Greg Durrett is an Associate Professor in the Department of Computer Science at University of Texas at Austin, leading the TAUR Lab (Text Analysis, Understanding, and Reasoning ). His research focuses on advancing Large Language Models (LLMs) for knowledge-intensive tasks in medical information processing scientific discovery legal reasoning . He received his B.S. in Computer Science and Mathematics from MIT (2010) and Ph.D. in Computer Science from UC Berkeley (2016). His work develops techniques to train LLMs with new capabilities augment models for reliability assess model outputs improve reasoning frameworks . His 15 most recent publications (2021-2025) span knowledge propagation in LLMs chain-of-thought reasoning code generation benchmarks multi-modal reasoning fact verification discourse analysis . Scientific honors include NSF CAREER Award (2024) NSF grants (2018, 2024) Bloomberg Data Science Grant (2017) Facebook Fellowship (2014) Best Paper Finalist (EMNLP 2013) . Teaching: CS388: Natural Language Processing (graduate) CS371N: NLP (undergraduate) High school NLP module .
Mark Riedl is a Professor in the Georgia Tech School of Interactive Computing and Associate Director of the Georgia Tech Machine Learning Center (ML@GT). His research focuses on human-centered artificial intelligence, emphasizing the development of AI technologies that naturally interact with humans. Key areas include story understanding/generation, computational creativity, explainable AI, and ensuring AI safety. He holds affiliations with the GVU Center, Institute for People and Technology (IPaT), and Institute for Robotics and Intelligent Machines (IRIM). His work is supported by NSF, DARPA, ONR, and industry partners like Google and Meta. Notable awards include the DARPA Young Faculty Award and NSF CAREER Award, plus three Pulitzer Prizes (collaborative with Roko M. Bask). Riedl's recent projects include STORY2GAME (AI-driven game design) and ethical AI frameworks addressing transparency and accountability. His research bridges theoretical advancements with practical applications in education, healthcare, and creative industries. Research interests span AI ethics, narrative systems, and AI's societal impact. He explores how AI can be made more transparent through explainable mechanisms while maintaining creativity and safety. Collaborations with Roko Bask on futuristic culinary trends have produced influential works. His labs and teams focus on interdisciplinary approaches, combining computer science with social sciences to shape responsible AI development. Key grants and projects include NSF-funded initiatives on AI in education and DARPA-supported work on AI safety. His contributions to explainable AI challenge traditional XAI paradigms, advocating for human-centered approaches that prioritize user understanding and ethical implications. Current efforts emphasize adapting LLMs for world modeling and enhancing RL agents with causal reasoning capabilities.
Matthew Kay is an Associate Professor in the Department of Communication Studies at Northwestern University's School of Communication, with a secondary appointment in Computer Science. He serves as Co-Director of Graduate Studies for the PhD in Technology and Social Behavior program. His research focuses on human-computer interaction and information visualization, specializing in uncertainty communication, usable statistics, and personal informatics. He employs mixed-method approaches including behavioral analysis, interactive system development, and visualization technique evaluation to address real-world data interpretation challenges. Analysis of his recent publications reveals dominant themes in visualization literacy development, uncertainty representation for decision-making, and health informatics applications. His work consistently bridges theoretical frameworks with practical implementations, particularly in educational assessment tools and election forecast visualizations. Professor Kay co-directs the Midwest Uncertainty Collective (MU collective), a research group advancing uncertainty communication methodologies. Previously faculty at the University of Michigan School of Information, he maintains active contributions to visualization tool development including the ggdist R package for uncertainty visualization.
Nick Montfort is a Professor of Digital Media at Massachusetts Institute of Technology (MIT) and Professor II & Principal Investigator at the University of Bergen 's Center for Digital Narrative. He directs MIT's Trope Tank lab/studio and contributes to platform studies, critical code studies, and electronic literature. Ph.D. in Computer and Information Science from University of Pennsylvania (2007) M.A. in Creative Writing (Poetry) from Boston University (2001) S.M. in Media Arts and Sciences from MIT (1998) B.S./B.A. in Computer Science and Liberal Arts from University of Texas at Austin (1995) Montfort's research focuses on computational creativity and digital poetics , particularly through exploratory programming and constraint-based writing . He has developed over ten computer-generated print books and co-edited MIT Press volumes like The Future and The New Media Reader . His work bridges computer science and literary arts , emphasizing algorithmic expression and platform-specific creation. His recent computational works include All the Way for the Win (2025), written entirely in three-letter words, and collaborations like The Deletionist and Sea and Spar Between . Notable books cover interactive fiction ( Twisty Little Passages ), platform studies ( Racing the Beam ), and generative literature ( 10 PRINT ). He also co-edits the Hardcopy and Using Electricity book series. Montfort's academic contributions span digital narrative systems , creative code , and computer-generated literature . His 101 BASIC Poems and Output: An Anthology of Computer-Generated Text exemplify his focus on code as cultural artifact and algorithmic literary practice . He has presented at major conferences including ELO, C&C, and Digital Arts and Culture, addressing topics from minimalist computational poems to narrative modeling . Through collaborations with institutions like University of Bergen and MIT's Comparative Media Studies/Writing, Montfort explores digital storytelling , machine-generated poetry , and platform-specific creativity . His academic leadership includes directing the Trope Tank and co-founding Bad Quarto press.
Andrew McCallum is a Distinguished Professor and Director of the Center for Data Science at the University of Massachusetts Amherst. He holds a PhD in Computer Science from the University of Rochester (1995) and a BS from Dartmouth College (1989). His research focuses on machine learning, natural language processing, and information extraction, with applications to scientific literature and knowledge base construction. He has pioneered work on conditional random fields and probabilistic databases, and led the development of systems like Rexa, an advanced research paper search engine. Affiliations: Center for Data Science, Center for Intelligent Information Retrieval, Computational Social Science Institute Key Projects: OpenReview.net, Unified Information Extraction, Automated Knowledge Base Construction His work emphasizes extracting actionable knowledge from unstructured text, with contributions to social network analysis, entity resolution, and semi-supervised learning. McCallum has over 300 publications and has received awards including the NSF ITR Grant, IBM Faculty Partnership Awards, and ACM/AAAI Fellowships. He has advised numerous students and served as ICML General Chair (2012). Recent Research Trends: Probabilistic box embeddings, case-based reasoning for knowledge bases, scalable clustering algorithms, and applications in biomedical informatics.
Chanchal K. Roy is Professor of Software Engineering/Computer Science at the University of Saskatchewan and Co-Director of the Software Research Lab. He leads an NSERC CREATE graduate program on Software Analytics Research and co-leads the Data Management group for an NSERC CFREF project on Food Security, with over 170 publications cited 6,000+ times. His research centers on software clone detection using the widely adopted NICAD system, software evolution, empirical studies, and AI-driven software analytics. Recent work integrates large language models for code generation, clone detection in the AI era, and developer interactions with tools like ChatGPT, emphasizing practical applications in maintenance and analytics. Analysis of his 15 most recent publications reveals a strong trend toward AI/ML integration in software engineering: 12 of 15 articles (2025) explore LLMs, quantum computing, or deep learning for tasks like bug localization, code snippet generation, and feature-toggle analysis. Key themes include empirical validation of AI tools, Stack Overflow data mining, and cross-domain frameworks for Society 5.0. His scientific awards include: Most Influential Paper Awards (SANER 2018, ICPC 2018) Outstanding Young Computer Science Researcher Award (CS-Can/Info-Can, 2018) New Researcher Award (University of Saskatchewan, 2019) New Scientist Research Award (College of Arts and Science, 2019) As lead of the NSERC CREATE program and CFREF data group, he mentors graduate students in software analytics while securing major grants. He actively serves on program committees for ASE, ICSE, and FSE, reviewing journals and organizing workshops on clone detection and empirical methods. His lab focuses on real-world applications in food security data management and software evolution. The Software Research Lab, co-directed by Roy, drives projects like NICAD and the NSERC CREATE initiative, emphasizing open-source contributions and industry collaboration. Current efforts include quantum-SE integration and AI-augmented maintenance tools under the CFREF food security mandate.
Desmond Elliott is an Associate Professor in the Natural Language Processing section at the Department of Computer Science, University of Copenhagen (UCPH). His research focuses on multimodal and multilingual models with specific emphasis on vision-language integration and tokenization-free NLP approaches. He teaches Bachelor and Master's level courses including Advanced Topics in Natural Language Processing (since 2019), Grundlæggende Data Science (since 2023), and previously Data Science (2021-2023). His research interests center on building and understanding multimodal and multilingual models , particularly exploring vision and language interactions through billion-parameter systems. Current work investigates cultural representation disparities in vision-language models, parameter-efficient captioning, and multimodal distributional semantics across diverse domains including food culture and medical imaging. His methodology emphasizes real-world applicability in non-English contexts and ethical considerations in multimodal systems. Elliott's recent publications (2025) demonstrate leadership in multimodal NLP, with significant contributions to vision-language pretraining, multilingual evaluation frameworks, and clinical NLP applications. His work spans theoretical advancements in model architectures and practical implementations addressing challenges in low-resource languages and domain adaptation. Best Long Paper Award at EMNLP 2021 Best Poster Award at COLING 2019 As an active educator, Elliott contributes to courses on Fair and Transparent Machine Learning and previously taught Information Retrieval. His research collaborations span international institutions with particular focus on European and non-English language contexts, reflecting UCPH's recognition as Europe's #1 institution for HCI research over the past decade.
James A. Evans is the Max Palevsky Professor of Sociology and Data Science at the University of Chicago, where he is a faculty member in the Department of Sociology within the Division of the Social Sciences. He is the director of Knowledge Lab and the Faculty Director of the Masters Program in Computational Social Science . He holds additional affiliations as an External Professor at the Santa Fe Institute , External Faculty at the Complexity Science Hub, Vienna , and Visiting Faculty Researcher at Google . Education: B.A. in Anthropology, Brigham Young University (1994) M.A. in Sociology, Stanford University (1999) Ph.D. in Sociology, Stanford University (2004) His research centers on the collective system of thinking and knowing , exploring how ideas emerge, spread, and evolve through social and technical systems. He investigates innovation, collective intelligence, and the science of science , using large-scale data modeling, machine learning, generative AI, and network analysis to study knowledge creation. His work spans domains including science, technology, law, and religion, with a focus on how AI is reshaping discovery processes. The most recent publications highlight trends in AI and scientific discovery , with a strong emphasis on innovation, knowledge systems, and human-machine intelligence . His research increasingly explores AI as a transformative agent in science , including the concept of 'alien intelligence' and the development of complementary AI to augment human capacity. Projects like the $20M NSF-funded APTO initiative aim to build language models that predict technological outcomes by analyzing historical data. Scientific Recognition and Funding: Research supported by the National Science Foundation (NSF) , National Institutes of Health (NIH) , Air Force Office of Scientific Research (AFOSR) , and philanthropic sources Work published in Nature, Science, PNAS , and leading social science journals Featured in The New York Times, The Economist, The Atlantic, Wired, NPR, BBC, Le Monde , and others James Evans advises on science policy and funding strategies, emphasizing the importance of diversity, interdisciplinary collaboration, and demographic balance in fostering innovation. He critiques current academic incentives and proposes alternative discovery regimes. He leads Knowledge Lab , a collaborative research environment that conducts seminars, grants, and employment opportunities in computational social science and AI.
Dimitris N. Metaxas is a Professor in the Department of Computer Science within the School of Arts and Sciences at Rutgers University. His research spans computer vision, medical image analysis, and artificial intelligence, with a particular focus on medical applications including cardiac MRI analysis and foundation models for healthcare. Dr. Metaxas's research interests encompass medical image analysis, computer vision, deep learning, and artificial intelligence. His work demonstrates a strong emphasis on applying advanced machine learning techniques to medical imaging problems, particularly in cardiac analysis. He has made significant contributions to diffusion models, multimodal learning, and efficient AI techniques for medical applications. His research bridges the gap between theoretical computer vision and practical healthcare solutions, with numerous publications in top-tier conferences and journals. His recent publications show a clear trend toward foundation models for medical image analysis, with significant contributions to cardiac MRI segmentation, diffusion models, and multimodal learning. The research spans both theoretical advancements in AI techniques and practical applications in healthcare, particularly focused on improving medical diagnostics through computer vision. His work demonstrates expertise in adapting cutting-edge AI techniques like diffusion models and large language models for specialized medical applications. Dr. Metaxas has mentored numerous students and researchers, as evidenced by his extensive publication record with multiple co-authors across various institutions. His work has received significant attention in the research community, with numerous publications in top venues including CVPR, ICCV, MICCAI, and Medical Image Analysis. His research group focuses on medical image computing, computer vision, and machine learning applications in healthcare. The team works extensively with cardiac MRI data, developing advanced techniques for segmentation, reconstruction, and analysis of 4D cardiac imaging. They are particularly known for their contributions to foundation models in medical imaging and efficient adaptation techniques for specialized medical tasks.
Jian Zhao is an Associate Professor at the University of Waterloo's School of Computer Science, specializing in Information Visualization (InfoVis), Human-Computer Interaction (HCI), and Data Science. With a Ph.D. from the University of Toronto (2016), his research emphasizes interactive visualization techniques, AI integration in design processes, and socio-technical systems. He explores how human-AI collaboration can enhance data analysis, presentation, and user experience in complex systems. Key research areas include: 1) AI-Driven Design (e.g., code generation via sketching, infographic creation), 2) Health Informatics (therapeutic AI tools for autism support), 3) Immersive Technologies (VR/AR interfaces for presentations and education), and 4) Social Computing (remote family communication, multi-modal emoticons). His work bridges technical innovation with human-centered design principles. His publications (2021–2025) reflect a focus on interactive visualization frameworks (e.g., iTrace for cross-view data analysis), AI-human collaboration (CoLadder for hierarchical code editing), and specialized applications like TherAIssist for art therapy and EMooly for autism support. Zhao frequently explores novel interaction modalities , including gesture-based VR interfaces and sketch-based programming tools. He leads projects in computational notebooks (EDAssistant, Slide4N), visual analytics (MissBin for bipartite networks), and neurofeedback training games (Eggly). His work often emphasizes systematic design considerations for missing data, cross-view analysis, and contextual visualization in spatial AR environments.
Dr Glenda Cooper is Reader in Journalism Studies and Head of the Department of Journalism at City St George's, University of London , where she leads a team of 25 staff, 50 visiting lecturers, and over 500 students from 53 countries. She holds a PhD in Journalism from City and an MA in English Language and Literature from Oxford, and has played a pivotal role in elevating the BA Journalism programme to 1st place nationally in the Guardian and Complete University Guide (2023), as well as 1st for Graduate Prospects (Sunday Times 2022). Education: MA in Academic Practice, City, University of London (2022) PhD in Journalism, City, University of London MA in Creative Writing, City, University of London BA (Hons) English Language and Literature, St Hilda's College, University of Oxford PG Diploma in Newspaper Journalism, City University of London Glenda's research focuses on crisis and humanitarian reporting , journalism ethics , live journalism , and the intersection of media and politics, particularly through the legacy of Alistair Cooke’s Letter from America . She has led major funded projects with the British Academy, ESRC, Google Digital News Initiative, and HEIF. Her current research explores AI’s impact on journalism through the DMINR project and innovative storytelling formats via the News on Stage initiative and the Contemporary Narratives Lab . Her recent publications span topics such as AI in newsrooms, humanitarian scandals (#AidToo), refugee representation, and digital narrative innovation. These works reflect a strong interdisciplinary trend, combining journalism studies with media technology, ethics, performance, and political communication. The articles indicate a growing focus on digital transformation, ethical challenges in sourcing, and the evolving role of journalists in public discourse. Scientific Awards and Recognition: Laurence Stern Fellow at the Post (2001) Guardian Research Fellow at Nuffield College, Oxford (2006–2007) AURORA Role Model for Future Women Leaders in Academia (2018) Glenda has supervised PhD students on topics ranging from political communication in Istanbul to the manosphere's influence on journalism. She has been instrumental in curriculum development, notably designing a core ethics module for final-year journalism students. She also serves as Reviews Editor for Journalism: Theory, Practice and Criticism , UK Director of the European Journalism Observatory, and Board Member of The Conversation UK . She co-founded and co-presented the Knowhow podcast, bridging academia and media. She co-directs the Contemporary Narratives Lab and co-founded the News on Stage project, which merges journalism with theatre through live events such as News Cabaret and News on the Street . Her play Aid Memoir , based on her PhD research, was performed at the Pleasance Theatre and featured in the ESRC Festival of Social Science.
Christian Wolff is a University Professor and Chair of Media Informatics at the Institute for Information and Media, Language and Culture at the University of Regensburg. Since April 2022, he has served as the founding Dean of the Faculty of Computer Science and Data Science, while maintaining secondary membership in the Faculty of Languages, Literature and Cultural Studies (SLK). His academic career spans over three decades with significant contributions to multiple disciplines at the intersection of computer science and humanities. Wolff's research interests center around multimedia and multimodal information systems, electronic publishing, and text technology, particularly text mining. His work bridges computer science with digital humanities, legal informatics, and social media analysis. Recent publications demonstrate a strong focus on large language models, sentiment analysis applications across various domains, legal technology innovations, and virtual reality research for cognitive studies. His interdisciplinary approach has produced significant contributions in both technical and humanities domains. His recent publication trends reveal a strategic shift toward applied AI research, particularly in legal technology (LegalTech), social media analysis, and sentiment analysis using large language models. The publications show increasing collaboration across disciplines, connecting computer science with law, political science, literature, and psychology. His work on the digital basis document for legal proceedings represents a major practical application of his research in the German justice system. East Bavarian Cultural Prize Doctoral Award of the University of Regensburg Wolff has led numerous interdisciplinary research projects connecting computer science with humanities and legal studies. His leadership extends to institutional roles including Dean of Research, Vice Dean, and Dean of Faculty positions. He has been instrumental in establishing the new Faculty of Computer Science and Data Science at the University of Regensburg, demonstrating significant impact on institutional development and research infrastructure. Wolff directs research initiatives focused on text technology, digital humanities, and legal informatics. His work with the INDIGO - Internet and Digitization Eastern Bavaria initiative and the TRIO project demonstrates commitment to regional technology transfer and innovation. The interdisciplinary nature of his research groups connects computer scientists with legal scholars, linguists, and social scientists to address complex digital transformation challenges.
Santiago Ontañón is an Associate Professor in the Department of Computer Science at Drexel University's College of Computing and Informatics. He is also a Senior Research Scientist at Google DeepMind, reflecting a strong dual affiliation in both academic and industrial AI research. His work bridges theoretical AI with practical applications in gaming and machine learning. PhD in Computer Science (Artificial Intelligence), cum laude, Autonomous University of Barcelona Postdoctoral Researcher, Georgia Institute of Technology Researcher, Artificial Intelligence Research Institute (IIIA), Barcelona, Spain Dr. Ontañón's research focuses on artificial intelligence, machine learning, and robotics, with a particular emphasis on game AI. His interests span case-based reasoning, reinforcement learning, Monte Carlo tree search, player modeling, and procedural content generation. He has made significant contributions to AI in real-time strategy games and explainable AI systems. His recent publications reflect a consistent trend in AI for games, hierarchical planning, and learning from demonstration. The articles span topics such as reproducible deep reinforcement learning, adaptive player modeling, and integrating domain knowledge into search algorithms, indicating a mature and impactful research trajectory in AI and game technologies. Senior Research Scientist, Google DeepMind Organizer, microRTS AI Competition Advising multiple PhD students in AI and game-related topics He has advised numerous PhD students, many of whom have completed their theses on advanced AI topics in games and reasoning. His research is supported by access to substantial computational resources and collaborative networks in both academia and industry. He actively promotes open science by releasing software, data, and teaching materials. He leads research efforts in AI for games and maintains an active lab focused on game AI, with projects like microRTS, FTL, and Darmok. His team develops systems for reinforcement learning, planning, and natural language understanding in game environments.
Dr. Jasmijn van Gorp is a Senior Lecturer in the Department of Media and Culture Studies at Utrecht University's Faculty of Humanities. She specializes in Digital Humanities , Audiovisual Heritage , and Digital Culture with a focus on Television History and Eastern European Media . As co-founder of the CLARIAH Media Suite, she develops digital research infrastructure for audiovisual heritage in the Netherlands. PhD in Social Sciences from the University of Antwerp (Belgium) MA in Communication Sciences from KU Leuven (Belgium) Her research examines digital methods for film and television studies , metadata in digital archives , and AI-generated analysis of broadcast schedules . She leads projects like Re-Frame (2021-2025) and AI TaDa , exploring the reuse of audiovisual archives in journalism and algorithmic metadata generation. Recent work includes tutorials on Data Visualization and Flow Analysis using the Media Suite. She advocates for critical digital tool literacy in education and co-authored the Digital Skills learning trajectory for Utrecht's BA programs. Her teaching includes Digital Television History and Programming and Curation at BA and MA levels, supervising theses in film and television studies. As workpackage leader for CLARIAH-PLUS WP5 , she integrates computer vision , automatic speech recognition , and linked data into sustainable tools for cross-media research. She trains researchers via the Media Suite Learn initiative and collaborates on digital infrastructure projects with institutions like NISV and CLARIAH.
Pekka Abrahamsson is a Professor at the Faculty of Information Technology and Communication Sciences at Tampere University , Finland. He actively contributes to research in Software Engineering , Artificial Intelligence , and AI Ethics , with recent work focusing on generative AI, multi-agent systems, and ethical software design. Published over 42 research outputs (2016–2025) Editorial roles in multiple international conferences (2016, 2019, 2022–2024) His research emphasizes practical applications of AI in software development, including tools like ChatGPT for full-stack coding, multi-agent systems for requirements engineering, and frameworks for AI ethics in software practices. He also explores challenges in continuous software engineering and quantum computing architecture. Key publication trends (2024–2025) include: Agile methodologies enhanced by AI Ethical alignment in AI systems Autonomous software development platforms AI tool adoption in programming education Quantum software architecture reviews Technical debt in embedded systems Awards and recognitions : PlumX Metrics highlight 1 scientific prize (unspecified) High readership on platforms like Mendeley (up to 211 readers) Multiple citations in Scopus (up to 53 citations for quantum computing work) Grants and collaborations include global studies on work-from-home impacts, AI tool usage in programming courses, and projects like CodePori for autonomous development. His work influences policy and industry practices, particularly in AI ethics and multi-robot systems.
