Prof. Dr. Ralph Bergmann is a full professor at the University of Trier since 2004 and leads the Experience-Based Learning Systems research group. Since 2020, he serves as topic-field leader for experience-based learning systems at the Trier Branch of the German Research Center for Artificial Intelligence (DFKI) . He has directed approximately 35 EU/DFG/BMBF-funded projects and authored over 200 papers (h-index 40) with four books and 13 edited proceedings. Academic Rank: Professor (Business Information Systems II) Key Collaborations: DFKI, KI-AIM, KIAFlex, DZW (Digital Twins), myRPA, SPELL Ralph Bergmann's research focuses on hybrid AI systems that combine data-driven methods (machine learning, case-based reasoning) with semantic technologies (ontologies, knowledge graphs). His work addresses knowledge-intensive processes like emergency call handling, healthcare discharge management, smart factory automation, and political argumentation analysis. He explores similarity assessment, workflow flexibility, and context-sensitive reasoning through frameworks like ProCAKE and CBRkit . Recent publications highlight IoT data integration (SensorStream, DataStream XES), large language models for knowledge engineering, and graph neural networks for similarity ranking. Application domains span Industry 4.0 , oncology decision support, water resource management, and clinical guideline conformance checking. His teaching includes courses on Data Mining , Semantic Technologies , and Research Internships in business informatics, with consultation hours held both in-person and online. Current projects like KI-AIM and SPELL emphasize AI anonymization in medicine and semantic platforms for control centers .
Jiabin Wu is a researcher at the Chair of Computing in Civil and Building Engineering, Technical University of Munich (TUM), specializing in Building Information Modeling (BIM) and AI-driven solutions for building design compliance. His work focuses on automating regulatory adherence in early design stages through computational innovation, with contact via j.wu@tum.de and +49 (89) 289-23147. His research centers on automated code compliance checking, design adaptation, and BIM conflict resolution using artificial intelligence techniques. Key methodologies include reinforcement learning for conflict resolution, graph-based semantic enrichment, and parametric modeling to enhance IFC (Industry Foundation Classes) models. This work addresses critical gaps in construction informatics by enabling self-healing building designs that dynamically adapt to regulatory requirements while preserving design intent. Analysis of Wu's 2022-2025 publications reveals a cohesive trajectory toward intelligent BIM ecosystems. Core themes include the 'Design Healing' framework for automated compliance, spatial logic integration in IFC models, and machine learning applications for conflict resolution. His research bridges civil engineering with computer science, emphasizing practical implementations that reduce manual review cycles and improve design efficiency in architectural workflows. Wu actively mentors graduate students, supervising theses on BIM-based issue resolution, circulation design optimization, and egress compliance. He contributes to TUM's educational mission through teaching 'BIM.fundamentals' in summer semesters 2022 and 2023, focusing on scalable assessment methods for large student cohorts. His academic service extends to developing pedagogical frameworks that integrate industry standards with computational thinking. As part of TUM's research infrastructure, Wu collaborates within the BIM-Lab and specialized groups including Information Management and Digital Twinning. His work leverages the university's Robotic Fabrication Lab resources and aligns with cross-departmental initiatives in spatial computing, advancing the integration of physical construction processes with digital model evolution.
Grégoire Sutre is a CNRS Research Fellow at LaBRI, University of Bordeaux, specializing in formal verification and model checking of infinite-state and concurrent systems. His research includes theoretical and practical aspects of verification, with applications ranging from systems code to biological models. Research Interests: Model-checking of safety properties Infinite-state and distributed systems Abstraction refinement techniques Vector addition systems and Petri nets Software verification and concurrent systems Teaching: He currently teaches Software Verification at the Master 2 level, with lab sessions in OCaml focusing on abstract interpretation and static analysis techniques. Students: He supervises several PhD students working on reachability, concurrency, and binary analysis. Projects: He has led or participated in multiple ANR-funded projects such as BraVAS, ReacHard, VACSIM, and SPaCIFY, focusing on formal methods and verification of critical systems.
Marc Zeitoun is Professor of Computer Science at the University of Bordeaux, teaching within the UFR Informatique and conducting research in the M2F team at LaBRI. His work focuses on theoretical computer science, especially logics, automata, formal languages, and verification. Research interests: Logics and automata theory Formal languages and expressiveness Finite-model theory and algebraic/topological connections Algorithms for finite automata and quantitative games Automatic verification and model-checking Distributed and infinite systems He has led or participated in several ANR projects (UnReAL 2025–2029, Delta 2016–2022, FREC 2010–2014) and develops the MeSCaL software for computations on regular languages. Teaching: Introduction à la calculabilité (L2 Math-info & CMI) Théorie de la complexité (M1 Cryptologie & Sécurité Informatique) Student supervision: Co-supervised Thomas Place (Master CSI, Complexity Theory)
Michalis Kokologiannakis is an Assistant Professor in the Department of Computer Science at ETH Zurich, Switzerland. Previously, he was affiliated with the Max Planck Institute for Software Systems (MPI-SWS) in Germany where he completed his PhD. His research focuses on programming languages, compilers, and software verification, with particular emphasis on: Automated verification and testing of concurrent programs Weak memory models employed by modern microprocessors Stateless model checking techniques Formal methods for program analysis His work has led to the development of several verification tools including GenMC (a stateless model checker for C/C++ programs under weak memory models) and Kater (a tool that automates weak memory model metatheory and consistency checking). Dr. Kokologiannakis has published extensively at top-tier programming languages and verification conferences including PLDI, POPL, OOPSLA, and CAV. His research has pioneered advances in stateless model checking, partial order reduction, and verification under weak memory consistency models. He completed his MEng at the National Technical University of Athens (NTUA) before earning his PhD from MPI-SWS.
Silvia Lizeth Tapia Tarifa is an Associate Professor in the Department of Informatics at the University of Oslo, specializing in formal methods for parallel and distributed systems. She serves as one of the principal investigators for the NFR SJM (Smart Journey Mining) project, which runs until 2026, and actively participates in Digital Twins research with a focus on GDPR-compliant data management. Her academic affiliations include the Reliable Systems research group and the Analytical Systems and Reasoning (ASR) group at the Department of Informatics. Professor Tapia Tarifa's research spans formal methods, concurrency theory, and distributed systems with particular emphasis on self-adaptive systems, semantics of concurrent languages, compositional reasoning about distributed system behavior, and formal modeling of resource usage. Her work bridges theoretical computer science with practical applications in digital twins, GDPR compliance, and resource management in distributed environments. She has made significant contributions to the ABS language framework and active object models for parallel and distributed computing. Her publication record shows a consistent focus on formal verification techniques applied to emerging challenges in distributed computing. Recent work demonstrates increasing attention to digital twins technology, user journey modeling, and privacy-preserving systems. The research trajectory reveals evolution from foundational work on concurrent language semantics toward applied research in self-adaptive systems and GDPR-compliant architectures, while maintaining strong theoretical underpinnings in formal methods. Young Research Talent grant from Research Council of Norway (2017), the only computer science grant in that call Fellow at United Nations University, International Institute for Software Technology (2007) Active participation in formal methods community as general chair, PC chair, and committee member Professor Tapia Tarifa has supervised PhD and master's students while teaching graduate-level courses. She has led significant research initiatives including the Analysis and Complex System Research Program at SIRIUS Center (ended 2023) and the EU MSCA-ITN REMARO project on Reliable AI for Marine Robotics (ended 2024). Her current research portfolio includes multiple active grants focused on digital twins, user journey analysis, and privacy-preserving distributed systems. She collaborates extensively with researchers across Europe through various EU-funded projects including FP7 ENVISAGE, FP7 FET UpScale, and FP7 FET HATS. Her research activities are centered around the ABS language framework and its applications to distributed systems verification. She maintains active collaborations through the SIRIUS Center and participates in the international formal methods community through conference organization and program committees.
Leandro Madrazo Agudin serves as Professor in the Department of Architecture at La Salle School of Architecture, Universitat Ramon Llull. His research integrates technical engineering with social sciences to address building sustainability and urban identity through data-driven approaches. His research spans Energy Efficiency Engineering (61% fingerprint match), Building Systems Engineering (52%), and Semantic Technology (40%), focusing on municipal data platforms for building renovation and socio-physical aspects of urban environments. Current work emphasizes semantic data models for energy management and the social construction of neighborhood identities in renewal processes. Analysis of recent publications (2019-2023) reveals three dominant trends: 1) Development of municipal data platforms like OpenSantCugat for building information management, 2) Semantic data-driven models connecting building physics with urban energy systems, and 3) Socio-spatial studies on placemaking and neighborhood identity. These strands demonstrate consistent interdisciplinary methodology bridging technical engineering solutions with social science perspectives. Scientific Awards: No awards were documented in the source material. Grants and projects include leadership in the European Commission's ACCORD project (2022-2025) for automated compliance checks in construction, RETABIT (2021-2024) developing municipal building rehabilitation platforms, and Energy Communities (2023-2024) addressing interdisciplinary renovation diagnostics. These demonstrate sustained funding for municipal-scale sustainable building initiatives. He actively contributes to the Human-Environment Research group at La Salle, which investigates socio-technical interactions in built environments through projects like OpenSantCugat and RETABIT. This team specializes in translating building data into municipal services for energy efficiency and urban renewal.
Dr. Nour Ali serves as a Professor and Vice-Dean of Education in the College of Engineering, Design and Physical Sciences at Brunel University London, where she co-heads the Brunel Software Engineering Lab. With a PhD in Software Engineering from Universidad Politecnica de Valencia and a Computer Science degree from Bir-Zeit University, she brings extensive international experience from previous positions at University of Brighton, Lero (Irish Software Engineering Research Centre), and Politecnico di Milano. PhD in Software Engineering, Universidad Politecnica de Valencia, Spain Major in Computer Science, Bir-Zeit University, Palestine PG Certificate in Teaching and Learning in Higher Education, University of Brighton Fellow of the Higher Education Academy (HEA) Her research focuses on developing software architecture techniques for distributed, mobile, and adaptive systems, with particular expertise in microservice architecture recovery and visualization. With over 70 publications spanning two decades, her work bridges theoretical architecture principles with practical implementation challenges. Recent research demonstrates a strong focus on microservice architecture recovery tools like MiSAR and analysis of service granularity adaptation. Dr. Ali has made significant contributions to the software engineering community through editorial roles including Deputy Editor in Chief of IET Software, committee memberships for major conferences like ICSE and ASE, and reviewer positions for EPSRC and other funding bodies. Her publications reveal consistent contributions across architecture consistency, microservice systems, and adaptive requirements engineering, with recent work increasingly focusing on practical tool development for architecture recovery. External Examiner, York St John University (2021-2025) Deputy Editor in Chief, IET Software Committee member for ICSE, ASE, EASE, ICSA, MOBILESoft conferences EPSRC Full College Member (2018-present) Reviewer for Dutch Research Council (NWO), UK UNESCO Newton Prize As an educator, Dr. Ali leads CS3100 Software Project Management and contributes to multiple undergraduate and postgraduate courses. Her teaching philosophy integrates research insights with practical software engineering skills, supported by her PG Certificate in Teaching and Learning and HEA Fellowship. She actively supervises PhD students and contributes to curriculum development as Vice-Dean of Education for her college.
Joanne M. Atlee is a Professor in the Department of Computer Science at the University of Waterloo, Faculty of Mathematics. She has been an active member of the software engineering research community since at least 2015, with continuous involvement in major conferences through 2025. Her research focuses on software product lines, domain-specific languages, cyber-physical systems, and model-driven engineering. She has made significant contributions to variability management and formal methods in software engineering, with publications spanning industrial case studies, model analysis techniques, and language workbenches. Her recent publications demonstrate a strong emphasis on practical applications of software engineering theory, particularly in the areas of dynamic product lines, declarative analysis, and human-in-the-loop verification systems. Her work bridges theoretical foundations with industrial applicability. Dr. Atlee has held numerous leadership positions in the academic community, including serving as General Chair for ICSE 2019, one of the most prestigious conferences in software engineering. She has also been active as a committee member, session chair, and mentor across multiple conferences including ASE, ICSE, ESEC/FSE, and MODELS. In terms of academic service, she has contributed to doctoral symposia, new faculty development, and equity initiatives, demonstrating a commitment to mentoring the next generation of researchers and promoting diversity in computing.
Nian-Ze Lee is an Assistant Professor at the Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Taiwan University, Taiwan, where they lead the Formal Methods and Analysis for Computing and Engineering Laboratory (ForMACE Lab). Additionally, Lee holds a position as a Gastprofessor (Guest Professor) affiliated with the Software and Computational Systems Lab (SoSy-Lab) at LMU Munich, Germany. Lee's research focuses on formal methods, with particular expertise in model checking, program analysis, and electronic design automation. Their work bridges hardware and software verification, developing techniques that transfer methodologies between these domains. A significant portion of their research involves stochastic Boolean satisfiability and threshold logic circuits, with applications in both hardware and software verification. Lee's recent publications demonstrate a strong trend toward developing cross-domain verification techniques, particularly focusing on how hardware model checking approaches can be adapted for software verification. Their work on interpolation-based model checking and configurable program analysis has received recognition through multiple best paper and artifact awards at top-tier conferences. ACM SIGSOFT Distinguished Paper Award (FSE 2024) Best Artifact Award (FSE 2024) Distinguished Artifact Award (TACAS 2024) Best Paper Award (SPIN 2024) Lee has secured research funding including a grant from the German Research Foundation (DFG) for the project 'Bridging Hardware and Software Analysis.' They actively maintain several software projects including Btor2C, Btor2-Cert, CPV, MoXIchecker, and contribute to CPAchecker and BenchExec. Lee is currently recruiting Ph.D., Master's, and Bachelor's students to work on formal methods research, with Ph.D. students potentially enrolled at LMU Munich through a DFG-funded project.
Matthew Lease is a Professor at the School of Information, University of Texas at Austin, where he serves as Director of Doctoral Studies and Assistant Graduate Advisor. He is a Distinguished Member of the Association for Computing Machinery (ACM), a Senior Member of the Association for the Advancement of Artificial Intelligence (AAAI), and an Amazon Scholar. Lease co-directs the $20M NSF-Simons AI Institute for Cosmic Origins (CosmicAI) and is a faculty founder and leader of UT's Good Systems, an eight-year, $20M university-wide Grand Challenge aimed at designing responsible AI technologies. In 2023-2024, he was invited four times to address the Texas Legislature on responsible AI. Ph.D. Computer Science, Brown University, 2010 M.Sc. Computer Science, Brown University, 2004 B.Sc. Computer Science, University of Washington, 1999 Lease directs the UT Austin Laboratory for Artificial Intelligence and Human-Centered Computing (AI&HCC), where his research spans artificial intelligence modeling and human-computer interaction design. His work focuses on creating novel datasets, building AI models, and evaluating both model performance and their impact on end-users. When automated AI falls short, his team designs human-in-the-loop approaches, leveraging AI model explanations and creative user interfaces. To promote fair AI, they focus on better annotation techniques to avoid bias and develop modeling strategies to mitigate dataset biases. Their work tackles real-world problems as part of UT Austin's Good Systems Grand Challenge, with an ongoing emphasis on content moderation—exploring automated, human-in-the-loop, and human-safe practices to combat disinformation, hate speech, and online polarization. Lease's recent publications (2021-2024) demonstrate a strong focus on human-centered AI, particularly in the areas of fair and explainable AI, content moderation, fact-checking, and crowdsourcing. His research integrates technical AI development with human factors considerations, emphasizing the importance of designing AI systems that work effectively with human users. The publications reveal a consistent theme of addressing bias in AI systems, improving human-AI collaboration, and developing methods to ensure the ethical deployment of AI technologies in sensitive domains like content moderation and misinformation detection. His work shows progression from foundational techniques in crowdsourcing and human computation toward more sophisticated approaches that consider psychological impacts and ethical implications. 2024 Test of Time Paper Award, AAAI Conference on Human Computation and Crowdsourcing (HCOMP) 2024 Most Influential Paper Award, IEEE/ACM International Conference on Automated Software Engineering (ASE) 2024 Best Paper Honorable Mention, ACM Conference on Computer Supported Cooperative Work (CSCW) 2022 Best Student Paper, Conference on Information Systems and Technology (CIST) 2020 Conference Award Track, Journal of Artificial Intelligence Research (JAIR) 2019 Best Student Paper, European Conference for Information Retrieval (ECIR) Early Career awards from DARPA, NSF, and IMLS Lease has secured significant funding for his research, including the $20M NSF-Simons AI Institute for Cosmic Origins and the $20M Good Systems Grand Challenge. His lab, AI&HCC, has developed numerous tools and methodologies for human-AI collaboration, particularly in the context of content moderation and fact-checking. He has advised numerous students who have gone on to publish in top-tier conferences and journals in AI, HCI, and NLP. Lease actively collaborates with industry partners including Amazon, where he serves as an Amazon Scholar, and has served on advisory boards for JASIS&T, Texas Advanced Computing Center (TACC), and UT Austin-Amazon Science Hub. His research has led to practical tools like SQUARE for aggregating crowd responses and methods for transparent AI evaluation. Lease leads the UT Austin Laboratory for Artificial Intelligence and Human-Centered Computing (AI&HCC), which has developed innovative approaches to human-AI collaboration. The lab's work on content moderation addresses critical challenges in online safety, including the psychological well-being of content moderators who face traumatic material. Their research on fair and explainable AI has produced methods for detecting toxic speech while maintaining accuracy across demographic groups. The lab actively collaborates with fact-checking organizations through co-design processes to create tools that meet real-world needs. As part of UT Austin's Good Systems initiative, the lab is developing AI technologies that prioritize human values and social responsibility from the outset of the design process.
KC Sivaramakrishnan serves as an Adjunct Professor at the Indian Institute of Technology, Madras and CTO of Tarides, bridging academic research and industrial application in programming languages and systems. His dual roles reflect a commitment to advancing both theoretical foundations and practical implementations in computer science. His research spans Functional Programming, Language Runtimes, Concurrency/Parallelism/Distribution, and Weak Memory/Consistency models, with particular focus on the OCaml programming language. Sivaramakrishnan's work addresses fundamental challenges in concurrent and distributed systems through innovative language design and runtime techniques. Analysis of his publication record reveals a consistent trajectory from foundational work on effect handlers and concurrency models (2015-2020) to more recent contributions in verified systems, multicore programming, and distributed data types (2021-2025). His research demonstrates strong continuity in exploring how programming language principles can solve real-world systems challenges. Sivaramakrishnan actively contributes to the academic community through program committee memberships across major PL conferences including POPL, PLDI, ICFP, and SPLASH. His service spans multiple roles from committee member to track chair and diversity co-chair. As CTO of Tarides, he leads industrial efforts in OCaml compiler development and systems programming, maintaining a productive synergy between his academic research and industry leadership. His work with the OCaml community includes significant contributions to multicore support and effect handler implementations.
Manju Puri is a Professor at Duke University's Fuqua School of Business and affiliated with the National Bureau of Economic Research (NBER). Her research focuses on banking, financial decision-making, and relationship lending. Her primary research interests include Finance , Banking , Loan Defaults , Relationship Lending , Risk Assessment , and Financial Data Analysis . Her work examines how banks use relationship-specific information to reduce loan defaults and improve lending decisions. Her recent publications analyze the impact of customer-bank relationships on loan default rates using large datasets from German banks. Her research demonstrates that banks with relationship-specific information act differently in screening and monitoring borrowers, which helps reduce loan defaults. She has found that even establishing a simple savings or checking account and observing activity prior to loan granting can significantly reduce default rates. Her work has been highly cited in the field of banking and finance, particularly in understanding how information asymmetry affects lending practices.
Sara Riva is an Associate Professor (Maître de Conférences) in Computer Science at Université de Lille, affiliated with the CRIStAL laboratory (UMR 9189). She is a member of the BioComputing research group and the MSV thematic group. Her academic journey includes a PhD jointly supervised by Université Côte d'Azur and Università degli Studi di Milano-Bicocca (2019-2022) and postdoctoral research at Université de Bordeaux (2022-2023). Her research explores Discrete Dynamical Systems , Cellular Automata , and Boolean Networks , with emphasis on equation solving, factorization methods, and dynamics modeling. She develops algorithmic approaches to analyze complex behaviors in computational and biological systems. Publications (2019-2023) demonstrate consistent focus on theoretical foundations of discrete systems, with applications in systems biology and complex modeling. Key themes include Boolean network dynamics, computational pipelines for equation solving, and sensitivity analysis in cellular automata. Awards: First prize for PhD students (Computer Science), STIC doctoral school Teaching: Extensive instructional experience at Université de Lille and Université Côte d'Azur covering: Algorithms & Programming (72+ lab hours) Databases (39+ lab hours) Logic, Graph Theory, Web Technologies (18+ lab hours each) IT Security and Information Coding (18 hours each) Academic Service: Member of CRIStAL's parity commission; Program Committee for AUTOMATA 2024; President of ADSTIC PhD association (2021-2022); Organized summer schools (EJCIM 2022).
David N. Jansen is an Associate Professor at the Institute of Software , Chinese Academy of Sciences , Beijing, China. Previously, he held an Assistant Professor position at Radboud University Nijmegen (2007–2016) and a Postdoc role at RWTH Aachen University (2007). His work bridges academic research, software development, and international collaboration. Education : PhD in Computer Science from University of Twente (1998–2003); Diploma in Mathematics (with minors in Computer Science and Comparative Linguistics) from University of Bern (1990–1997); Certificate of Proficiency in English from University of Cambridge (2000). Research interests include stochastic model checking , UML extensions for probabilistic systems , Markov reward models , and formal verification of embedded systems . He has contributed to tools like MRMC and TCM , focusing on efficient data structures and lumping techniques for system validation. Publication trends show expertise in formal methods , probabilistic verification , and equivalence relations . His work spans automated analysis of probabilistic programs , stuttering equivalence algorithms , and applications to real-world systems like the European Train Control System. Leadership and Service : He has served on doctoral advisory boards (SIKS), supervised student projects (e.g., XML/XMI interface for TCM), and volunteered in Christian organizations. His technical skills include C, C++, UML, model checking, and database systems like MySQL.
