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Lin TanView profile

Professor

• Software-AI synergy
• Software dependability
• Software engineering
• +7 more

Lin Tan is a Professor of Computer Science at Purdue University , holding the Mary J. Elmore New Frontiers Professorship . She joined Purdue in 2019 after serving as a Canada Research Chair and associate professor at the University of Waterloo. She is an ACM Distinguished Member and IEEE Senior Member . Education: PhD in Computer Science, University of Illinois Urbana-Champaign BS in Computer Science and Technology, Zhejiang University Research Interests: Professor Tan’s research lies at the intersection of software engineering , artificial intelligence , and security . Her work focuses on software-AI synergy , software dependability , defect detection & repair , and software text analytics . She leverages machine learning and natural language processing to enhance software reliability, and conversely uses software techniques to improve the dependability of AI systems. Her recent projects include building binary foundation models (Nova), evaluating large language models for code generation and repair, and developing interactive debugging tools that reduce debugging time by one-third. She also explores robot task planning with LLMs and automated front-end development . Awards & Honors: Best Paper Award Finalist, ICRA 2025 ELATES Fellow, 2024-2025 ACM SIGSAC Distinguished Paper Award, CCS 2024 J.P.Morgan AI Faculty Research Awards (2020, 2021, 2022) ACM SIGSOFT Distinguished Paper Awards (ASE 2020, MSR 2018, FSE 2016) Canada Research Chair (2017) Ontario Early Researcher Award (2015) NSERC Discovery Accelerator Supplements Award (2015) Google Faculty Research Awards (2010, 2014) IEEE Micro Top Picks (2006) Advising & Funding: Professor Tan currently advises eight PhD students and has graduated 20+ PhD and Master’s students now thriving in academia (York University, Concordia University, University of Alberta) and industry (Microsoft, Meta, Amazon, Google). Her group is generously supported by NSF , Meta/Facebook Research Awards , J.P.Morgan AI Faculty Awards , and NSF REU programs. Labs & Teams: She leads the Software Reliability & AI Lab at Purdue, recruiting postdocs, PhD, MS, and undergraduate researchers year-round. Lab interests span binary recovery , LLM-based program repair , testing deep-learning libraries , and data-free model extraction .

Anett HoppeView profile

Researcher

• Search as Learning
• Educational Technology
• Artificial Intelligence in Education
• +5 more

Dr. Anett Hoppe is a research staff member at the Leibniz Information Centre for Science and Technology (TIB) in Hannover, Germany, where she works in the Visual Analytics research group. Her research focuses on the intersection of artificial intelligence, education technology, and information science, with particular emphasis on how people learn through search processes and educational video consumption. Dr. Hoppe completed her academic journey with: Ph.D. in Semantic Web technologies for online user profiles from the University of Burgundy, Dijon, France Her primary research interests span Search as Learning, software-based support for scientific reproducibility, and ethical considerations in computer-based decision making. She investigates how visual elements, reading sequences, and AI technologies impact knowledge acquisition during web search and educational video consumption. Her work bridges human-computer interaction, educational psychology, and information retrieval to create more effective learning experiences, with recent publications examining the role of large language models, vision-language models, and visual complexity in educational contexts. Analysis of her recent publications (2024-2025) reveals a strong interdisciplinary focus combining computer science, educational psychology, and information science. Her research examines video-based learning effectiveness, knowledge gain prediction, educational resource discovery, and the impact of visual elements on learning outcomes. She consistently explores how AI technologies can be leveraged to enhance educational experiences while maintaining attention to ethical considerations and scientific reproducibility. Dr. Hoppe maintains active collaborations with researchers across multiple institutions, with frequent co-authorship patterns indicating strong research partnerships, particularly with Ralph Ewerth and other members of the Visual Analytics group at TIB. Her work supports TIB's mission to advance knowledge infrastructure and scholarly communication through innovative technological solutions while directly addressing practical challenges in educational technology and information retrieval.

• Software Engineering
• Data Analytics
• Mining Software Repositories
• +5 more

Ahmed E. Hassan is a Professor and Canada Research Chair in Software Analytics at the School of Computing, Queen's University. He serves as NSERC RIM Industrial Research Chair and leads the Software Analysis and Intelligence Lab (SAIL). Dr. Hassan pioneered the Mining Software Repositories (MSR) conference and co-edited special issues in the IEEE Transactions on Software Engineering and the Journal of Empirical Software Engineering on MSR topics. Education Ph.D. in Computer Science, University of Waterloo (2005) MMath in Computer Science, University of Waterloo BMath in Computer Science, University of Waterloo His research focuses on software analytics, mining software repositories, and systems engineering. Projects at SAIL include analyzing version control systems, predicting software defects, and improving software quality through empirical methods. The lab's work spans software evolution, architecture analysis, and debugging of distributed systems. Dr. Hassan has taught courses like CISC 322: Software Architecture and CISC 880: Mining Software Engineering Data at Queen's University, University of Victoria, and University of Waterloo. His teaching philosophy emphasizes hands-on learning with tools like WEKA and R, and includes project-based assignments using MSR Challenge datasets. Scientific Awards NSERC RIM Industrial Research Chair in Software Engineering Canada Research Chair in Software Analytics He has industrial experience from RIM (Blackberry platform), IBM Research (Almaden Lab), and Nortel Networks. Dr. Hassan is a named inventor on patents in the US, Europe, Canada, Japan, and India. SAIL lab is funded by NSERC, ORF, CFI, and industry partners.

Gabriel WainerView profile

Professor

• Modelling and Simulation Methodologies
• Discrete Event Systems
• Real-Time Modelling
• +7 more

Dr. Gabriel Wainer is a Professor in the Department of Systems and Computer Engineering at Carleton University's Faculty of Engineering and Design. He leads the Advanced Real-Time Simulation Lab and specializes in modeling and simulation methodologies, particularly focusing on discrete event systems, real-time modeling, cellular automata, and DEVS formalism. Research Interests: Discrete event systems, DEVS formalism, cellular automata, real-time simulation, IoT applications, and parallel/distributed simulation Affiliation: Carleton University Recent publications highlight his work in advanced simulation frameworks, energy-efficient 5G systems using deep reinforcement learning, and pandemic modeling with cellular automata. His lab develops tools like PROMETHEUS and Devsmap for standardized DEVS model representation, while also exploring applications in wireless communication, building energy systems, and behavioral epidemiology.

Dimitris KolovosView profile

Professor

• Automated and Model-Based Software Engineering
• Model-Driven Software Engineering
• GenAI for Software Engineering
• +4 more

Roles and Affiliations : Dimitris Kolovos is a Professor of Software Engineering at the University of York's Department of Computer Science. He leads the Automated Software Engineering (ASE) research group and is an Eclipse Foundation committer, leading development of the Epsilon open-source platform. His roles include research leadership, teaching, and academic service. Education : PhD in Software Engineering - University of York MSc in Software Engineering with Distinction - University of York First Class Honours Degree in Informatics - Athens University of Economics and Business Research Interests : Kolovos focuses on advancing Model-Driven Engineering (MDE), GenAI integration in software development, low-code platforms, and data analytics. His work emphasizes scalable modeling tools, education technology (e.g., MDENet platform), and industry collaboration with organizations like NASA, BAE Systems, and Siemens. Labs and Projects : He leads the Epsilon project under the Eclipse Modelling initiative, developing tools for model transformation, validation, and code generation. His research group also explores AI-driven model transformations and hybrid graphical-textual editors.

Claudia Patricia Ayala MartinezView profile

Lecturer

• Empirical Software Engineering
• Off-The-Shelf Adoption
• Requirements Engineering
• +4 more

Claudia Patricia Ayala Martinez serves as a Lecturer in the Department of Service and Information Systems Engineering at the Barcelona School of Informatics (FIB), Polytechnic University of Catalonia (UPC). She is actively involved in research through the GESSI - Group of Software and Service Engineering and the UPC inSSIDE - integrated Software, Services, Information and Data Engineering research groups. Her career spans over two decades of academic contributions in software engineering with consistent publication output. Dr. Ayala Martinez's research focuses on Empirical Software Engineering, Off-The-Shelf Adoption, Requirements Engineering, and Software and Architectural Quality. Her work demonstrates an evolution from traditional software engineering topics toward increasing integration with machine learning and AI systems. Recent publications show particular emphasis on software quality indicators, ML pipeline design principles, trustworthiness of ML models, and green computing in software systems. Analyzing her publication trends reveals a consistent research trajectory with growing focus on AI/ML integration in software engineering. Her work spans empirical studies, systematic literature reviews, and practical industrial applications. The research shows strong connections between software quality metrics, architectural decisions, and emerging technologies, with increasing attention to ethical considerations in ML systems and sustainability in software development. Most-Influential Paper Award at the 30th IEEE International Requirements Engineering Conference Dr. Ayala Martinez has participated in numerous competitive R&D projects including those funded by the Spanish National Research Plan, Horizon 2020, and the Catalan Innovation Strategy. Her collaborative network includes extensive work with Professor Javier Franch Gutierrez (69 joint publications), Silverio Juan Martinez Fernandez (26 joint publications), and Cristina Gomez Seoane (20 joint publications). Her research has been supported by various national and European funding programs focusing on software engineering, quality assessment, and open source adoption. She is actively involved with the GESSI and inSSIDE research groups at UPC, which focus on integrated software, services, information, and data engineering. These groups maintain strong industry connections and have produced significant research in empirical software engineering, reference architectures, and quality assessment methodologies. Her recent work shows increasing collaboration with researchers working at the intersection of software engineering and artificial intelligence.

Tim ConradView profile

Researcher

• Bioinformatics
• Computational Biology
• Machine Learning
• +5 more

Dr. Tim Conrad is a researcher at the Zuse Institute Berlin in the Visual and data-centric computing department under the Mathematics of Complex Systems division. He leads projects at the intersection of computational biology, AI, and medical data analysis. Projects: Geometric Learning for Single-Cell RNA Velocity Modeling, MODAL MedLab, Sparse Compressed Sensing in -Omics Data, BIFOLD (Big Data and Machine Learning) Research Networks: Affiliated with MATH+ and MODAL Research Campus His research focuses on applying machine learning , network optimization , and sparse data analysis to biological and medical challenges including disease modeling, microbiome dynamics, and ECG classification. Recent work explores hybrid PDE-ODE epidemic models and federated learning in healthcare. 2023-2025 publications highlight trends in AI for biological networks , temporal community detection , and medical signal processing . He co-authored studies on SARS-CoV-2 simulations, proteomics feature selection, and multi-label ECG analysis. His 2004 doctoral thesis at Monash University laid foundations for later work in metabolic pathway analysis. Awarded as a Zuse Fellow , he contributes to open science initiatives like FAIR data sharing . Collaborations span institutions including Freie Universität Berlin and Charité in medical informatics and clinical applications.

Nicole NovielliView profile

Associate Professor

• Affective Computing
• Software Engineering
• Natural Language Processing
• +5 more

Nicole Novielli, Ph.D., is Associate Professor at the University of Bari “A. Moro” , Italy, where she conducts research on affective computing applied to software engineering and human-computer interaction. She leads the Collaborative Development Group and coordinates national projects investigating emotions in software teams, AI quality and IoT ecosystems. Education: Ph.D. in Computer Science, University of Bari, 2010 – thesis on “Lexical Semantics of Dialogue Acts” M.Sc. in Computer Science (Knowledge & Software Engineering), University of Bari, 2006 – summa cum laude B.Sc. in Computer Science, University of Bari, 2004 – summa cum laude Visiting researcher at USC-ICT, University of Aberdeen, FBK-irst (Trento) Research interests revolve around recognizing and exploiting affective and cognitive states in computer-mediated cooperative work. She studies sentiment and emotion mining in developers’ textual communication, multimodal emotion recognition via low-cost biometric sensors, and natural-language dialogue simulation for intelligent interfaces. Her work couples software engineering with natural language processing , social media analytics and human-computer interaction . Recent articles (2021-2025) reveal a clear trend: integrating deep learning and large language models into software engineering tasks—automated issue labelling, sentiment classification, technical-debt detection—while validating these techniques through rigorous empirical studies and biometric experiments . A parallel stream explores developer experience , measuring how emotions and cognitive load influence productivity, code quality and collaboration. Scientific awards include the 2020 Apex Award for Publication Excellence , multiple Distinguished Reviewer Awards at flagship venues (ESEC/FSE, ICSME, MSR), the Best Paper Award SANER 2019 and the Best Student Paper Award ACII 2009 . She currently teaches “Sentiment Analysis” in the Data-Science MSc and “Computer Networks” in the ITPS programme. She has advised numerous B.Sc., M.Sc. and PhD projects and is PI or Co-PI of four ongoing grants: EmoQuest (SIR), EMPATHY (PRIN), FAIR-Spoke 6 (PnRR), and QualAI (PRIN 2022). Dr. Novielli serves on the editorial boards of Empirical Software Engineering and Journal of Systems and Software , has guest-edited special issues on affect awareness in SE, and has chaired tracks at ICSE, SANER, MSR, ICSME and SSBSE. She co-leads the Collaborative Development Group and actively releases datasets and open-source tools for the community.

• Artificial Intelligence
• Semantic Web
• Ontology Engineering
• +2 more

Asunción Gómez Pérez is a Spanish computer scientist and Full Professor at the Technical University of Madrid (UPM) . She currently serves as Vice-Rector for Research, Innovation and Doctoral Studies at UPM and holds a seat at the Real Academia Española . She has authored over 300 publications and accumulated 20,000 citations. Education : PhD in Computer Science (UPM, 1993), MBA (Comillas Pontifical University) Leadership Roles : Director of the Department of Artificial Intelligence (2008–2016), Academic Director of AI Master’s/PhD programs (2009–2016), Executive Director of UPM’s Artificial Intelligence Lab (1995–1998) Her research focuses on Semantic Web and Ontology Engineering , with applications in knowledge representation, machine-machine communication, and multilingual data integration. She pioneered methods for ontology validation, metadata licensing, and AI-driven social inclusion. Key publication trends include: Ontology evaluation frameworks (e.g., OOPS!) Linked Data quality models and validation tools Multilingual and cross-lingual AI applications Interoperability solutions for smart cities and healthcare Machine Learning for social exclusion prediction Ontology-driven library and lexicography systems Scientific Awards Fellow of the European Academy of Sciences Ada Byron Prize She has led projects like the NeOn Methodology for ontology development and contributed to the European framework for linked data rights (LD Terms). Her work bridges theoretical research with practical implementations in AI and Semantic Technologies.

Daniel CatchpooleView profile

Professor

• Childhood Cancer Research
• Genomics
• Biomedical Data Science
• +6 more

Professor Daniel Catchpoole serves as Deputy Head of School (Research) at the School of Computer Science, University of Technology Sydney (UTS), holding dual appointments at UTS and The Children's Hospital at Westmead. With over 20 years of research experience, he bridges computational sciences and pediatric cancer research through the Biomedical Data Science Lab in the Australian Artificial Intelligence Institute. His work integrates data analytics, artificial intelligence, and software development with molecular cancer biology to transform pediatric cancer treatment pathways. PhD in Cancer Cell Biology, University of New South Wales (1991-1995) Founding Fellow, Royal College of Pathologists Australasia (2010-present) Head, Children's Hospital at Westmead Tumour Bank (2001-present) Professor Catchpoole's research focuses on translational applications of genomics in childhood cancers, particularly acute lymphoblastic leukemia and neuroblastoma. His work combines high-throughput genomic technologies with advanced computational analysis to develop systems biology approaches for cancer patient assessment. Recent projects explore virtual reality applications for complex genomic data visualization and copper chelation therapies to enhance neuroblastoma immunotherapy. His research has received significant funding from Cancer Institute NSW, Sony Foundation, ARC, and NHMRC. His publication record spans biomedical data science, cancer genomics, and virtual reality applications in oncology. Recent work demonstrates leadership in 3D latent diffusion models for tumor segmentation, biobank economics, and innovative immunotherapies. His research consistently addresses the critical need for actionable knowledge from complex multidimensional biomedical data. Editorial Board Member, Cancers (2023) Associate Editor, Innovations in Digital Health, Diagnostics and Biomarkers (2019) Founding member and first President, Australasian Biospecimens Network Association Professor Catchpoole has supervised 17 Honours students (including 6 First Class Honours), 3 MSc students, and 12 PhD candidates across multiple institutions, with 6 current PhD students. His collaborative research bridges UTS's Faculty of Engineering and IT with The Children's Cancer Research Unit at The Children's Hospital at Westmead. Significant research funding includes Cancer Institute NSW grants, Sony Foundation VR projects, and ARC Discovery Projects focused on genomic data analysis and clinical decision support systems. His leadership extends to building frameworks for translational research, managing biobanks and clinical data linkages, and navigating governance requirements for cancer research. The Tumour Bank at Kids Research, CCRU, represents his long-standing commitment to pediatric cancer infrastructure development.

• Business Process Management
• Process Mining
• Complex Event Processing
• +5 more

Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data

Kenneth Edwin BarkerView profile

Professor

• Privacy Preserving Data Repositories
• Database Management Systems
• Data Privacy and Security
• +4 more

Dr. Kenneth Edwin Barker is a Professor in the Department of Computer Science within the Faculty of Science at the University of Calgary, where he also serves as Director of the Institute for Security, Privacy and Information Assurance (ISPIA). His academic career spans several decades with significant contributions to database systems and privacy research. Dr. Barker earned his B.S. and M.S. in Computer Science from the University of Calgary in 1982 and 1984 respectively, followed by a Ph.D. in Computer Science from the University of Alberta in 1990. His educational background established the foundation for his extensive research career in database systems and information security. His primary research interests focus on Privacy Preserving Data Repositories , with specific attention to protecting privacy in mobile applications, understanding privacy's impact on data analytics, and architecting database management systems that inherently respect user privacy. His work also extends to distributed database environments, integration of legacy systems, and multidatabase environments. Dr. Barker's research bridges theoretical foundations with practical applications, making significant contributions to how privacy is implemented in real-world systems. An analysis of his recent publications reveals a strong trend toward practical privacy-preserving techniques for cloud data, social networks, and location-based services. His work consistently addresses the tension between data utility and privacy protection, developing innovative methods to maintain data value while safeguarding personal information. The publications span multiple subfields including encrypted search, graph privacy, high-dimensional data privacy, and privacy metrics. Best Paper Award at DBSec 2012 Best Paper Award at CODASPY 2012 Best Paper at BNCOD 2009 Dr. Barker has been instrumental in establishing privacy research infrastructure at the University of Calgary through his leadership of ISPIA. His research has attracted significant funding from various sources supporting privacy and security initiatives. While specific grant details aren't provided in the text, his extensive publication record indicates sustained research funding throughout his career. He has collaborated extensively with researchers both within and outside the University of Calgary, particularly with R. Alhajj and other colleagues on numerous projects. As Director of ISPIA, Dr. Barker oversees a research environment focused on advancing security and privacy technologies. The institute serves as a hub for interdisciplinary research, bringing together computer scientists, social scientists, and legal experts to address complex privacy challenges. His leadership has positioned the University of Calgary as a significant player in privacy research within Canada.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +5 more

Prof. Dr. Mehmet Reşit Tolun is a full-time Professor in the Department of Software Engineering at Çankaya University (Turkey) since 2022. Previously held full-time professor positions at Konya Food and Agriculture University (2020-2022), Aksaray University (2013-2017), and TED University (2011-2013), along with a part-time professorship at Başkent University (2017-2020). Specializes in Artificial Intelligence , Machine Learning , and Data Mining , with a focus on deep learning applications in aerospace, biomedical data analysis, and software process improvement. PhD in Computer Science (University of Kent, 1985) MSc in Computer Science (University of Kent, 1982) BSc in Physics and Computer Science (University of Kent, 1981) Research Interests span deep learning frameworks, hybrid expert systems, software engineering methodologies, and biomedical signal processing. Publications emphasize practical implementations in medical diagnostics, robotics, and agricultural pest detection. Scientific Awards include the IEEE Third Millenium Medal (2000). Supervised over 55 graduate students, including Burak Çetin, Uğur Özotuk, and Mahinur Doğan. Collaborated with researchers from Orta Doğu Teknik Üniversitesi , Çankaya University , and Aksaray University .

Patrick T. BrandtView profile

Professor

• Political Dynamics
• Time Series Analysis
• Machine Learning
• +8 more

Patrick T. Brandt is a Professor of Political Science, Public Policy, and Political Economy at the University of Texas at Dallas , affiliated with the School of Economic, Political and Policy Sciences . His work integrates advanced statistical methods with political science, focusing on time series analysis, machine learning, and Bayesian modeling to study political dynamics. His research spans international relations , political economy , terrorist targeting , and conflict forecasting . He specializes in developing novel models for event count time series, including the Bayesian Poisson Vector Autoregression and MS-BVAR packages for R. His NSF-funded projects focus on event data generation and real-time conflict forecasting. Recent publications emphasize domain-specific language models (ConfliBERT variants), graph neural networks for conflict prediction, and machine translation challenges in political text analysis. He maintains the OpenEvent Data Repository and develops software like MSBVAR and PESTS for academic use. Scientific Awards : Robert H. Durr Award for Best Methodology Paper, Midwest Political Science Association (2006)

Sabine GrafView profile

Professor

• Educational Technology
• User Adaptive Systems
• Learning Analytics
• +7 more

Dr. Sabine Graf is a Full Professor at the School of Computing and Information Systems, Athabasca University. She holds a PhD in Computing and Information Systems from Vienna University of Technology (2007) and has been a faculty member since 2009. Her research focuses on user adaptive systems, learning/academic analytics, personalization, and game-based learning, with over $2.3M in external funding and 130+ peer-reviewed publications (cited 8,200+ times). She leads the OMEGA+ educational game project and the User Adaptive Systems (UAS) research cluster. Education: PhD in Computing and Information Systems, Vienna University of Technology, 2007 MSc in Computing and Information Systems, University of Vienna, 2003 Research Interests: Dr. Graf specializes in making learning systems more intelligent through adaptive interfaces, AI-driven recommendations, and data analytics. Her work bridges educational technology, artificial intelligence, and collaborative learning. Recent projects include developing OMEGA+, analyzing student behavior in online courses, and enhancing adaptive learning systems with context-aware features. Grants & Funding: NSERC Discovery Grant ($205,000), 2020 CFI John R. Evans Leaders Fund ($164,573), 2020 AU IDEA Lab Grant ($5,000), 2021 Multiple Mitacs Globalink Internships, NSERC awards, and AU-funded projects. Advising & Team: Dr. Graf has mentored over 30 students, including PhD, MSc, and postdoctoral fellows. Notable advisees include Moustafa Mahmoud (NSERC Scholar) and Mohammad Belghis-Zadeh (3MT Competition winner). The research team collaborates globally, with members from Brazil, Taiwan, Spain, and beyond. Labs & Initiatives: The Academic Analytics Tool (AAT) project and the OMEGA+ game platform are central to her work. The UAS cluster connects researchers worldwide to advance adaptive systems research.