David Bamman is an Associate Professor in the School of Information at UC Berkeley, specializing in applying Natural Language Processing (NLP) and machine learning to cultural and social science questions. He leads research in born-literary NLP, computational humanities, and cultural analytics, with affiliated roles in EECS, Linguistics, and Computational Precision Health. Bamman holds degrees from Carnegie Mellon (Ph.D., 2015), Boston University (M.A., 2006), and University of Wisconsin-Madison (B.A., 1998). His work is supported by NEH, NSF, and industry grants. Educations: Ph.D. in Computer Science (2015), Carnegie Mellon University M.A. in Applied Linguistics (2006), Boston University B.A. in Classics (1998), University of Wisconsin-Madison Research Interests: NLP for underserved domains (e.g., literature, social media), coreference resolution, cultural analytics, and computational methods for studying literature and culture. Projects include LitBank and BookNLP datasets. Grants & Awards: Hellman Fellow (2019), Amazon Research Award (2017), NSF CAREER Award, and NEH funding. Teaching: Courses include Natural Language Processing (Info 159/259), Computational Humanities (INFO 190), and Applied NLP (INFO 256). His research group explores topics like racial representation in high school literature, Hollywood diversity metrics, and the sociocultural implications of LLMs. Bamman advises multiple PhD students and collaborates on datasets like CMU Book Summaries and 11K Latin Books.
Jim Smith is a Professor in Interactive Artificial Intelligence at the University of the West of England (UWE), Bristol, affiliated with the School of Computing and Creative Technologies and the Department of Computer Science and Creative Technologies. He serves as Director of the Computer Science Research Centre and leads the AI@UWE theme. His research is supported by UKRI, Innovate UK, and partnerships with organizations including Health Data Research UK, Office for National Statistics, NHS Scotland, and DSTL. University: University of the West of England School: School of Computing and Creative Technologies Department: Department of Computer Science and Creative Technologies Role: Professor in Interactive Artificial Intelligence Leadership: Director, Computer Science Research Centre Research Interests : Jim Smith's work focuses on Interactive Artificial Intelligence, particularly at the intersection of AI and privacy preservation when using sensitive data for public good. His research includes statistical disclosure control, privacy leakage from AI models, evolutionary computation, machine learning, and systems that learn through human interaction or self-adaptation. He explores how AI can automate privacy checks in research outputs and assess vulnerabilities in trained models. Recent Publications : His recent work spans AI privacy in trusted research environments (e.g., SACRO, SDC-Reboot), dialogue act classification, human-robot interaction, and visualization of deep learning models. Themes include privacy-preserving AI, automated disclosure control, interactive machine learning, and neuromorphic computing. Machine Learning & Privacy Evolutionary Computation Interactive AI Systems Human-Computer Interaction Statistical Disclosure Control Federated Learning Security Scientific Awards : No specific awards are mentioned in the provided texts. Advising and Grants : He currently supervises PhD students on topics including spatio-temporal air quality modeling, federated learning privacy, and threat detection in mobile networks. He leads Innovate UK and UKRI-funded projects such as SACRO and SDC-Reboot, focusing on AI-driven solutions for data confidentiality in public sector research. Interactive Machine Learning for Claim Settlement (Innovate UK) SDC-Reboot (DARE UK/Health Data Research UK) Threat Identification in Mobile Networks (Ribbon Communications) Labs and Teams : He leads the AI@UWE initiative and the Computer Science Research Centre at UWE. His work involves collaboration through DARE UK and open-source development via the AI-SDC GitHub organization, which hosts tools from SACRO and GRAIMATTER projects.
Nicole Novielli, Ph.D., is Associate Professor at the University of Bari “A. Moro” , Italy, where she conducts research on affective computing applied to software engineering and human-computer interaction. She leads the Collaborative Development Group and coordinates national projects investigating emotions in software teams, AI quality and IoT ecosystems. Education: Ph.D. in Computer Science, University of Bari, 2010 – thesis on “Lexical Semantics of Dialogue Acts” M.Sc. in Computer Science (Knowledge & Software Engineering), University of Bari, 2006 – summa cum laude B.Sc. in Computer Science, University of Bari, 2004 – summa cum laude Visiting researcher at USC-ICT, University of Aberdeen, FBK-irst (Trento) Research interests revolve around recognizing and exploiting affective and cognitive states in computer-mediated cooperative work. She studies sentiment and emotion mining in developers’ textual communication, multimodal emotion recognition via low-cost biometric sensors, and natural-language dialogue simulation for intelligent interfaces. Her work couples software engineering with natural language processing , social media analytics and human-computer interaction . Recent articles (2021-2025) reveal a clear trend: integrating deep learning and large language models into software engineering tasks—automated issue labelling, sentiment classification, technical-debt detection—while validating these techniques through rigorous empirical studies and biometric experiments . A parallel stream explores developer experience , measuring how emotions and cognitive load influence productivity, code quality and collaboration. Scientific awards include the 2020 Apex Award for Publication Excellence , multiple Distinguished Reviewer Awards at flagship venues (ESEC/FSE, ICSME, MSR), the Best Paper Award SANER 2019 and the Best Student Paper Award ACII 2009 . She currently teaches “Sentiment Analysis” in the Data-Science MSc and “Computer Networks” in the ITPS programme. She has advised numerous B.Sc., M.Sc. and PhD projects and is PI or Co-PI of four ongoing grants: EmoQuest (SIR), EMPATHY (PRIN), FAIR-Spoke 6 (PnRR), and QualAI (PRIN 2022). Dr. Novielli serves on the editorial boards of Empirical Software Engineering and Journal of Systems and Software , has guest-edited special issues on affect awareness in SE, and has chaired tracks at ICSE, SANER, MSR, ICSME and SSBSE. She co-leads the Collaborative Development Group and actively releases datasets and open-source tools for the community.
Matthew Collinson is a Senior Lecturer in Computing Science at the University of Aberdeen, where he also serves as Head of Computing Science and Academic Line Manager. He holds an affiliation with the Scottish Informatics and Computer Science Alliance (SICSA) and leads the EPSRC-funded project SSPEDI (Supporting Security Policy with Effective Digital Intervention). Education: BSc Mathematics, University of Edinburgh (1997) MSc Mathematical Logic, University of Manchester (1998) PhD Computer Science, University of Manchester (2003) Research Interests: His research spans theoretical computer science and cybersecurity , focusing on non-classical logics (intuitionistic, modal, substructural), semantics of computation , concurrency theory , and type theory . He applies these foundations to information security , particularly in modelling security policies, access control, and the economics of cybersecurity decisions. His work integrates formal verification , simulation tools (e.g., Gnosis), and game-theoretic models . Publications Trends: Recent publications (2016–2022) emphasize human-centred security , exploring how persuasion and behavioural interventions can reduce cybersecurity vulnerabilities. Earlier works (2008–2015) concentrate on mathematical systems modelling , layered graph logics , and trust domains , bridging high-level policy and low-level system configurations. Projects & Grants: SSPEDI (2017–2020, EPSRC): Human dimensions of cybersecurity policy compliance. ALPUIS (EPSRC consortium): Algebra and logic for security policy and utility. Trust Domains (RCUK/TSB, 2011–2014): Framework for modelling secure information sharing. Seconomics (EU FP7, 2012–2015): Socio-economic impacts of cybersecurity regulation. PhD Supervision: He has successfully supervised PhD students including Kevin McDonald (2014), Barry Taylor (2015), and Robert (Bob) Duncan (2016), whose theses addressed logic-based security architectures, vulnerability analysis, and cloud stewardship respectively. Labs & Teams: His research is conducted within the Computing Science section of the School of Natural and Computing Sciences, leveraging collaborations with National Grid, HP Labs, and other academic partners.
Stuart Shieber is the James O. Welch, Jr. and Virginia B. Welch Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. He is a prominent researcher in computational linguistics and natural language processing, with significant contributions across multiple related fields including theoretical linguistics, computer-human interaction, automated graphic design, and the philosophy of artificial intelligence. Professor Shieber's research interests focus primarily on computational linguistics, examining natural language from the perspective of computer science. His work spans scientific and engineering goals, utilizing foundational formal and mathematical tools. He has made significant contributions to grammar formalisms, psycholinguistics, semantics, and synchronous grammars with applications in machine translation and sentence compression. Beyond computational linguistics, his research extends to automatic layout of charts and maps, novel interaction techniques for document reading and diagram layout, online auction mechanisms, library book access prediction, biological evolution tree reconstruction, and the philosophical basis for Turing's test for machine intelligence. His recent publications demonstrate a continued focus on neural language models, syntactic agreement mechanisms, readability assessment, conversational understanding, and bias detection in language models. His research has evolved from traditional grammar formalisms to incorporate modern neural network approaches while maintaining a strong theoretical foundation. The trend shows increasing attention to ethical considerations in NLP, particularly around bias detection and mitigation, alongside continued theoretical work on language structure. Presidential Young Investigator award (1991) Presidential Faculty Fellow (1993) John L. Loeb Associate Professorship in Natural Sciences (1993) Harvard College Professorship (2001) Fellow of the American Association for Artificial Intelligence (2004) Fellow of the Association for Computing Machinery (2014) Fellow of the Association for Computational Linguistics (2017) Professor Shieber has advised numerous PhD students who have gone on to successful careers at institutions including UCSD, Cornell University, Microsoft Research, Google, and various academic institutions. His work on open access and scholarly communication policy, particularly his development of Harvard's open-access policies, led to his appointment as the first director of the university's Office for Scholarly Communication. He is also the founding director of the Center for Research on Computation and Society and a faculty co-director of the Berkman Center for Internet and Society. His laboratory work has focused on advancing computational linguistics through both theoretical and applied research, with numerous patents and co-founding of Cartesian Products, Inc., a high-technology research and development company. His future work appears to be focusing on the intersection of neural network approaches with traditional linguistic theory, particularly in understanding and mitigating bias in language models, while continuing his long-standing interest in the theoretical foundations of language processing.
Rob van der Goot is an Associate Professor at the IT University of Copenhagen , specializing in robustness in natural language processing (NLP). His work focuses on non-standard language varieties, low-resource languages, and scenarios with limited training data, particularly in syntactic tasks. He is involved in projects like MaChAmp and MoNoise , which address multi-task learning and lexical normalization. Supervises PhD student Arzu Burcu Güven and mentors postdoc Elisa Bassignana Member of the HPC committee at ITU and the AI Pioneer Center Active in research collaborations including the COST Action Multi3Generation His research has earned accolades, including a Best Paper Award at WNUT 2022, an Outstanding Paper Award at EACL2021, and the Most Reviews Award at NAACL 2019. He frequently presents at international venues, such as LMU, DeiC Konference, and the University of Helsinki. His recent publications explore cross-domain dialogue act classification, code-switched lexical normalization, and multi-task learning frameworks. His work has been featured in media outlets like Omrop Fryslan and Linear Digressions. 2022 : Best Paper Award for Increasing Robustness for Cross-domain Dialogue Act Classification on Social Media Data (WNUT) 2021 : Outstanding Paper Award for MaChAmp (EACL) 2019 : Most Reviews Award (NAACL) As part of the HPC committee and DeIC Science Forum, he contributes to computational infrastructure and data management initiatives. His supervision history includes advising Anders Giovanni Møller (shared first place in WNUT 2021 shared task) and collaborations with researchers like Barbara Plank and Malvina Nissim.
Associate Professor Wayne Wobcke is a faculty member in the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he has been employed since 2002. His academic career includes previous positions at the University of Sydney until 1998, British Telecom Labs in the UK for three years, and the University of Melbourne for one year. He holds a PhD in Computer Science from the University of Essex (1989), an MSc from the University of Queensland (1985), and a BSc (Hons) in Mathematics/Computer Science from the University of Queensland (1984). Dr. Wobcke's research spans both theoretical and practical aspects of artificial intelligence and data science. His work encompasses intelligent agents, data mining, agent-based modeling, dialogue management, personal assistants, recommender systems, and computational social science. He has collaborated extensively with industry through three Cooperative Research Centres (Smart Internet Technology CRC, Smart Services CRC, and Data to Decisions CRC), where he served as a Programme Manager and Project Leader for over 10 years. Notable achievements include developing a voice-controlled mobile application for email and calendar interaction (a precursor to Apple's Siri) and deploying a people-to-people recommender system for online dating on one of Australia's largest dating sites. His recent research focuses on data science in humanitarian contexts and machine learning applications in official statistics, conducted in collaboration with BPS (Statistics Indonesia) and STIS (Politeknik Statistika, Indonesia). His publication record shows a consistent trajectory of impactful research, with recent work concentrating on poverty targeting, domain adaptation, natural language processing for recommender systems, and political opinion mining. Scientific Awards: Best Paper Nomination, 11th Workshop on Argument Mining (2024) UNSW Arc Postgraduate Research Supervisor Award (2017, 2018) AAAI Deployed AI Application Award, Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (2014) Best application paper runner up, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (2013) Dr. Wobcke has successfully supervised numerous research students, with Irwan Rahadi currently working on 'Causal Modelling and Machine Learning for Official Statistics'. His grant portfolio includes significant funding from the Australian Research Council and various Cooperative Research Centres, totaling over $3.7 million since 2003. He teaches COMP9414 Artificial Intelligence and COMP9727 Recommender Systems at UNSW.
Professor Vincent Wade is a prominent academic and co-founder of the ADAPT SFI Research Centre, holding the Professorial Chair of Computer Science (established 1990) and a Personal Chair in Artificial Intelligence at Trinity College Dublin's School of Computer Science and Statistics. He co-directs the DREAL Centre for Research Training and leads ADAPT, a globally recognized centre for digital media technology and AI research. His work spans intelligent systems, personalisation, machine learning, and ethical AI applications in healthcare and education. Research interests include AI-driven personalisation, multimodal interaction, knowledge graphs, and ethical considerations in digital technologies. He has published over 350 peer-reviewed papers, earned the prestigious Provost Innovation Award (2018), and holds patents in personalisation technologies. He co-founded EmpowerTheUser, a TCD spin-out focused on simulation-based learning analytics. Major Achievements: 2018 Provost Innovation Award (Trinity College Dublin) 2010 European Language Label Award Fellow of Trinity College Dublin Over 350 scientific publications Key Contributions: Developed the ADELE corpus for social conversation analysis Pioneered cross-site personalisation frameworks Advanced adaptive e-learning systems through platforms like Slicepedia and AMASE His research bridges technical innovation with societal impact, addressing challenges in healthcare, education, and digital ethics.
Neal Martin Kingston is a University Distinguished Professor in the Department of Educational Psychology at the University of Kansas. He serves as Director of the Achievement and Assessment Institute (AAI) and Vice Provost for Jayhawk Global and Competency-Based Education. His work focuses on large-scale assessment, learning maps, and psychometric methods. He leads AAI, overseeing 400+ staff and 150+ temporary employees, managing projects like the Dynamic Learning Maps Alternate Assessment. Education includes a B.A. from SUNY Stony Brook (1974) and multiple degrees from Teachers College, Columbia University: M.A. (1977), M.Ed. (1978), M.Phil. (1983), and Ph.D. (1983) in Educational Measurement. Research emphasizes integrating assessment with learning, including formative assessment, games-based testing, and support for students with disabilities. Over 250 grants funded his work, notably the Dynamic Learning Maps project (then KU’s largest grant). Notable awards include the University Distinguished Professor title. His advising spans 17 listed students, many now in academia or assessment roles. Key labs include the AAI, involved in projects like I-SMART and Kansas Assessment Program.
Susan Ariel Aaronson is a Research Professor of International Affairs at George Washington University (GWU) and Director of the Digital Trade and Data Governance Hub. She leads research on AI governance, data governance, and digital trade policies as co-PI of the NSF-NIST TRAILS initiative. Her work focuses on global challenges like AI nationalism, data accuracy, and extended reality (XR). Aaronson is a GWU Public Interest Technology Scholar (2024) and Cross-Disciplinary Scholar (2017). Education: Ph.D., Johns Hopkins University. Research areas include international trade, human rights, and the governance of AI/data-driven technologies. She explores topics like AI protectionism, cross-border data flows, and the ethical implications of generative AI. Her op-eds appear in Barron’s and Fortune , and she contributes to media outlets like NPR and the BBC. Key Projects: Digital Trade Hub (mapping global data governance), TRAILS (AI governance research) Funded by: Ford Foundation, Minderoo Foundation Publications span peer-reviewed journals ( Oxford Review of Economic Policy ), policy briefs ( CIGI Papers ), and op-eds. Recent work highlights the need for rethinking trade policies to address digital-era challenges and fostering trust through inclusive governance frameworks. Awards: GWU Public Interest Technology Scholar, Cross-Disciplinary Scholar Grants: NSF-NIST TRAILS, Ford Foundation, Minderoo Foundation Labs/Teams: Leads the Digital Trade and Data Governance Hub, a global research initiative addressing data governance and AI policy.
Nirmalie Wiratunga is a Professor in Intelligent Systems at the School of Computing , Robert Gordon University, and serves as the Associate Dean for Research . She is also an Adjunct Professor at the Norwegian University of Science and Technology (IDUN program). Her academic excellence spans over two decades in Artificial Intelligence and Machine Learning , with a focus on Explainable AI (XAI) , Case-Based Reasoning (CBR) , and Natural Language Processing (NLP) . Her research explores innovative methodologies for knowledge-rich representations to automate decision-making through CBR for Retrieval-Augmented Q&A systems and human-centered AI platforms . She co-founded Attendr.app , a spinout for student and conference attendance tracking, and leads the Artificial Intelligence & Reasoning Research Group at RGU. Recent publications (2024–2025) highlight her work on LLM hallucination detection , counterfactual explanations in finance , cross-lingual biomedical review automation , and multi-query resolution in legal domains . Themes span AI explainability , NLP , CBR , and domain-specific knowledge integration across healthcare, law, and education. Her leadership extends to organizing international workshops on XAI , digital health , and Deep Learning , and co-chairing the ICCBR 2021 and 2022 conferences. She actively contributes to program committees for ECCBR , ECML/PKDD , and IJCAI .
National and Kapodistrian University of AthensGreece
Alexandri Christina is a Lecturer at the Faculty of German Language and Literature at the National and Kapodistrian University of Athens. Her research focuses on computational linguistics and natural language processing, with applications in sentiment analysis, machine translation, and human-computer interaction systems. Her work examines how implicit meaning and unspoken context in spoken interactions can be computationally modeled, particularly in political, journalistic, and technical domains. Recent projects explore the integration of generative AI with traditional linguistic approaches to enhance human-machine communication across specialized fields including medical, engineering, and maintenance contexts. Research trends show consistent focus on multilingual processing challenges, with innovations in knowledge graph construction for bias detection and cross-domain adaptation techniques. Publications demonstrate strong interdisciplinary connections between linguistics, computer science, and social sciences.
Dr. Sara Gallardo González is a full Professor at the Faculty of Humanities and Education, Catholic University of Ávila (Spain). She holds the Santa Teresa de Jesús Chair of Women's Studies since 2014 and directs the Master's Degree in Bioethics since 2008. Her academic journey includes a doctorate from Complutense University of Madrid (2002) under Dr. Juan José García Norro, with research periods at institutions in Munich, Bonn, and Fribourg. Accredited as contracted professor with doctorate by ANECA (2021) Member of Neuen Schülerkreis J. Ratzinger-Benedikt XVI (since 2018) Director of University Extension Service (2008-present) Research Pillars : Anthropology (with focus on sexual difference, women's studies, and relational personhood) Bioethics (end-of-life care, corporate ethics) Philosophy-Theology Dialogue (Brentano, Ratzinger/Benedict XVI, Spaemann) Metaphysics of Causality and Free Will Academic Impact : Over 40 publications including peer-reviewed articles, book chapters, and monographs. Active participation as speaker in international conferences across Spain, Germany, Peru, Colombia, and USA. Notable awards include DAAD, La Caixa Foundation, and Eskas Foundation scholarships.
Marie Tahon is a Professor at Le Mans University and Director of the LST (Langage, Signal et Texte) team at LIUM (Laboratoire d'Informatique de l'Université du Maine). Her research spans expressive speech processing with applications in speech synthesis, emotion recognition, and speaker identification, complemented by expertise in musical acoustics for automatic song analysis and organology. Education : Engineering degree from École Centrale de Lyon (2007), M.S. in Acoustics from École Centrale & INSA Lyon (2007), and Ph.D. in Computer Science from University of Paris-Sud (Orsay, 2012). Postdoctoral positions at LIMSI-CNRS (affective computing), LMSSC CNAM (acoustics), and IRISA (Expression team). Research Focus : Tahon's work centers on developing interpretable systems for expressive speech processing. Key contributions include the ALLIES corpus for speech segmentation/diarization and AlloSat for call-center emotion analysis. Her recent publications demonstrate strong emphasis on low-resource speech translation (e.g., Kurdish), speaker verification after resynthesis, and turn-taking analysis in French media using explainable AI techniques. She integrates acoustic and linguistic features for continuous emotion prediction and develops noise-robust models for digital holography. Collaborations & Infrastructure : Leads the COMMUTE and ESPERANTO projects while directing LIUM's LST team. Her work leverages specialized resources like the ALLIES corpus (segmentation, diarization, recognition) and AlloSat (satisfaction/frustration analysis). Current efforts focus on lifelong learning for MOS prediction, multilingual speech translation, and perceptual evaluation of turn-taking phenomena in broadcast media.
Yangming Li is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work bridges applied mathematics, theoretical physics, and machine learning, with a focus on developing innovative models for image analysis, generative processes, and natural language understanding. Research interests include Fourier Neural Operators, diffusion models, generative adversarial networks (GANs), and their applications in solving complex problems across scientific computing and data-driven domains. His recent contributions explore operator learning for PDEs, robust diffusion models under noisy conditions, and adversarial attacks in text watermarking systems. Publications highlight advancements in operator-based neural networks, risk-sensitive generative modeling, and domain-aware NLP frameworks. His methodologies emphasize mathematical rigor while addressing practical challenges like missing data and model expressivity limitations. Active collaborations span interdisciplinary teams at DAMTP and the broader University of Cambridge research community.
