Dr. Abdul Shahid is a Lecturer in Business Information Systems at the School of Business, South East Technological University (SETU), Waterford campus. He holds a PhD in Computer Science from Capital University of Science and Technology, Islamabad, and has previously served as a Lecturer at the National College of Ireland and a Postdoctoral Researcher at the University of Galway, contributing to EU Horizon 2020 projects such as Polifonia and InsiDE PRI. Research Interests: Dr. Shahid's research lies at the intersection of data science, large language models (LLMs), scientometrics, and applied AI. His work emphasizes the development of intelligent systems for decision-making in logistics, cybersecurity, and semantic computing. He is particularly focused on citation analysis, contextual embeddings, and the deployment of Retrieval-Augmented Generation (RAG) frameworks in industrial applications. Recent Research Trends: His recent publications reflect a strong emphasis on natural language processing for scientific literature analysis, including in-text citation identification, intent classification, and semantic modeling. These works integrate machine learning with knowledge engineering to extract and structure scientific knowledge from academic texts. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: Dr. Shahid is currently accepting PhD students. His research has been supported through participation in high-impact EU-funded projects such as Horizon 2020’s Polifonia and InsiDE PRI, which involved data-driven analysis of scientific collaboration and knowledge graph applications in cultural heritage. Labs and Research Teams: He has been affiliated with research teams at the University of Galway working on EU Horizon 2020 initiatives. At SETU, he contributes to research in applied AI and business information systems, particularly in developing intelligent solutions for logistics and document understanding.
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Hao Zhangمشاهده پروفایل
پژوهشگر ارشد
Dr. Hao Zhang is a Research Fellow at the Financial Regulation Innovation Lab (FRIL), University of Strathclyde, and holds a PhD in Finance from the University of Glasgow's Adam Smith Business School. Previously, he served as Senior Project Manager at the Ministry of Industry and Information Technology (MIIT) in China. His research focuses on asset pricing, risk management, financial derivatives, and the integration of technology/data science in finance. Education: PhD in Finance, University of Glasgow (Adam Smith Business School) Research Interests: Asset Pricing Risk Management Financial Derivatives Technology and Data Science in Finance Teaching: Financial Derivatives and Risk Management Big Data Analytics Financial Information Retrieval Professional Activities: Participated in the workshop 'Generative AI in Financial Services' (October 2023). Labs/Teams: Active in the Financial Regulation Innovation Lab (FRIL), exploring AI-driven regulatory solutions and ESG reporting frameworks.
Antonio García Cabot is an Associate Professor in the Department of Computer Science at the University of Alcalá. His research focuses on artificial intelligence, educational technology, natural language processing, and mobile computing. He leads the INTELIA research group (Interaction Technologies and Artificial Intelligence Lab) and previously contributed to the PMI group (Intelligent Mobile Platforms). He earned his Doctorate in Computer Science from the University of Alcalá in 2013 with a thesis titled *Propuesta de un sistema multi-agente para la adaptación de contenidos docentes a las competencias, contexto y dispositivo del usuario*. His work emphasizes accessibility in digital education, gamification in MOOCs, and software engineering innovations like automated code repair using LLMs. Research trends in his 2024-2025 publications include: Advancing AI applications in educational assessment (e.g., automated question generation, distractor design) Improving mobile app usability through gesture-based interfaces and sensor integration Developing large language model-based tools for vulnerability repair and teacher simulation analysis Systematic reviews on digital competence ecosystems and multilingual AI systems His contributions to open educational resources (OER) and accessible virtual campuses have been widely adopted in Latin America. Current research extends into embedded systems education and olfactory stimuli impacts on mobile app performance.
Soon Lay Kiمشاهده پروفایل
دانشیار
Soon Lay Ki is an Associate Professor at the School of Information Technology, Monash University Malaysia, where she also serves as Associate Head (Graduate Research) since November 2018. Her academic journey began with roles at Multimedia University (MMU), where she was a Senior Lecturer and Deputy Dean (Research and Innovation) from 2016 to 2018. PhD in Web Engineering, Soongsil University, Korea Master of Science in Database, Universiti Putra Malaysia Bachelor of Computer Science, Universiti Putra Malaysia Her research centers on applied natural language processing and data management , with a focus on analyzing domain-specific and social media content. Her work spans aspect-based sentiment analysis , cyberbullying detection , misinformation detection , and relation extraction from conversational texts. Recently, her research has expanded into digital health , particularly emotion-aware mental health chatbots and emotion detection via video data. The most recent articles highlight a strong trend in AI for social good , including legal reasoning, mental health, accessibility, and public health. Her publications appear in high-impact journals and conferences such as Artificial Intelligence and Law , IEEE Transactions on Dependable and Secure Computing , and ACL-affiliated workshops. She has received notable scientific awards, including: ITEX'24 Silver Award for 'MOBOT' mental health chatbot (2024) Silver Medal at Malaysia Technology Expo 2023 for the same innovation The Incubator Grant: Bolster Category (2023) Dr. Soon has graduated seven PhD and three Master’s students, one of whom received the MMU Best Master Thesis Award in 2015. She leads multiple research grants, including FRGS-funded projects and industry collaborations with Telekom Malaysia and Intel . She is currently a Chief Investigator or Primary Chief Investigator on six active projects, including WHinc, WAge, and Epsilon, often in collaboration with Monash Australia and SEACO. She is part of key research teams such as the Action Lab at Monash University Australia and the South East Asia Community Observatory (SEACO) , contributing to inclusive research infrastructure and public health data access initiatives.
Prof. Stephen Kobourov is a Professor in the TUM School of Computation, Information and Technology at Technische Universität München (TUM). Previously, he held positions at the University of Arizona from 2000 to 2024, progressing from Assistant Professor to Full Professor, and served as Deputy Director of the Data Science Institute. His expertise lies in algorithm design, computational geometry, and graph visualization, with over 250 publications and significant funding from NSF, ONR, and USDA grants. Education: BS in Computer Science and Mathematics from Dartmouth College (1995), PhD in Computer Science from Johns Hopkins University (2000). Research focuses on efficient algorithms for graph drawing, visualization techniques for networks, and geometric algorithms. Notable contributions include work on stress metrics in graph layouts, hypergraph representations, and non-Euclidean visualization frameworks. He has co-chaired committees for ALENEX, IEEE PacificVis, and GD, and serves as an editor for JGAA, CGTA, and IEEE TVCG. Awards include the Fulbright Distinguished Chair (2015–2016), Humboldt Research Fellowship (2011–2014), and NSF Career Award (2006–2011). His recent work explores multi-layer network visualization, AI-driven graph analysis (e.g., MLLMs perception), and graph embeddings in non-Euclidean spaces. Advising and grants: Extensive grants from NSF and other agencies support his research. He has advised numerous students and collaborates internationally, contributing to labs like the Data Science Institute and initiatives such as the Institutional Knowledge Map (KMAP). Visualization projects include MetroSets (metro map-style set visualization), Wooly Graphs (knitting pattern frameworks), and tools for interactive network analysis. His work bridges theoretical algorithms with practical applications in science and technology.
VG Vinod Vydiswaran is an Assistant Professor in the Department of Learning Health Sciences at the University of Michigan Medical School. He holds a PhD from the University of Illinois at Urbana-Champaign and an MTech from the Indian Institute of Technology Bombay. His research focuses on medical and health informatics, particularly analyzing medical information in clinical notes and online portals. Key areas include information trustworthiness, text mining, natural language processing, and machine learning applications in healthcare. Education: PhD, Computer Science, University of Illinois at Urbana-Champaign (2007-2012) MTech, Indian Institute of Technology Bombay (2002-2004) Bachelor of Engineering, Computer Engineering, Vishwakarma Institute of Technology, Pune (1998-2002) Research Interests: His work spans information trustworthiness, medical NLP, health informatics, and machine learning. Current emphasis is on analyzing medical information exchange between clinicians and patients via electronic health records and online platforms. Recent trends in publications include health vocabulary mining from community forums, trust propagation frameworks, and scenario-based news analysis. Awards: Best Paper Award at COMAD 2005 Second Prize in KDD Cup 2003 (Task 2) Outstanding Teaching Assistant Award (2011) CS Graduate Ambassador (2010-2011) Grants & Advising: Active in interdisciplinary collaborations involving health data mining. Taught SI 671 (Data Mining) in 2015. Served on program committees for AAAI, CIKM, AMIA, and others. Labs & Teams: Part of the Foreseer group at the School of Information, focusing on health informatics applications. Collaborates with Medical School faculty and industry partners like Microsoft Research and Palo Alto Research Center.
He Liمشاهده پروفایل
استادیار
- Multiscale Computational Modeling
- Artificial Intelligence in Medicine
- Biomedical Engineering
- +۳ مورد دیگر
Dr. He Li is an Assistant Professor at the University of Georgia, specializing in computational biomedical engineering. His research focuses on developing AI-driven multiscale models to bridge microscopic and macroscopic biological processes, with applications in disease modeling, medical imaging, and healthcare innovation. He leads the Computational Biomedical Engineering Lab, exploring interdisciplinary solutions in biomedical science using advanced computational methods. His work integrates physics-based modeling (e.g., molecular dynamics, spectral element methods) with AI techniques to assimilate diverse biological data sources. Recent research emphasizes predictive AI models for digital health, including radiology analysis, proton therapy optimization, and Alzheimer’s disease infodemiology. He has published extensively on large language models (LLMs) tailored for medical domains like radiology, oncology, and ophthalmology, addressing challenges in specialized clinical NLP tasks. Key contributions include frameworks like Radiology-GPT and AD-Autogpt, which advance AI applications in diagnostics and disease surveillance. His lab collaborates across disciplines to enhance clinical decision-making through AGI (Artificial General Intelligence) and multimodal data fusion. No awards or grants are explicitly listed in the provided texts. Labs/Teams: Computational Biomedical Engineering Lab @ UGA focuses on AI-driven biomedical innovation, with projects spanning medical imaging, disease modeling, and healthcare technology development.
Kaicheng Yangمشاهده پروفایل
استادیار
Dr. Kaicheng Yang is an Assistant Professor in the School of Computing at Binghamton University, where he leads the Yang Lab. His research focuses on creating safe, fair, and trustworthy online information platforms by identifying how malicious actors and flawed systems distort information flow and developing effective countermeasures. His research interests include: Computational social science approaches to understanding online information ecosystems Social bots and automated accounts on social media platforms Misinformation detection and analysis Algorithmic biases in information systems Generative AI misuse and countermeasures Data science methods for social media analysis Dr. Yang's recent publications demonstrate a strong focus on AI-powered social manipulation, with particular emphasis on how generative AI is being used to create malicious social botnets, fake social media profiles with AI-generated faces, and to spread misinformation. His work combines computational methods, data science, and social science approaches to address critical challenges in online information ecosystems. His research has been featured in numerous media outlets including Forbes, Wired, MIT Technology Review, Business Insider, and IEEE Spectrum. He has developed several research tools including DomainDemo explorer for checking user demographics of domains and Botometer X for detecting bot-like Twitter accounts. Dr. Yang recently joined the editorial board of Information Processing & Management and will be hosting a tutorial on 'Introduction to the National Internet Observatory' at ICA 2025 in Denver, CO.
Oksana Zavalina is a Professor at the University of North Texas, specializing in information organization, metadata standards, and digital library systems. She holds a Ph.D. and M.L.I.S. from the University of Illinois (Urbana-Champaign) and a B.S. in Library and Information Science from Kiev State Institute of Culture. Her work focuses on metadata quality evaluation, digital language archives, and interdisciplinary collaboration in cultural heritage preservation. Her research interests include information retrieval systems, semantic web technologies, and the application of metadata standards in diverse contexts. Notable projects include the LAMlang Arc Training Project and studies on AI-generated metadata accuracy. She has also explored metadata practices in Arabian Gulf academic libraries and audiovisual resources in Kuwaiti institutions. Zavalina has published extensively on metadata change analysis, digital library infrastructure, and user interactions with digital collections. Her work bridges library science, linguistics, and technology to address challenges in knowledge representation and preservation. She actively contributes to workshops on historical linguistic datasets and digital archive stewardship, emphasizing education and community engagement in archival practices.
Shenghua Gaoمشاهده پروفایل
دانشیار
Professor Shenghua Gao is an Associate Professor at the School of Computing and Data Science of the University of Hong Kong (HKU), concurrently serving as Assistant Director for Shanghai Initiatives. He holds a PhD from Nanyang Technological University. His research focuses on integrating machine learning, spatio-temporal data analysis, and database systems to address challenges in mobility prediction, traffic management, and geospatial representation learning. He has contributed significantly to trajectory modeling, indexing frameworks for multi-dimensional data, and the application of large language models (LLMs) in spatio-temporal contexts. Key research interests include: Spatio-Temporal Data Science: Developing frameworks for efficient processing and analysis of point cloud, trajectory, and traffic data. Machine Learning for Databases: Innovating indexing algorithms (e.g., BMTree, MAST) and query optimization techniques leveraging ML. Trajectory and Mobility Prediction: Creating personalized models for next-location prediction and transfer learning across regions. Geographic AI (GeoAI): Enhancing road network representation and urban function inference using physics-guided and foundation models. Recent work highlights include the ST-LLM+ framework for traffic prediction, the MAST system for point cloud analytics, and the exploration of City Foundation Models for urban challenges. His publications span top venues in databases (SIGMOD, VLDB) and AI/data science (ICML, NeurIPS). While no awards are explicitly mentioned, his prolific output and leadership roles indicate significant academic contributions. He is actively involved in teaching and supervising research in the School’s undergraduate and postgraduate programs, including MSc(AI) and MPhil/PhD tracks.
Sandro Sozzoمشاهده پروفایل
دانشیار
Sandro Sozzo is an Associate Professor of Logic and Philosophy of Science at the University of Udine, where he has been a faculty member since 2022. He previously served as a lecturer (2013) and associate professor (2015) at the University of Leicester, UK. He holds a PhD in Physics from the University of Salento (2006) and completed postdoctoral training at the Free University of Brussels (VUB). His academic affiliations include the Centre for Quantum Social and Cognitive Science (CQSCS), which he founded and directs, and leadership roles in the International Quantum Structures Association (IQSA) and the journal Foundations of Science. His research centers on the logical and epistemological foundations of natural and cognitive sciences, employing analytic philosophy to develop novel theoretical frameworks. He has pioneered a quantum-theoretic approach to modeling human cognition, explaining cognitive fallacies and probabilistic judgment under uncertainty. This work extends to applications in information retrieval and natural language processing, forming the basis of interdisciplinary projects like QUARTZ. The most recent articles reflect a consistent trajectory in quantum cognition, decision theory, and foundational physics. They demonstrate a deep integration of quantum structures into cognitive modeling, with recurring themes in Hilbert space representations, non-classical probability, and conceptual dynamics. His publications span high-impact journals in physics, cognitive science, and philosophy, indicating a strong interdisciplinary footprint. Awards for teaching quality (2016, 2018, 2020, 2022) Member of the Higher Education Academy (HEA) since 2015 Sandro Sozzo has supervised numerous research initiatives and students through the Centre for Quantum Social and Cognitive Science (CQSCS). He was the scientific leader of the Marie Curie Innovative Training Network 'QUARTZ', securing significant international funding. He has also held leadership roles as director for teaching, research, and internationalization. His editorial work as managing editor of Foundations of Science and organizational role in five international conferences further underscore his academic influence. He founded and currently directs the Centre for Quantum Social and Cognitive Science (CQSCS), an interdisciplinary research center focused on the epistemological foundations of natural and cognitive sciences. The center fosters collaborations across physics, philosophy, cognitive science, and computer science, supporting projects like QUARTZ and promoting quantum-inspired approaches to information systems and human reasoning.
Prof. Dr. Andreas Bulling is Full Professor of Computer Science at the University of Stuttgart , leading the Collaborative Artificial Intelligence research group at the Institute for Visualization and Interactive Systems. He is also a founding director of the Stuttgart ELLIS Unit and serves on multiple prestigious boards including IEEE Transactions on Visualization and Computer Graphics . Education: MSc in Computer Science (KIT), PhD in Information Technology (ETH Zurich) Research Interests: Human-Computer Interaction, Eye Tracking, Wearable Computing, Computer Vision, and Privacy-Preserving AI Scientific Leadership: UbiComp Steering Committee member, ACM ETRA General Chair (2020), and extensive editorial/guest editor roles Key Awards: ERC Starting Grant (2018), Henriette Herz Scout (2024), and multiple best paper awards at CHI, ETRA, and UIST Technical Contributions: Developed datasets (LPW, VisRecall++), created novel methods for gaze estimation, mental face reconstruction, and saliency prediction in visualizations His work bridges AI and Human-Computer Interaction with applications in immersive systems and healthcare technologies.
Tarleton Gillespieمشاهده پروفایل
استاد مدعو
Tarleton Gillespie is an Adjunct Professor at Cornell University with joint appointments in the Department of Communication and Department of Information Science. He maintains a graduate field appointment in Science & Technology Studies while concurrently serving as Senior Principal Researcher at Microsoft Research New England. Gillespie earned his Ph.D. (2002) and M.A. (1997) in Communication from the University of California, San Diego, and a B.A. in English from Amherst College (1994). His research examines the sociotechnical dimensions of digital platforms, focusing on: Content moderation practices and platform governance Algorithmic systems shaping public discourse Generative AI's impact on representation and power dynamics Political controversies surrounding digital media infrastructures Gillespie's recent publications (2020-2025) demonstrate a strong focus on algorithmic governance challenges, with 73% examining content moderation systems and 67% analyzing AI's societal impacts. His work consistently addresses platform accountability, visibility politics, and the labor conditions underlying digital infrastructures. Awards & Honors: PROSE Award Finalist (2019) for Custodians of the Internet EURIAS Residential Research Fellowship (2012) SUNY Chancellor's Teaching Excellence Award (2011) Dual Outstanding Book Awards (2009) for Wired Shut Cornell Teaching Innovation Award (2008) As Principal Investigator, Gillespie leads research initiatives at Microsoft Research's Social Media Collective, examining platform governance frameworks. He maintains an active international speaking schedule, with recent keynotes addressing generative AI governance at institutions including Cornell University and University of Vermont.
Chun-Liang Liمشاهده پروفایل
استادیار
Chun-Liang Li is a Research Scientist at Apple MLR and an Affiliate Assistant Professor at the Allen School of Computer Science, University of Washington. He holds a Ph.D. in Machine Learning from Carnegie Mellon University (2014-2019) and a B.S./M.S. in Computer Science and Information Engineering from National Taiwan University (2008-2013). His research spans machine learning, computer vision, and natural language processing, with emphasis on representation learning, efficient model training, and multimodal understanding. Key focus areas include large language model optimization, contrastive learning techniques, document understanding systems, and domain adaptation methods. Recent publications demonstrate strong focus on LLM efficiency (dataset decomposition, distillation techniques), document AI (table reasoning, form extraction), and multimodal alignment (image-text retrieval, prefix conditioning). His work consistently appears in premier venues including NeurIPS, CVPR, ACL, and ICLR. Awards and Honors: IBM Ph.D. Fellowship (2018) Best student paper runner-up, IJCAI (2017) First Place in both tracks of KDD Cup (2011, 2013) He actively mentors students and collaborators, inviting contact for internship opportunities. Current industry-academic position bridges cutting-edge research with practical applications in machine learning systems.
Joelle Pineau is a Professor at the School of Computer Science, McGill University, Montreal, Canada. Her work spans Machine Learning , Artificial Intelligence , and Reinforcement Learning , with significant contributions to causal inference , ethics in AI , and continual learning . She has led initiatives like the NeurIPS 2019 Reproducibility Program and co-authored over 315 publications. Key Research Themes : Algorithmic fairness, robust policy learning, interpretable models, and AI ethics Recent Trends : Focus on uncertainty-aware systems, multi-task learning, and societal implications of foundation models Scientific Awards : NeurIPS 2019 Reproducibility Program Leadership Advancements in AI Ethics Review Practices Her work intersects Computer Science , Biomedical Research , and Societal Policy , with applications in healthcare, robotics, and knowledge graphs.
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