Kaiwen Zhang is a prolific researcher affiliated with institutions including École de Technologie Supérieure (Montréal, Canada), Technical University of Munich, and McGill University. His work spans blockchain technologies, distributed systems, federated learning, and privacy-preserving protocols, with a focus on applications in electric vehicle infrastructure, smart grids, and online gaming. Research Interests: He specializes in blockchain-based solutions for supply chain traceability, IoT security, and federated learning monetization. His studies often integrate decentralized architectures, cryptographic protocols, and event-driven systems, addressing challenges in scalability, privacy, and resource allocation. Publication Trends: Over 17 years (2008–2025), Zhang’s 39 publications (323 citations) emphasize blockchain’s intersection with cybersecurity, transportation, and distributed computing. Key subtopics include smart contracts, privacy-preserving EV charging, and federated learning frameworks. Collaborations: He frequently collaborates with colleagues like Hans-Arno Jacobsen, Mohammad Sadoghi, and Syed Muhammad Danish, contributing to conferences such as Middleware, DEBS, and ACM/SIGAPP Symposium on Applied Computing.
Longbing Cao is a prominent academic researcher in data science and artificial intelligence, currently holding dual affiliations with Macquarie University's School of Computing in Sydney, Australia, and the University of Technology Sydney (UTS). He leads the Data Science Lab and has established himself as a leading authority in machine learning, data mining, and anomaly detection research with a prolific publication record spanning over two decades. Dr. Cao's research encompasses multiple critical domains within data science, with particular expertise in deep learning for anomaly detection, time series analysis, recommender systems, and behavior informatics. His scholarly work bridges theoretical foundations with practical applications across diverse industries including finance, cybersecurity, and e-commerce. He has pioneered innovative approaches in coupled behavior analysis, representation learning, and cross-domain collaborative filtering that have significantly advanced these fields. His research demonstrates a consistent trajectory from foundational data mining techniques to sophisticated deep learning methodologies. His publication record shows remarkable impact with 240 publications as of 2025 and 4,725 total citations. His highly influential survey papers have shaped research directions across multiple domains, with "Deep Learning for Anomaly Detection: A Review" (2021) accumulating over 1,600 citations and 25,586 downloads, establishing it as a seminal reference in the field. His recent work (2025) focuses on cutting-edge topics including diffusion models (SepDiff, ProgDiffusion), dynamic spectral graph analysis, and non-stationary time series modeling, reflecting his position at the forefront of AI research evolution. Data Science: A Comprehensive Overview (2017) - 298 citations, 41,677 downloads Data science: challenges and directions (2017) - 87 citations, 31,525 downloads AI in Finance: Challenges, Techniques, and Opportunities (2022) - 201 citations, 21,892 downloads A Survey on Session-based Recommender Systems (2021) - 346 citations, 4,563 downloads As a research leader, Dr. Cao has mentored numerous students and collaborators, contributing significantly to the development of next-generation data science researchers. His Data Science Lab serves as an interdisciplinary hub where computer scientists, statisticians, and domain experts collaborate to tackle complex data challenges. The lab's research spans theoretical advancements in machine learning algorithms to practical implementations with tangible real-world impact across multiple sectors.
Prof. David Garcia is a Professor of Social and Behavioral Data Science at the University of Konstanz, affiliated with the Department of Politics and Public Administration and the Center for Data and Methods. His research focuses on analyzing human behavior through digital traces using complexity science and computational social science methodologies. Previously, he held positions at Graz University of Technology and the Complexity Science Hub Vienna. Affiliations: University of Konstanz, Department of Politics and Public Administration, Center for Data and Methods Roles: Professor, Principal Investigator of grants like PRODEMINFO (Protecting Democratic Information Space in Europe), and leader of the AG Becerra research group. His work spans computational analysis of political discourse, misinformation dynamics, emotional expression in social media, and algorithmic impacts on democracy. Key projects include studying congressional speech patterns, suicide-related content detection, and polarization modeling. Garcia’s interdisciplinary approach combines data science with social theory to address societal challenges. Recent articles focus on topics like honesty perceptions in political communication, AI discourse on Reddit, and emotional responses to crises. He has secured funding for initiatives like the WHAT-IF project simulating democratic information environments. Grants & Projects: PRODEMINFO (2021–2026): Protecting democratic information spaces WHAT-IF (2025–2027): Testing information environment impacts on democracy Labs/Teams: Leads the Social Data Science Lab and collaborates with interdisciplinary networks on digital thinking and computational social science.
Andra Lutu is a network architecture expert with Telefonica Research in Madrid, Spain since 2021, previously serving as Associate Researcher in Barcelona (2018-2021) and Postdoctoral Fellow at Simula Research Laboratory. She holds a PhD in Telematics Engineering from IMDEA Networks Institute and University Carlos III of Madrid (2014), advised by Marcelo Bagnulo, and graduated cum laude. Her research focuses on mobile network performance, IoT connectivity, interdomain routing, and roaming solutions. PhD: University Carlos III of Madrid (2014) Advisor: Marcelo Bagnulo Research interests include: Mobile broadband optimization Cellular coverage analysis Network measurement frameworks (MONROE platform) Interdomain routing security 5G performance evaluation Crowdsourced network data Recent publications highlight mobile network performance analysis, roaming optimization, and pandemic-era traffic patterns. She has received multiple scientific awards : Best Paper Award (2016, 2015) Best Demo Award (2017, 2016) Andra contributes to experimental platforms like MONROE and has co-authored journal articles in IEEE Transactions, Computer Networks, and ACM SIGCOMM publications.
Prof. Katharina Scherf is an Associate Professor of Food Biopolymer Systems at the Technical University of Munich (TUM) and heads the Food Biopolymer Chemistry research group at the Leibniz Institute for Food Systems Biology (LSB@TUM). Her research focuses on plant-based food proteins, particularly wheat-related disorders like celiac disease and wheat allergy. She develops analytical methods to study protein structure-functionality-immunogenicity relationships, aiming to enhance food safety and quality. Her career includes roles at KIT (2019–2024) and leadership in multiple research groups. Key contributions include ERC Starting Grant (2022), Harald Perten Prize (2022), and numerous awards for allergy and celiac disease research. Education & Career: PhD in Food Chemistry (TUM, 2014) Habilitation in Food Chemistry (2018) Head of Functional Biopolymer Chemistry Group (Leibniz LSB@TUM, 2017–2019) Professor at Karlsruhe Institute of Technology (2019–2024) Current roles: Head of Section I (LSB@TUM) and TUM professorship since 2024 Research Focus: Protein composition of cereals Immunological effects of gluten proteins Development of gluten-free food standards Proteomic analysis of baking processes Food allergy mechanisms Grants & Awards: ERC Starting Grant (2022) Bernhard van Lengerich Prize (2017) Multiple German Celiac Society Research Awards (2014, 2019) Young Investigator Network Grants (KIT, 2020–2023) Professional Roles: Associate Editor, Journal of Agricultural and Food Chemistry Scientific Advisory Boards for Celiac Societies and Food Industry Groups Leadership in DIN Standards Committee for Food Analysis Labs & Collaborations: Leibniz LSB@TUM facilities International collaborations on cereal chemistry and allergy research
Prof. Dr. Petra Schubert is a Professor of Business Application Systems at the University of Koblenz-Landau, leading the Business Application Systems research group. She directs the Competence Center for Collaboration Technologies (UCT) and co-directs the Center for Enterprise Information Research (CEIR). Her work focuses on ERP systems, collaborative technologies, and digital workplace transformation. Education: Economics & IT Management from University of St. Gallen (PhD, Habilitation in Business Informatics from University of Basel). Prior roles include heading the Institute for Business Information Systems at FHNW and leading international projects like 3gERP at Copenhagen Business School. Research emphasizes enterprise collaboration systems (ECS), including the eXperience methodology for case studies and the IndustryConnect initiative. Projects like 2C-NOW investigate hybrid work practices using trace ethnography and log数据分析. She co-founded the UCT, operating the UniConnect platform, and collaborates with industry partners like HCL Technologies. Notable contributions include over 10 books on ERP implementation, 150+ case studies in eXperience databases, and participation in major conferences (HICSS, ECSCW). Her work bridges academic research with industry practice through initiatives like CEIR.
Tom Alby is a Lecturer at Hamburg University of Applied Sciences and Humboldt University of Berlin , specializing in digital technologies and data science. His expertise spans Data Mining , Machine Learning , Web Analytics , and Search Engine Optimization . He has extensive industry experience in the digital sector since 1994, working with companies like Google and Ask.com. Core Competencies : Statistics, Python/R programming, Cloud platforms (AWS/GCP), Digital Transformation, Web Technologies Research Focus : Web Analysis, Data-driven Applications, Digital Literacy, and Internet-based Self-diagnosis His publications address challenges in using top websites lists for research, digital marketing strategies, and health informatics. He has authored multiple books on data science, web analytics, and search engine optimization. Despite no explicit awards listed, his work impacts digital libraries, web analysis, and online marketing practices.
Kokil Jaidka is an Assistant Professor in the Department of Communications and New Media at the National University of Singapore (NUS), Faculty of Arts and Social Sciences. She is the founding Principal Investigator of the SMOL (Social Media, Online behavior and Language) project, focusing on computer-mediated communication and online human behavior. Her research spans social media analytics, computational social science, digital well-being, and ethical AI. She has contributed to policy discussions on age verification and platform regulation in Singapore and has developed tools like Twilly and SMOLgram for longitudinal online experiments. Her recent scholarly work explores emotional expression in political discourse, emoji use in digital communication, affordances of social platforms, misinformation dynamics, and ethical frameworks for computational research. She advocates for responsible experimentation and has co-developed ethical guidelines for top-tier conferences. Key research trends in her recent publications include: Use of NLP and machine learning to analyze social media content Design and ethics of longitudinal online behavioral studies Impact of platform design on user behavior and well-being Political communication and disinformation in digital spaces Policy implications of social media governance Her scientific contributions emphasize interdisciplinary approaches at the intersection of communication, computer science, and psychology. As a PI and faculty member, she leads the SMOL research team, mentors students, and secures research grants for innovative projects in digital behavior. She actively engages with public discourse through blog posts and media collaborations, translating academic insights for broader audiences. She has developed and shared resources for online experimentation using platforms like Amazon Mechanical Turk, oTree, Empirica, and custom-built tools, supporting open and reproducible research practices.
Prof. Dr. Stefan Wagner is a Full Professor of Software Engineering at the Technical University of Munich (TUM), where he leads the Chair of Software Engineering within the TUM School of Computation, Information and Technology. Based at the TUM Campus Heilbronn, Prof. Wagner joined TUM in 2024 after serving as Professor of Empirical Software Engineering at the University of Stuttgart since 2011. His research has significant practical relevance, often conducted in collaboration with industry partners, particularly in automotive and AI-based software domains. Prof. Wagner's educational background spans multiple disciplines: Computer Science studies at Augsburg and Edinburgh Psychology studies at Hagen Doctorate in Computer Science from TUM (2007) His primary research interests focus on software engineering with particular emphasis on software quality, human factors in development processes, AI-supported engineering methods, and empirical studies. Prof. Wagner's work bridges theoretical foundations with practical applications, especially in automotive software systems and AI-based domains. His interdisciplinary approach, combining computer science with psychology, enables unique insights into developer behavior and software quality assessment. The research conducted at his chair addresses critical challenges in modern software development, including quality assurance in complex systems and the integration of artificial intelligence into engineering processes. Prof. Wagner's extensive publication record demonstrates evolving research trends from traditional software quality models toward increasingly sophisticated integration of AI and human factors in software development. His recent work shows a growing emphasis on empirical studies of developer behavior, AI-assisted programming, virtual reality applications in software engineering, and the psychological aspects of technical debt. The publications reveal a consistent focus on practical applicability while maintaining scientific rigor, with strong industry collaboration evident throughout his career. Prof. Wagner has received numerous prestigious awards recognizing his contributions to software engineering: Class of IEEE Computer Society Distinguished Contributors (2022) Best Full Paper Award, ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (2020) IEEE Computer Society TCSE Distinguished Paper Award, IEEE International Conference on Software Maintenance and Evolution (2019) Most Influential Paper Award, IEEE International Conference on Software Maintenance and Evolution (2017) Google Research Award (2009) Prof. Wagner actively mentors students and researchers, with several PhD candidates and master's students working under his supervision. His research is supported by various grants, including industry collaborations and competitive research funding. He serves on multiple editorial boards including IEEE Software and Empirical Software Engineering, contributing significantly to the academic community. At TUM, he chairs the aptitude assessment committee for the M.Sc. Information Engineering program and serves on the scientific board for the TUM Global Postdoc Fellowship. The Chair of Software Engineering at TUM Heilbronn maintains strong connections with industry partners, particularly in the automotive sector. The research group actively participates in multiple collaborative projects focusing on AI in software development, software quality assessment, and empirical studies of development practices. The team combines expertise in software engineering, psychology, and artificial intelligence to address complex challenges in modern software systems.
Prof. Dr. Marc Rittberger serves as Deputy Managing Director of the Leibniz Institute for Research and Information in Education (DIPF) since April 2025, concurrently holding a Professorship for Information Management at DIPF and Darmstadt University of Applied Sciences since 2005. His institutional leadership spans multiple directorial roles including Managing Director (2008-2012) and current oversight of the Education Information Center. Rittberger's research focuses on open science infrastructure development , digital educational architectures , and information management systems . His work examines practical implementations of Open Educational Resources (OER), bibliographic database optimization for systematic reviews, and metadata standards for distributed learning environments. Recent publications demonstrate growing emphasis on AI-driven knowledge graphs and machine learning applications in scholarly communication. Analysis of his 15 most recent publications reveals consistent engagement with Open Science implementation challenges Systematic review methodology in educational research Teacher practices in digital resource sharing Quality metrics for research infrastructures His work bridges theoretical informatics with practical educational applications, particularly in K-12 digital transformation contexts. Rittberger actively shapes research policy through key committee roles including: Spokesperson of Leibniz Association's Open Science Strategy Forum (since 2023) Deputy Spokesperson of Standing Commission for Scientific Infrastructure Facilities (since 2024) Former Scientific Advisory Board positions at GESIS, ZBW, and Know Center His academic supervision extends through leadership in major research initiatives including Digi-EBF (Digitalization in Education), ABIBA (reducing educational barriers), and EduArc (digital educational architectures). Current projects emphasize open scholarship quantification, AI-based search infrastructures, and internationalization of educational research infrastructures.
Prof. Dr. Ivo Feussner is a Professor of Biochemistry at the Department of Plant Biochemistry, Georg August University, Göttingen. He leads the Plant Biochemistry research group at the Albrecht von Haller Institute, focusing on lipid metabolism in plants, algae, mosses, and fungi. His academic journey includes a Diploma (1990) and PhD (1993) from Philipps University, Marburg, followed by habilitation (2000) and leadership of independent research groups in Halle/Saale and Gatersleben before assuming his current position in 2002. Research Interests: Feussner's work centers on lipid biosynthesis and signaling pathways, particularly oxylipin metabolism, sphingolipid function, and structural lipid networks. Techniques used include metabolomics, molecular genetics, and biochemical analyses. Key projects involve plant defense mechanisms during stress, seed oil content optimization in crops, and insect-plant communication via chemical signals. Scientific Recognition: Honors include the Ernst Schering Habilitation Prize (2001), Terry-Galliard Medal (2012), and Fellowships at the Saxonian and Göttingen Academies of Sciences. His lab is internationally recognized for lipidomics innovations and contributions to plant stress biology. Professional Contributions: Feussner collaborates with global institutions, supervises interdisciplinary training programs (e.g., IMPRS GoteNeuro), and leads projects on drought-tolerant poplar trees and lipid-based industrial applications. His lab's address and contact details are available at www.plant-biochem.uni-goettingen.de .
Dr. Christof Barth is a Researcher at the Department of Media Studies, University of Trier. He joined the university as a research associate in 1998 and obtained his doctorate in 2001 with a dissertation on media transformation processes. His work focuses on media quality, media criticism, social media analysis, and qualitative research methods. Barth studied German linguistics, modern German literature, general rhetoric, and speech communication at the Universities of Tübingen and San Diego. Before joining academia, he worked as a freelance media researcher at Südwestfunk in Baden-Baden. His research emphasizes empirical studies on online media use, radio evolution, and political communication dynamics in digital spaces. Key research themes include the interplay of hate speech and deliberation in social media, agent-based modeling of communication networks, and critical evaluations of media policy frameworks. His publications span over two decades, addressing media quality standards, radio innovation, and digital transformation processes in broadcasting. Barth has contributed to interdisciplinary projects analyzing audience reception patterns, media policy in Luxembourg, and the evolution of web radio formats. His work bridges linguistic analysis with contemporary media challenges, offering insights into both traditional and digital media ecosystems.
Zhaopeng Qiu is an active researcher in computer science with a strong publication record spanning from 2012 to 2025. His work primarily focuses on recommendation systems, machine learning, and their applications in healthcare and online services. He frequently collaborates with researchers including Xian Wu, Zhi Zheng, Hengshu Zhu, and Hui Xiong on projects related to large language models, medical informatics, and job recommendation systems. Dr. Qiu's research interests include Recommendation Systems, Machine Learning, Medical Informatics, Natural Language Processing, Artificial Intelligence, Data Mining, and Healthcare AI. His work demonstrates a clear evolution from earlier research in mobile robotics (2012-2015) to current cutting-edge work applying large language models to recommendation problems across various domains. Analysis of his recent publications shows a strong trend toward leveraging large language models for recommendation tasks, with significant contributions in medication recommendation, job matching, and fairness-aware systems. His work bridges theoretical AI advancements with practical applications, particularly in healthcare contexts where AI can have significant real-world impact. While specific scientific awards aren't documented in the available publications, his work appears in top-tier venues including WWW, AAAI, KDD, and IEEE/ACM transactions journals, indicating recognition within the research community. Dr. Qiu's research has practical implications for online platforms, healthcare systems, and labor market technologies. His recent focus on large language models for recommendation suggests he's at the forefront of integrating emerging AI capabilities with traditional recommendation paradigms.
Chul Lee is a Research Fellow at Rockefeller University (Laboratory of Neurogenetics of Language, Supervisor: Dr. Erich D. Jarvis). He holds a Ph.D. in Interdisciplinary Bioinformatics from Seoul National University (2022), an M.Sc. in the same field from Seoul National University (2015), and a B.Sc. in Bioscience and Biotechnology from Hankuk University of Foreign Studies (2011). Ph.D. - Interdisciplinary Program in Bioinformatics, Seoul National University (2022) M.Sc. - Interdisciplinary Program in Bioinformatics, Seoul National University (2015) B.Sc. - Bioscience and Biotechnology, Hankuk University of Foreign Studies (2011) Lee's research focuses on comparative genomics , integrative multi-omics , and genome editing to unravel molecular mechanisms behind human-specific traits, brain evolution, vocal learning, and zoonotic pathogen transmission. His work spans viral infectivitiy (SARS-CoV-2, HIV), mental disorders , and evolutionary adaptation . His 15 most recent publications (2018-2025) cover themes in vertebrate genome evolution (Vertebrate Genomes Project), pathogen-host interactions , and computational tools for genome assembly (e.g., Galaxy platform). Notable topics include convergent evolution in vocal learners , viral transmission mechanisms , and genomic adaptations in transitional species (mudskippers, coelacanth). Lee collaborates with leading institutions on integrative data browsers for multi-omics datasets and contributes to functional validation of genetic signatures using genome-editing techniques. His work bridges evolutionary biology , neurogenetics , and computational biomedicine .
Philipp Lorenz-Spreen is a Computational Social Scientist at the Max Planck Institute for Human Development in Berlin, where he works within the Center for Adaptive Rationality. He simultaneously leads the junior research group "Computational Social Science" at TU Dresden's Center Synergy of Systems (SynoSys) and ScaDS.AI. His research focuses on the societal impact of digitalization, particularly examining how increasingly complex online discourse affects democracies worldwide. Education: Dr. rer. nat. (PhD) in Theoretical Physics, TU Berlin (2018) M.Sc. in Physics, LMU Munich (2016) B.Sc. in Physics, LMU Munich (2013) Lorenz-Spreen's research centers on understanding the interplay between human behavior and online platform functionality, with particular emphasis on how this relationship shapes public discourse and democratic processes. His work spans computational social science, complex systems analysis, social network theory, and opinion dynamics. He investigates how digital platforms create echo chambers and polarization while exploring untapped opportunities for improving information landscapes and fostering online participatory democracy. His research combines empirical methods with theoretical modeling to analyze collective attention dynamics and online behavior patterns. His publication record demonstrates a clear research trajectory from theoretical physics to computational social science applications in democratic contexts. The most recent works focus on digital media's impact on democracy, vulnerability to misinformation, and interventions against online misinformation. His research shows increasing interdisciplinary reach, with publications spanning computer science, psychology, political science, and social science journals, reflecting the complex nature of digital democracy research. Scientific Awards: Association for Psychological Science, Rising Star (2024) Leopoldina Prize for Junior Scientists (2021) Lorenz-Spreen has taught the "Applied Network Science" course in the Master of Data Science program at the Hertie School and is currently researching new data access rights for researchers under the Digital Services Act. His grant-funded projects include "Reclaiming Autonomy Online (RAO)" and "Social Media for Democracy (SOME4DEM)," which examine tools for meaningful democratic discourse and how to counteract polarization in online environments. He leads the Computational Social Science research group that investigates self-organized online discourse, with a particular focus on understanding how platform design influences democratic processes worldwide. His team works at the intersection of complex systems theory, network science, and democratic theory to develop evidence-based approaches for improving digital public spheres.
