Dan Zhu is a Professor of Supply Chain and Information Systems at Iowa State University. His research spans interdisciplinary areas including data mining, healthcare analytics, fraud detection, software engineering, and information systems. He holds a courtesy professorship and actively contributes to advancing methodologies in supply chain management and technical debt analysis. Research interests focus on leveraging data-driven approaches to solve real-world problems in healthcare, fraud prevention, and software quality. His work integrates machine learning, statistical modeling, and domain-specific knowledge to address challenges in both academic and industrial contexts. Key contributions include studies on hybrid creative learning spaces, imbalanced classification models, and technical debt identification. His publications reflect a strong emphasis on practical applications, such as fraud detection in health insurance and predictive analytics in agile software development. Zhu has no listed awards but maintains an active publication record across journals and conferences. His research often intersects with organizational memory management, recommendation systems, and electronic medical records.
Dr. Liz Shanahan is a Professor in the Department of Political Science at the University of Montana and serves as the Associate Vice President of Research Development in the Office of Research & Economic Development. Her roles combine academic leadership with interdisciplinary research in policy processes and analysis. Education: B.A., Dartmouth College, 1986 M.S., Idaho State University, 1994 M.P.A., Idaho State University, 2004 D.A., Idaho State University, 2005 Research Interests: Shanahan’s work focuses on the power of policy narratives in shaping governmental and individual decisions. She employs the Narrative Policy Framework to study risk communication in contexts like flood hazards, pandemic responses, and human-wildlife conflict. Her research bridges human systems and ecological systems, emphasizing coupled natural and human systems, social ecological systems, and opinion surveys. She explores how narratives influence policy realities, particularly in environmental and public health domains. Grants and Funding: She leads multiple National Science Foundation (NSF) grants including: "The Impacts of Narrative-Based Risk Communication on Hazard Preparedness" "CNH-L (44%): Dynamics of zoonotic systems: human-bat-pathogen interactions" "Risk Narratives Across Time and Space in Urban-Wildlife Conflict" Scientific Awards: Meritorious Research and Creativity Award (2018) from the MSU College of Letters and Science Service and Engagement: Shanahan is actively involved in academic service, including roles as: Board of Advisors (2019–Present) APSA STEP Editorial Review Board Member (2019–Present) Policy Studies Journal Reviewer (2017–Present) NSF Reviewer (2011–Present) She has also served as a guest speaker at Dartmouth College Alumni Association (2020), Institute on Ecosystems (2016–2017), and North Carolina State University (2015). Labs and Teams: Her research integrates cross-disciplinary teams through the Office of Research & Economic Development, focusing on topics like zoonotic systems and urban-wildlife conflict. She collaborates with institutions such as the Institute on Ecosystems and leverages computational tools for narrative analysis.
Fangju Wang is a retired Professor with expertise in artificial intelligence, machine learning, and intelligent systems. His research focuses on applying partially observable Markov decision processes (POMDP) and reinforcement learning to develop efficient algorithms for intelligent tutoring systems (ITS), emphasizing computing cost reduction and uncertainty management. He has contributed to interdisciplinary computational science education through collaborative academic program development. Key research interests include algorithm optimization, policy tree efficiency, Bellman equation solutions in POMDP frameworks, and adaptive teaching strategies in educational technology. His work bridges theoretical advancements with practical applications in speech recognition and user behavior modeling in dialogue systems. No academic awards, grants, or advising roles are explicitly documented in the provided materials. His publications span conferences like CSEDU and journals such as International Journal of Information and Education Technology, focusing on computational methods in education and AI.
Kevin Brown is an Associate Professor at Oregon State University with joint appointments in the College of Pharmacy (Department of Pharmaceutical Sciences) and College of Engineering (Chemical, Biological, and Environmental Engineering). As a complex systems scientist, his research examines biological networks using methodologies from machine learning, dynamical systems, and network theory. Education: B.S. Physics and B.A. Mathematics, Louisiana State University (1998) Ph.D. Theoretical Physics, Cornell University (2003) Helen Hay Whitney Foundation Fellow, Harvard University (2004-2007) Postdoctoral Scholar, UC Santa Barbara Physics (2007-2012) Research Focus: Dr. Brown investigates complex biological systems including gene/protein networks, neural systems, and cognitive processes. His pioneering 'Sloppy Models' theory analyzes parameter space geometry in nonlinear models, with applications spanning ecology, neuroscience, and atmospheric science. His work combines data-driven and model-driven approaches through interdisciplinary collaborations. Publication Trends: Recent works demonstrate strong emphasis on network science applications across biological and cognitive systems, including microbiome-host interactions, semantic network analysis, computational linguistics, and multi-omics integration. Publications frequently intersect machine learning, systems biology, and cognitive modeling. Awards: Helen Hay Whitney Foundation Fellowship (2004-2007) Laboratory: Leads the Brown Lab at OSU/OHSU College of Pharmacy focusing on complex systems research in systems biomedicine and cognitive science.
Mattia Albertini is a Post-Doctoral Researcher at the Institute for Economic Research (IRE) within the Faculty of Economics at Università della Svizzera italiana (USI). He previously completed his Ph.D. at the Institute of Economics (IdEP), USI, and participated in the Swiss Program for Beginning Doctoral Students in Economics. He holds a Master of Science in Economics with a minor in Data Science from USI and a Bachelor in Economics from the University of Pavia. Bachelor in Economics, University of Pavia Master of Science in Economics (minor in Data Science), Università della Svizzera italiana Ph.D. in Economics, Università della Svizzera italiana Swiss Program for Beginning Doctoral Students in Economics, Swiss National Bank His research focuses on applied microeconometrics with applications in health, labor, public, urban, and environmental economics. A recurring theme in his work is the spatial dimension of economic outcomes, including residential integration, real estate market dynamics, and geographic proximity to borders or hazards. He integrates data science techniques, particularly programming in Python and STATA, to develop tools for empirical analysis and reproducible research. The collection of articles reflects a strong trend in applied econometrics, causal inference, and spatial analysis. His work spans health economics (e.g., benzodiazepine prescribing), labor and urban economics (e.g., migration, job accessibility), and environmental and insurance economics (e.g., natural hazard impacts). Methodologically, he engages with difference-in-differences, event study designs, spatial modeling, and text analysis, often building or applying computational tools to address research questions. He has contributed to teaching at both the Bachelor's and Master's levels, particularly in microeconomics, macroeconomics, and data-intensive courses such as Textual Analysis and Spatial Data for Economists. Teaching Assistant, Microeconomics A (Bachelor) Teaching Assistant, Macroeconomics B (Bachelor) Teaching Assistant, Textual Analysis and Spatial Data for Economists (Master) Mattia Albertini is actively involved in open science, sharing code for econometric methods, data visualization, and application development (e.g., a gluten-checking app) on GitHub. His interdisciplinary approach combines economic theory, statistical modeling, and computational tools to investigate real-world policy-relevant questions.
Prof. Dr. rer. nat. Malte Prieß is a Professor for Cloud Technologies at Kiel University of Applied Sciences since October 20224, following eight years as Dean and Professor of Applied Computer Science at Schleswig-Holstein Cooperative State University (DHSH) . He teaches modules including Cloud Computing , Web Applications , and Advanced Cloud Computing , with additional involvement in Agile Development Methods and Software Engineering . Academic Background: Diploma in Physics (with distinction) from Leibniz University Hanover and Max Planck Institute for Gravitational Physics (2002-2007) Dr. rer. nat. (magna cum laude) from Kiel University in Algorithmic Optimal Control (2008-2012) Research Interests focus on the intersection of cloud computing, artificial intelligence, and modern software engineering . His work includes surrogate-based optimization for climate models, AI-driven document capture systems , and ethical considerations in AI deployment within project work. Recent Publications demonstrate expertise in AI vulnerability assessment , deep learning training optimization , and document search algorithms for governmental agencies. Scientific Recognition: Best Paper Award at CLOUD COMPUTING 2025 for "Graph of Effort" vulnerability assessment Accepted fellowship at AI Campus (Stifterverband) for "Teaching AI, learning AI at DHSH" (2022) Research Projects: Central Innovation Programme for SMEs (ZIM): "AI MODULES for the skilled trades" (2024/25) HR dashboard for DRK Schwesternschaft, Lübeck Scalable Data Analytics project under BMBF FHprofUnt program (2018)
Dr. Gregor Wiedemann serves as a Senior Researcher in Computational Social Science at the Leibniz Institute for Media Research (Hans Bredow Institute) since September 2020, co-heading the Media Research Methods Lab (MRML) with Sascha Hölig. His work bridges computer science and social sciences through methodological innovation in empirical media research. Wiedemann holds a doctorate in computer science from Leipzig University (2016), where his dissertation focused on automating discourse analysis using text mining and machine learning. His educational background combines political science and computer science studies at Leipzig University and the University of Miami, followed by postdoctoral work in Language Technology at the University of Hamburg under Prof. Chris Biemann. His research centers on natural language processing and text mining applications for social and media analysis, with significant contributions in hate speech detection, argument mining, and cross-platform misinformation tracking. Recent work demonstrates a strategic shift toward building research infrastructures for sensitive data handling, including the Community Data Trust model for extremism research and the Social Media Observatory open-science platform. His methodology development specifically targets unsupervised information extraction from large document corpora to support investigative journalism and social science inquiry. Wiedemann's publication trends reveal deepening specialization in computational infrastructure development, with 7 of his 11 most recent works (2024-2025) focusing on data trust frameworks, cross-platform methodologies, and AI-driven analysis systems. His projects consistently intersect computational linguistics with pressing social issues including election integrity, climate discourse, and child safety in digital spaces. He has secured major funding through the German Research Foundation (DFG) for the FAME project on argument mining and evaluation, and leads collaborative initiatives including NOTORIOUS (mis- and disinformation tracking) and ComAI (communicative AI impact studies). His work with state media authorities on family influencing content demonstrates applied policy relevance. As co-director of the Media Research Methods Lab, Wiedemann oversees a dynamic team developing cutting-edge computational approaches for media analysis. The lab functions as an interdisciplinary hub connecting computer scientists with social researchers, with current projects spanning TikTok political campaigning analysis, right-wing extremism data infrastructure, and ethical AI applications in public discourse monitoring.
Joan C. Timoneda is an Assistant Professor in the Department of Political Science at Purdue University. His research focuses on authoritarian regimes, democratic backsliding, and the application of big data and natural language processing techniques to comparative politics. He received his Ph.D. from the University of Maryland, College Park (2019), MPhil in Latin American Studies from Oxford University (2011), M.A. in Political Science from University of Maryland, College Park, and B.A. from Arizona State University. Ph.D. : Political Science, University of Maryland, College Park (2019) MPhil : Latin American Studies, Oxford University, St Antony's College (2011) M.A. : Political Science, University of Maryland, College Park B.A. : Arizona State University Timoneda's research spans authoritarian regime dynamics, democratic erosion, and computational methods. His book project How Dictators Survive examines institutional personalization in authoritarian regimes using archival work and NLP. He also develops forecasting models for political trends via Twitter and Google Trends, and contributes to methodological advancements in panel data analysis and political text classification. At Purdue, he co-leads the Advanced Methods at Purdue program and teaches courses on political methodology and dictatorship dynamics. Recent publications highlight his work on power personalization, social media's role in democratic subversion, transformer model performance in political science, and economic shocks in authoritarian systems. His projects include detecting racism in Spanish texts, analyzing cabinet formation in dictatorships, and addressing rare events data challenges in logistic regression.
Artak Sargsyan Piloyan is an Assistant Professor and Acting Head of the Department of Cartography and Geomorphology at Yerevan State University's Faculty of Geography and Geology. With a Candidate of Sciences degree (2018) and extensive experience in GIS methodologies, he specializes in geomorphometric analysis, environmental hazard mapping, and spatial data infrastructure development. His work focuses on integrating geospatial technologies for ecosystem assessment, disaster risk management, and sustainable resource planning. Education: Masaryk University (postgraduate studies), Yerevan State University (Bachelor's to PhD) Research Interests: Geomorphometry, GIS Applications, Environmental Monitoring Technical Contributions: Python-based SDI optimization, Landsat vegetation analysis, semi-automated landform classification His publications demonstrate a strong emphasis on Armenian environmental challenges, including Lake Sevan's aquatic vegetation dynamics, Aghstev River Basin risk assessment, and Yerevan's urban green space accessibility. Piloyan collaborates with international researchers like Milan Konečný and Vladimir Boynagryan, contributing to national spatial data frameworks and disaster risk atlases.
Kangwook Lee serves as an Associate Professor in the Electrical and Computer Engineering Department with a courtesy appointment in Computer Sciences at the University of Wisconsin-Madison, where he also holds a Discovery Fellowship. He concurrently leads deep learning research initiatives at KRAFTON, bridging academic and industry innovation in artificial intelligence. His academic foundation includes a PhD in Electrical Engineering and Computer Sciences from UC Berkeley (2016), preceded by research assistant and postdoctoral positions at KAIST's Information and Electronics Research Institute. Hailing from Seoul, South Korea, Lee maintains active research operations through his laboratory in Madison's Discovery Building. Lee's research program centers on Large Language Models and LLM agents, with rigorous theoretical and empirical investigations into their operational mechanisms and improvement pathways. His work spans in-context learning dynamics, agent-based social simulations, multi-domain reward modeling, and efficient inference techniques, emphasizing both fundamental understanding and practical enhancement of AI capabilities. Recent publications reveal a concentrated effort on overcoming length generalization barriers, enabling compositional reasoning with rare concepts, and developing robust feature selection frameworks. His 2025 publications demonstrate significant advancements across LLM architecture, evaluation methodologies, and application domains. Key trends include the emergence of task vector representations in in-context learning, development of superposition techniques for multi-task processing, and innovative approaches to speculative decoding for multimodal systems. These works collectively advance the field toward more efficient, generalizable, and interpretable language models. Lee's research excellence is recognized through prestigious accolades: NSF CAREER Award (premier early-career grant) IEEE Joint Communications Society/Information Theory Society Paper Award Amazon Research Award KSEA Young Investigator Grant Award As principal investigator of the Lee Lab, he directs a dynamic research group focused on cutting-edge AI challenges. His work integrates theoretical analysis with empirical validation to address fundamental limitations in modern language models, while industry collaborations through KRAFTON ensure real-world impact. Current projects emphasize agent-based social dynamics modeling, efficient inference architectures, and robustness frameworks for diverse deployment scenarios.
Marc Freixes Guerreiro is a Researcher at La Salle School of Engineering, Department of Engineering, part of La Salle - Universitat Ramon Llull in Barcelona, Spain. His work focuses on interdisciplinary research at the intersection of acoustics, human-environment interaction, and biomedical engineering. He actively participates in multiple research projects investigating soundscape perception, vocal tract modeling, and human comfort in built environments. His research interests span a diverse range of fields including Acoustics , Soundscape , Vocal Tract Modeling , Urban Environment , Sensor Networks , and Citizen Science . His work combines computational modeling with practical applications in both urban settings and biomedical contexts. He has particular expertise in analyzing vocal phenomena, from animal vocalizations for welfare monitoring to human phonation mechanics for speech synthesis and clinical applications. Analysis of his recent publications reveals a strong interdisciplinary trend, connecting engineering principles with applications in healthcare, urban planning, and cultural heritage. His work demonstrates expertise in both theoretical modeling (such as finite element analysis of vocal folds) and practical implementation (like sensor networks for environmental monitoring). The research spans from fundamental acoustics to applied projects addressing real-world challenges in animal welfare, medical diagnostics, and urban comfort. Marc Freixes Guerreiro leads and participates in multiple significant research projects including the BeNeXT project for Turner syndrome diagnostics, DISTRESIA for stress biomarker identification in educational contexts, Sons dels Monestirs for sacred space acoustics in Catalan monasteries, and Inhabiting Gaudí for comfort assessment in Gaudí's architectural masterpieces. His collaborative work demonstrates strong connections across engineering, medical, and humanities disciplines. His laboratory work focuses on the Human-Environment Research group within the Department of Engineering, where he develops and applies acoustic measurement techniques, computational models, and sensor network solutions. His research teams typically include interdisciplinary collaborators from engineering, medical, and humanities backgrounds, reflecting the cross-cutting nature of his research interests.
Dr. Natalia Beloff is a Reader (Associate Professor) in Software Engineering at the University of Sussex's School of Engineering and Informatics, with prior roles including Deputy Head of Department (2018-2023). She holds a PhD in Mathematical Physics from Rostov State University (now Southern Federal University) and completed postgraduate lecturer training there. Her research bridges geophysical data techniques and modern computational challenges: Core methodologies : Data quality algorithms for dynamic systems, advanced regularization techniques Application domains : Blockchain, IoT, e-health records, financial technology, space/solar physics Regional focus : Technology adoption frameworks for Saudi Arabian healthcare, education, and finance She secured 12 grants from UK societies (Royal Society, RAEng) supporting space physics and international collaboration. Recent publications emphasize: Saudi digital transformation in universities/hospitals Arabic NLP tools and cross-cultural fintech adoption Federated learning for cybersecurity She teaches e-business systems and supervises student projects, though no lab or award details are available.
Stéphane Gauvin is a Full Professor in the Marketing Department at Université Laval's Faculty of Business Administration, where he maintains an active research program examining technology adoption across public and private sectors. His work bridges marketing theory, e-government implementation, and digital policy analysis with current affiliations including ongoing university research and historical collaborations with Quebec government agencies. His educational foundation includes a Doctor of Economics in Marketing (Ph.D.) from Pennsylvania State University, complemented by a Master of Science in Marketing from the University of Sherbrooke and a Bachelor of Business Administration in Marketing from the same institution. This tripartite business education anchors his applied research approach. Professor Gauvin's research centers on technology adoption dynamics across three interconnected domains: e-government implementation (evidenced by 12+ Quebec government reports 2005-2012), digital ecosystem analysis (including 2024 algorithmic studies of YouTube comments), and sector-specific applications in healthcare marketing and wine economics. His methodology integrates quantitative big data analysis with policy evaluation frameworks, consistently examining visibility-power relationships in digital spaces and barriers to technology dissemination. Recent work demonstrates increasing focus on computational social science approaches while maintaining practical policy relevance. Publication trends over the past decade reveal expanding investigation into algorithmic measurement (2024), digital inclusion (2014 Waswanipi study), and SME technology assimilation (2012 framework), building upon foundational work in virtual collaboration (1996). Key thematic threads include digital divide mitigation, political campaign tracking through internet metrics, and optimization of technology-driven markets. His academic service includes leading the French adaptation of major marketing textbooks (2011-2020 editions) and developing pedagogical frameworks for international online teaching. Current research directions examine social media analytics applications in consumer behavior and policy impact assessment through digital trace data.
