mentored numerous graduate students, including current PhD candidates Zuzanna Osika (working on explainable multi-objective decision support), Shubhalaxmi Mukherjee (focusing on fact checking using LLMs), and several recent PhD graduates whose work has contributed to the fields of opinion diversity through hybrid intelligence and context-specific value inference. As co-director of the Hippo Lab and through his leadership roles in the Hybrid Intelligence center, he actively shapes research directions that emphasize fairness, interpretability, and human-centered AI approaches for addressing complex societal challenges.
