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Kelly GatesView profile

Professor

• Digital Media Technologies
• Surveillance Studies
• Facial Recognition Technology
• +3 more

Kelly Gates is a Professor in the Department of Communication at the University of California, San Diego (UCSD). Her research critically examines digital media technologies, focusing on the politics and social implications of computerization, automation of surveillance, and the evolving relationship between policing and corporate surveillance infrastructure. She holds a PhD in Communications Research from the University of Illinois, Urbana-Champaign (2004). Her key works include Our Biometric Future: Facial Recognition Technology and the Culture of Surveillance (2011) and Targeted: Corporations and the Police Surveillance Economy (2025), which explore the societal and ethical dimensions of surveillance technologies. Her research emphasizes how visual technologies like facial recognition and video analytics shape perceptions of identity, security, and governance. Gates teaches courses on surveillance studies, digital media, and the cultural history of photography. Her recent work addresses police media infrastructures, video forensics, and the entanglement of corporate and governmental surveillance systems. Awards and grants are not explicitly listed in the provided texts, but her contributions to surveillance studies are widely recognized in academic circles. In advising and grants, while specific details are not mentioned, her extensive publication record suggests active engagement in research networks and possibly collaborative projects. She is affiliated with UCSD’s Science Studies Program and contributes to interdisciplinary dialogues on media, technology, and society.

Richard BoyceView profile

Associate Professor

• Clinical Decision Support Systems
• Pharmacovigilance
• Drug Interaction Analysis
• +3 more

Richard Boyce is an Associate Professor in the Department of Biomedical Informatics at the University of Pittsburgh's School of Computing and Information. His work focuses on clinical decision support systems (CDSS), pharmacovigilance, and distributed knowledge bases. He leads research in drug interaction analysis, particularly involving natural products like cannabis, and develops machine learning tools for healthcare applications. His address is 530 DBMI Building, Pittsburgh, PA 15260, with contact email rdb20@pitt.edu. Boyce’s research integrates clinical and pharmacological data to improve patient safety through advanced analytics. Recent projects include predicting Alzheimer’s disease using EHR data, falls prediction in nursing homes, and optimizing CDSS modules for resource-limited settings. His team also develops open-source knowledge graphs for life sciences, enhancing data interoperability. Education: Details not explicitly listed in the provided text. Labs/Teams: Affiliated with the Department of Biomedical Informatics and School of Computing and Information at Pitt. Publications highlight his contributions to pharmacovigilance signal detection, machine learning in healthcare, and ontology engineering. Notable works include studies on cannabis-drug interactions and CDSS design for clinical practice.

• Multiple Sclerosis
• Neurology
• Neuropathic Pain
• +7 more

Henrik Boye Jensen is a Clinical Associate Professor at the Department of Regional Health Research, IRS - Sygehus Lillebælt, Forskningsenhed for Neurologi (Kolding), affiliated with the University of Southern Denmark. His work bridges clinical neurology and research, with a strong focus on multiple sclerosis, stroke, and neurorehabilitation. Institution: University of Southern Denmark Department: Department of Regional Health Research Research Unit: Research Unit for Neurology, Kolding Academic Rank: Clinical Associate Professor His research interests center on multiple sclerosis , particularly disease progression, pharmacological treatments, and the role of exercise in neuroprotection. He also investigates stroke rehabilitation and diagnostic challenges in neurological conditions such as Lyme neuroborreliosis. His work frequently employs advanced methodologies including randomized controlled trials , cohort studies , and neuroimaging (MRI) . The 15 most recent articles reflect a strong trend in clinical neuroscience , with emphasis on MS phenotypes , pharmacological interventions , exercise as therapy , and diagnostic accuracy . His publications span high-impact journals in neurology and rehabilitation, demonstrating consistent scholarly output. Scientific Awards: No specific awards listed in the provided text. Henrik Boye Jensen has been actively involved in advising and research projects. He has contributed to at least three supervised works, including a Ph.D. thesis, and is a working partner in the RoboRehab project on stroke rehabilitation. He has participated in multiple collaborative research initiatives and has delivered presentations in both academic and public settings. He is also involved in public discourse through media contributions on neurological health issues. Labs and Teams: He is associated with the Research Unit for Neurology in Kolding and collaborates extensively within the Region of Southern Denmark and with national and international research groups, particularly in MS and stroke research.

Alex MesoudiView profile

Professor

• Cultural Evolution
• Social Learning Experiments
• Agent-Based Modeling
• +3 more

Professor Alex Mesoudi is a leading academic in cultural evolution, serving as the Professor of Cultural Evolution at the University of Exeter’s Penryn Campus in Cornwall, UK. He is also the President of the Cultural Evolution Society. His work bridges evolutionary theory with social sciences, focusing on how cultural traits evolve through psychological and social processes. Key research areas include experimental studies of social learning, agent-based modeling of cultural dynamics, and analysis of large-scale cultural datasets (e.g., pop music, football tactics). Mesoudi’s lab investigates migration’s impact on cultural diversity and employs interdisciplinary methods to explore cultural change. Research Interests: Human cultural evolution mechanisms Experimental transmission chains and technological innovation Agent-based models of cultural transmission Cross-cultural variation in learning strategies Big data analysis of cultural trends Lab and Collaborations: Mesoudi leads a vibrant research group with current PhD students Andoni Sergiou and Ishaan Sinha. Former students include Dugald Foster, Angel V. Jimenez, and Alice Williams. His lab has hosted visiting scholars from institutions worldwide, including Télécom Paris and the Federal Rural University of Pernambuco. He collaborates with global researchers on topics like cultural group selection and migration’s psychological impacts. Publications and Media: Mesoudi has authored influential books such as Cultural Evolution: How Darwinian Theory Can Explain Human Culture and contributed to high-impact journals like PNAS and Proceedings of the Royal Society B . He frequently engages in media discussions about cultural evolution through podcasts and documentaries, emphasizing the societal relevance of his work.

• Higher Education Research
• Student Motivation
• Academic Dropout
• +7 more

Markus Rump, M.Sc., is a Researcher and Research Assistant at Bielefeld University, affiliated with the Faculty of Economics, the Department of Management Science & Business Analytics, and the Chair of Business Administration, specifically in Marketing, under Prof. Dr. Decker. He also holds institutional roles in research data management at the Competence Center for Research Data, Bielefeld IT Service Center (FDM Team), and the Bielefeld Center for Data Science (BiCDaS), reflecting his interdisciplinary engagement in data infrastructure and digital scholarship. Faculty of Economics Department of Management Science & Business Analytics Chair of Business Administration, esp. Marketing Competence Center for Research Data Bielefeld Center for Data Science (BiCDaS) His research spans two primary domains: higher education and marketing. In higher education, he investigates student motivation, academic dropout, dispositional traits (optimism/pessimism), and heterogeneity in learning environments, often in collaboration with Wiebke Esdar and Elke Wild. In marketing, his work focuses on customer experience, branding, voice assistants, and brand selfies, frequently co-authored with Reinhold Decker. These interests reflect a strong interdisciplinary profile bridging psychology, education, and business. His recent publications (2016–2023) show a consistent output in both peer-reviewed journals and conference venues. Themes include the impact of voice assistants on customer experience, motivations behind brand selfies, and structural models analyzing early college dropout. The research employs quantitative methods and surveys, contributing to both theoretical understanding and practical applications in education and marketing. Markus Rump has no listed scientific awards in the provided texts. He is actively involved in research projects such as HeLGA (Heterogeneity-Oriented Teaching), focusing on conditions for successful teaching in diverse student populations. While no formal students or grants are explicitly mentioned, his role in large-scale research suggests collaborative supervision and project-based funding. He contributes significantly to research data management, supporting institutional data governance and digital research practices.

Gustavo Ovando-MontejoView profile

Assistant Professor

• Geographic Information Science (GIScience)
• Environmental and Natural Resource Management
• Human-Environment Interactions
• +7 more

Gustavo Ovando-Montejo is an Assistant Professor in the Department of Environment & Society within the College of Natural Resources at Utah State University, based at the Blanding campus. He contributes to environmental and geospatial education and research, teaching undergraduate and graduate courses in GIScience, environmental data science, and natural resource management. Education: PhD in Geography (Geographic Information Science), Oklahoma State University, 2019 MS in Geography (Geographic Information Science), Oklahoma State University, 2015 BS in Geographic Information Systems, Brigham Young University, 2013 Research Interests: Dr. Ovando-Montejo's research centers on human-environment interactions, land cover change, and landscape ecology, with a focus on linking spatial patterns to ecosystem services. He explores issues of environmental equity, indigenous segregation, and applies advanced geospatial and data science techniques to environmental and natural resource problems. His work bridges technical GIScience with social and ecological applications. Publication Trends: His recent publication focuses on web-based visualization of large-scale geospatial data, emphasizing equitable access to advanced analytics. This reflects a growing trend in democratizing petascale computing for broader environmental research and education, combining technical innovation with social responsibility. Scientific Awards: QCNR Teacher of the Year, 2024 Advising and Grants: While no formal advisees or grants are listed in the provided text, Dr. Ovando-Montejo has been actively involved in teaching and extension since 2011. He teaches core courses such as Environmental Data Science, Geospatial Analysis, and Physical Geography, and contributes to USU Extension Sustainability efforts, indicating a long-standing commitment to outreach and applied environmental education. Labs and Teams: Specific research labs or collaborative teams are not mentioned in the available content. However, his work in geospatial analysis and environmental data science suggests potential affiliations with cyberinfrastructure or sustainability research initiatives at Utah State University.

• Digital Twins
• Smart Cities
• Blockchain Technology
• +5 more

Noel Crespi is a Professor and Director of Studies at Telecom SudParis, part of Institut Polytechnique de Paris. He leads research in the NeSS group and is affiliated with SAMOVAR, a prominent research laboratory focusing on networks, systems, and services. His work spans multiple domains in telecommunications, networking, and smart systems. His primary research interests include digital twins for smart cities, blockchain technologies, Internet of Things (IoT) security and applications, 5G/6G networking, and machine learning applications in network management. He has published extensively on these topics, with over 100 publications in top-tier journals and conferences. His recent work shows a strong focus on digital twin applications for urban management, particularly in traffic and air quality monitoring. He has developed modular frameworks for smart city digital twins that integrate real-time data from multiple sources. His research also explores blockchain applications for network security, access control, and service provisioning in next-generation cellular networks. Dr. Crespi has received significant recognition for his work on digital twins, with his 2020 paper "Digital twin in the IoT context" in Proceedings of the IEEE being highly cited. His current research explores the integration of AI with digital twin technologies for sustainable urban development. He has supervised numerous PhD students and collaborates extensively with researchers across Europe and internationally. His work often involves interdisciplinary approaches, bringing together computer science, networking, urban planning, and environmental science. Dr. Crespi has been instrumental in developing frameworks for network digital twins, with applications in traffic management, air quality monitoring, and resource allocation in smart cities. His research demonstrates practical implementations in cities like Madrid, showing measurable improvements in urban management.

Haihua ChenView profile

Assistant Professor

• Artificial Intelligence
• Data Science
• Data Quality
• +7 more

Haihua Chen is an Assistant Professor of Data Science in the Department of Information Science at the University of North Texas (UNT), with a co-affiliation in Health Informatics. They lead the Intelligent Data Engineering and Analytics (IDEA) Lab, focusing on interdisciplinary research in artificial intelligence, data science, and informatics. Chen earned a Ph.D. in Information Science (concentrating in Data Science) from UNT in 2022, an M.S. in Information Science from Wuhan University, and dual B.S. degrees in Information Science and English Literature from Central China Normal University. Research interests span applied machine learning, data quality evaluation, NLP, and informatics applications in legal and healthcare domains. Notable work includes developing frameworks for measuring scientific novelty, constructing high-quality legal and biomedical datasets, and leveraging AI for precision medicine and disaster response. Chen has secured over $499K in external grants, including NSF REU and HSI projects, and $20K+ in internal grants. Their work has been published in top journals like Journal of Informetrics , IEEE Transactions on Reliability , and Scientometrics , with a strong focus on innovation measurement and data-centric AI. Teaching includes courses on computational methods, data analysis, and AI in healthcare. Professional leadership roles include chairing ASIS&T SIG-STI and editorial roles for Journal of the Association for Information Science and Technology , Knowledge and Information Systems , and others. Awards include UNT’s Great Grads Award and the Linda Schamber Writing Award.

Stan MatwinView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +7 more

Stan Matwin is a Professor and Director of the Institute for Big Data Analytics at the Faculty of Computer Science, Dalhousie University. He is also affiliated with the Institute for Computer Science of the Polish Academy of Sciences. Previously, he was a Distinguished University Professor at the University of Ottawa and has held academic positions at Warsaw University, University of Guelph, Acadia University, and visiting roles across the U.S., Europe, and Latin America. His educational background includes a Ph.D. from Warsaw University (1977) and a D.Sc. (habilitation) from the Polish Academy of Sciences (2010). Stan Matwin's research focuses on Artificial Intelligence , Machine Learning , Data Mining , Text Mining and Analytics , Big Data , and Data Privacy . He is internationally recognized for pioneering work in visual text analytics and the application of machine learning to real-world data challenges. His work bridges academic research and industrial innovation, particularly in technology transfer and data-driven applications. The publications linked from his profile are inaccessible due to a server restriction (403 Forbidden), but he is known to have authored or co-authored over 250 research papers in top-tier venues. Scientific Awards and Honors: Canada Research Chair (Tier 1) in Visual Text Analytics Fellow of the European Coordinating Committee on Artificial Intelligence (ECCAI) Fellow of the Canadian Artificial Intelligence Association (CAIAC) Ontario Champion of Innovation Stan Matwin has secured significant research funding from organizations including the Canada Research Chairs program, Canada Foundation for Innovation (CFI) , NSERC , Ontario Centres of Excellence , and the Canadian Bureau for International Education . He has supervised numerous graduate students and led major research initiatives. He was former president of the Canadian Artificial Intelligence Association (CAIAC) and IFIP Working Group 12.2 on Machine Learning. He is also an active innovator, co-founding technology companies such as Distil Interactive Inc. and Devera Logic Inc. He leads the Big Data Analytics, AI & Machine Learning research cluster at Dalhousie and continues to be a key figure in advancing data science research and training through the Institute for Big Data Analytics.

Ralf KellnerView profile

Professor

• Economics
• Data Science
• Statistics
• +2 more

Professor Ralf Kellner holds the Chair of Financial Data Analytics at the University of Passau, Faculty of Economics. His work integrates economics, data science, and statistics, focusing on empirical and application-oriented research to explore how statistical learning and AI can uncover insights in data-driven decision-making processes that generate economic value. He also teaches courses such as Deep Learning and Text Analysis in Finance, Financial Data Analytics and Machine Learning, and Scientific Computing with Python. His research examines the intersection of financial markets, statistical learning, and artificial intelligence, with specific interests in modeling adverse financial developments, systemic risks, and analyzing text data via domain-specific language models. Publications highlight collaborations with researchers like D. Rösch and N. Gatzert. Recent publications include work on hybrid service agents, quantile neural networks, default resolution time analysis, Bayesian sovereign bond risk models, and international diversification studies. His methodological approaches span extreme value theory, quantile regression, and multivariate statistical techniques applied to financial and insurance contexts. Contact: ralf.kellner@uni-passau.de

Olusegun Michael OtunugaView profile

Assistant Professor

• Stochastic Modeling
• Mathematical Epidemiology
• Biostatistics
• +7 more

Olusegun Michael Otunuga is an Assistant Professor in the Department of Mathematics at the College of Science and Mathematics and the Department of Population Health Science: Division of Biostatistics and Data Science at Augusta University. He holds a Ph.D. in Pure & Applied Mathematics from the University of South Florida and has been actively contributing to interdisciplinary research in stochastic modeling, epidemiology, biostatistics, and financial mathematics. Ph.D., Pure & Applied Mathematics, University of South Florida, 2014 M.S., Mathematics, Marshall University, 2009 B.Sc., Mathematics, Federal University of Agriculture, Nigeria, 2006 Dr. Otunuga's research focuses on the development and analysis of stochastic models to understand complex systems in biology, medicine, and economics. His work spans tumor growth dynamics under environmental stress, multi-strain epidemic modeling (including HIV, TB, measles, and COVID-19), vaccine breakthrough infections, and financial time series forecasting. He employs advanced techniques such as stochastic differential equations, parameter estimation, and first-passage-time analysis. The recent publications highlight a strong trend in applying mathematical rigor to public health challenges, particularly in modeling infectious disease spread with stochastic realism. His work bridges theoretical mathematics with practical applications in biostatistics and data science, often involving real-world data from U.S. states and global health contexts. The integration of time-varying parameters and random perturbations enhances model accuracy and predictive power. Scientific Awards: Excellence in Research Award, College of Science and Mathematics, Augusta University, 2024 Dr. Otunuga actively mentors students and serves as a faculty advisor for Pi Mu Epsilon and Goldwater Scholarship candidates. He has not received mention of specific grants in the provided text, but his sustained publication record and patent suggest active research funding. He contributes extensively to academic service through multiple committees at the department, college, and university levels, and has served as a reviewer for scholarly journals. He is involved in research teams and workshops such as the POPUPS Workshop at Augusta University, where he leads training on biostatistics and data analysis using R. His collaborative work includes joint research with the Ladde research group, culminating in a patented method for dynamic process modeling.

Jennifer GrayView profile

Researcher

• Materials Science
• Transmission Electron Microscopy
• Nanomaterials
• +7 more

Jennifer Gray is an Assistant Research Professor at the Materials Characterization Lab within the College of Earth and Mineral Sciences at Pennsylvania State University. She is actively engaged in cutting-edge interdisciplinary research involving advanced materials characterization, with applications in energy, biomedicine, and planetary science. Her work leverages high-resolution electron microscopy and spectroscopy techniques to understand nanoscale structures and properties. Her research interests span a broad range of topics including Materials Science , Transmission Electron Microscopy , Nanomaterials , Thermoelectric Materials , Biomaterials , and Planetary Materials . She has made significant contributions to the analysis of asteroid Ryugu samples, studying space weathering, mineral alteration, and extraterrestrial organic matter. Her work also extends to energy materials, particularly thermoelectrics and magnetic composites, as well as biomedical applications such as nanoparticle delivery in cancer therapy. The recent publications highlight a strong trend toward interdisciplinary collaboration, especially in the characterization of complex materials using advanced electron microscopy techniques. Her work frequently appears in high-impact journals in materials science, planetary science, and biomedicine. The keywords across her articles reflect expertise in electron microscopy , nanoscale characterization , semiconductors , energy materials , and astrogeology . Scientific Awards: No awards explicitly mentioned in the text. Advising and Grants: Jennifer Gray collaborates extensively with large research teams, particularly in NASA-related asteroid sample analysis and energy materials research. While specific grants or students are not listed, her role as a research professor suggests involvement in funded projects and mentorship within the Materials Characterization Lab. She contributes to open science initiatives such as the STEP Initiative, promoting resource sharing in materials research. Labs and Teams: She is a key member of the Materials Characterization Lab at Penn State, which provides centralized facilities for advanced materials analysis. Her work is integral to interdisciplinary teams studying thermoelectrics, biomaterials, and extraterrestrial samples, often in collaboration with national and international researchers.

• Computer Graphics
• Virtual Reality
• Cultural Heritage Visualization
• +7 more

Carlos Antonio Andújar Gran is a faculty member at the Faculty of Informatics of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), where he is affiliated with the Department of Computer Science. He actively contributes to the academic community through teaching and supervising numerous student projects, particularly in advanced computing domains. His research interests center on the application of computer graphics, virtual reality, and artificial intelligence to real-world problems. Key areas include the 3D visualization and virtual reconstruction of cultural heritage, physically accurate rendering, intelligent segmentation of images, and the use of machine learning and reinforcement learning in sports analytics, particularly for padel. His work bridges technical innovation with practical applications in cultural preservation and sports technology. The recent publications under his supervision reflect a strong trend in digital cultural heritage, with multiple projects focused on tools for virtual visits, reconstruction, lighting simulation, and web-based 3D visualization of heritage models. Another significant trend is the application of AI to padel, including ball and player tracking, virtual coaching, and training virtual agents using reinforcement learning. These projects demonstrate a consistent focus on computer vision, interactive systems, and data-driven modeling. Carlos Antonio Andújar Gran has supervised a substantial number of students, guiding both undergraduate and master's theses. While specific grant information is not available in the text, his sustained supervision across many years suggests active involvement in research projects. He plays a key role in mentoring the next generation of computer scientists at UPC. His work is closely tied to the Department of Computer Science at FIB, where he collaborates with other faculty members such as Imanol Muñoz Pandiella, Núria Pelechano Gómez, and Mohammadreza Javadiha on various student projects, indicating a collaborative research environment focused on graphics, AI, and VR applications.

Zhuping LiuView profile

Associate Professor

• Marketing Analytics
• Machine Learning
• Big Data
• +6 more

Zhuping Liu is an Associate Professor at the Allen G. Aaronson Department of Marketing & International Business within the Zicklin School of Business at Baruch College, City University of New York. She holds a PhD in Marketing from the University of Texas at Austin (2017), an MS in Statistics from the University of Connecticut (2012), an MS in Management Science from Beijing University of Aeronautics and Astronautics, and a BS in Management Information Systems from Qingdao University. Her research focuses on marketing analytics , machine learning applications , and consumer behavior in digital environments . Key areas include mobile advertising effectiveness, online consumer decision-making, crowdfunding dynamics, visual marketing analytics, and behavioral modeling in retail and sports contexts. She employs advanced methodologies such as Bayesian statistics, field experiments, and econometric modeling. Her publication portfolio demonstrates a strong focus on digital marketing trends, particularly mobile consumer engagement, online platform strategies, and visual attention analytics. Recent work explores push-notification advertising, eye-gaze effects in imagery, and omni-channel retail returns. Awards and Honors: MSI Alden G. Clayton Doctoral Dissertation Proposal Award (2017) SYLFF Doctoral Fellowships in Business (2016) University of Texas Graduate Continuing Fellowship (2016) AMA-Sheth Foundation Doctoral Consortium Fellow (2016) Grants and Funding: PSC CUNY grants totaling over $21,000 for projects including: Geo-messaging strategies (2021-2023) Mobile advertising congruency (2020-2022) Visual attention in marketing images (2022-2023) Push-notification effectiveness (2023-2024) She serves as Director of the Advertising and Marketing Communication Track and participates in multiple committees including the Zicklin Academic Standing Committee and Graduate Curriculum Committee. She also reviews for leading journals including Journal of Marketing and International Journal of Research in Marketing .

Daniel HardtView profile

Associate Professor

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +4 more

Daniel Hardt serves as Associate Professor in the Department of Management, Society and Communication at Copenhagen Business School. His interdisciplinary research bridges computational linguistics, artificial intelligence, and social analysis, with particular focus on natural language processing applications and theoretical linguistic phenomena. His primary research domains include Computational Linguistics (specializing in ellipsis resolution and sluicing phenomena), Natural Language Processing (developing methods for psychographic classification and sentiment analysis), and Artificial Intelligence (examining large language model capabilities and limitations). Recent work analyzes travel behavior during crises, gender effects in evaluations, and GDPR policy comprehension through NLP techniques. His publications span top venues including Linguistic Inquiry , Tourism Management , and ACL proceedings. Hardt actively engages with practical business applications through 27 media contributions discussing AI implementation, ChatGPT transparency, and data-driven leadership strategies. His academic service includes organizing events like the 2019 "Fake News" conference at CBS and presenting at international venues including JSAI 2024. With 28 supervised academic works documented, he maintains substantial mentoring activity while contributing to public discourse on digital transformation challenges.