Professor Kara Morgan-Short holds a joint appointment at the University of Illinois at Chicago in the Department of Hispanic and Italian Studies and the Department of Psychology. She directs the Cognition of Second Language Acquisition Laboratory and is affiliated with the Laboratory of Integrative Neuroscience. Her research focuses on the cognitive and neural mechanisms underlying second language acquisition, integrating linguistics, cognitive psychology, and neuroscience. She has held editorial roles for Language Learning and contributed to advancing open science practices in applied linguistics. Education: PhD in Spanish Linguistics (Georgetown University, 2007), MATL in Spanish (University of Southern Mississippi, 1998), BA in Humanities (UT Austin, 1991). Research interests include the role of declarative/procedural memory, attention, and context in SLA. She employs behavioral and electrophysiological methods (e.g., ERP) to study linguistic and cognitive processes. Key grants include NSF funding for doctoral research (2018–2022) and Language Learning grants (2014–2016). Awards include the 2018 Excellence in Teaching Award and the 2009 Harold N. Glassman Dissertation Award. Her work emphasizes interdisciplinary approaches to understanding bilingualism and SLA at both behavioral and neurocognitive levels.
Sanem Kabadayı is an Assistant Professor at the Department of Computer Engineering, Istanbul Technical University. Her research focuses on sensor networks, middleware frameworks, and pervasive computing. Research Interests Sensor Networks (Wireless, Ad-hoc, Heterogeneous) Pervasive and Ubiquitous Computing Middleware Design for Smart Environments Data Abstraction and Communication Paradigms Advisory Role Advisor for Yakup Kayatas' thesis on AI-assisted IoT agents for smart spaces (2025). Recent Research Trends Her 15 most recent publications (2006–2024) emphasize sensor network architectures, middleware innovations for programmable environments, and abstractions for heterogeneous sensor integration. Key themes include AI-driven IoT agents, positioning techniques in wireless networks, and virtual sensor frameworks.
Professor Tova Milo is the Chair for Information Management at the School of Computer Science, Tel Aviv University, leading the prominent Databases Lab (DB Group). Her research spans databases, big data management, crowd-based data sourcing, and business process querying, with significant contributions to data integration and semi-structured data systems. Her research interests focus on innovative approaches to data management challenges through machine learning integration, crowd computing, and business process analysis. Key projects include Business Process Querying (BPQ) for analyzing BPEL specifications, MoDaS for crowd-based data management, and PROX for data provenance summarization. Her work bridges theoretical foundations with practical applications in fraud detection, recommendation systems, and data cleaning. Analysis of her recent publications reveals strong trends in human-in-the-loop data management systems, with growing emphasis on crowd integration for data cleaning and knowledge acquisition. Her research increasingly combines traditional database theory with machine learning techniques for scalable big data processing, while maintaining focus on business process modeling and provenance tracking. ACM PODS Alberto O. Mendelzon Test-of-Time Award (2010) ERC Advanced Investigators grant MoDaS (2011) The Weizmann Prize for Exact Sciences (2017) VLDB Women in Database Research award (2017) IEEE TCDE Impact award (2022) ISF Breakthrough Research Grant (2022) Doctorate Honoris Causa, University of Zurich (2023) ACM Fellow Member of Academia Europaea Professor Milo has advised over 30 graduate students including PhD candidates like Yael Amsterdamer and Ohad Greenshpan, and numerous MSc students working on projects including MoDaS, BPQ, and EDOS. Her research has been supported by major grants including the ERC Advanced Investigators grant and ISF Breakthrough Research Grant. She actively collaborates with industry partners including IBM and Microsoft, particularly in business process management standards. She directs Tel Aviv University's Databases Lab, which maintains the DB Group with multiple research streams including business process querying (BPQ), crowd-based data management (MoDaS), and self-adaptive data dissemination (EDOS/COLT). The lab operates from the Schreiber Building (M-20) and maintains strong international collaborations, particularly with European institutions through the ERC-funded MoDaS project.
Tijs Slaats is an Associate Professor in the Software, Data, People & Society section at the Department of Computer Science, University of Copenhagen. His research focuses on Business Process Management with particular emphasis on declarative and hybrid process notations to provide flexible workflow support for knowledge workers, funded by the Danish Council for Independent Research. His educational background includes: M.Sc. in Information Technology from IT University of Copenhagen Ph.D. in Computer Science from IT University of Copenhagen (supervised by Thomas Hildebrandt) Dr. Slaats specializes in declarative process modeling, particularly Dynamic Condition Response (DCR) graphs which enable flexible workflow systems. His work bridges theoretical foundations with practical applications in cross-organizational settings. Recent research extends into blockchain technologies, smart contracts, and applications in sensitive domains like asylum processing and refugee law. He combines formal methods with empirical evaluation to ensure both correctness and usability of workflow systems. His publication pattern shows increasing application of process mining techniques to blockchain technologies and socially impactful domains, while maintaining core research in Business Process Management. Notable trends include integration of DCR graphs with smart contracts, privacy-preserving techniques for asylum data, and object-centric approaches to process discovery. Research funding includes: Hybrid Business Process Management Technologies project (Danish Council for Independent Research) Technologies for Flexible Cross-organizational Case Management Systems (FLExCMS) industrial Ph.D. project Alongside his academic work, Dr. Slaats maintains strong industry connections through Exformatics A/S where he developed the DCR Graphs workflow solution (www.dcrgraphs.net), and previously worked as a software engineer in the Dutch e-commerce sector. His dual expertise in academia and industry ensures his research addresses real-world workflow challenges while maintaining theoretical rigor.
Hajo A. Reijers is a Professor at the University of Utrecht, Netherlands, with a former affiliation at Vrije Universiteit Amsterdam. His research focuses on Business Process Management (BPM), Process Mining, and Robotic Process Automation (RPA), emphasizing practical applications in healthcare, organizational processes, and human-computer interaction. He contributes to developing tools like SWORD for detecting workarounds and DEUCE for auditing electronic health records. His work spans algorithm development for process discovery, predictive analytics, and optimization techniques. Key areas include analyzing event logs, modeling workplace behavior, and enhancing process transparency. Reijers collaborates extensively with industry partners, addressing challenges in process automation, employee acceptance of AI, and ethical monitoring. His contributions to conferences like BPM, CAiSE, and ICIS highlight interdisciplinary approaches, combining computer science with organizational studies. Notable projects include frameworks for task mining, reinforcement learning in care processes, and pattern recognition in government transparency assessments. Research initiatives often involve cross-disciplinary teams, exploring topics like workplace well-being through process mining, decision-making support systems, and overcoming barriers to BPM adoption. His work bridges theoretical advancements with real-world impact, influencing both academic discourse and practical business solutions.
Jennifer Tang is a Postdoctoral Associate at the Massachusetts Institute of Technology (MIT), holding dual appointments in the Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems (LIDS). She conducts her research under Professor Ali Jadbabaie, focusing on interdisciplinary problems at the intersection of information theory, network science, and social dynamics. Her position is temporary as she actively seeks a permanent academic role through the 2025 job market. Her academic credentials include: Ph.D. in Electrical Engineering and Computer Science from MIT, advised by Professor Yury Polyanskiy Bachelor of Science in Engineering (B.S.E.) in Electrical Engineering from Princeton University, with independent work supervised by Paul Cuff Dr. Tang's research program centers on theoretical and applied aspects of information theory, including channel capacity, quantization, and data compression. She investigates prediction and estimation in high-dimensional settings, data analytics for complex systems, and mathematical modeling of social dynamics and inference in multi-agent networks. Her work employs tools from statistics, optimization, and network theory to address challenges in communication, decision-making, and societal systems, with particular emphasis on opinion dynamics under social pressure and efficient representation of probability distributions. Analysis of her publication record reveals consistent contributions to information-theoretic limits, social network modeling, and compression techniques. Her works frequently appear in top venues like IEEE Transactions on Information Theory and major conferences (ISIT, CDC, ACC), demonstrating expertise in bridging theoretical foundations with real-world applications in networked systems and societal challenges. Her scientific achievements have been recognized with: Best Student Paper Award at IEEE International Symposium on Information Theory (ISIT) 2022 Best Student Paper Award at IEEE Machine Learning for Signal Processing (MLSP) 2022 Student Competition Winner at the Shannon Centennial Celebration Dr. Tang maintains an active teaching portfolio, having served as instructor for MIT 1.022: Introduction to Network Models (Spring 2025) and teaching assistant for multiple core courses including 6.008 (Introduction to Inference), 6.041/6.431 (Probabilistic Systems Analysis), 6.437 (Inference and Information), and 6.439 (Statistics, Computation and Applications). She also contributed to the MIT Women's Technology Program as a Mathematics Instructor during summer 2017. Her research is embedded within MIT's Laboratory for Information and Decision Systems (LIDS) and Institute for Data, Systems, and Society (IDSS), two premier interdisciplinary laboratories fostering collaboration on data-driven decision-making, societal challenges, and foundational theory in information and systems.
Athinagoras Skiadopoulos is a computer systems researcher at Stanford University's School of Engineering, Department of Computer Science, focusing on the intersection of database systems and operating systems. His work centers around the innovative DBOS (Database-oriented Operating System) project and large-scale machine learning infrastructure, collaborating with prominent researchers including Christos Kozyrakis and Michael Stonebraker. His primary research interests include: Database-oriented Operating Systems (DBOS) Distributed systems for large-scale machine learning Resource management and optimization in data-intensive systems Transaction processing and data governance High-performance networking for accelerated computing Fault tolerance in distributed training systems Skiadopoulos's research trajectory shows a clear evolution from foundational DBOS architecture toward applications in large-scale machine learning systems. His early publications established the DBOS framework for operating system design using database principles, while his recent work addresses critical challenges in distributed training of massive neural networks. Systems like ReCycle and SlipStream demonstrate innovative approaches to pipeline adaptation and failure recovery during distributed training. His most recent 2025 work on accelerating Mixture-of-Experts training represents the cutting edge of efficient large model training infrastructure. Through his research, Skiadopoulos has established himself in both the database and systems research communities, with publications in premier venues including SOSP, OSDI, VLDB, and CIDR. His work consistently bridges theoretical database concepts with practical systems implementations, demonstrating how database techniques can solve real-world systems challenges in modern computing environments.
Yann Ponty is a tenured CNRS Researcher at the Computer Science Department (LIX) of École Polytechnique (Institut Polytechnique de Paris, France). He leads the AMIBio team and serves as Deputy Director of LIX. His work focuses on developing bioinformatics methods at the intersection of computer science, mathematics, and molecular biology, particularly for RNA structure prediction, design, and evolution. He holds leadership roles in the ISCB Board of Directors (2025-2027) and the HDR referent for the IDIA department (CS&Interactions) at IP Paris. Research Interests: RNA folding/design/evolution, RNA-RNA/RNA-protein interactions, random generation, enumerative combinatorics, discrete algorithms, parameterized complexity, RNA visualization Key Contributions: Developed algorithms for RNA inverse folding, pseudoknot modeling, and dynamic programming optimization Collaborations: Partnerships with institutions like Simon Fraser University, Boston College, and Université Paris-Saclay His recent publications (15 most recent) span RNA structure prediction, pseudoknot partition functions, linear-time inverse folding algorithms, and parameterized sampling techniques. The work emphasizes dynamic programming, combinatorial approaches, and integration of experimental data for improving RNA modeling. Scientific awards include election to the ISCB Board of Directors (2025-2027) and leadership roles in academic networks like GdR BIM. He actively contributes to software development (VARNA, RNANR, SPARCS, IncaRNAtion, RNARedPrint) and serves as Associate Editor for Bioinformatics (OUP). Teaching engagements include graduate-level courses in combinatorial optimization, RNA bioinformatics, and algorithms at Université Paris-Saclay and École Polytechnique.
Gerhard Friedrich is a Full Professor at the University of Klagenfurt, leading the Institute for Artificial Intelligence and Cybersecurity. He previously served as Dean of the Faculty of Technical Sciences (2013–2021). His roles include Coordinator for International Relations of the Faculty of Engineering and Member of the Faculty Conference of Technical Sciences. He holds a PhD in Computer Science from Vienna University of Technology and has extensive industry experience, including heading departments at Siemens Austria and research roles at Siemens Corporate Research and Stanford Research Institute. Research focuses on knowledge-based systems, recommender systems, configuration and planning, and production informatics. His work bridges theoretical AI with practical applications in manufacturing, software development, and business processes. He has authored a book on recommender systems (translated into Japanese and Chinese) and contributed to prestigious journals like Artificial Intelligence and IEEE Transactions . He has organized major conferences such as the German Conference on Artificial Intelligence (2016) and served as editor and program committee member for leading venues. His awards include Fellowships from the European and Asia-Pacific AI Associations (2012, 2023). His advising and grants include leadership in applied AI projects and international collaborations. He directs the Intelligent Systems and Business Informatics research group, emphasizing interdisciplinary innovation.
Tilman Zuckmantel is a Research Assistant at the Software, Data, People & Society (SDPS) section of the Department of Computer Science , University of Copenhagen. His work focuses on distributed computing and data-centric systems, particularly in asynchronous choreographies and microservices architecture. Recent research outputs include: DACEO (2025): A declarative framework for asynchronous choreographies with event-ordering and object-oriented extensions. Event-based Data-Centric Semantics (2022): A model for consistent data management in microservices environments.
Renata Medeiros de Carvalho is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Process Analytics and EAISI Health groups. She holds a PhD in Computer Science from Federal University of Pernambuco (Brazil), an MSc and BSc in Computer Engineering from University of Pernambuco, and has conducted postdoctoral research at UQAM (Canada). Her research focuses on adaptive and declarative business processes, with particular emphasis on healthcare and data privacy. Education: PhD in Computer Science, Federal University of Pernambuco (2015) MSc in Computer Engineering, University of Pernambuco BSc in Computer Engineering, University of Pernambuco Research Interests: Flexible business processes and Process Mining Declarative modeling (e.g., OCBC language) Healthcare process optimization GDPR compliance frameworks Key Projects: PATIENCE 2 : Patient-centric healthcare through nomadic sensing BPR4GDPR : GDPR compliance toolkit Awards: Xerox University Affairs Committee Grant NSERC Engage Grant Teaching & Leadership: Local coordinator for EIT Digital Data Science and Erasmus Mundus BDMA master programs Teaches courses like Advanced Process Mining and DBL Data Challenge
Omar Lizardo is the LeRoy Neiman Term Chair Professor of Sociology at the University of California, Los Angeles, where he conducts empirical and theoretical research at the intersection of multiple sociological fields. He holds office in Haines Hall and maintains an active research program that spans cultural sociology, historical sociology, social movement studies, cognitive sociology, and network science. His research interests focus on the relationship between social position and cultural taste across multiple levels of analysis and time scales, with particular emphasis on patterns of cultural change and cultural stratification. He also contributes to the interdisciplinary field of Network Science, examining social tie formation, the dynamics of social networks, and the influence of personal attributes on network structure. His work bridges sociology with physics, computer science, and complex systems theory. Lizardo's recent publications demonstrate a consistent focus on the intersections of culture, cognition, and networks, with articles appearing in top sociology journals including American Sociological Review, Sociological Theory, and Social Forces. His research often employs innovative methodological approaches to analyze cultural patterns and social networks. American Sociological Association Section on Theory Theory Prize for Outstanding Article (Honorable Mention), 2017 American Sociological Association Section on Emotions Recent Contribution Award (Honorable Mention), 2015 American Sociological Association Section on Comparative and Historical Sociology Charles Tilly Best Article Award, 2014 Best Paper Award at The Fifth ACM Hotplanet Workshop, 2013 American Sociological Association Section on Theory Lewis Coser Award for Theoretical Agenda Setting, 2013 American Sociological Association Section on Culture Clifford Geertz Prize for Best Article, 2008 Lizardo has successfully secured significant grant funding from multiple sources including the National Science Foundation, Army Research Laboratory, and National Institutes of Health, with projects focused on network science, social dynamics, and health behaviors. He has advised graduate students like Isaac Jilbert and collaborates extensively with scholars across disciplines. Beyond his research, he serves in important editorial roles, having been former co-editor of American Sociological Review and currently co-editing Sociological Theory, while also serving on the Board of Reviewing Editors for the journal Science.
Nikolaos Tsiogkas is an Assistant Professor in the Declarative Languages and Artificial Intelligence (DTAI) group at KU Leuven's Faculty of Engineering Technology, Department of Computer Science. His research focuses on robotics, artificial intelligence, and autonomous systems, with particular emphasis on cognitive reasoning, knowledge representation, and sensor fusion for robotic navigation. Projects: Promotor of initiatives like 'Harnessing Robotics for Safe Agriculture' (2024-2028) and 'ROSANA: Robust Semantic Navigation in Orchards' (2022-2027); co-promotor in demining robotics and multi-arm manipulation research. Research: Combines symbolic AI with robotics, exploring knowledge graphs for explainable navigation, reinforcement learning frameworks, and computationally efficient free-space detection algorithms.
Georgios Bardis is a permanent Assistant Professor at the Department of Informatics and Computer Engineering , School of Engineering , University of West Attica . He holds a PhD in Informatics from University of Limoges (2006), an MSc in Software Systems from University of California, Santa Barbara (1994), and a Diploma in Computer Engineering & Informatics from University of Patras (1992). His career spans multiple academic roles, including Lecturer at University of West Attica (2018-2021) and Professor of Applications at TEI of Athens (2010-2018). Research Interests : Focus on Intelligent Computer Graphics , Declarative Modeling , and Multicriteria Decision Analysis . His work integrates AI into 3D scene synthesis, urban planning, and semantic decision systems. Awards : Master Microsoft Office Specialist (MOS), 2003 NAT Scholarships for Academic Excellence (1989-1992) 1984 Monetary Prize from Hellenic Mathematical Society Leadership : Member of AKIIS Research Lab (University of West Attica), Editorial Board of International Journal of Systems Biology and Biomedical Technologies , and Reviewer for International Journal of Digital Earth . Publications : 5 peer-reviewed journals, 2 books, 8 book chapters, and 22 conference papers. Key areas include WebGL avatars, urban data analysis, and 3D modeling with AI.
Siegfried Nijssen is an Assistant Professor of Data Mining and Artificial Intelligence at the Catholic University of Louvain (UCLouvain) in Belgium, working within the ICTEAM research institute's Artificial Intelligence and Algorithms group. He has been at UCLouvain since 2016, previously serving as an Assistant Professor at University Leiden (2012-2016) and completing postdoctoral work at KU Leuven (2006-2015). He earned his PhD in Computer Science from University Leiden in 2006. His research focuses on making data analysis simpler through intersections between pattern mining, exploratory data analysis, and programming paradigms in Artificial Intelligence, particularly constraint programming and probabilistic programming. He has developed techniques for analyzing diverse data types including graphs, networks, and multi-relational data. His work bridges theoretical foundations with practical applications in decision tree learning, probabilistic networks, and source code analysis. Nijssen's recent publications demonstrate a strong focus on optimal decision trees, constraint-based pattern mining, and applications in bioinformatics and education. His research shows consistent evolution from foundational graph mining work (including the Gaston algorithm developed in 2004) to current work integrating machine learning with constraint programming for interpretable AI solutions. As an educator, he teaches courses including Mining Patterns in Data, Databases, and Artificial Intelligence and Machine Learning seminars at UCLouvain. He has advised numerous PhD students and postdocs, primarily in collaboration with Pierre Schaus, with former students like Tias Guns now holding professorships.
