Dr. Zoe Tieges is a Lecturer in Computing at Glasgow Caledonian University , with a research focus spanning delirium diagnostics, natural language processing, and neurorehabilitation. She holds an ORCID and is based at 70 Cowcaddens Road, Glasgow Caledonian University, United Kingdom. Primary affiliation: Glasgow Caledonian University, School of Computing Email: zti1@gcu.ac.uk Research activity: 59 outputs (2004–2025), 21 h-index Research Interests Dr. Tieges specializes in: Delirium detection and subtyping Natural language processing for clinical trial communication Neurorehabilitation for multiple sclerosis Blue space health benefits Smartphone-based diagnostic tools Meta-analytic methods in healthcare Selected Projects Current initiatives include: Data2Action : Developing AI networks for social innovation Clinical Trial Summarization : Making medical research accessible via NLP MS Rehabilitation : Exercise programs for mobility improvement Scientific Awards Recipent of: Post-Doctoral Trainee Oral Abstract Award (2019) Best oral presentation (2018) NIDUS Junior Investigator Pilot Award (2017) Paper of the Year (2015) Poster presentation prize (2014) Publications Trends Her 15 most recent publications (2023–2025) focus on delirium assessment tools, NLP applications in clinical trials, and MS rehabilitation technology. Recurring subfields include cognitive disorder diagnostics, healthcare AI, and environment-health interactions.
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.
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.
Santu Karmaker is an Assistant Professor at the University of Central Florida in the Department of Computer Science within the College of Engineering. His research focuses on democratizing AI and data science through advancements in natural language processing, information retrieval, and machine learning. He earned a Ph.D. in Computer Science from the University of Illinois Urbana-Champaign and a Master of Science in Computer Science from Bangladesh University of Engineering and Technology. Ph.D. in Computer Science – University of Illinois Urbana-Champaign M.S. in Computer Science – Bangladesh University of Engineering and Technology Karmaker's research explores semantic similarity alignment, conversational data science, infrastructure as code defects, and embedded NLP applications. His work addresses zero-shot learning, Bangla language processing, and prompt taxonomy for LLMs. He has secured over $1.4 million in grants from the NSF, AFOSR, ARO, and USDA. Recent publications span premier venues like EMNLP, ACL, TMLR, and ACM Transactions on Intelligent Systems and Technology. He actively contributes to academic service as an action editor for ACL Rolling Review and communication/tutorial chairs for conferences. Current affiliations include the Laboratory for Information and Decision Systems (MIT) postdoctoral collaboration and UCF's embedded systems research initiatives.
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.
Dr. Kyung Hun Jung is a faculty member at Kennesaw State University, affiliated with the Department of Psychology. He teaches courses including Cognitive Psychology, Engineering Psychology, Experimental Design, and Research Methods and Statistics. Ph.D. in Experimental Psychology (University of New Mexico, 2013) M.S. in Experimental Psychology (Korea University, 2006) B.A. in Psychology (Korea University, 2004) His research spans cognitive psychology, human factors, and ergonomics, with recent work focusing on automated vehicle interaction and driver training using moving-base driving simulators and virtual-reality headsets. Earlier research includes studies on attentional mechanisms, facial attractiveness, color perception, and document similarity analysis. Recent publications highlight applications of human factors in transportation technology and usability engineering. Data and code from his studies are publicly shared for replication purposes.
Dr. Ali Farzamnia is a Lecturer in Electronic and Electrical Engineering at the Department of Engineering , School of Computing and Engineering , University of Huddersfield, United Kingdom. He is actively involved in research and supervises PhD students. Research focus areas: Signal Processing, Deep Learning, and Wireless Communications His recent work demonstrates interdisciplinary applications of machine learning in diverse domains including sign language recognition, financial trading, agricultural stress detection, and software testing automation. Dr. Farzamnia employs cutting-edge techniques like third-generation transformers and few-shot learning frameworks in his research projects.
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.
Aditi Gandotra is an Assistant Professor at the Department of Personality and Health Psychology, Eötvös Loránd University (ELTE), Hungary. She is affiliated with the Emotion and Mind Integration for Neuropsychological Development Research Group . Her research focuses on motor skills development in children, executive functions, autism spectrum disorder, and the application of digital tools in mental health. She holds a doctoral degree (as indicated by the 2021 dissertation entry) and has conducted studies in both Hungarian and Indian contexts. Her work bridges developmental psychology, clinical psychology, and technology-driven mental health solutions. Key Research Themes: Motor skill-cognitive function interplay in preschoolers Neuropsychological development in neurotypical and neurodivergent populations Accessibility of mental health interventions via digital platforms Her recent publications (2017-2023) emphasize: Exploring motor skills as predictors of socio-emotional outcomes Cultural and contextual factors in mental health app usage (focusing on Indian populations) Systematic reviews on motor skill deficits in autism spectrum disorder Awards & Grants: No specific awards or grants listed in provided texts. Advising & Teams: Currently, no listed students or specific grant details. She contributes to the Emotion and Mind Integration Research Group, focusing on neuropsychological development trajectories.
Dr Marco Palomino is a Senior Lecturer in the School of Natural and Computing Sciences at the University of Aberdeen and holds a Visiting Associate Professor position at the University of Plymouth in the School of Engineering, Computing and Mathematics. He is actively involved in research, teaching, and PhD supervision, with a focus on data science and computing. His roles include Admissions Tutor, Programme Manager for the Digital and Technology Solutions Professional Degree Apprenticeship, and Athena Swan Lead at Aberdeen. His research interests span Natural Language Processing , Sentiment Analysis , Social Media Analytics , Spatiotemporal Databases , and Human-AI Interaction . He employs interdisciplinary methods to analyze public opinion, improve cybersecurity training, support elderly care through robotics, and develop decision-support systems. His work contributes to Sustainable Development Goals related to technology and societal well-being. His recent publications (2022–2025) reflect a strong trend in applying machine learning and data science to real-world problems, including sentiment analysis during the pandemic, adaptive cybersecurity training, human-AI collaborative decision-making, and assistive social robots for older adults. These works emphasize practical applications in healthcare, security, urban mobility, and policy. Scientific recognition includes: Highly Commended Paper award, Emerald Literati Network (2014) He has supervised numerous students on topics including sentiment analysis, malware detection, and cybersecurity education. His research is supported by affiliations with both the University of Aberdeen and the University of Plymouth, and he has contributed as a Guest Editor and reviewer for journals such as Applied Sciences , Big Data and Cognitive Computing , and Mathematics . He is a Fellow of the Higher Education Academy.
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.
Philipp Cimiano is a Professor at the Faculty of Engineering, Bielefeld University, and leads the Semantic Databases Group. He holds additional roles as Coordinator of the Cognitive Interaction Technology Center (CITEC) and Director of the Joint Artificial Intelligence Institute (JAII). His research focuses on the intersection of language, semantics, and knowledge representation, with applications in Explainable AI , Knowledge Graphs , and AI in Medicine . Education: University of Stuttgart, University of South Australia, Karlsruhe Institute of Technology (KIT) Key Research Areas: Knowledge Representation, Ontologies, Explainable AI, Clinical Decision Support His recent work emphasizes dialogue-based XAI , federated learning , and semantic data integration . He has secured funding from the German Research Foundation (DFG) and the European Union for projects like TRR 318 "Constructing Explainability" and Pret-a-LLOD. Scientific Awards Carl Adam Petrie Prize, KIT Faculty of Business and Economics Editorial Roles Co-editor, Journal of Applied Ontology Area Editor, Semantic Web Journal Editorial Board, Journal of Web Semantics Notable Projects TRR 318 (Subprojects B01, C05, INF) 3B: Bots Building Bridges for online deliberation LLM4KMU: Open Source LLMs for SMEs
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).
David Enrique Losada Carril is a Full Professor in Computer Science and Artificial Intelligence at the CiTIUS Research Center of the University of Santiago de Compostela (Spain). He holds a PhD in Computer Science (2001, University of A Coruña) and has been active in Information Retrieval (IR) research since joining the university in 2003 as a senior research fellow under the Ramón y Cajal program. BS and PhD (with honors) from University of A Coruña ACM Senior Member awardee (2011) Co-founder of the eRisk CLEF Lab for early risk detection on the internet Active in IR community with roles in ACM SIGIR, ECIR, and CLEF Research Focus spans probabilistic IR models, novelty detection, health search technologies, and mental health analysis via social media monitoring. His recent work explores large language models for depression symptom assessment and misinformation detection in health contexts. Scientific Leadership includes: Principal Investigator for projects like Big-eRisk and LudoTrack Co-author of over 20 top-tier publications (SIGIR, ECIR, ACL, ECIR, Springer series) Technical Contributions feature real-time social media analysis platforms (e.g., Catenae , eXtream ) and benchmark datasets like DepreSym for depression detection.
