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Dr. Alfonso José López Rivero is a Professor at the School of Computer Science , Pontifical University of Salamanca , specializing in Statistics and Operations Research . He earned his PhD from the Universidad Pontificia de Salamanca in 2004 with a thesis on Software Quality in Information Selection and Classification , supervised by Dr. Luis Joyanes Aguilar. Education: PhD in Computer Science (2004), Universidad Pontificia de Salamanca. His research spans Digital Transformation , Machine Learning , and Sustainable Mobility , with a focus on applications in healthcare, battery recycling, and strategic management. Recent work includes IoT systems for voice-based disease detection and AI-driven sustainability in SMEs. Key trends in his publications include Electric Vehicle (EV) Optimization , IoT in Healthcare , and Ethical AI . He leads the Gestión tecnológica y ética del conocimiento research group, emphasizing technological ethics and data-driven decision-making.

• Statistics
• Operations Research
• Strategic Management
• +12 more

Andreas Bjerre-Nielsen is an Associate Professor at the Department of Economics and Copenhagen Center for Social Data Science (SODAS) within the Faculty of Social Sciences at the University of Copenhagen. His work bridges economics and data science to analyze education-related behavior and policies. Research Focus: School choice, digital technology in education, predictive analytics for interventions, and social network effects. Methodology: Combines econometrics with machine learning techniques to evaluate policy impacts. Research Trends: Recent publications emphasize algorithmic fairness in college admissions, socioeconomic impacts of school boundary policies, and behavioral insights from large-scale datasets. His 2025 Scientific Reports study reveals nation-scale social network dynamics. Awards and Grants: Tietgen Prize (2021) for young social science researchers 2024: Independent Research Fund Denmark grant for 'Coded Clues' project 2023: Major grant for school choice research Collaborations: Works with Danish Ministry of Children and Education through UDDanKvant unit, and collaborates with multidisciplinary researchers including Sune Lehmann and David Dreyer Lassen.

• Educational Public Policy
• AI for Public Policy
• Social Networks
• +6 more

Xiaoyang Wang is a Senior Lecturer in the School of Computer Science and Engineering (CSE) at the University of New South Wales (UNSW). He holds a Bachelor's and Master's degree in Computer Science from Northeastern University, China, and earned his PhD from CSE UNSW. Dr. Wang's research focuses on database systems with a special emphasis on query processing and data mining on large-scale graph, spatial, and streaming data. His expertise extends to data-driven machine learning, smart contract analysis on blockchain, and FinTech with financial network analysis. His work spans Graph Processing, Graph Neural Networks, Spatial Data Processing, AI for Databases (AI4DB), Database for AI (DB4AI), and FinTech applications. His publication record shows significant contributions to the field with 7 book chapters, 56 journal articles, 61 conference papers, 7 edited conference proceedings, and 4 conference abstracts. Recent publications (2022-2025) demonstrate his strong research trajectory in advanced graph processing techniques, neural network applications, and innovative database approaches. Key themes include hierarchical contrastive learning, robust attack frameworks, temporal graph processing, influence maximization, knowledge graph-enhanced reasoning, and rumor mitigation. Dr. Wang actively recruits PhD students interested in pursuing research in related fields and encourages current undergraduate and master's students at UNSW to contact him about research opportunities. He maintains an active research agenda with practical implications for industries dealing with large-scale network data, financial technology applications, and data-intensive systems. He can be reached at xiaoyang.wang1@unsw.edu.au and is located in Engineering building K17-501D at UNSW.

• Graph Processing
• Graph Neural Networks
• Spatial Data Processing
• +10 more

Sanna Sundqvist serves as Associate Professor in the Department of Marketing at LUT University, with a research focus spanning international business, export performance, and entrepreneurial strategy. Her work bridges empirical analysis with practical applications in global markets. Her academic credentials include a Doctoral degree in Engineering and Technology (awarded November 29, 2002) and a Master's degree in the same field (awarded December 21, 2000), both from LUT University. Professor Sundqvist's research examines how entrepreneurial orientation and cognitive biases influence international business decisions, with particular emphasis on structural equation modeling techniques. Her work explores the interplay between prior experience and export performance, Finnish market dynamics, and sales strategy optimization in global contexts. Recent investigations extend into consumer behavior analytics, examining online data patterns and high-involvement service interactions. Analysis of her 2018-2025 publications reveals a concentrated trajectory in international entrepreneurship, with increasing attention to cognitive microfoundations of business decisions. Her methodological approach combines bibliometric analysis with computational text mining, while maintaining strong empirical grounding in structural equation modeling for export performance studies. As principal investigator, she has secured multiple research projects including Wisdom2 (2022-2024), KATAJA/New Product Development (2022), KATAJA/LISREL (2021-2022), and KATAJA/Asikainen (2020), demonstrating consistent funding success in international business research.

• Export Performance
• International Business
• Entrepreneurship
• +3 more

Hans Vernooij is a Lecturer in Farm Animal Health at the Faculty of Veterinary Medicine, Utrecht University. He specializes in statistical methods and data science applications in veterinary epidemiology and animal health. His areas of expertise include: Statistical methods for veterinary research Applied Data Science in Life Sciences Epidemiological modeling Machine learning applications in animal health Vernooij has extensive experience in developing statistical models for animal health applications. His research focuses on applying advanced statistical techniques and data science methods to solve problems in veterinary epidemiology and farm animal health. He has particular expertise in Random Forest models, as demonstrated during his sabbatical at the Human Sciences Research Council in Pretoria where he developed a model for HIV status prediction based on demographic information and knowledge of HIV prevention from large-scale survey data. His publication record shows consistent contributions across veterinary epidemiology, with recent work emphasizing machine learning applications and big data analytics in animal health surveillance. The research demonstrates a clear trajectory from traditional statistical methods toward more advanced data science approaches. Vernooij is actively involved in teaching and mentoring: Teaches statistics to Bachelor students at the veterinary faculty Supports PhD candidates and Master students during data analysis phases of their research Provides statistics education for the Master of Epidemiology program at the Julius Centre of University Medical Centre

• Statistics
• Applied Data Science
• Veterinary Epidemiology
• +2 more

Dr. Grey Ballard is an Associate Professor in the Department of Computer Science at Wake Forest University . He earned a B.S. in Math and Computer Science (2006), M.A. in Math (2008) from Wake Forest, and PhD in Computer Science (2013) from the University of California, Berkeley. He was a Truman Fellow at Sandia National Laboratories before joining Wake Forest. Research Focus: Ballard develops communication-optimal algorithms for high-performance computing , particularly in tensor decompositions , symmetric matrix computations , and nonnegative matrix factorization . His work combines numerical linear algebra with parallel algorithm design to reduce data movement costs in distributed systems. Publications demonstrate expertise in communication lower bounds , randomized tensor rounding , and visualization tools for parallel algorithms. He has contributed software packages such as TuckerMPI , GentenMPI , and PLANC for large-scale data compression and clustering. Scientific Awards: Wake Forest Excellence in Research Award NSF CAREER Award SIAM Linear Algebra Prize Three Conference Best Paper Awards (SPAA, IPDPS, ICDM) C.V. Ramamoorthy Distinguished Research Award (UC Berkeley) ACM Doctoral Dissertation Award – Honorable Mention Teaching: Courses include Introduction to Computer Science , Numerical Linear Algebra , and Parallel Algorithms . He has developed educational tools using the Thread-Safe Graphics Library to visualize parallel dynamic programming and collective communication.

• Numerical Linear Algebra
• High Performance Computing
• Computational Science
• +6 more

Antonios Deligiannakis is a Professor at the School of Electronic and Computer Engineering of the Technical University of Crete, specializing in database systems and distributed data processing. His academic career includes a postdoctoral position at the National and Kapodistrian University of Athens (2006-2007) and a visiting researcher role at AT&T Labs-Research (2003). His educational background includes: PhD in Computer Science, University of Maryland, USA (2005) Master's Degree in Computer Science, University of Maryland, USA (2001) Diploma in Electrical and Computer Engineering, National Technical University of Athens (1999) Professor Deligiannakis's research spans Databases , Stream Processing , and Sensor Networks , with pioneering work in Approximate Query Evaluation for massive datasets and Complex Event Processing in distributed environments. His contributions enable efficient analytics in resource-constrained settings through techniques like synopses-based engines and windowed outlier detection. His 15 most recent publications (2020-2025) reveal a dominant focus on distributed streaming analytics, with recurring themes of cross-platform integration, federated learning, and extreme-scale interactive systems. Key innovations include the INFORE framework for interactive analytics, DAG* for IoT workflow optimization, and communication-efficient federated learning techniques—demonstrating consistent translation of theoretical advances into production-ready platforms. Scientific Awards: No specific awards were listed in the provided material. Information about advisees and research grants was not provided in available documentation, though his leadership in the Distributed Information Systems and Applications laboratory suggests active mentorship and project direction. He directs research in the Distributed Information Systems and Applications laboratory, developing systems for real-time analytics across domains including maritime surveillance, financial technology, and IoT platforms, with emphasis on scalability and fault tolerance in geo-distributed environments.

• Databases
• Sensor Networks
• Analytical Data Processing
• +4 more

Xu Xiang is a Tenured Professor and Vice Dean at Tongji University School of Art and Media , where he serves as Director of the Big Data and Computational Communication Research Center. He is also Editor-in-Chief of Computing, Intelligence, and Communication , reviewer for CSSCI journals, and former Deputy Secretary-General of Beijing Society of Literature and Art. PhD in Literary Theory (Beijing Normal University, 2009) M.A. in Chinese Language and Literature (Tsinghua University, 2006) B.A. in Sociology (Nanjing University, 2004) His research focuses on computational communication, particularly analyzing social media networks, user behavior, and information dissemination. He established key theoretical frameworks including 'Opinion Paradigm', 'Social Echo Chamber', 'Influence Circle', 'Idol Conformity', 'Emotional Setting', and 'Silent Public Opinion', with extensive publications on social media emotional dynamics, information narrowing, and cultural communication mechanisms. His 15 most recent publications (2017-2022) demonstrate consistent exploration across computational communication theory, algorithmic platform analysis, and social media's role in cultural dissemination. Key research trends include information narrowing mechanisms, emotional preference patterns, and structural transformations in social media ecosystems. Fourth China 'News Communication Academic Award' (2018) Youth New Media Academic Research 'Qi Hao Award' (2018) National News Academic Youth Scholar Award (2012) Beijing-Taiwan Research Award (2013) Beijing Outstanding Talent Funding (2011) Tongji University '13th Five-Year Plan' Scientific Research Advanced Individual (2020) As an award-winning mentor, he guided student teams to first, second, and third prizes in multiple national competitions including China Data Journalism Competition and National Undergraduate New Media Creative Competition. His research bridges academic theory with policy practice, with findings receiving Central Politburo member instructions and extensive national media coverage.

• Journalism
• Communication
• Computational Communication
• +5 more

Sarah Kaufman serves as Director of the NYU Rudin Center for Transportation and Assistant Clinical Professor of Public Service at New York University's Wagner Graduate School of Public Service. Her work bridges academic research and practical policy solutions for urban transportation systems, with emphasis on equity, technology, and resilience. Her research spans transportation policy, urban planning, emergency management, and climate change adaptation. Key focus areas include mobility equity (notably the "pink tax" on transportation), micromobility systems, autonomous vehicle governance, and disaster response protocols. She investigates how technology and policy can create more inclusive and resilient urban mobility networks, with particular attention to gender disparities and climate vulnerabilities. Analysis of her 15 most recent publications reveals consistent themes in transportation equity, emerging technologies, and crisis management. Her work demonstrates methodological diversity—from agent-based modeling (MATSim-NYC) to policy analysis of scooter sharing—and maintains strong practical relevance through partnerships with transportation agencies. Notable trends include the evolution of micro-mobility regulation, gender-based mobility disparities, and transportation's critical role in pandemic and extreme weather response. As Director of the Rudin Center, she leads a multidisciplinary research team that produces policy-influential reports on congestion pricing, transit innovation, and sustainable mobility. The center serves as a hub for collaboration between government agencies, industry stakeholders, and community organizations, translating academic research into actionable transportation solutions for New York City and beyond.

• Transportation
• Urban Planning
• Urban Policy
• +7 more

Professor Tova Milo is the Chair for Information Management at the School of Computer Science, Tel Aviv University, leading the prominent Databases Lab (DB Group). Her research spans databases, big data management, crowd-based data sourcing, and business process querying, with significant contributions to data integration and semi-structured data systems. Her research interests focus on innovative approaches to data management challenges through machine learning integration, crowd computing, and business process analysis. Key projects include Business Process Querying (BPQ) for analyzing BPEL specifications, MoDaS for crowd-based data management, and PROX for data provenance summarization. Her work bridges theoretical foundations with practical applications in fraud detection, recommendation systems, and data cleaning. Analysis of her recent publications reveals strong trends in human-in-the-loop data management systems, with growing emphasis on crowd integration for data cleaning and knowledge acquisition. Her research increasingly combines traditional database theory with machine learning techniques for scalable big data processing, while maintaining focus on business process modeling and provenance tracking. ACM PODS Alberto O. Mendelzon Test-of-Time Award (2010) ERC Advanced Investigators grant MoDaS (2011) The Weizmann Prize for Exact Sciences (2017) VLDB Women in Database Research award (2017) IEEE TCDE Impact award (2022) ISF Breakthrough Research Grant (2022) Doctorate Honoris Causa, University of Zurich (2023) ACM Fellow Member of Academia Europaea Professor Milo has advised over 30 graduate students including PhD candidates like Yael Amsterdamer and Ohad Greenshpan, and numerous MSc students working on projects including MoDaS, BPQ, and EDOS. Her research has been supported by major grants including the ERC Advanced Investigators grant and ISF Breakthrough Research Grant. She actively collaborates with industry partners including IBM and Microsoft, particularly in business process management standards. She directs Tel Aviv University's Databases Lab, which maintains the DB Group with multiple research streams including business process querying (BPQ), crowd-based data management (MoDaS), and self-adaptive data dissemination (EDOS/COLT). The lab operates from the Schreiber Building (M-20) and maintains strong international collaborations, particularly with European institutions through the ERC-funded MoDaS project.

• Databases
• Big Data Management
• Machine Learning for Data Management
• +7 more

Prof. Dr. Edgar Kreilkamp is a full-time Professor at Leuphana University of Lüneburg , affiliated with the Institute for Management & Organization (IMO) . His academic and research expertise spans business administration , tourism management , and sustainability science , with a strong focus on integrating sustainable practices into tourism systems through digital innovation and strategic stakeholder collaboration. Research Focus: Sustainable tourism development and policy Digital transformation and big data in tourism Service marketing and consumer behavior Climate adaptation and environmental impact assessment Gamification and experiential design in tourism Projects & Contributions: Prof. Kreilkamp has led and contributed to multiple high-impact projects funded by the Federal Ministry of Education and Research (BMBF), including: Green Travel Transformation : Developing models for sustainable tourism management and labeling of travel packages. Digitalization as a Success Factor in Tourism : Exploring how digital tools can enhance sustainability and competitiveness. Climate-Neutral Island of Juist : A long-term initiative to transform Juist into a global model for climate-neutral tourism. KUNTIKUM : Investigating climate trends and sustainable tourism development in coastal and low mountain regions. Publications & Media: He has authored and co-authored numerous scholarly articles, book chapters, and policy reports. His work has appeared in both peer-reviewed journals and industry-focused publications, and he is a frequent keynote speaker at international conferences and policy forums. His media contributions include expert commentary on sustainable travel, climate-conscious tourism strategies, and digital innovation in the tourism sector. Contact: Email: edgar.kreilkamp@leuphana.de Phone: +49.4131.677-2171 Office: C6.124, Universitätsallee 1, 21335 Lüneburg, Germany

• Sustainable Tourism
• Tourism Management
• Business Administration
• +4 more

Mahsa Jessri is an Associate Professor in the Food, Nutrition and Health Program at the University of British Columbia's Faculty of Land and Food Systems. She holds the prestigious Canada Research Chair (Tier 2) in Nutritional Epidemiology for Population Health and is affiliated with the Centre for Health Services and Policy Research (CHSPR) and the Department of Health Services and Policy at the UBC School of Population and Public Health, Faculty of Medicine. Dr. Jessri also serves as an Affiliate Investigator of Evidence to Innovation (E2i) Theme at the BC Children's Hospital Research Institute. Her educational background includes a PhD (Vanier Scholar) in Nutritional Sciences from the University of Toronto, an M.Sc. in Nutrition and Metabolism from the University of Alberta, and a Banting Postdoctoral Fellowship from Statistics Canada. Dr. Jessri's research is uniquely positioned at the intersection of nutritional sciences, big data analytics, AI, and population and public health, with the overarching goal of providing evidence-based decision tools to inform guidelines and policies for reducing the burden of chronic diseases. Dr. Jessri's research program focuses on using big data and methodological advancements (including AI and machine learning) to identify dietary patterns and their interactions with health outcomes, population health impact assessment of food and nutrition policies, nutritional monitoring and population surveillance, development of robust methods for complex data analysis, and design of personalized nutrition risk assessment tools. Her Nutritional Epidemiology and Big Data Analytics Laboratory provides a stimulating environment for research in these areas. Her publication portfolio demonstrates a strong focus on dietary patterns, chronic disease prevention, and the application of advanced statistical methods to nutritional epidemiology. Her recent work has examined nutrient profiling models, cardiovascular disease risk, dietary pattern assessment tools, and the impact of dietary factors on health outcomes across diverse populations including Canadian adults, Inuit communities, and international cohorts. Among her notable honors are the Royal Society of Canada (RCS) Alice Wilson Award (2018), Emerging Leader in Nutrition Science from the American Society for Nutrition, and the Canadian Institute of Health Research (CIHR) Banting Postdoctoral Fellowship. She has also received the INMD/CNS Early Career Researcher Partnership Prize and the Discovery Award from the Banting Research Foundation. Dr. Jessri teaches FNH 398: Research Methods in Human Nutrition and has previously taught graduate-level courses at the University of Ottawa. Her academic career builds on decades of clinical and public health dietetics and nutrition policy experience working at academic and regulatory institutions. She has served as a past Core member of Health Canada's Nutrition Science Advisory Committee (NSAC) and is active in the American Society for Nutrition and American Academy of Nutrition and Dietetics Artificial Intelligence (AI) in Nutrition Research Taskforce.

• Nutritional Epidemiology
• Population Health
• Big Data Analytics
• +5 more

Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.

• Cloud Computing
• Hardware Acceleration
• Field-Programmable Gate Arrays
• +7 more

Domingo Savio Rodríguez Baena is a Professor at Pablo de Olavide University, affiliated with the Department of Computer Languages and Systems. His research focuses on data mining, bioinformatics, and computational biology, with a particular emphasis on biclustering algorithms, gene co-expression networks, and high-performance computing applications. PhD in Engineering, Data Science, and Bioinformatics (2012) from Pablo de Olavide University His work spans interdisciplinary domains, including recommender systems , livestock behavior analysis , and biological data interpretation . Recent articles highlight his contributions to multi-GPU optimization , ensemble learning , and historical database construction . Key collaborations include the DATAi Intelligent Data Analysis and DASE Data Analytics Science & Engineering research groups. He has developed tools like the CyEnGNet–App for gene network visualization and BIGO for gene enrichment analysis. Contact: dsrodbae@upo.es

• Data Mining
• Bioinformatics
• Machine Learning
• +3 more

Martha Tsigkari serves as an Associate Professor at The Bartlett School of Architecture, University College London (UCL), where she bridges architectural practice with cutting-edge computational research. Her position situates her at the forefront of digital transformation in the built environment, with institutional affiliations spanning UCL's Faculty of the Built Environment and direct contributions to UN Sustainable Development Goals 4 (Quality Education), 11 (Sustainable Cities), and 13 (Climate Action). Her research program critically examines the integration of artificial intelligence, machine learning, and cognitive psychology into architectural design processes. Key investigations include spatial and visual connectivity analysis, XR-enhanced collaborative design environments, and AI-driven optimization of building performance. She explores how digital tools reshape creativity, professional identity, and sustainability outcomes in architecture, with particular focus on data commoditization, skills evolution, and human-AI collaboration in design workflows. Her interdisciplinary approach connects architectural theory with computational neuroscience and industrial digitalization trends. Tsigkari's publication trajectory reveals a clear evolution from computational structural analysis (2012-2017) toward AI ethics and professional transformation (2022-2024). Early work established foundations in performance-driven facades and material systems, while recent output confronts existential questions about architectural practice in the AI era. Her scholarship consistently addresses the tension between technological capability and human-centered design values, with growing emphasis on sustainable development frameworks and educational implications. Scientific Awards: No major awards are documented in the available records. Advising and Grants: While specific supervisees and funding mechanisms aren't detailed in current sources, her extensive collaborative network across 30+ publications indicates active mentorship and research leadership. Co-authorship patterns suggest involvement in multi-institutional projects addressing AECO industry digitalization, with potential ties to UK research councils and industry partnerships like RIBA. Labs and Teams: Tsigkari operates within The Bartlett's digital research ecosystem through recurring collaborations with Kosicki, Tarabishy, and Psarras. Her work manifests in experimental toolsets including Glaucon (XR design environment), HYDRA (optimization framework), and SandBOX (conceptual design system), indicating leadership in UCL's computational design labs focused on human-AI interaction and sustainable building technologies.

• Architecture
• Artificial Intelligence
• Machine Learning
• +4 more