Find Academic Supervisors

• Urban Mobility
• Traffic Simulation
• Smart City Technologies
• +2 more

Maria Paz Linares Herreros is a Senior Lecturer at the Universitat Politècnica de Catalunya (UPC) in the Department of Statistics and Operations Research within the Faculty of Mathematics and Statistics (FME). She is affiliated with the IMP - Information Modeling and Processing research group and inLab FIB. Her educational background includes a Licenciada en Matemáticas, a Doctorate from UPC, and a Master's in Logistics, Transportation, and Mobility. Licenciada en Matemáticas Doctorat from Universitat Politècnica de Catalunya Máster en Logística, Transporte y Movilidad Her research focuses on urban mobility , traffic simulation , and smart city technologies . She develops data-driven models for transportation systems and parking management, integrating deep learning techniques for real-time predictions. Her work addresses environmental impact assessment of traffic policies and inclusive mobility solutions . Recent publications analyze urban mobility trends through macroscopic and microscopic traffic models , deep learning applications for parking predictions, and dynamic ride-sharing systems . She explores IoT interoperability and data integration in transportation. Scientific awards include the IV International Award on Transport Infrastructure Management Research (2014). She participates in competitive R+D+I projects like ALGORAE and Virtual Mobility Lab, focusing on transportation innovation and smart city policy evaluation . She contributes to multimodal transport simulation frameworks such as CitScale and Barcelona Virtual Mobility Lab, which evaluate emerging mobility concepts and city policies using integrated modeling approaches .

E. Thomas EwingView profile

Professor

• Russian History
• European History
• World History
• +7 more

E. Thomas Ewing is a Professor of History and the Associate Dean for Graduate Studies and Research at the College of Liberal Arts and Human Sciences, Virginia Tech. He is based in the Department of History and plays a key role in advancing interdisciplinary research and graduate education at the university. His research spans Russian, European, and world history, with a distinctive focus on the history of education, medical history, and the digital humanities. He has authored and edited several influential books on Soviet education and pandemic history. His current research investigates the transmission of information during the 1889–1890 Russian Influenza, employing digital humanities methodologies to analyze historical medical data. Ewing leads the Data in Social Context program at Virginia Tech, which integrates data analytics, computational thinking, and critical humanities inquiry into undergraduate and graduate education. Funded by the National Endowment for the Humanities, this initiative includes workshops for graduate teaching assistants and supports interdisciplinary teaching across the humanities and social sciences. His recent work emphasizes ethical dimensions of data, the societal impact of algorithms, and historical perspectives on public health crises. His publications and media engagements reflect a strong commitment to public scholarship. Notable articles in The New York Times , The Washington Post , and Fortune connect historical insights to contemporary issues such as pandemic responses and media responsibility. His research program combines rigorous historical scholarship with innovative digital methods, making significant contributions to both academic and public understanding of history in the age of data. National Endowment for the Humanities (NEH) funding for workshops on the 1918 Spanish Influenza and Images and Texts in Medical History He advises graduate students through the Data in Social Context workshop and supports interdisciplinary research training. He also coordinates the Data & Decisions Minor and collaborates with library data consultants and research groups across campus. His leadership extends to curriculum development, particularly in integrating quantitative and computational thinking with critical humanities education.

• Virtual Geosciences
• Clastic Sedimentology
• Sequence Stratigraphy
• +7 more

Professor John Howell is a Chair in Virtual Geosciences at the School of Geosciences, University of Aberdeen. He has been a Professor there since 2012, following academic and industry roles in Norway and the UK. His research bridges sedimentology, digital field methods, and energy transition geoscience. He leads multiple international research initiatives and collaborates widely across academia and industry. Education: BSc in Geology, Cardiff University, 1988 PhD in Geology, University of Birmingham, 1992 — Dissertation: Sedimentology of the Rotliegend of the UK Southern North Sea John Howell's research is centered on virtual geosciences, focusing on digital outcrop modeling using LiDAR and drones, virtual field trips, and the use of geological analogues to improve subsurface models for reservoirs, carbon capture and storage (CCS), and hydrogen storage. His work spans clastic sedimentology, sequence stratigraphy, and reservoir modeling, with field areas including the Colorado Plateau (Utah), the North Sea, and global deep-water systems. He pioneered virtual outcrop geology and co-founded V3Geo, a public repository hosting over 500 virtual models. His recent publications reflect a strong trend toward integrating digital technologies—such as machine learning and photogrammetry—into geological analysis and education. Themes include reservoir heterogeneity, virtual field trip efficiency, and the application of modern depositional systems to ancient subsurface analogues. His work increasingly supports the energy transition, particularly in subsurface storage and sustainable resource management. Scientific Awards: Perce Allen Award (2018) ExxonMobil Young Researcher Award (2000) Alaister Pilkinton Award for Excellence in Teaching (2000) John Howell has supervised 56 PhD students and leads or co-leads five major Joint Industry Projects (JIPs), including the fifth phase of the SAFARI project, funded by nine energy companies and the Research Council of Norway. His research has attracted significant industry and public funding, supporting numerous postdocs and PhD candidates. He collaborates with institutions in Bergen, Leeds, Manchester, Oslo, and Barcelona. He is actively involved in knowledge exchange, having developed V3Geo, presented TEDx talks, and appeared in numerous TV documentaries such as 'The Big Monster Dig', 'Dinosaur Detectives', and 'World's Deadliest Jobs'. He also produced educational content like 'Drone vs Volcano' with over 375,000 views.

Anna SidorovaView profile

Professor

• Artificial Intelligence in Business
• AI Ethics and Governance
• Digital Transformation
• +7 more

Anna Sidorova is a Professor and Chair of the Department of Information Technology and Decision Sciences at the G. Brint Ryan College of Business, University of North Texas. She holds a Ph.D. in Information Systems from Washington State University and has extensive experience in both academia and industry. Ph.D., Information Systems, Washington State University MBA, Washington State University B.A., Washington State University Her research focuses on Artificial Intelligence in Business , AI Ethics and Governance , Business Intelligence and Analytics , Text Mining , and Digital Transformation . She explores how organizations adopt and benefit from advanced technologies, particularly in decision-making and process improvement. The analysis of her publications reveals a strong trajectory in AI and machine learning applications in business, with a growing emphasis on ethical and governance aspects. Her work spans technical implementation and strategic implications, often published in premier journals like MIS Quarterly and Decision Support Systems . Dr. Sidorova has held significant leadership roles, including Academic Associate Dean for Undergraduate Programs at RCOB. She previously served as an Assistant Professor at the University at Albany, SUNY, and worked as a business consultant at PricewaterhouseCoopers, advising on business strategy and IT systems. She teaches graduate courses in Artificial Intelligence in Business, Information System Development, and Information Systems Theory and Research, contributing to the development of future business and technology leaders.

Agnes HaryantoView profile

Research Fellow

• Big Data Management
• Data Engineering
• Geospatial Databases
• +3 more

Dr. Agnes Haryanto is a Research Fellow in the Embodied Visualisation Group at Monash University's Faculty of IT. Her work focuses on improving healthcare quality through live-streaming clinical analytics and dashboards for accreditation. She holds a PhD from Monash University (2015) in Spatial and Graph Databases. Education: Doctor of Philosophy, Monash University Research interests span Big Data Management, Geospatial Databases, and Health Informatics. Her current projects include a digital health intervention for Emergency Department clinicians (2023–2026), funded by Australia's Department of Health and Aged Care. She has contributed to advancing spatial query optimization and clinical data warehouse systems. Collaborations involve interdisciplinary teams addressing SDGs like quality education and health equity. Teaching commitments include units like FIT3003 (Business Intelligence) and FIT5137 (Advanced Database Technology). Key projects include developing real-time clinical analytics systems to bridge gaps in healthcare data utilization. Her work aligns with UN SDG 3 (Good Health) and SDG 4 (Quality Education).

Oscar Romero MoralView profile

Professor

• Data Management
• Data Science
• Data Governance
• +7 more

Oscar Romero Moral is a Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Department of Services and Information Systems Engineering at the Barcelona School of Informatics (FIB). He leads research in the inSSIDE, inLab FIB, and DTIM groups, focusing on data management, data science, and big data technologies. His work emphasizes knowledge graphs, data governance, and machine learning integration with data systems. Affiliations: UPC, inSSIDE, inLab FIB, DTIM Group Research Interests: Data Management, Data Engineering, Big Data, Knowledge Graphs, Data Governance, Machine Learning Integration He has authored over 276 academic contributions, including peer-reviewed articles on federated healthcare data systems, GPU-accelerated workflows, and graph-driven data integration. His recent work addresses challenges in heterogeneous computing, automated data governance, and scalable data architectures. Romero has served on the program committees of major conferences like VLDB, ICDE, and EDBT, and led competitive research projects in data systems and analytics. He collaborates extensively with industry partners and academic institutions, driving innovations in distributed data management and edge computing.

• Information Retrieval
• Digital Libraries
• Informetrics
• +5 more

Philipp Mayr-Schlegel is a Professor at the University of Göttingen’s Institute of Computer Science, leading the team 'Information & Data Retrieval' at GESIS - Leibniz-Institute for the Social Sciences. His research focuses on interactive information retrieval systems, scholarly recommendation mechanisms, and the integration of bibliometric methods with digital library technologies. University of Göttingen - Institute of Computer Science GESIS - Leibniz-Institute for the Social Sciences His work spans information retrieval , digital libraries , and applied informetrics , with particular emphasis on: Interactive search systems Non-textual ranking algorithms Scholarly document processing Knowledge representation Semantic search technologies User behavior analysis Recent research outputs (2025) include studies on LLMs for scholarly search, bibliometric reproducibility tools, scientific uncertainty annotation, and preprint adoption disparities. He has published extensively in Scientometrics , International Journal on Digital Libraries , and top conference proceedings like ACL and ECIR . As organizer of the International Workshop on Bibliometric-enhanced Information Retrieval (BIR) and Scholarly Document Processing (SDP) series, he has shaped academic discourse through editorial roles at ISSI , JCDL , and SocInfo conferences. His projects have attracted significant national and European funding.

Javier CabreraView profile

Professor

• Biostatistics
• Data Mining
• Clinical Trial Methodology
• +5 more

Javier Cabrera is a Professor in the Department of Statistics at Rutgers University with a joint affiliation at the Cardiovascular Institute. He holds a Ph.D. from Princeton University and is recognized as a Fulbright Scholar. His office is located at Hill Center 471, 110 Frelinghuysen Road, Piscataway, NJ. His research focuses on: Biostatistics and clinical trial methodology Data mining for functional genomics and DNA/protein arrays Statistical computing, machine vision, and high-dimensional data analysis Cardiovascular health applications using statistical modeling Recent publications (2022-2025) demonstrate strong emphasis on: Novel statistical methods for medical/biological data Machine learning applications in diagnostics and genomics Clinical risk modeling and epidemiological studies Big data reduction techniques and computational efficiency He frequently publishes in interdisciplinary collaborations at the intersection of statistics, biomedicine, and computational science. Awards: Fulbright Scholar He collaborates extensively with the Cardiovascular Institute, contributing statistical expertise to research on cardiovascular outcomes, disease risk modeling, and clinical data analysis.

Cresantus BiambaView profile

Senior Lecturer

• Educational Science
• Teacher Education
• Education for Sustainable Development
• +3 more

Cresantus Biamba is a Senior Lecturer at the University of Gävle, specializing in Educational Science. His research bridges education theory with technological advancements, focusing on teacher training, sustainability in education, and inclusive pedagogy. Researcher at University of Gävle (Education, Educational Science) Research interests include: Education for Sustainable Development (ESD) in global contexts Teacher education reform and policy analysis Inclusive classroom practices in the Global South Technological integration in educational systems Curriculum development for post-pandemic resilience Publication trends reveal interdisciplinary work combining AI, cloud computing, and IoT applications with educational challenges, particularly in African institutions. His articles address security optimization, healthcare technology, and sustainability frameworks. Academic activities involve collaborations with researchers in cybersecurity, AI, and energy systems, though specific grants or mentoring roles are not explicitly documented here.

• Database Systems
• Web Data Extraction
• Information Integration
• +4 more

Jayant Madhavan is a researcher at Google specializing in database systems, web data extraction, and information integration. His work primarily focuses on extracting structured data from the web, schema matching, and developing techniques for managing and visualizing large datasets, particularly through projects like Google Fusion Tables and WebTables. Madhavan's research interests center around the challenges of working with web data. His work explores methods for extracting structured information from unstructured web content, particularly focusing on tables and lists. He has made significant contributions to the field of schema matching, developing techniques that enable integration of data from diverse sources. His research also extends to geospatial data processing and visualization, where he has developed algorithms for efficiently handling large geographical datasets for map visualization. His publication record shows a consistent focus on practical applications of database research to web-scale problems. The evolution of his work demonstrates a progression from foundational research on schema matching and data integration to applied work on Google products like Fusion Tables, which enable non-experts to work with structured data. His most recent work examines the ecosystem of structured data on the web and how to effectively extract and utilize this information. Madhavan has collaborated extensively with Alon Y. Halevy (43 co-authored papers) and other researchers at Google, forming a core group that has advanced the state of the art in web data management. His work bridges theoretical database research with practical applications, making significant contributions to both academic literature and real-world data management systems.

• e-business
• supply chain management
• inter-organizational information systems
• +5 more

Dr. Evangelia Kopanaki is an Associate Professor at the Department of Business Organization and Administration within the School of Economics, Business and International Studies at the University of Piraeus, Greece. With a strong academic background in Mathematics, Informatics, and Information Systems, she has established herself as a prominent researcher and educator in the fields of digital transformation, e-business, and supply chain management. Dr. Kopanaki earned her BA in Mathematics and MSc in Informatics from the National and Kapodistrian University of Athens, followed by a PhD in Information Systems from the London School of Economics and Political Science. Prior to her current position, she worked as a researcher at the Department of Informatics and Telecommunications of the National and Kapodistrian University of Athens for over three years, participating in numerous Greek and European research projects. She also has industry experience as an analyst-programmer in the private sector. Her research interests focus on the intersection of information systems and business operations, particularly in the areas of e-business, supply chain management, inter-organizational information systems, Internet technologies, and Business Agility. Dr. Kopanaki has published extensively in international conferences and journals, with a recent emphasis on digital transformation across various sectors including retail, banking, tourism, and transportation. Her work often explores how digital technologies can enhance sustainability, operational efficiency, and strategic decision-making in complex business environments. A significant portion of Dr. Kopanaki's recent research (2022-2025) examines the role of digital transformation in creating sustainable business practices, with particular attention to supply chain management, tourism, and financial services. Her publications demonstrate a strong focus on practical applications of emerging technologies like blockchain, AI, and big data analytics to solve real-world business challenges. The research shows a clear trajectory toward integrating sustainability considerations with digital innovation across multiple industry sectors. Co-author of two Academic Books in the fields of e-business and Internet technologies Extensive publication record in international conferences and journals Dr. Kopanaki has taught a variety of undergraduate and postgraduate courses at the University of Piraeus, including Operations Research, Management Information Systems, E-Commerce, and E-Commerce Programming & Design. She has also shared her expertise at the National and Kapodistrian University of Athens and the Singapore Institute of Management through the University of London International Programmes. Her teaching philosophy emphasizes the practical application of theoretical concepts, preparing students for the digital challenges of modern business environments. Through her work with the University of Piraeus, Dr. Kopanaki contributes to several research laboratories and initiatives focused on digital transformation, sustainable business practices, and innovative information systems. Her research projects often involve collaboration with industry partners and international academic institutions, creating valuable bridges between theoretical knowledge and practical business applications.

• human genetics
• genomics
• bioinformatics
• +7 more

Sophie LIMOU is a Professor in Human Genetics at the University of Nantes , affiliated with the Mathematics, Informatics and Biology (MIB) department and leading Team 3 at the CR2TI (Center for Research in Transplantation and Translational Immunology) under Inserm UMR1064. Her work bridges genomics, bioinformatics, and immunology in kidney transplantation and complex disease research. Cell Biology Molecular Biology Physiology Human Genomics Big Data Bioinformatics Her research focuses on genetic epidemiology of complex diseases, integrating genomics, bioinformatics, and biostatistics to explore kidney function and pathologies in five key areas: end-stage renal disease, kidney graft survival, transplantation comorbidities, recurrent diseases post-transplantation, and living donor kidney function. She emphasizes nephrogenomics and precision medicine applications. Recent publications highlight her work in HLA typing , polygenic risk scores , and computational immunogenetics , particularly in transplantation outcomes. Tools developed include SHLARC reference panels and synthetic HLA twin models for privacy-preserving data sharing. She actively collaborates with international consortia and welcomes researchers to join her team at CR2TI, where she integrates multi-omics data into clinical care frameworks.

Peter LugtigView profile

Professor

• Survey Methodology
• Data Quality
• Longitudinal Data Analysis
• +5 more

Peter Lugtig serves as Professor of Data Quality and Head of the Department of Methodology and Statistics within the Social and Behavioural Sciences faculty at Utrecht University. He holds a PhD in Survey Methodology from Utrecht University (2012) and an MSc in Political Science from the University of Amsterdam (2006). His academic leadership extends to serving on the management board of the European Master of Official Statistics. His research spans multiple critical areas in modern social science methodology, with particular expertise in data quality assessment , survey methodology , and integration of traditional survey data with big data sources . A key focus of his work involves leveraging smartphone sensor data (GPS, accelerometer) to enhance understanding of human behavior through innovative data fusion techniques. His methodological interests include statistical modeling of data errors, longitudinal data analysis using Structural Equation Models, and addressing nonresponse patterns in emerging data collection approaches. Lugtig's recent scholarly output demonstrates consistent contributions to survey methodology, with publications focusing on smartphone-based data collection, mode effects in surveys, and advanced techniques for handling missing data in mobility trajectories. His work shows a clear trajectory toward integrating traditional survey methods with novel data sources and analytical approaches. As an educator, Lugtig is deeply committed to teaching, contributing to Bachelor's, Master's, and post-graduate courses in statistics and survey methodology. He also provides consulting services to governmental agencies and companies on data collection methods and data quality assessment, demonstrating the practical application of his research.

Stanley Joel GreensteinView profile

Senior Lecturer

• Law and Information Technology
• Artificial Intelligence Ethics
• Data Privacy
• +3 more

Stanley Joel Greenstein is a Senior Lecturer in Law and Information Technology at the Department of Law, Faculty of Law, Stockholm University. He serves as Course Director for the optional Cyberlaw course. PhD in Law (Stockholm University, 2017) South African legal education background with civil/common law expertise His research focuses on technology-society interactions , particularly: Artificial Intelligence ethics and regulation Data protection and privacy frameworks Cybersecurity legal implications Algorithmic decision-making transparency Human dignity in digital environments Commercial data practices and autonomy Current projects include EXTREMUM (Explainable and Ethical Machine Learning for medical data) funded by Digital Futures initiative through 2024. His 2017 dissertation Our Humanity Exposed proposed empowerment strategies for regulating predictive modeling. Key research areas span: Digital society governance Algorithmic fairness in public services Legal aspects of environmental sustainability Digital innovation frameworks AI implementation in healthcare Explainable AI for regulatory compliance

Kalogeraki VanaView profile

Professor

• Distributed Systems
• Real-Time Systems
• Big Data Systems
• +8 more

Prof. Vana Kalogeraki is a Faculty Member at the Department of Informatics , Athens University of Economics and Business (AUEB) , and serves as the Dean of the School of Information Sciences and Technology and Director of the Computer Systems and Communications Laboratory . She has held academic positions at the University of California, Riverside and was a Research Scientist at Hewlett-Packard Labs . PhD: University of California, Santa Barbara M.S. & B.S.: University of Crete, Greece Her research focuses on Distributed and Real-Time Systems , Big Data Systems , Cloud Computing , Human-Centered Systems , and Crowdsourcing . She has published over 200 papers at top journals (IEEE TPDS, ACM TOS, etc.) and conferences (RTSS, DSN, ICDCS, VLDB, MDM), including co-authoring the OMG CORBA Dynamic Scheduling Standard . Her 2023-2025 publications address AI pipelines in serverless environments, edge computing for trauma detection via eye-tracking, and fairness in resource scheduling. She has received prestigious awards including an ERC Starting Grant , Best Paper Awards (DEBS 2017, IPDPS 2009), a Best Poster Award (EuroSys 2024), and multiple UC Research Awards . Her research is funded by the European Union (ARISTEIA, THALIS), NSF, and industry partners like SUN and Nokia. Advising Legacy : Supervised 10 PhD graduates (now at Google, Amazon, IBM, Apple) and over 60 MS/PhD committees Labs & Teams : Leads the Computer Systems and Communications Laboratory at AUEB, focusing on mobile human-centered systems and urban data analytics