Dr. Lydia Cui is a Senior Lecturer at La Trobe University's Department of Computer Science and Information Technology. She holds a PhD and MPhil from the University of Sydney and a Bachelor's from Harbin Institute of Technology. Her research focuses on AI-driven biomedical image analysis, machine learning, and precision oncology, with emphasis on multi-modality imaging fusion, cancer diagnosis, and graph neural networks. She actively collaborates with industry and hospitals to translate AI technologies into clinical workflows. Dr. Cui leads the Department’s Teaching & Learning and Postgraduate Course Coordination roles. She has received notable awards, including the SNMMI 2015 International Best Paper Award. Her teaching includes Data Mining, Computer Vision, and Image Processing courses. Research interests include segmentation of biomedical images, AI for disease prognosis, and integration of imaging with non-imaging biomarkers. Recent publications span top-tier journals like IEEE Transactions on Medical Imaging and conferences such as MICCAI. She supervises students in AI and biomedical informatics. Funded projects include 'Multi-modality data-driven health monitoring in Industry 4.0' with Rudder Technology.
Professor Kyriazis Dimosthenis holds a faculty position at the Department of Digital Systems, University of Piraeus. He earned his diploma in Electrical & Computer Engineering from the National Technical University of Athens (2001) and a cross-disciplinary MSc in Techno-Economic Systems (2004). His academic rank is Professor specializing in service-oriented architectures with a focus on quality of service and workflow management. He has led European projects like BigDataStack, CrowdHEALTH, and CYBELE, addressing challenges in cloud computing, edge computing, and AI-driven solutions for healthcare, finance, and industrial sectors. His research emphasizes resilient service-oriented systems, AI explainability, and human-centric digital transformation. Notable contributions include frameworks for dynamic resource allocation in hybrid cloud/edge environments, AI applications for maritime safety, and data governance solutions for cross-sector integration. He coordinates initiatives such as the Future Internet Architecture Board and Cloud QoS&SLAs, driving advancements in federated data marketplaces and sustainable computing practices. His recent work explores Large Language Model (LLM) applications in financial decision-making, conversational AI for MLOps, and neurosymbolic systems for defect detection. He also investigates XAI methodologies, including VirtualXAI, which leverages GPT-generated personas for explainability assessment. His projects often bridge technical innovation with societal impact, such as the iHELP platform for holistic health records and SmartCHANGE for behavioral change strategies in youth health. Key themes in his publications include bias mitigation in machine learning, dynamic deployment prediction in hybrid cloud settings, and energy-efficient data spaces for mobility. He has contributed to standards like the H2020-funded IRMOS and 5GTANGO, emphasizing interoperability and fault-tolerant architectures. His work frequently intersects with EU policy frameworks, particularly in data governance and ethical AI implementation.
Tijl De Bie is a Senior Full Professor at the University of Ghent, specializing in machine learning, data science, and their applications in bioinformatics, computational social sciences, and HR analytics. He leads the AI and Data Analytics (AIDA) research group within IDLab-ELIS. PhD in Machine Learning (KU Leuven, 2005) Worked at U.C. Berkeley, U.C. Davis, University of Southampton, and University of Bristol His research focuses on foundational aspects of data science, including fairness in AI, network embeddings, and human-centric methodologies. Recent work explores temporal network simulation, bias mitigation, and large-scale career trajectory datasets. Notable awards include an FWO Odysseus Group I grant and three ERC grants (Consolidator, Proof of Concept, Advanced). Current projects involve ethical AI frameworks and dynamic network analysis. Scientific Awards : FWO Odysseus Group I, ERC Consolidator, ERC Proof of Concept, ERC Advanced Grant He collaborates extensively in interdisciplinary research, applying machine learning to social media analysis and financial domains. His team develops open-source tools like EvalNE and Fondue for network embedding evaluation.
Hadi Tabatabaee is an Assistant Professor at the School of Computer Science, University College Dublin (UCD), leading the Sustainable Orchestration in Computing Continuum (SOC² Lab). His research focuses on sustainable orchestration of services across edge-cloud environments, emphasizing energy efficiency, carbon-aware systems, and AI-driven applications like large language models (LLMs). Key roles include Associate Editor for IEEE Access and Management Committee member of COST Action CA22151 (CYPHER). He holds a PhD in Computer Engineering from the University of Isfahan and has held academic positions at Maynooth University, Shahid Beheshti University, and Trinity College Dublin's CONNECT research program. Education: PhD (Computer Engineering, University of Isfahan), MSc (Computer Engineering), with a research visit at TU Delft (2010-2011). Certifications include Epigeum's Research Leadership and Research Integrity courses. Languages: Persian (fluent), Azerbaijani (spoken). Research Interests: Edge-cloud continuum, dynamic service placement, distributed AI workloads, LLM optimization, and sustainable resource management. Recent work includes zero-trust vehicular networks, parallel algorithms for recommender systems, and geospatial event processing. Awards: None explicitly listed, though his contributions include over 20 journal articles in IEEE/Elsevier/Springer venues. Professional Activities: IEEE Senior Member, TPC member for IEEE conferences, and reviewer for multiple journals. Teaching: Coordinates/teaches Cloud Computing, Computer Networks, and Principles of Computer Organization at UCD.
Mehwish Alam is an Associate Professor of Language, Knowledge, and Artificial Intelligence at Télécom Paris, part of the Institut Polytechnique de Paris. She leads the Data, Intelligence, and Graphs (DIG) team at the Laboratoire Traitement et Communication de l'Information (LTCI). Her research focuses on machine/deep learning, language models, knowledge graphs, and natural language processing. She is actively involved in organizing workshops such as DL4KG and chairs roles at major conferences like EKAW 2024 and ECAI 2025. Education includes a Ph.D. in informatics from LORIA, INRIA, Nancy Grand-Est, France, and an Erasmus Mundus MSc in Language, Communication & Technology. She has held postdoctoral roles at institutions including Karlsruhe Institute of Technology, CNR Rome, and University of Bologna. Research interests span neurosymbolic AI, knowledge graph embeddings, and applications in cultural heritage and materials science. She supervises multiple PhD students and has mentored interns across institutions like BNP Paribas and Nokia Bell Labs. Her work bridges deep learning and symbolic methods, emphasizing interdisciplinary collaborations.
Jens Ulrik Hansen is an Associate Professor at Roskilde University's Department of People and Technology. His work bridges artificial intelligence, data science, and social epistemology. He holds a PhD and MSc, and his research focuses on explainable AI, machine learning applications in healthcare, social network dynamics, and logical frameworks for multi-agent systems. Hansen participates in interdisciplinary projects like #echopol (studying social media polarization) and ROROGREEN (digital innovation in maritime shipping). He has published widely on topics including AI ethics, expert knowledge integration in ML systems, and opinion dynamics in social networks. Research Interests: Artificial Intelligence & Machine Learning Data Science & Big Data Analysis Explainable AI Social Network Analysis Formal Epistemology Key Projects: #echopol: Echo chambers and polarization dynamics ROROGREEN: Green RORO shipping innovations FeedbackBox: Collaborative workshop methodologies Awards: No specific prizes listed, though his work has received media attention for innovations in AI research and healthcare applications.
Andreas Manfred Pointner is an Assistant Professor at FH Hagenberg , specializing in interdisciplinary research at the intersection of computer science, healthcare informatics, and software engineering. He leads research in graph databases, process mining, and attribute grammars with applications in healthcare IT and automated data systems. His affiliations include the Web Intelligence and Innovation Laboratory , AIST Center of Excellence , and Medical Engineering/TIMed Center . He has contributed to projects such as RiskAI (risk management in enterprises), PASS (plan analysis automation), and REPO (radiology e-health platforms). Research Interests: Graph database optimization, interoperability in healthcare systems (HL7 standards), fuzzing techniques for software testing, and process mining for audit event analysis. His work bridges theoretical formal methods with practical applications in clinical workflows and automated data cleansing. Notable Contribution: Developed a graph transformation framework for complex data structures Recipient of the Best Paper Award 2022 for contributions to intelligent systems Collaborative Projects: Focus on AI-driven solutions for enterprise risk management and healthcare interoperability He actively participates in international conferences and has supervised projects involving 3D model analysis, contour extraction, and global disease monitoring systems.
Zaiwen Feng is a researcher actively contributing to data governance, semantic modeling, and causal inference. His work focuses on knowledge graphs, graph-based methods, and service-oriented architectures through collaborations with institutions like the University of Queensland and universities in China. Research Focus : Graph Differential Dependencies, Entity Resolution, Causal Effect Estimation, and Ontology Alignment Methodologies : Machine Learning, Variational Autoencoders, Prompt Engineering, and Semantic Retrieval Application Areas : Biomedical Data, Property Graph Recommendation, and Process Model Repositories Key trends in his publications include automated semantic modeling , neural approaches for entity resolution , and causal inference with graph structures . He frequently collaborates with researchers like Keqing He, Wolfgang Mayer, and Selasi Kwashie across conferences such as HPCC, BIBM, and WISE.
Mariano Rico is an Associate Professor at the Polytechnic University of Madrid (UPM), affiliated with the OEG research group in the Artificial Intelligence Department. Previously, he served as a Senior Researcher at OEG (2016-2020) and held teaching roles at the Autonomous University of Madrid (UAM). His primary affiliations include the UPM's Faculty of Computer Science and the UAM's Computer Engineering Department. Education: PhD in Computer Science (UAM, 2009), MSc in Physics (UAM, 1992), and postgraduate studies in Telecommunications Engineering. He conducted research stays at DERI (Ireland) and Freie Universität Berlin, focusing on Semantic Web and Linked Data. Research interests center on Linked Open Data, Natural Language Processing (NLP), and Semantic Web technologies, with contributions to DBpedia's Spanish branch and projects like Wf4Ever and LIDER. He actively collaborates with institutions in Leipzig, Bielefeld, and Berlin on Linked Data and linguistic applications. Teaching: Coordinates courses in NLP, Linguistic Engineering, and Big Data Visualization at UPM and online programs. Has instructed over 300 UAM faculty through teacher training programs on LaTeX, bibliographic management, and digital scholarly practices. Projects: Lead roles in European and national initiatives including SlideWiki, UpGrid, and Neptune. Current work focuses on NLP applications like text summarization (esT5s) and terminology tools (TermInteract). Labs/Teams: Core member of the OEG group, contributing to semantic web infrastructure and NLP tool development. Maintains international collaborations through AKSW and CITEC groups.
Dr. Xin Wang is a Research Fellow at the University of Oxford's Institute of Biomedical Engineering, affiliated with the Computational Health Informatics (CHI) Lab under Professor David Clifton. He joined Oxford in 2024 after completing his PhD in Computer Science and Technology at Tsinghua University, where he was advised by Professor Ling Feng. His research bridges Data Mining and Natural Language Processing with healthcare applications, focusing on: Computational mental health diagnostics using social media/video analysis Knowledge graph development for biomedical contexts AI agent design for therapeutic interventions Large language model applications in healthcare Wang's publications demonstrate consistent focus on AI-driven mental health solutions , evolving from social media text analysis to multimodal systems incorporating video, knowledge graphs, and real-time intervention frameworks. Recent work shows increased emphasis on clinical applicability and human-AI collaboration. He actively contributes to academic communities as reviewer for premier venues including NeurIPS, ACL, KDD, and IEEE journals. He maintains open-source research outputs like the SSE framework for stress-specific NLP modeling.
Jun Liu is a Professor in the Department of Statistics at Harvard University, renowned for his contributions to computational statistics, bioinformatics, and Bayesian methods. He leads research in statistical genetics, genomic data analysis, and algorithm development for biological systems. His work integrates advanced statistical theory with computational tools, such as the Gibbs Motif Sampler and Bayesian Aligner, widely used in bioinformatics. Research interests include Monte Carlo methods, statistical genetics, and machine learning applications in biology. He has developed influential software tools like BPPS, MDScan, and CLIC, addressing problems in motif discovery, genomic sequence analysis, and pathway expansion. Liu’s interdisciplinary approach bridges statistics and computational biology, with applications in cancer genomics, immune repertoire analysis, and evolutionary biology. Notable recognition includes fellowships from the American Statistical Association, Institute of Mathematical Statistics, and International Society for Bayesian Analysis. He advises numerous Ph.D. students and postdoctoral researchers, many of whom hold academic and industry positions globally. His lab collaborates internationally, organizing workshops on Monte Carlo methods and statistical forums in China. Liu’s publications span statistical methodology, computational biology, and genetics, with recent work on genomic element evolution, immune cell profiling, and algorithmic advancements in high-dimensional data analysis. He emphasizes inverse modeling and Bayesian approaches to tackle complex biological questions.
University of California , Santa Barbara (UCSB)United States
Tao Yang is a Professor in the Department of Computer Science at the University of California, Santa Barbara, where he has been a faculty member since 1993. His research spans web search and mining, database and information systems, machine learning and data mining, parallel and distributed systems, and cloud computing. He serves as an active educator, teaching courses including CS170 Operating Systems (Spring 2024), CS291A Neural Information Retrieval (Fall 2024), and CS140 Parallel Computing (Winter 2025). PhD in Computer Science, Rutgers University ME in Artificial Intelligence, Zhejiang University MS in Computer Science, Rutgers University BS in Computer Science, Zhejiang University Professor Yang's research focuses on advancing the field of information retrieval with particular emphasis on neural approaches to search and ranking. His recent work explores neural document ranking, privacy-aware search systems, and versioned data search. He has led significant projects including Neptune clustering infrastructure, Sorrento self-organizing storage cluster, and TMPI for MPI execution optimization. His research bridges theoretical advances with practical implementations, particularly in scaling search architectures to handle billions of documents while maintaining relevancy, performance, and freshness. His publication record shows a clear evolution from foundational work in parallel and distributed systems toward contemporary research in neural information retrieval. Recent publications demonstrate expertise in optimizing both sparse and dense retrieval methods, with particular focus on efficiency improvements for multi-vector representations. His work consistently addresses real-world challenges in search scalability and privacy preservation. Faculty Research Award, Google Research Research Initiation Award, NSF (1994) UC Regents' Junior Faculty Award (1994) Computer Science Faculty Teacher Award (1995) CAREER Award, NSF (1997) Noble Jeeviant Award, AskJeeves (2002) Professor Yang has supervised numerous graduate students, many of whom have gone on to prominent positions at companies like Google, Apple, and Coursera, or academic positions at universities worldwide. His industry experience as Chief Scientist for Ask.com (2001-2010) and founding Chief Scientist for Teoma (2000-2001) has informed his research direction and provided valuable practical context for his academic work. He has served on program committees for major conferences including WWW, SIGIR, KDD, WSDM, CIKM, ECIR, and EMNLP. His research group maintains active projects in neural information retrieval, privacy-aware search, similarity computing, and parallel computing systems. The group collaborates closely with industry partners, particularly in the search technology space, and has developed systems that power major search engines serving over 100 million users.
Abdulkadir Celikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark. He is affiliated with The Technical Faculty of IT and Design and the Data, Knowledge and Web Engineering research group. His research focuses on graph representation learning, network analysis, bioinformatics, and machine learning applications in dynamic systems. Key projects include the Villum Foundation-funded 'DarkScience: Illuminating microbial dark matter through data science,' which explores metagenomic binning and microbial ecology using advanced data science techniques. He has been recognized with the Best Paper Award (2023) for contributions to temporal graph analysis and modeling. His work spans continuous-time dynamic node representations, scalable genome profiling, and polarization detection in social networks. Celikkanat collaborates widely, contributing to interdisciplinary research at the intersection of computer science, biology, and environmental science. Recent publications highlight innovations in graph embeddings, citation network modeling, and hybrid membership latent distance models. His research addresses challenges in low-dimensional graph representations, efficient kernel methods, and integrating biological networks for protein analysis.
Dr. Zhiqiang Lin is an Associate Professor in the Computer Science Department at the University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds a PhD in Computer Science from Purdue University (2011). His research focuses on software security, cloud computing security, and memory data analysis, with applications in vulnerability discovery, malware analysis, and virtualization security. He has received prestigious awards including the NSF CAREER Award and Air Force YIP Award. Education: PhD in Computer Science, Purdue University, 2011 Research Interests: Software Security: Binary code analysis, kernel malware detection, and vulnerability discovery. Cloud Computing: Virtual machine introspection, cloud security mechanisms, and data protection in distributed systems. Memory Analysis: Data structure identification in memory/disk, forensic recovery, and semantic data extraction. Teaching: CS 4393: Computer and Network Security (Spring 2013) CS 6324: Information Security (Fall 2012) CS 6V81: Systems Security/Binary Code Analysis (Spring 2012) CS 6V81: Advanced Digital Forensics (Fall 2011) Grants & Projects: Lead researcher on DARPA-funded project to transition legacy system data to secure platforms (collaboration with Purdue University). Developed "space travel" technique for cross-VM monitoring, enhancing cloud security. Air Force-funded framework to protect computer cores from advanced threats. Service & Leadership: NSF proposal review panel member (2012+) Publication Chair for IEEE IPCCC (2012) TPC member for ICDCS, AsiaCCS, CCGrid, and other conferences.
Pei-Chi Lo serves as Assistant Professor in the Department of Information Management at National Sun Yat-sen University (NSYSU), Taiwan, where she leads research at the intersection of information retrieval, computational linguistics, and user profiling. Her work leverages knowledge graphs and large language models to advance contextual understanding systems and social media analysis. Education: PhD in Computer Science, Singapore Management University (supervised by Prof. Ee-Peng Lim) Research Focus: Dr. Lo pioneers knowledge-based information retrieval systems including contextual path generation and knowledge graph reasoning. Her computational linguistics work spans task-specific language models, Singlish (English Creole) processing, and LLM-based knowledge extraction. User profiling research examines behavior-based modeling, adaptive crowdsourcing, and social media personality analysis through community-specific language features. Publication Trends: Her 14 publications (2017-2025) reveal evolving expertise from foundational knowledge graph embeddings to contemporary LLM integration. Recent work (2023-2025) emphasizes causal reasoning with LLMs, judicial document analysis, and temporal knowledge discovery, while maintaining core strengths in contextual retrieval and low-resource language processing. Academic Leadership: Dr. Lo advises 8 Master's students across 2024-2025 cohorts and supervises undergraduate research teams. She has secured multiple competitive grants including NSTC projects on LLM-based causal reasoning (2025-2027) and temporal knowledge discovery (2024-2026), plus institutional funding for sustainable e-commerce and elderly care technology initiatives. Laboratory: Her NSYSU research lab actively recruits students for projects spanning judicial reasoning analysis, personalized travel planning systems, and Singlish processing tools, maintaining strong industry and community engagement through practical NLP applications.
