Dr. June Cao is an Associate Professor at the University of Southampton . Her research focuses on Environmental, Social, and Governance (ESG) Accounting , Corporate Social Responsibility (CSR) , and Sustainability Reporting , with a particular emphasis on Greenwashing , Carbon Emission Trading , and Accounting Education . Key research areas include ESG, CSR, and sustainability frameworks. Prominent publications analyze environmental regulation, green revenues, and digital transformation in sustainability. Her recent work explores greenwashing behaviors, peer benchmarking, and labor investment dynamics. Scientific Awards : None explicitly mentioned in the data. Notable collaborations include co-authoring papers with scholars from Curtin University , Satya Wacana Christian University , and Xiamen University . Her contributions to Systematic Literature Reviews and Bibliometric Analysis highlight her methodological expertise.
Hayato Yamana serves as Professor at Waseda University's Faculty of Science and Engineering and concurrently holds the position of Vice President for IT Promotion and Chief Information Officer since October 2020. His academic journey began with a Dr. Eng. degree from Waseda University in 1993, followed by positions at the Electrotechnical Laboratory of MITI, before joining Waseda University as Associate Professor in 2000 and becoming full Professor in 2005. He has held significant leadership roles including Director of the Database Society of Japan and the Information Processing Society of Japan, as well as Vice Chair of IEICE's Information and Communication Society. Waseda University, Faculty of Science and Engineering (2005-Present) National Institute of Informatics, Visiting Professor (2005-Present) Waseda University, Vice President for IT Promotion (2020-Present) Deputy Chief Information Officer (2015-2020) His research spans homomorphic encryption, big data analysis, and computer architecture, with notable contributions in privacy-preserving computation, recommender systems, and secure data processing. His work bridges theoretical cryptography with practical applications in smart cities, healthcare, and e-commerce security. Recent publications demonstrate strong focus on accelerating homomorphic encryption operations, improving recommendation system diversity, and developing novel authentication mechanisms. Analysis of his 15 most recent publications reveals consistent emphasis on privacy-preserving technologies (particularly homomorphic encryption applications), innovative recommender system architectures, and biometric security solutions. His research group produces highly cited work at the intersection of cryptography, machine learning, and systems security, with practical implementations in real-world scenarios including smart grids, e-commerce, and healthcare. Fellow, Information Processing Society of Japan (IPSJ), 2020 Golden Core Award, IEEE Computer Society, 2018 Fellow, Institute of Electronics, Information and Communication Engineers (IEICE), 2018 IBM Faculty Award, 2009 Multiple Best Paper Awards from IEICE, IPSJ, and ITE Yamana has secured substantial research funding for projects in homomorphic encryption, smart city infrastructure, and privacy-preserving systems. His leadership extends to advising numerous doctoral students and directing major research initiatives including the Smart Systems and Services Innovative Professional Education Program. He maintains active collaborations with industry partners through projects involving secure computation and data analytics. His research group operates at the forefront of secure computing, with specialized laboratories focused on homomorphic encryption acceleration, privacy-preserving machine learning, and secure mobile authentication. The team actively develops practical implementations of cryptographic protocols for real-world applications in healthcare, finance, and smart city infrastructure, bridging theoretical cryptography with deployable security solutions.
Walid G. Aref is a Professor of Computer Science at Purdue University since Fall 1999. His research focuses on database systems, spatial and spatio-temporal data management, query processing, and indexing. He has led projects funded by NSF, NIH, and industry partners. Aref is a Fellow of the IEEE and has received awards including the NSF CAREER Award (2001) and VLDB’s Ten-Year Best Paper Award (2016). He serves as Editor-in-Chief of ACM Transactions on Spatial Algorithms and Systems and has authored numerous influential papers. Education: BSc/MSc from Alexandria University (Egypt), PhD from University of Maryland (1993). Research Interests: Extending database functionality for emerging applications like spatial, graph, and sensor databases. Notable contributions include the Chameleon project, AQWA system for big spatial data, and privacy-preserving Casper framework. Selected Projects: NSF-funded work on multi-predicate spatial queries, in-memory graph relational systems, and adaptive spatio-textual processing. Systems like Tornado (spatio-textual streams) and LocationSpark (distributed spatial data) exemplify his contributions. Awards: Multiple best paper awards, IEEE Fellow, and leadership roles in ACM SIGSPATIAL.
Athanasios Rontogiannis is an Associate Professor at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA). He holds a PhD in Signal Processing from the National University of Athens (1997) and has held roles including Research Director at the National Observatory of Athens (2017–2021). His research focuses on signal processing, machine learning, and hyperspectral image analysis. Education: MEng (Electrical Engineering, NTUA, 1991), M.A.Sc. (University of Victoria, Canada, 1993), PhD (Signal Processing, National University of Athens, 1997). Research interests include adaptive algorithms, sparse representations, and tensor models. He has served on editorial boards of IEEE Transactions on Signal Processing and EURASIP journals, receiving an honorary distinction in 2020. He is a Senior Member of IEEE and affiliated with EURASIP and the Technical Chamber of Greece. Key contributions span hyperspectral unmixing, Bayesian algorithms, and space data exploitation. His work integrates machine learning for applications in space science and signal processing.
Dr. Daphné Chopard is a Researcher affiliated with the Professorship for Medical Data Science at ETH Zürich. Her work focuses on advancing medical data science through machine learning, clinical informatics, and multimodal learning applications in healthcare. She specializes in areas such as time-series analysis in critical care, generative models for medical data, and natural language processing for clinical texts. Her research emphasizes improving healthcare outcomes through innovative data-driven approaches, including projects like the SwissPedHealth pediatric data network and foundational work on multimodal variational autoencoders. Dr. Chopard’s contributions span clinical decision support systems, adverse event detection in trials, and acronym disambiguation in medical narratives. Her recent projects include studies on ventilation protocols in pediatric critical care and weakly-supervised learning applied to medical imaging datasets like MIMIC-CXR. She collaborates on initiatives to enhance representation learning in multimodal healthcare contexts, reflecting her commitment to bridging AI advancements with practical clinical applications.
Fattane Zarrinkalam is an Assistant Professor in the School of Engineering at the University of Guelph. She holds a PhD from Ferdowsi University of Mashhad, Iran, and completed a Postdoctoral Research Fellowship at Ryerson University (2018–2020). Her research focuses on social media mining, semantic technologies, and user modeling, with applications in healthcare, legal tech, and e-commerce. She is a Vector Institute Postgraduate Affiliate and serves on editorial boards for journals like Information Processing & Management and IEEE Transactions on Network Science and Engineering . Her work emphasizes actionable insights from social data, including sarcasm detection, user interest prediction, and fairness in social media analytics. Zarrinkalam has contributed to over 30 peer-reviewed publications and holds multiple patents in data analysis and social media sentiment modeling. Education: PhD, Ferdowsi University of Mashhad, Iran Postdoctoral Fellowship, Ryerson University Research Scientist, Thomson Reuters Labs Research Interests: Semantic interpretation of social content User modeling via temporal analysis Social good applications (e.g., mental health, telecommunication) Fairness in social media mining Recent Work Trends: Her articles span network representation learning, dynamic user interest prediction, and interdisciplinary applications. Notable themes include neural networks for sarcasm detection, heterogeneous graph embeddings, and leveraging Twitter data for psychological insights. Awards & Service: Co-chair, International Workshop on Mining Actionable Insights from Social Networks (MAISoN) Editorial board roles for top journals Labs & Teams: Involved in interdisciplinary collaborations at the Vector Institute and partnerships with industry on legal tech and social analytics projects.
Jeeyeon Kim is a Lecturer in the Department of Management and Marketing at La Trobe Business School, La Trobe University. Prior to this, she served as an Assistant Professor at National Sun Yat-sen University in Taiwan. Her research focuses on digital marketing, omnichannel retailing, social media/influencer marketing, and digital healthcare. She holds a PhD and MA from Yonsei University, South Korea, and has held visiting roles at Yonsei University and IE University. Her work emphasizes empirical data analysis and statistical modeling to address marketing challenges. Jeeyeon has secured multiple research grants, including AUD 82,500 from Taiwan's Ministry of Science and Technology (MOST) for projects on big data-driven marketing and digital transformation. She also coordinates grants with Yonsei University and AACSB initiatives. Her editorial roles include serving on the Asia Marketing Journal and Korean Scholars of Marketing Science boards. Her research spans topics such as virtual influencers' impact on social media, omnichannel strategies, and healthcare consumer behavior. Teaching awards include the 'Outstanding Course Teaching Award' and 'Excellent Mentor Award.' She actively reviews for journals like the Asian Pacific Journal of Marketing and Logistics and conferences like the Global Fashion Management Conference.
Associate Professor Sonny Pham leads research in artificial intelligence at Curtin University's School of EECMS. His work balances theoretical foundations with practical applications in computer vision, data mining, and deep learning. As head of the IAMAI research group, he collaborates with industry partners on security systems, healthcare AI, and sustainable technologies. His research explores: Computationally efficient deep learning architectures Compressed sensing for high-dimensional data Robust statistical methods for real-world problems Applications in computer vision and industrial automation Recent publications demonstrate a focus on medical imaging interpretation and efficient neural networks, with applications spanning radiology report generation, semantic segmentation for autonomous systems, and cybersecurity. His team's work consistently bridges theoretical AI advancements with industrial applications. Honors include: Multiple WANMA Awards (2021-2024) for industry-impactful research INCITE Award for social impact technology (2024) IEEE Young Author Best Paper Award (2010) Over $5M in competitive research funding including MRFF and DFAT grants He leads the IAMAI research group with 12+ graduate students and coordinates Curtin's Master of Artificial Intelligence program. Industry collaborations include Alcoa Australia, iCetana, and HyprFire.
Nicole Novielli, Ph.D., is Associate Professor at the University of Bari “A. Moro” , Italy, where she conducts research on affective computing applied to software engineering and human-computer interaction. She leads the Collaborative Development Group and coordinates national projects investigating emotions in software teams, AI quality and IoT ecosystems. Education: Ph.D. in Computer Science, University of Bari, 2010 – thesis on “Lexical Semantics of Dialogue Acts” M.Sc. in Computer Science (Knowledge & Software Engineering), University of Bari, 2006 – summa cum laude B.Sc. in Computer Science, University of Bari, 2004 – summa cum laude Visiting researcher at USC-ICT, University of Aberdeen, FBK-irst (Trento) Research interests revolve around recognizing and exploiting affective and cognitive states in computer-mediated cooperative work. She studies sentiment and emotion mining in developers’ textual communication, multimodal emotion recognition via low-cost biometric sensors, and natural-language dialogue simulation for intelligent interfaces. Her work couples software engineering with natural language processing , social media analytics and human-computer interaction . Recent articles (2021-2025) reveal a clear trend: integrating deep learning and large language models into software engineering tasks—automated issue labelling, sentiment classification, technical-debt detection—while validating these techniques through rigorous empirical studies and biometric experiments . A parallel stream explores developer experience , measuring how emotions and cognitive load influence productivity, code quality and collaboration. Scientific awards include the 2020 Apex Award for Publication Excellence , multiple Distinguished Reviewer Awards at flagship venues (ESEC/FSE, ICSME, MSR), the Best Paper Award SANER 2019 and the Best Student Paper Award ACII 2009 . She currently teaches “Sentiment Analysis” in the Data-Science MSc and “Computer Networks” in the ITPS programme. She has advised numerous B.Sc., M.Sc. and PhD projects and is PI or Co-PI of four ongoing grants: EmoQuest (SIR), EMPATHY (PRIN), FAIR-Spoke 6 (PnRR), and QualAI (PRIN 2022). Dr. Novielli serves on the editorial boards of Empirical Software Engineering and Journal of Systems and Software , has guest-edited special issues on affect awareness in SE, and has chaired tracks at ICSE, SANER, MSR, ICSME and SSBSE. She co-leads the Collaborative Development Group and actively releases datasets and open-source tools for the community.
Jonathan Cullen is Professor of Sustainable Engineering at the University of Cambridge and President of Fitzwilliam College, specializing in resource efficiency and decarbonization through top-down analysis of industrial material and energy systems. His work bridges academic research with industry applications across energy-intensive sectors. Education: Bachelor's in Chemical and Process Engineering, University of Canterbury, New Zealand MPhil in Engineering for Sustainable Development, University of Cambridge PhD in Engineering Fundamentals of Energy Efficiency, University of Cambridge His research develops metrics for quantifying energy and material consequences of production systems, focusing on circular economy implementation, minimum energy requirements, and zero-carbon transition pathways. Key applications target cement, steel, plastics, and petrochemicals where he pioneers methods like exergetic analysis and material flow accounting to expose carbon lock-ins and circularity opportunities. Recent publications reveal three dominant trends: (1) Frameworks for theoretical minimum energy requirements across industrial processes, (2) Geopolitical analysis of critical mineral flows and ownership structures, and (3) Circular economy metrics for plastics and construction materials. These consistently employ system-scale modeling validated through industry partnerships. Research Funding: Lead: C-THRU ($4M, VKRF) - carbon clarity in petrochemical supply chains Co-I: UK FIRES (£5.2M, EPSRC) - industrial decarbonization program Co-I: CirPlas (£1.25M, UKRI) - plastic waste elimination 7+ projects (EPSRC, Innovate UK, Horizon 2020) Academic Leadership: Teaching: Energy Systems and Policy (MPhil in Energy Technologies) Undergraduate supervision in Materials/Mathematics Graduate Tutor at Fitzwilliam College IPCC AR6 Lead Author (Industry Chapter) He directs the Resource Efficiency Collective, which develops open-source tools like Mat-dp for material demand projections and Starter Data Kits for energy planning. Current work focuses on scaling circular business models for construction retrofitting and quantifying geopolitical risks in critical mineral supply chains.
Luc Sels has served as Rector of KU Leuven since August 1, 2017, and was re-elected for a second four-year term in May 2021. He is a full professor in the Faculty of Economics and Business (FEB), where he joined in 1996, became full professor in 2004, and served as Dean from 2008 to 2017. He concurrently holds an honorary professorship at Cardiff University and advisory roles at Friedrich-Alexander Universität Erlangen-Nürnberg, National University of Singapore, and Korea University. His research centers on labour market projections (employment, skill levels, regional forecasts), active labour market policies, and cooperative entrepreneurship. As Chairholder of the Randstand Chair on Employer Branding, VBO-FEB Chair on Belgian Business Champions, and CERA-Boerenbond Chair on Cooperative Entrepreneurship, he publishes in top journals including Journal of Management and Harvard Business Review. His work bridges academic research and policy impact through the Centre of Expertise in Labour Market Monitoring. Analysis of Sels' 2021-2025 publications reveals two dominant research streams: human resource management (particularly employer branding mechanics and line manager roles in training effectiveness) and cooperative enterprise dynamics (focusing on social capital in agri-food cooperatives). Methodological innovations in survival analysis for organizational research complement these themes, demonstrating interdisciplinary rigor across management, economics, and policy domains. Scientific Awards: No specific prizes, fellowships, or medals are mentioned in the source text. Sels actively supervises doctoral research, including S. Friedel's work on agri-food cooperative legitimacy, A. Billiet's study of member 'voice' mechanisms, K.G. Tamayo's analysis of line manager HRM involvement in Philippine call centers, and S. Ghielen's employer branding equity research. His current funded projects include Intercooperation (2024-2028) examining cross-cooperative legitimacy, Centre of Expertise for Labour Market Monitoring (2021-2025), and Relationships Matter (2019-2024) investigating social capital in agricultural cooperatives. He conducts research through KU Leuven's Department of Work and Organisation Studies and Leuven Economics of Education Research Centre (LEER), while directing the Centre of Expertise in Labour Market Monitoring for Flemish government policy advising. His cooperative entrepreneurship work operates through the CERA-Boerenbond Chair, integrating academic research with industry partnerships.
Andrea Burattin is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark. His work bridges formal methods and practical process analysis, focusing on process mining, business process management, and hybrid modeling techniques. He actively contributes to research in healthcare process optimization, streaming data analysis, and system verification through Petri nets and CCS transformations. UN Sustainable Development Goals: Poverty eradication, environmental protection, and prosperity for all (via process optimization) Active projects: Immersive Process Mining (2024-2027), Usability and Understandability of Hybrid Process Models (2018-2021) His research explores large language model integration with process mining, proposing frameworks like Tiramisù for multi-faceted process visualization and PN2CCS for formal model translation. Recent work emphasizes real-time monitoring, conformance checking, and IoT-driven process analytics. Key trends in his publications include: 1) Streaming process mining pipelines (2022-2025); 2) LLM-plan generation frameworks (2024); 3) Formal verification techniques (Petri nets, CCS); 4) Healthcare process modeling (2019-2023); 5) Behavioral pattern analysis in process compliance. Scientific Awards Best Demo Award (2022, 2016) Best Process Mining Dissertation Award (2014) Best Workshop Paper (EDBA and PODS4H, 2023) As advisor, he supervises PhD projects on process mining and hybrid modeling. His editorial roles include Information Systems reviewer (2024-2025) and past editor for Engineering Applications of AI (2022-2023). Collaborations span Denmark, Italy, and the Netherlands.
Professor Ioannis Katakis is a Faculty Member at the University of Nicosia, where he is affiliated with the School of Sciences and Engineering and the Department of Computer Science. He has held various academic positions across multiple institutions including Aristotle University of Thessaloniki, University of Cyprus, Cyprus University of Technology, Open University of Cyprus, Hellenic Open University, Athens University of Economics and Business, and National and Kapodistrian University of Athens. His educational background includes a PhD in Machine Learning for Automated Text Classification (2005-2009), a Master's in Information Systems (2005-2007), and a Bachelor's in Computer Science (2000-2004), all from Aristotle University of Thessaloniki. Professor Katakis specializes in several cutting-edge areas of computer science and data analysis. His primary research interests include Mining Social, Web and Urban Data , Sentiment Analysis and Opinion Mining , Data Streams , and Multi-label Learning . His work bridges theoretical machine learning approaches with practical applications in social media analysis, healthcare informatics, privacy protection, and smart city technologies. He has published extensively in top venues including CIKM, ECML/PKDD, IEEE TKDE, and ECAI. His recent publications demonstrate a clear trend toward applying machine learning techniques to real-world problems with societal impact. He has focused on areas such as GDPR compliance in smart devices, sentiment analysis in crowd-sourced content, healthcare applications including drug reaction classification and brain disease monitoring, and privacy protection in wearable technologies. His work often involves multi-modal data analysis and addresses challenges in data streams and multi-label classification. Professor Katakis has made significant contributions to his field, with his research cited over 4,200 times. He serves as an Editor for the journal Information Systems and has edited four special issues in journals such as DAMI and InfSys. He regularly contributes to the academic community by serving on program committees for major conferences including ECML/PKDD, WSDM, DEBS, and IJCAI, and by reviewing for prestigious journals like TPAMI, DMKD, TKDE, TKDD, JMLR, TWEB, and ML. He has been actively involved in European research projects, notably serving as Quality Assurance Coordinator and Senior Researcher for projects such as VAVEL (www.vavel-project.eu) and INSIGHT (www.insight-ict.eu). His grant activities demonstrate a strong focus on collaborative, interdisciplinary research with practical applications in urban data management, social media analysis, and healthcare informatics. He has organized three workshops at major conferences (ICML, ECML/PKDD, EDBT/ICDT) and has extensive experience translating research into practical applications through his involvement in European projects.
Abdullah Mueen is a Professor and Associate Chair in the Department of Computer Science at the University of New Mexico (UNM), where he has been since 2013. Previously, he worked as a Scientist in the Cloud and Information Sciences Lab at Microsoft Corporation. Research Interests : His work focuses on Temporal Data Mining , with emphasis on efficiency , interactivity , and interpretability . Key areas include Blockchain Data Mining (e.g., BitLink for Bitcoin cluster analysis), Seismic Data Mining (e.g., PAW for aftershock detection), and Social Media Mining (e.g., DeBot for Twitter bot detection). Article Trends : His recent publications span four domains: Seismology : Algorithms for earthquake data analysis (e.g., focal depth inference, aftershock classification). Blockchain : Temporal linkage of Bitcoin addresses (BitLink) and cryptocurrency fraud detection. Traffic Safety : Multi-LiDAR data fusion for real-time road safety monitoring. Time Series Methods : Innovations like MASS similarity search and DAMP anomaly detection for massive datasets. Scientific Awards : ACM SIGKDD Test-of-Time Award (2022) UNM Provost Research Leader Award UNM School of Engineering Junior Faculty Research Excellence Award KDD 2012 Doctoral Dissertation Contest Runner-Up KDD 2012 Best Paper Award Advising and Grants : He has mentored 11 PhD students now employed at institutions like Microsoft, Meta, and Lawrence Livermore National Lab. His research is funded by NSF , NIH , DARPA , AFRL , NEC , Exxon , Microsoft , and LANL .
Dong Li is an Assistant Professor in the Department of Computer Science and Electrical Engineering (CSEE) at the University of Maryland, Baltimore County (UMBC). His research focuses on wireless sensing, mobile computing, wearable sensing, multi-modal sensing, and smart health, aiming to develop affordable and accessible technologies to address healthcare equity and environmental sustainability challenges. He holds a PhD from the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst, an M.Eng. in Software Engineering from Shanghai Jiao Tong University, and a B.S. in Computer Science from the University of Electronic Science and Technology of China. His work has been published in prestigious venues such as MobiCom, SenSys, IPSN, UbiComp, and HotNets. Key research themes include anomaly detection via knowledge graphs, privacy prediction models for social networks, and interactive recommendation systems. His interdisciplinary approach integrates machine learning, data mining, and cybersecurity to tackle real-world problems in health and environmental sustainability. Dr. Li’s contributions span theoretical advancements and practical system development, with a focus on bridging the gap between cutting-edge research and societal impact. His recent publications highlight innovations in data stream processing, weak supervision frameworks, and privacy behavior analysis in online platforms.
