Susan Fussell is a Professor at Cornell University in both the Department of Communication and Department of Information Science. She received her Ph.D. in psychology from Columbia University and directs graduate studies in Communication. Key research areas: Computer-Mediated Communication, Intercultural Communication, Human-Computer Interaction, Multilingual Collaboration, and Collaborative Intelligence Analysis. Teaching focus: Theories of communication, research methods in social computing, and behavioral foundations of technology. Her publications from 2015-2025 reveal trends in cross-cultural communication technology , AI-mediated interaction , and collaborative problem-solving . Scientific awards include NSF grants #1025425, #1318899, #1314778 Google Faculty Gift She advises students like Nanyi Bi, Ge Gao, and Bin Xu. Notable collaborations include work with Claire Cardie, Sara Kiesler, and Dan Cosley. Projects include Tools for multilingual collaboration Energy conservation through social computing Telepresence robotics and human-robot interaction Contact: 226 Gates Hall, Ithaca, NY 14853 | sfussell@cornell.edu
Isabelle Augenstein is a Professor at the University of Copenhagen, Department of Computer Science (DIKU), where she heads the Copenhagen Natural Language Understanding (CopeNLU) research group and the Natural Language Processing section. She is also a co-lead of the Danish Pioneer Centre for Artificial Intelligence, Denmark's largest research center initiated by the Danish Ministry of Higher Education and Science. In October 2022, she became Denmark's youngest ever female full professor. Dr. Augenstein earned her undergraduate degree in Computational Linguistics and Psychology from Heidelberg University, followed by a Master's in Computational Linguistics. She completed her PhD in Computer Science at the University of Sheffield under the supervision of Dr. Diana Maynard and Prof. Fabio Ciravegna. In 2021, she earned a Habilitation at the University of Copenhagen in Explainable Fact-checking. Professor Augenstein's primary research focuses on fair and accountable Natural Language Processing, with particular emphasis on explainability, factuality, and bias detection. Her work spans multiple subfields including automated fact-checking, stance detection, gender bias analysis, and cultural bias in language models. She has pioneered research in explainable fact-checking, developing methods that not only predict claim veracity but also provide meaningful explanations of the decision-making process. Her research group has produced numerous influential papers on measuring model fragility, quantifying gender biases, and developing robust fact-checking systems that account for distribution shifts. Her significant contributions have been recognized with several prestigious awards: ERC Starting Grant on 'Explainable and Robust Automatic Fact Checking' DFF Sapere Aude Research Leader fellowship on 'Learning to Explain Attitudes on Social Media' Karen Spärck Jones Award from the British Computing Society and Bloomberg Hartmann Diploma Prize from the Hartmann Foundation Member of the Royal Danish Academy of Sciences and Letters since 2024 Professor Augenstein has secured significant research funding including her ERC Starting Grant supporting five years of blue-sky research. She actively mentors PhD students and postdoctoral researchers through her 'ExplainYourself' project. She served as President of SIGDAT (which organizes the EMNLP conference series), having previously held leadership roles as Vice President and Vice President-Elect. She is a co-founder of Widening NLP (WiNLP), an initiative to increase diversity in the NLP community, and maintains the BIG Directory of underrepresented groups in NLP. She leads the Copenhagen Natural Language Understanding (CopeNLU) research group, which relocated to the historic Østervold Observatory in Copenhagen's Botanical Gardens in 2023. The group focuses on developing methods for explainable and robust natural language understanding, with applications in fact-checking, bias detection, and social media analysis. Professor Augenstein also co-leads the Speech and Language collaboratory at the Pioneer Centre for Artificial Intelligence, where her team investigates how language models can better serve diverse populations while maintaining accountability and transparency.
Dr. Shirin Nilizadeh is an Associate Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington's College of Engineering. She leads the Security and Privacy Research Lab, conducting interdisciplinary research at the intersection of cybersecurity, privacy, machine learning, and social media analysis. Her work addresses critical societal issues related to online security, privacy, and safety through data-driven approaches. Dr. Nilizadeh received her PhD in Computer Science from Indiana University in 2014, followed by MS in Computer Science from Amirkabir University (2007) and BS in Computer Engineering from Islamic Azad University (2004). Her research focuses on security and privacy in systems and social networks, employing techniques from machine learning and big data analytics. She takes a highly interdisciplinary approach, integrating AI, NLP, social sciences, and public health to address societal issues in cybersecurity and privacy. Her research objectives include: (1) detecting and characterizing emerging threats in online social networks like social engineering attacks, misinformation, and online hate speech; (2) advancing the adversarial robustness and fairness of ML and NLG systems; and (3) studying humans' online behaviors through data-driven interdisciplinary research. Analysis of her recent publications reveals a strong focus on AI-generated security threats, particularly phishing scams using LLMs, NFT fraud detection, social media toxicity analysis, and content moderation systems. Her work bridges theoretical security research with practical applications, often addressing real-world security challenges through innovative technical solutions. Among her notable scientific achievements are the prestigious NSF CAREER award (2023), Comcast Innovation Awards (2022 and 2024), College of Engineering Outstanding Early Career Research award (2024), and IEEE SP 2024 Distinguished Paper Award. Her work has also received best paper and technical poster awards at eCrime 2021 and NDSS 2022. Dr. Nilizadeh has successfully mentored numerous doctoral and master's students while securing significant research funding, including multiple NSF grants and Comcast Innovation Fund awards. She leads a vibrant research group that has produced impactful work cited in official reports submitted to The Supreme Court and the EU Committee on Civil Liberties, Justice, and Home Affairs. Her lab has also received coverage from WIRED, MIT Technology Review, Orange's Hello Future, and Communications of the ACM. She serves on numerous program committees for top international conferences including ACM CCS, USENIX Security, and POPETS, and has organized outreach programs like OurCS@DFW to broaden participation of underrepresented students in computing.
Mayank Goel serves as an Assistant Professor in the Software and Societal Systems Department (S3D) at Carnegie Mellon University's School of Computer Science. His research bridges computer science and societal impact through practical sensing systems that leverage existing environmental devices for health monitoring and human-computer interaction without requiring hardware modifications. Dr. Goel specializes in mobile computing, signal processing, and machine learning to develop unobtrusive health technologies applicable to real-world scenarios. His core research areas include passive activity recognition for chronic disease management (particularly multiple sclerosis), privacy-preserving acoustic sensing, smartwatch-based clinical interventions for post-operative care, and equitable healthcare systems for global development contexts. He emphasizes end-to-end solutions through close collaboration with medical professionals and designers to ensure immediate deployability outside laboratory environments. Analysis of his 2024-2025 publications reveals a strong interdisciplinary focus spanning computer science, biomedical engineering, and clinical practice. Key trends include longitudinal digital phenotyping for neurological conditions, on-device privacy preservation in activity recognition, and multimodal procedural assistance systems. His work consistently addresses real-world challenges in sensor placement flexibility, user adoption barriers, and equitable access to medical technologies. No scientific awards were mentioned in the available documentation. Information regarding student advising, research grants, or laboratory affiliations was not specified in the provided materials, though his publication record indicates active collaboration with medical professionals and bio-engineers for clinical validation of health technologies.
Milena Stróżyna is an Assistant Professor at the Department of Economic Informatics in the University of Economics in Poznan , Poland. Her work focuses on data modeling, AI applications in disinformation detection, and maritime data analysis . Email: milena.strozyna@ue.poznan.pl Research interests span: Data Modeling & Analysis : Extracting insights from diverse data sources, ensuring quality, and implementing ERP systems Disinformation Studies : Developing AI tools for fake news detection and semantic mapping of misinformation topics Maritime Data Science : Crisis impact analysis in shipping, anomaly detection in maritime transport Scientific Contributions include: Pioneering OpenFact system for information verification Creating adversarial text detection methods Leading research on generative AI risks in information integrity Notable Awards : 2018: Most innovative article at NATCON conference Multiple first-place international competition wins with OpenFact system (2022-2024)
Timothy Baldwin is a Professor at the University of Melbourne, School of Computing and Information Systems, with additional affiliation at Mohamed bin Zayed University of Artificial Intelligence in UAE. His research spans natural language processing, large language models, and multilingual AI systems. His research interests focus on the safety, reliability, and ethical aspects of large language models. He investigates bias evaluation and debiasing techniques, uncertainty quantification methods, fact-checking systems, and multilingual model safety. His work addresses critical challenges in making AI systems more transparent, reliable, and culturally aware, with particular attention to low-resource languages and cross-cultural differences. Baldwin's recent publications demonstrate a strong focus on evaluating and improving the safety of language models across diverse linguistic contexts, developing tools for fact verification, and understanding the internal mechanisms of large language models. His research shows increasing emphasis on practical applications with real-world impact, particularly in multilingual settings and safety-critical domains. His scientific contributions include foundational work on multilingual NLP, bias mitigation techniques, and frameworks for evaluating LLM safety across different cultural contexts. His research has been published in top-tier venues including ACL, NAACL, EMNLP, and ICLR. Baldwin actively mentors students and junior researchers, with frequent collaborations with Haonan Li, Xudong Han, and Fajri Koto, among others. His research group appears to focus on practical applications of NLP with strong ethical considerations, particularly regarding model safety and cultural sensitivity.
Florian Leiser is a Professor at the Chair of Information Infrastructures (led by Prof. Dr. Ali Sunyaev) at Technical University of Munich's Heilbronn campus. His research focuses on human-AI collaboration, privacy-preserving algorithms, and explainability in machine learning systems. Current research areas include Hybrid Intelligence, Human-centered Generative AI (LLMs), Federated Learning, and Health Information Systems Recent publications demonstrate expertise in Explainable AI for medical imaging LLM hallucination detection Federated learning architectures Human-in-the-loop systems Healthcare data applications He contributes to teaching through Human-Centered Artifact Design courses Collaborative teaching roles in machine learning Supervising student projects
Maria-Luciana Blaha is an Assistant Professor in Business Management and Intelligent Automation at Heriot-Watt University, affiliated with the School of Social Sciences and Edinburgh Business School. She leads the Intelligent Automation Systems (IAS) Lab and coordinates the Graduate Apprenticeships in Business Management Year 1 program. With a PhD from the University of Aberdeen and extensive professional experience across sectors, her research focuses on AI, RPA, chatbots, and their impact on organizational behavior. She was awarded the 2023 Early Career Researcher of the Year by the School of Social Sciences. Education : PhD in Business Management, University of Aberdeen (Elphinstone Scholarship, British Federation of Women Graduates Bursary) Research Interests : Blaha investigates Intelligent Automation systems, posthumanism, organizational behavior, and ethical AI adoption. Her work bridges science/technology studies, business management, and computing science. Current projects include automation in Ghana/UK manufacturing, ethical AI frameworks for healthcare, and generative AI in HR. Grants & Projects (2025-): TransiT: Decarbonisation via Digital Twinning (EPSRC) Lighthouse Project: SME Manufacturing Audit (Innovate UK) Local Authority AI Readiness Review (Interface Scotland) 2024 Projects : Thermo Fisher/National Robotarium AKTP (Innovate UK) Extend Robotics Feasibility Study (Interface Scotland) Media & Outreach : Blaha contributes to Scottish AI Alliance initiatives, speaks globally on AI/automation, and engages in public lectures like the 'Age of AI' series. Her media features include Financial Times and pandemic-era fact-checking insights. Labs & Teams : Leads the IAS Lab, collaborates with National Robotarium, and oversees AKT projects with industry partners.
Francisco Javier Canton Correa is a researcher at the Universidad de Granada, specializing in sociology. He earned his PhD in 2019 with the thesis Socialización digital y creatividad audiovisual , supervised by Dr. Jordi Alberich Pascual. Education: PhD in Sociology (2019), Universidad de Granada Research Interests focus on digital sociology, social media analysis, and the societal impact of artificial intelligence. His work explores disinformation combat, visual communication, and urban culture through computational methods. Recent Publications highlight AI-driven tools for misinformation detection, social media analytics, and critical perspectives on digital humanities. Key trends include cross-platform verification systems and sociological risk frameworks for global disinformation. Labs & Collaborations include Medialab UGR, where he contributes to cross-media communication experiments.
Yu Fu is an Assistant Professor at the University of Central Florida (UCF) Department of Computer Science, leading the Designing Interactive & Intelligent Data (DiiD) Lab. His research focuses on data visualization, human-computer interaction, and AI-powered data analysis with applications in sports analytics, journalism, and digital twin systems. Educational Background: Ph.D. in Human-Centered Computing from Georgia Institute of Technology. His work emphasizes designing interactive systems and visualization techniques to enhance data storytelling and critical thinking in data-rich environments. Recent research explores automated fact-checking, basketball performance analysis, and journalistic data communication. Publications highlight interdisciplinary applications of visualization in sports analytics, journalism, and digital twin systems, combining empirical studies, interaction design, and system development.
J. Nathan Matias is an Assistant Professor in the Cornell University Department of Communication, where he leads the Citizens & Technology Lab (CAT Lab). His work bridges social psychology, computer science, and digital governance, focusing on human-algorithm interaction and technology policy. Education: Doctorate (2017), Massachusetts Institute of Technology Master of Science (2013), Massachusetts Institute of Technology Master's Degree (2008), University of Cambridge Bachelor of Arts (2008), Elizabethtown College Research focuses on digital governance, human-algorithm behavior, and social media ethics. He investigates how AI systems shape group dynamics, develops methods for public accountability in technology policy, and explores ethical frameworks for digital research. His lab collaborates with social media platforms, news organizations, and policymakers. Recent publications examine algorithmic compliance challenges, data refusal strategies, and behavioral impacts of digital technologies. His work addresses systemic issues like faculty diversity in academia and misinformation mitigation. He received the Rise25 Award from Mozilla (2023) for science-oriented public engagement. Awards & Recognition: Rise25 Award, Mozilla (2023)
Jean Wagemans is a Professor at the University of Amsterdam's Faculty of Humanities, where he leads research in the Department of Speech Communication, Argumentation Theory and Rhetoric. His work specializes in the interdisciplinary study of argumentative discourse, bridging philosophy, linguistics, and computational analysis. He maintains an active research profile with recent publications exploring AI-generated argumentation, legal/medical discourse, and digital misinformation. Wagemans' research centers on argumentation theory, rhetoric, and debate, with emphasis on practical applications in AI ethics, healthcare communication, and public discourse. His recent investigations include: Developing computational models like Adpositional Argumentation (AdArg) for natural discourse analysis Examining ethical frameworks for AI-generated arguments Creating argument-checking methodologies to combat misinformation Analyzing normative structures in public deliberation His scholarly publications (2022-2024) demonstrate a distinct trajectory toward computational argumentation, with recurring themes of AI ethics, misinformation detection, and applied discourse analysis. Recent works systematically address: The intersection of argumentation theory with AI systems Methodologies for evaluating reasoning in natural language Cross-disciplinary applications in law, medicine, and digital humanities
Raphaël Troncy is an Assistant Professor at EURECOM's Data Science Department, specializing in Semantic Web technologies, Knowledge Graphs, and Natural Language Understanding. He teaches courses like 'Human-computer interaction for the Web' and 'Semantic Web technologies.' His research focuses on semantic data integration, knowledge graph applications, and recommender systems. Notable projects include DOREMUS (musical work graph), entity2rec (knowledge graph-based recommendations), and 3cixty (city exploration knowledge bases). He actively contributes to semantic web challenges and conferences, winning multiple awards including the 2018 Best Poster Award at ESWC and 2015 First Prize in the Semantic Web Challenge. Troncy's work spans cultural heritage digitization (e.g., Odeuropa olfactory data modeling), cybersecurity anomaly detection (NORIA-O ontology), and interdisciplinary projects like SILKNOW's silk textile knowledge graph. He leads development of tools like DAGOBAH for semantic table interpretation and KG Explorer for knowledge graph exploration. Education: Not explicitly stated in text Labs/Teams: Active in EURECOM's Data Science group, collaborating on projects involving knowledge graphs, AI, and semantic technologies
Dr. Theresa Züger is an interdisciplinary researcher leading the AI & Society Lab at the Alexander von Humboldt Institute for Internet and Society (HIIG). She investigates how AI systems can be designed to serve the common good, focusing on initiatives that promote sustainability, strengthen social inclusion, and enable technology reuse through open systems. Her work addresses societal challenges associated with artificial intelligence at political, social, and cultural levels, contributing to accurate assessment of AI's societal implications. Züger received her Ph.D. in Media Studies from Humboldt University in Berlin in 2017 with her dissertation 'Reload Disobedience,' focusing on digital forms of civil disobedience. Previously, she earned an M.A. in Theatre, Film and Television Studies as well as German and Philosophy from the University of Cologne. She also headed the office responsible for the German government's Third Engagement Report on behalf of the Federal Ministry for Family Affairs, Senior Citizens, Women and Youth (BMFSFJ). Her research centers on Public Interest AI, examining how AI development and deployment can benefit society rather than primarily maximizing profit. Current projects include 'Impact AI' (funded by Volkswagen Foundation), which develops auditing methods to assess AI projects' impact on public interest and sustainability, and 'Human in the Loop,' investigating how automated decision-making processes should be designed for successful human-machine interaction. She previously led the 'Public Interest AI' project, developing a theoretically grounded understanding of public interest AI and creating prototypes like a web accessibility tool and fact-checking application. Analysis of her recent publications reveals a consistent focus on bridging AI technology with societal values. Her work spans technical aspects of AI implementation, ethical considerations, and practical applications that serve public interests. Key trends include human-AI collaboration frameworks, accessibility enhancements, critical examination of AI hype, and theoretical foundations for public interest-oriented AI development. Züger serves as Vice-Chair of the UNESCO Commission on Communication and Information and participates in several juries including the Deep Tech Award, Digital Places – Land of Ideas, and the Civic Innovation Fund. She is also a regular event moderator for organizations like the Berlin-Brandenburg Media Authority, re:publica, and transmediale. As project leader of Impact AI and head of the AI & Society Lab, Züger directs significant research initiatives with funding from organizations like the Volkswagen Foundation. Her work involves extensive collaboration with academic and practical partners across Europe and globally, particularly through projects addressing women in tech and international AI governance. The AI & Society Lab functions as an interdisciplinary interface for new research approaches and knowledge transfer in AI, promoting inclusive, human rights-friendly, and sustainable AI strategies in Europe.
Arjun Mukherjee is a Lecturer at the Department of Computer Science , University of Houston , where he teaches courses in Machine Learning , Data Mining , Natural Language Processing , and Data Structures . His research focuses on Bayesian Inference , Data Mining , Natural Language Processing , Sentiment Analysis , Opinion Spam , and Web Mining , with a strong emphasis on deception detection and social media analysis. His recent publications explore advanced techniques in LLM-generated content detection synthetic data applications cross-domain deception modeling temporal user behavior analysis , reflecting his commitment to addressing modern challenges in digital content authenticity and machine learning robustness. Dr. Mukherjee has developed educational materials for graduate-level courses, including a well-structured Machine Learning course (COSC 6342) covering probabilistic inference, supervised/unsupervised learning, and neural networks. He earned his Ph.D. from the University of Illinois at Chicago in 2014, with a thesis titled Probabilistic Models for Fine-Grained Opinion Mining: Algorithms and Applications .
