Moritz Staudinger is a PreDoc Researcher at the Data Science department of Technische Universität Wien . His research focuses on reproducibility in machine learning and information retrieval, with particular emphasis on query generation, data citation, and evolving database schemas. Current projects: FAIR-AI (2024–2026) , HumRec (2021–2025) , and DoSSIER (2019–2024) Collaborations: Works with Andreas Hanbury , Andreas Rauber, and others Research interests include large language models for scientific applications, temporal information retrieval, and FAIR data principles. His recent publications examine reproducibility challenges across machine learning, systematic literature reviews, and environmental data management. Supervisions : Mentors students working on topics like data sovereignty, multilingual fact-checking, and quality indicators for data management plans. Collaborates on the DBRepo semantic repository framework.
Magdalena Steinböck is a PreDoc Researcher at Vienna University of Technology (TU Wien), affiliated with the Security and Privacy research group (E192-06). She holds a Dipl.-Ing.in (Master's equivalent) and BSc in technical disciplines. Her research focuses on mobile ecosystem security , particularly cross-platform analysis of iOS and Android systems, including deep link security, local network permissions, and vulnerability mitigation strategies. Current research projects: IoTIO (2020–2025) , W4MP (2023–2027) Active contributor to software repository mining (MSR 2024 conference paper) Her technical work spans mobile application analysis frameworks, security hardening comparisons, and user-centric permission system evaluations. She has published on iOS/Android ecosystem differences and cross-platform app matching methodologies.
Gabriele Bavota is a Full Professor in the Faculty of Informatics at the Università della Svizzera italiana (USI). He holds a Laurea (cum laude) and PhD in Computer Science from the University of Salerno (2013). Before joining USI in 2016, he was an Assistant Professor at the Free University of Bolzano-Bozen (2014–2016) and a research fellow at the University of Sannio (2013–2014). Research Focus: Software maintenance, mining software repositories, empirical software engineering, and AI-driven code analysis. Awarded the 2018 ACM Sigsoft Early Career Researcher Award and leads the ERC Starting Grant project DEVINTA, exploring deep learning applications in software engineering. Authored over 180 publications in top venues like ICSE, FSE, and TOSEM, with multiple best/distinguished paper awards. His work bridges empirical studies, AI tools, and developer-centric solutions, addressing challenges in code quality, refactoring, and automated testing. He has served as program chair for ICPC 2016, SANER 2017, and ICSME 2023, and holds editorial roles in TSE, EMSE, and JSS. Labs/Teams: Principal Investigator of the DEVINTA project and part of the Software Institute at USI, fostering collaborations in AI, software engineering, and empirical research.
Risto Sarvas is a University Lecturer in the Department of Computer Science at Aalto University's School of Science, specializing in the Digital Ethics, Society and Policy (Digital-ESP) research area. His work bridges human-computer interaction, social media analysis, and the societal implications of digital technologies, with significant contributions to educational tools and public discourse through media engagements. Academic qualifications include: Doctoral degree in Engineering and Technology from Helsinki University of Technology (awarded December 18, 2006) Master's degree in Engineering and Technology from Helsinki University of Technology (awarded October 22, 2001) Sarvas's research trajectory evolved from foundational studies on domestic photography and metadata systems to contemporary investigations of digital ethics and educational technology. His early work analyzed photographic practices across technological eras (Kodak, Portrait, and Digital Paths), while recent publications apply natural language processing to social media health discussions and develop frameworks for transversal competences in Finnish high schools. This progression reflects a consistent focus on technology-society interplay, emphasizing ethical considerations and user-centered design in both historical and emerging contexts. Publication trends reveal a strategic shift from technical HCI and photography studies (2004-2011) toward societal applications in education and public health (2019-2020). His 33 publications demonstrate sustained expertise in metadata systems and mobile interaction, now channeled into solving real-world problems like curriculum design (EduHex tool) and health communication analysis. The Digital-ESP research area serves as the unifying framework for this applied ethical approach. Risto Sarvas maintains active public engagement through 16 media appearances addressing smart cities, student burnout, pandemic-driven professional development, and social media risks. His international collaboration is evidenced by visiting researcher positions at foreign academic institutions in 2003 and 2009. Within Aalto University, he contributes to the Digital-ESP research group's mission of examining ethical, societal, and policy dimensions of digital technologies, frequently partnering with Finnish high school educators to implement modular curriculum solutions.
Dr. Samantha Pearman-Kanza is a Principal Enterprise Fellow at the University of Southampton , specializing in the application of Semantic Web technologies and Artificial Intelligence to scientific domains. She leads the Careers and Skills for Data-driven Research (CaSDaR) project and serves as the Pathfinder Lead for Process Recording in the Physical Sciences Data Infrastructure (PSDI) initiative. Her work bridges computer science with chemistry , agriculture , and social sciences to enhance digital research environments. Research Focus: Digitisation of scientific research and knowledge management Development of electronic lab notebooks and smart laboratories Integration of IoT devices in scientific workflows Ontologies and linked data for cross-domain interoperability Ethical AI applications in food supply chains and education Key Achievements: Published interdisciplinary work in Science, Technology, & Human Values and the Journal of Cheminformatics Contributed to AI4Green4Students, advancing sustainable chemistry education through digital tools Collaborated on zombie cheminformatics projects for legacy data conversion Contact: S.Pearman-Kanza@soton.ac.uk
Dr. Owen Osborne is a Lecturer in Zoology at the School of Environmental and Natural Sciences, Bangor University. He is an active researcher in evolutionary biology and ecology, focusing on speciation, adaptation, hybridization, and their impacts on biodiversity. His work spans diverse biological systems including plants, fungi, amphibians, fish, birds, insects, and host-associated microbiomes. His research interests center on understanding how rapid evolutionary change affects ecological interactions, particularly host-microbiome dynamics, and how these interconnected processes generate and maintain biodiversity. He employs a multidisciplinary approach combining evolutionary genomics, experimental ecology, metagenomics, and spatial ecology. Notably, he develops computational tools, including software for hypothesis testing in spatial ecology and bioinformatics, such as the R package fauxcurrence for generating null species distributions. The trends in his recent publications (2021–2025) show a consistent focus on parallel adaptation, speciation mechanisms, phylogenetic and biogeographic modeling, host-microbiome coevolution, and the development of computational methods. His work frequently appears in top-tier journals such as Nature Ecology and Evolution , PNAS , Molecular Biology and Evolution , and The ISME Journal , often in collaboration with leading researchers in the field. Dr. Osborne has no listed scientific awards in the provided texts, but his extensive publication record and software development indicate significant scholarly contributions. He advises students and likely leads or contributes to research grants, though specific names and funding details are not mentioned. His GitHub profile (ogosborne) hosts several repositories related to his research, such as fauxcurrence , Silene.uniflora.genome.project , and fasta_alignment_filters , indicating active involvement in open science and computational research. Dr. Osborne is part of research teams investigating evolutionary processes in natural populations, with a particular emphasis on integrating genomic data with ecological and spatial analyses. His lab or research group likely involves interdisciplinary collaboration and the use of bioinformatics pipelines for large-scale data analysis.
Hui Chen is an Associate Professor in the Department of Computer and Information Science at Brooklyn College, City University of New York, and a member of the doctoral faculty in the CUNY Computer Science Ph.D. program. His research integrates software engineering, wireless networks, and system security. Education: B.E. (1993), M.S. (1996), M.S. (2003), Ph.D. (2007) in Computer Science and Geophysics. Chen's research spans modeling and analytics of software and systems , wireless sensor networks, network security, and computer science education . His lab focuses on accountable systems and predictive models for developer behavior. Recent publications emphasize just-in-time defect prediction , internet censorship detection , and wireless sensor applications . Awards include multiple PSC-CUNY grants and an NSF award for secure programming education innovations. Scientific Awards: PSC-CUNY Award #67751-00 55 (2024-2025) NSF #2235976 (2023-2026) Additional PSC-CUNY grants (2018-2024) Chen serves on IEEE/ACM technical program committees and advises students in his MASS lab , which emphasizes industry collaboration and hands-on research in software/hardware security domains.
Johannes Wachs is an Associate Professor at the Institute of Data Analytics and Information Science, Corvinus University of Budapest, and a Research Fellow at the Centre for Economic and Regional Studies. He is affiliated with the Complexity Science Hub Vienna, where he has been a faculty member since April 2020. His interdisciplinary research bridges data science, network science, and complexity to study digital economies, open source software, and societal challenges. PhD in Network Science, Central European University (2019) MS in Applied Mathematics, Central European University (2012) BS in Mathematics and Economics, Tulane University (2009) His research focuses on the application of network and data science to understand social, economic, and technical systems. Key interests include open source software ecosystems, AI’s impact on knowledge sharing, corruption detection, urban inequality, and digital innovation. He uses large-scale digital trace data to model complex behaviors in online communities, software development, and public policy. His recent publications reveal a strong trend in analyzing digital platforms such as GitHub and Stack Overflow, studying brain drain in tech, and assessing climate and health risks in Austria. His work combines network modeling, machine learning, and empirical analysis to uncover patterns in human behavior and systemic risk. IMF Anti-Corruption Challenge Winner (2020) Principal Investigator, CRISP Project (2021–2024), funded by FFG Grants from Hungarian Research Funding Agency (OTKA), City of Vienna, and WU Projects Johannes Wachs actively supervises PhD and Master’s students, including Hannah Schuster and Brigi Németh. He has taught courses in computational social science, social networks, and data mining at institutions including RWTH Aachen, CEU, and WU Vienna. He is launching a new MSc in Social Data Science at Corvinus in 2025. He leads the CRISP project, which builds semantic data pools for real-time crisis response and intervention, integrating heterogeneous data sources for impact forecasting and policy transparency.
Fabio Palomba is an Associate Professor at the Department of Computer Science , University of Salerno, Italy. He earned a European PhD in Management & Information Technology (2017), funded by University of Salerno and University of Molise, under advisor Prof. Andrea De Lucia. His research spans software maintenance and evolution , empirical software engineering , and ML systems quality . Recipient of IEEE Computer Society Best PhD Thesis Award (2017) Multiple Distinguished Paper Awards from ACM/SIGSOFT and IEEE/TCSE Recipient of prestigious SNSF Ambizione grant (2019) and IEEE Rising Star Award (2023) His work investigates fairness-aware practices in ML , technical debt in AI systems , and LLM applications in software engineering . Recent studies focus on automated requirements generation via RECOVER, quantum software engineering , and socio-technical community smells in ML-enabled systems, with empirical analyses across large datasets. Key editorial roles include Elsevier's Information and Software Technology Journal (2022-), Springer's Empirical Software Engineering Journal (2021-), and IEEE Transactions on Software Engineering (2020-). He has served as program co-chair for SANER 2024 , ICPC 2021 , and multiple conference tracks. 16 Distinguished Reviewer Awards for his refereeing work Co-authored 80+ journal papers , 100+ conference papers , and advised 300+ theses
Shane McIntosh is an Associate Professor at the David R. Cheriton School of Computer Science, University of Waterloo. He leads the Software Repository Excavation and Build Engineering Labs (Software REBELs), focusing on empirical studies of historical data in large-scale software development. Research: Specializes in release engineering (assembling, verifying, and delivering software releases) and software quality (deriving guidelines for reliable systems). Teaching: Has taught courses like ECSE 321 (Introduction to Software Engineering), ECSE 611 (Software Analytics), and ECSE 437 (Software Delivery). Service: Organized key conferences (e.g., ICSE 2022, PROMISE 2021–2022) and served as Senior Associate Editor for the Journal of Systems and Software (2019–Present). Labs: Leads the Software REBELs lab, emphasizing software repository analysis and build engineering.
Juan Carlos Farah is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL), holding dual appointments in the Fondation Bertarelli Chair in Neuroprosthétique Cognitive (School of Life Sciences/SV) and the SCI-STI-DG group (School of Engineering/STI). His work bridges neuroscience, artificial intelligence, and educational technology, focusing on innovative applications of AI in learning environments and neuroprosthetics research. Research Interests: Farah’s research spans educational chatbot design, AI-enhanced learning analytics, neuroprosthetic systems, and the ethical integration of technology in education. He has pioneered frameworks for task-oriented conversational agents, blockchain-based learning trace repositories, and gamified computational thinking tools. Key Projects: He contributed to the Graasp Desktop initiative for underconnected African schools and developed the TRACE model for educational chatbots. His work on code review notebooks and bot-mediated software engineering education has influenced pedagogical practices globally. Awards & Recognition: No specific awards listed, but his publications reflect high-impact contributions to IEEE, ACM, and Elsevier journals/conferences. Active in global initiatives like UNESCO’s Unequal World Conference on education equity. Technical Expertise: Proficient in Python, JavaScript, and AI toolkits. Specializes in building scalable educational platforms, learning analytics pipelines, and neuroimaging analysis for cognitive studies.
Daniel Feitosa is an Assistant Professor in the Faculty of Science and Engineering at the University of Groningen, affiliated with the Software Engineering and Architecture (SEARCH) group led by Prof. Paris Avgeriou. His research focuses on software quality, technical debt management, automation in software projects, and energy efficiency in AI/ML systems. He holds a Ph.D. in Computer Science from the University of Groningen and has extensive experience in both academia and industry. Research Interests: Automation of software processes and technical debt management Source code analysis and mining software repositories Energy efficiency in AI/ML applications Software architecture and design patterns Teaching Roles: He coordinates and teaches courses such as Software Architecture , Advanced Object-Oriented Programming , and Algorithmic Programming Contests at both BSc and MSc levels. Awards: Distinguished Paper Award at the 2nd International Conference on AI Engineering (CAIN 2023) Service & Engagement: He serves on the boards of the Dutch National Association for Software Engineering (VERSEN) and the Steering Committee of the European Conference on Software Architecture (ECSA). He has organized workshops and tracks at major conferences like TechDebt, SEAA, and SANER.
Nuwan Indika is an Assistant Professor of Business Analytics at the Department of Business Analytics, College of Business, Loyola University New Orleans. He holds a Ph.D. in Economics from Kansas State University (2019), an MA in Economics from Western Illinois University (2013), and a BA in Economics from the University of Colombo, Sri Lanka (2009). A Fulbright Scholar and econometrician, his research spans econometrics, big data, health data science, and biomedical text processing. Education: Ph.D. (Economics, Kansas State University, 2019); MA (Economics, Western Illinois University, 2013); BA (Economics, University of Colombo, 2009) Research Interests: Focus on econometric modeling of complex health data (human and veterinary medicine), biomedical text processing, and soda industry vertical merger analysis. Engages in large-scale data mining and computational epidemiology. Collaborations: Research associate with 1DATA Consortium (Kansas State University) and sports analytics collaborations with Prof. Sanders at Syracuse University. Scientific Awards: Recognized as a Fulbright Scholar. Collaborative work spans oncology, pharmacokinetics, and behavioral economics. Consulting: Analytics consultant for Plan International, World Bank, and GTZ projects.
Hayder Murad is a researcher with expertise in machine learning, sentiment analysis, and hybrid filtering algorithms, particularly applied to intelligent tutoring systems and medical diagnostics. His work bridges computational methods with practical applications in education and healthcare. He earned his PhD from the University of Portsmouth in 2019 with a thesis titled An integrated approach to recommending online video materials through sentiment analysis and hybrid filtering algorithms . His recent publications focus on knowledge graph construction, large language model applications, and open science initiatives. Notable contributions include enhancing systematic literature reviews, improving dataset interoperability, and advancing FAIR data principles across disciplines. Research Interests Machine learning for healthcare diagnostics Hybrid recommendation systems Knowledge graph development Open science infrastructure Intelligent tutoring systems LLM-based research synthesis
Anish Das Sarma is a researcher affiliated with Google, USA , specializing in uncertain data management, MapReduce algorithms, and knowledge graph systems. He earned a PhD from Stanford University in 2010 under the supervision of Jennifer Widom and Alon Halevy, with a dissertation on "Managing Uncertain Data." His career spans collaborations with leading institutions, focusing on scalable data integration, social choice theory, and machine learning applications in scholarly knowledge organization. PhD in Computer Science, Stanford University (2010) Key collaborations: Stanford, Google Research, NFDI4DataScience His research interests intersect uncertain data modeling , MapReduce optimization , and large language model applications for scientific synthesis. Recent work includes FAIR data frameworks, ontology learning, and clinical entity linking. Article trends highlight his evolution from foundational database systems (2004-2015) to modern applications of LLMs in scholarly communication (2023-2024). Key areas: scalable algorithms, research data management, and ethical AI.
