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.
Andrea Mocci is a Lecturer at the Faculty of Informatics of the Università della Svizzera italiana (USI). His work focuses on software engineering methodologies, developer productivity, and IDE interaction analysis. He is affiliated with the Software Institute and actively contributes to academic events such as the IEEE International Workshop on Mining and Analyzing Interaction Histories (MAINT). His research explores empirical software engineering techniques, including developer behavior analysis, code documentation improvement, and the application of natural language processing to software artifacts. Key areas of investigation include: IDE interaction and navigation efficiency Code redundancy and quality metrics Video tutorial analysis for educational content Runtime systems and annotation APIs Defect prediction and software maintenance Publications from 2016-2020 highlight trends in developer-centric tools, holistic recommender systems, and visualization techniques for software evolution. His work often bridges theoretical formal methods with practical developer workflows, aiming to improve both software quality and developer productivity through empirical studies and tool development.
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.
Professor Sebastian Sardina is a Professor in Artificial Intelligence at RMIT University's School of Computing Technologies. He holds a Bachelor's from South National University (Argentina) and a PhD from the University of Toronto (Canada). His research focuses on AI for dynamic systems, including automated planning, knowledge representation, and agent-oriented programming. He has contributed to enhancing agent programming languages with learning capabilities and advanced AI planning techniques. His work frequently appears in top AI venues like IJCAI and AAAI, with notable best paper nominations. Teaching interests include foundational CS courses such as Theory of Computation and Intro to AI. He actively promotes computational thinking through workshops for youth and educators, including roles in Victorian curriculum development (VCE Algorithmics). Supervision projects span hand gesture recognition, autonomous vehicle safety, and goal recognition in path-planning. His research has been presented globally and applied across domains like aviation safety, manufacturing systems, and healthcare. Recent trends in his publications emphasize goal recognition techniques (e.g., process mining applications), agent behavior modeling, and interdisciplinary AI applications in healthcare and automotive engineering. He has collaborated with industry and academic partners internationally, contributing to both theoretical advancements and practical AI solutions. Scientific Recognition: Multiple best paper nominations in AI conferences. Community Engagement: MAV conference presenter, VCAA Algorithmics curriculum panel member (2023). Supervision: Active mentor for 4+ research projects in AI planning and recognition.
Nguyen Thanh Son is a Research Scientist at the Artificial Intelligence Initiative under the Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore. He holds a PhD in Information Systems from the School of Information Systems, Singapore Management University (SMU), where he was advised by Associate Professor Hady Lauw. His research focuses on natural language processing, opinionated text mining, and multimodal deep learning. Previously, he completed a visiting PhD program at Carnegie Mellon University (CMU) and an internship at IBM Research Lab in Dublin, Ireland. He also earned a Bachelor of Information Systems from the University of Engineering and Technology (UET), Vietnam National University, Hanoi, with academic distinctions in research. Research Interests: His work spans natural language processing, emotion recognition, knowledge base systems, and agentic AI. He has contributed to advancements in large language models, multimodal encoding, and retrieval-augmented systems. Grants & Awards: He secured a Singapore Aerospace Programme grant (SGD 360,000) as PI and co-led an A*STAR grant (SGD 6 million). His accolades include the SMU Presidential Doctoral Fellowship and top research prizes during his undergraduate studies. Labs & Teams: Active in IHPC's AI initiatives and collaborated with CMU and IBM on projects like sentiment analysis and ontology building. His work bridges academic research and industrial applications in AI.
Stéphane BORDAS is a Full Professor in Computational Mechanics at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), leading the Computational Mechanics (Legato) research group. His work focuses on free boundary problems, method development for complex geometries, and applications in fracture mechanics, biomechanics, and computational engineering. He previously held roles at Cardiff University and the Swiss Federal Institute of Technology in Lausanne (EPFL). His research integrates computational methods like XFEM, isogeometric analysis, and meshfree techniques to address challenges in engineering and medicine. Education: Ph.D. in Theoretical and Applied Mechanics, Northwestern University (2003) M.Sc. in Civil Engineering, École Spéciale des Travaux Publics and Northwestern University (1999) Research Interests: Computational Mechanics, Biomechanics, Finite Element Methods, Fracture Mechanics, High-Performance Computing, Isogeometric Analysis. Grants & Projects: ERC Starting Grant (RealTCut) for surgical simulation and material cutting FP7 ITN INSIST for meshless methods Labs/Teams: Computational Mechanics (Legato) Group at the University of Luxembourg. His work bridges academia and industry, with applications in aerospace, biomedical engineering, and materials science. He is active in open-source software development, including codes for XFEM, isogeometric analysis, and meshfree methods.
Ulla Richardson is a Professor at the Centre for Applied Language Studies, part of the Faculty of Humanities and Social Sciences at the University of Jyväskylä. She serves as Vice Head of Department and holds a docentship in experimental psycholinguistics and speech processing at the Department of Languages. Richardson leads the UNESCO Chair on Inclusive Literacy Learning for All and the secretariat of the GraphoWORLD Network of Excellence, while also directing the multidisciplinary GraphoLearn (GraphoGame)/Ekapeli research team. Fields of Interest: Reading and writing skill development, dyslexia, phonology, speech processing, evidence-based technology in language learning, and cross-linguistic literacy interventions. Her research focuses on creating and validating digital learning environments like GraphoLearn (info.GraphoLearn.com), which supports reading and spelling skill development in 30 languages. She has conducted extensive randomized controlled trials in diverse contexts, including India and China, and collaborates on projects like LearnDigi (funded by the Academy of Finland) to study digitalization’s impact on learning. Her recent publications highlight trends in randomized trials, computational frameworks for literacy assessment, and adaptive learning tools for multilingual settings. Scientific Awards: UNESCO Chair on Inclusive Literacy Learning for All Richardson actively advises multidisciplinary teams, including her leadership in the GraphoLearn Rime intervention studies and collaborations with researchers like Heikki Lyytinen. Her work spans neurocognitive predictors of reading outcomes, phonological awareness training, and global literacy initiatives. She contributes to projects such as Lohkanlihkku (Sámi language literacy) and LearnDigi, emphasizing technology’s role in education.
Hervé VERJUS is an Associate Professor in Computer Science at Université Savoie Mont Blanc, affiliated with Polytech Annecy-Chambéry and the LISTIC Laboratory. He has held this position since 2002. His research focuses on decision-aiding software systems under uncertainty, integrating heterogeneous data, AI-driven anomaly detection, and dynamic adaptable architectures. Key areas include information fusion models, business process mining, and distributed software design. Education: BSc/MSc in Mathematics/Computer Science (University of Savoie, 1996), MSc in Information Systems (University of Grenoble, 1997), PhD in Computer Science (University of Savoie, 2001) under Professors Jacky Estublier and Flavio Oquendo. Research Applications: Snow avalanche risk assessment (mountain decision support), human resource processes, manufacturing systems, and tourism itinerary recommendations. Previously leveraged π-calculus for system architecture design. Current projects emphasize software prototypes for proof-of-concept validation. Academic Roles: Vice-Dean of IAE Savoie Mont-Blanc (2013–2018), permanent faculty at IAE Savoie Mont-Blanc. Member of LSR Lab (CNRS UMR 5526) and LLP Lab during his PhD and early career. Professional Background: Founded and managed an IT company (1996–1999) before academia. Worked in IT, communications, and NGOs from 1993–1999. Contact: Office A115 at LISTIC, reachable via +33(0)4 50 09 65 94. Personal website: https://hverjus.github.io/
Panagiotis G. Zervas is an Associate Professor at the Department of Electrical and Computer Engineering, University of Peloponnese (since 2020). His expertise spans audio signal processing, music information retrieval, and natural language processing for knowledge extraction. He teaches courses including Signals & Systems, Digital Signal Processing, and Machine Learning. His research focuses on AI-driven applications in sound analysis, music feature extraction, and multimodal information processing. Education: PhD (2007) in Electrical Engineering from the University of Patras, specializing in Greek prosody modeling for text-to-speech systems. Previous roles include Assistant Professorships at Hellenic Mediterranean University (2015–2020) and Technical Educational Institute of Crete (2008–2015). Research Interests: Natural Language Processing (NLP) for text analysis and large language models (LLMs) Audio signal processing, voice analysis, and embedded systems AI applications in job market analytics and skills frameworks Machine learning for music information retrieval Notable Projects: Principal Investigator in EU projects EU-ALMPO (2025–), Train4Blue (2025–), GROWTH4BLUE (2024–), and MICROIDEA (2024–) World Bank consultant (2023–) for AI-driven employment systems in Greece and Pacific Islands Publications in journals like 'Acoustics' and conferences like WAC 2022 and Forum Acusticum 2023 Office: Building K, Office K2.07 | Contact: pzervas@uop.gr
Ashok Agrawala is a Professor in the Department of Computer Science at the University of Maryland, with joint affiliations in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and the Department of Electrical Engineering. He directs the Maryland Information and Network Dynamics (MIND) Lab, a leading research group focused on real-time systems, network performance, and wireless localization technologies. Education: B.E. and M.E. in Electrical Engineering, Indian Institute of Science, Bangalore M.A. and Ph.D. in Applied Mathematics, Harvard University His research interests span computer systems, distributed algorithms, real-time operating systems, network performance modeling, traffic shaping, and indoor localization . He is renowned for pioneering the Ricart-Agrawala distributed mutual exclusion algorithm, developing the Maruti real-time operating system, and creating the Horus and Locus indoor positioning systems. His work has led to significant advances in temporal guarantees, jitter-free data delivery, and calibration-free localization. His recent publications show sustained innovation in wireless localization, context-aware computing, breath analytics, and energy-efficient IoT systems , reflecting an evolving research trajectory toward smart health and urban computing. He has authored over 200 papers and seven books, with consistent publication activity into 2024. Scientific Recognition: Fellow, IEEE Member, ACM Member, AAAS Member, Sigma Xi Agrawala has advised over thirty Ph.D. students, many of whom now lead research in academia and industry. His research has been funded by DARPA, NSF, and AFOSR, with corporate partnerships from IBM, Novell, and AT&T. He previously worked at Honeywell Information Systems, where he designed an Optical Character Reader. He has also served as a consultant to the UNDP and the Government of India, and as a technical expert in legal cases. He leads the MIND Lab, which focuses on mobile computing, wireless networks, and context-aware systems , continuing to innovate in real-time and networked systems.
Gao Min is a Professor and Doctoral Supervisor at the School of Big Data and Software, Chongqing University. He is a member of IEEE, CCF, and CAAI, and has held visiting scholar positions at Arizona State University and Reading University. His research focuses on personalized recommendation systems, anomaly detection, and social media mining, with strong emphasis on security aspects such as shilling attacks and fake news detection. His research interests include: Personalized Recommendation Systems Anomaly and Attack Detection in Recommender Systems Social Media Mining and Fake News Detection Graph-based and Contrastive Learning for Recommendations Domain Adaptation and Meta-Learning Time Series and Behavioral Forecasting The recent articles highlight a consistent trend in adversarial and robust learning for recommender systems and misinformation detection. His team leverages contrastive learning, graph neural networks, and meta-learning to enhance model robustness against poisoning and shilling attacks. There is a growing focus on simulating user behaviors, modeling fine-grained discrepancies, and applying domain adaptation techniques, particularly in detecting fake news and securing recommendation platforms. His scientific awards are primarily reflected through the recognition of his students, including multiple recipients of the National Graduate Scholarship, Huawei Scholarship, and Chongqing Outstanding Master’s Thesis Award. National Graduate Scholarship (awarded to students: Tian Renli, Yu Junliang, Song Yuqi, Zhao Zehua, Zhang Junwei, Wang Jia, Peng Lin, Ma Hao) Huawei Scholarship (awarded to students: Tan Kan, Wang Jia, Huang Yinqiu) Chongqing Outstanding Master's Thesis Award (awarded to students: Yu Junliang, Zhao Zehua, Zhang Junwei) Aerospace Scholarship (awarded to student: Zhang Junwei) Gao Min has secured significant research funding as principal investigator, including two National Natural Science Foundation projects, a sub-project of the National Key R&D Project, two Chongqing Natural Science Foundation projects, and one China Postdoctoral Fund project. He has also contributed as a main researcher in major national programs such as the 973 Program, National Key R&D Program, and National Science and Technology Support Program. He advises a vibrant research group that values autonomy, academic freedom, and practical research, with students regularly publishing in top venues and securing top-tier industry and academic positions. His team has developed key research platforms including QRec (Recommendation Algorithm Experiment Platform), Yue (Music Recommendation), ARLib (Data Pollution Attack Platform), and SDLib (Shill Attack Detection Platform). He serves as a reviewer for major journals and is a PC member of top conferences including CIKM, IJCAI, and AAAI.
Roberto Minelli is a Scientific Collaborator and Academic Coordinator at the Software Institute, Faculty of Computer Science, Università della Svizzera italiana (USI), where he also completed his Bachelor, Master, and PhD in Informatics. His work bridges research, education, and technology outreach, with a strong focus on software engineering, visualization, and developer interaction analysis. His research centers on leveraging interaction data from development environments to enhance software comprehension and evolution. Key areas include software visualization , mining software repositories , reverse engineering , and program comprehension . He has pioneered work in visual metaphors such as Software Cities and explored immersive environments like virtual reality for code visualization. The recent publications reflect a consistent trend in visual analytics for software engineering, with increasing emphasis on social and collaborative aspects of development (e.g., Discord, GitHub issues), large-scale system comprehension, and the integration of diverse data sources into unified visual models. His work combines empirical studies, tool development, and human-centered evaluation. Best Paper Award, IWESEP 2016 Most Influential Paper Award, ICPC 2015 Distinguished Reviewer Award, ICPC 2020 Minelli actively mentors students, co-supervising numerous Bachelor and Master theses in software visualization and analytics. He has secured multiple research and development mandates, including projects like Self-Driving Cars on Interactive Dynamic Tracks (SNF Agora) and Sphere Two: Swiss Pavilion @ Expo 2025 . He plays a central role in organizing key events such as VISSOFT, SIESTA, and #FormulaUSI, and contributes to curriculum development and outreach programs targeting secondary education. He is involved in several labs and teams, primarily the REVEAL research group (led by Prof. Michele Lanza) and the Software Institute at USI. His leadership in initiatives like CodeLounge and #FormulaUSI underscores his commitment to experiential learning and public engagement in computing.
João Caldeira is an Associate Researcher at ISTAR-Iscte (Research Center in Information Sciences, Technologies and Architecture), where he focuses on Process Science , Process Mining , and Software Development Analytics . He holds a PhD in Information Science and Technology from ISCTE - University Institute of Lisbon, a Master's in Computer Engineering from Universidade Nova de Lisboa, and a Bachelor's in the same field from the same institution. João is a Visiting Assistant Professor at ISCTE, teaching process modeling and software development topics, and a Professor at IPAM/Univ. Europeia, where he lectures on Blockchain and Digital Payments. Research Interests : João's work bridges Big Data , Machine Learning , and Process Mining to enhance software development practices. His research includes Augmented Business Process Management and Software Systems Engineering , with a focus on extracting process insights from refactoring and team efficiency. Publications & Projects : João has contributed to journals like Computer Standards and Interfaces and Archives of Computational Methods in Engineering . His project DataScience4NP explores visual programming paradigms for non-programmers in Data Science, proposing parameterized workflow templates for increased reuse. Academic Roles & Mentorship : He has guided multiple Master's dissertations at ISCTE, including topics like BPM with adaptive SLA and Process Mining for vaccine distribution . João serves as an official reviewer for journals such as Journal of Systems and Software and IEEE Access .
Ruhul Amin is an Assistant Professor at Fordham University's Department of Computer and Information Science, where he explores the intersection of Artificial Intelligence , Data Science , and Public Health . His work spans multiple domains including Bioinformatics , Natural Language Processing , and Computational Social Science . PhD in Computer Science (Stony Brook University, 2019) MS in Computer Science (Stony Brook University, 2015) BSc in Computer Science (Shahjalal University, 2007) His research focuses on developing deep learning algorithms for genome annotation, anomaly detection in high-cardinality spaces, and assistive technologies for visually impaired users. Notably, he designed the Mongol Dip bilingual screen reading software and the Pipilika Bengali text search engine. Recent publications (2025-2024) demonstrate his expanding interests in large language model alignment , sentiment analysis across languages, and distributed reinforcement learning for cybersecurity applications. His work frequently appears at IEEE conferences and in journals like PLOS One. Scientific Recognition BASIS National ICT Award (Bangladesh) Best Poster Awards at IEEE R10 HTC and CEWIT conferences Bangladesh Prime Minister’s Award (2012) mBillionth South Asia Award (2010) He maintains active collaborations with research teams at University of Toronto , University of British Columbia , and Stony Brook University . Contact: moamin@cs.stonybrook.edu
Dimosthenis Kyriazis is a Professor at the University of Piraeus, affiliated with the Research Center. He currently teaches courses such as C Programming, e-Business, and Information Systems. His research focuses on service-oriented architectures, cloud/edge computing, data management, and healthcare informatics. Kyriazis holds a PhD from the National Technical University of Athens (NTUA) and has contributed to EU-funded projects like BigDataStack and CrowdHEALTH, addressing quality of service, workflow management, and IoT applications. He leads research on data management in cloud/edge environments, socially-enhanced IoT management, and big data applications in sectors like finance and e-health. His expertise spans distributed systems, software engineering, and interdisciplinary collaborations with industries and academia. He has participated in EU working groups on Future Internet Architecture and Cloud QoS&SLAs. Kyriazis actively seeks interns and collaborates on initiatives like AI-driven healthcare platforms (iHELP) and sustainable computing practices. His work emphasizes human-centric AI, data interoperability, and ethical AI frameworks such as AI4Gov for transparent governance. Education: Diploma in Electrical and Computer Engineering (NTUA, 2001), MSc in Techno-economics (NTUA/University of Athens/University of Piraeus, 2004), PhD in Service-Oriented Architectures (NTUA, 2007). Key research areas include federated data marketplaces (FAME), healthcare data integration (holistic health records), and AI applications in finance (DeepVaR). His publications address topics like explainable AI (XAI), defect detection in manufacturing, and environmental risk correlation with health. Grants and collaborations involve coordinating EU projects targeting data governance frameworks, energy-efficient mobility data spaces (Mobispaces), and AI for policy-making (e.g., OECD AI policy analysis). His contributions bridge technical innovation with societal impact through sustainable computing and ethical AI practices.
