Carlos Olarte is an Associate Professor at the LIPN (Laboratoire d'Informatique de Paris Nord) within the Institut Galilée at Université Sorbonne Paris Nord . His research focuses on concurrent systems , formal methods , and rewriting logic , with affiliations to the AVISPA research group . He has contributed to tools like PTA2Maude , PITPN2Maude , and SiLVer , addressing formal verification and logic-based systems. He holds grants including NATO Science for Peace (SymSafe, 2023) and CNPq projects (LOGOCOSMICS, 2019–2022). His work spans theoretical computer science, with recent publications in Tableaux , JLAMP , and Coordination . He actively participates in academic service, including PC chair roles for PPDP 2025 and local organization of Petri Nets 2025 . His research tools emphasize formal analysis of real-time systems, modal logics, and strategic reasoning. He has developed frameworks for linear logic theorem proving and declarative system specification.
Ulle Endriss is a Professor of Artificial Intelligence and Collective Decision Making at the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam. He leads the COMSOC Group and is a member of both the Theoretical Computer Science research unit of the ILLC and the Centre for Explainable, Responsible and Theory-driven AI (CERTAIN). Endriss has held academic positions since 2005, progressing from Assistant Professor (2005-2011) to Associate Professor (2011-2019) and finally Full Professor since 2019. Endriss's research focuses on formal methods in AI, particularly in multiagent systems and knowledge representation, with special interest in computational social choice—the intersection of AI with economics and political science. His work spans voting theory, fair division, participatory budgeting, and game theory. He is actively involved in the ADDI project on digital democracy and regularly contributes to major AI conferences including AAMAS, IJCAI, and AAAI. His recent publications demonstrate a strong focus on computational social choice, with particular emphasis on voting systems, participatory budgeting mechanisms, and the theoretical foundations of collective decision making. His research combines theoretical rigor with practical applications in democratic processes and resource allocation problems. Runner-up for the Best Paper Award (2023) Best Paper Award (2016) Best Paper Award (2012) Best Paper Award (2005) Finalist for the Best Paper Award (2014) Finalist for the Best Paper Award (2008) IJCAI-JAIR Best Paper Prize 2016 Endriss regularly teaches courses on Computational Social Choice and Game Theory for MSc Logic and MSc AI programmes. He makes all his teaching materials openly available, consistent with his commitment to open access in both research and education. He has supervised numerous Master's theses that have led to published research. His work extends to outreach through platforms like Game-Academy.org and Pabuviz.org, which help explain complex voting concepts to broader audiences.
Dr. Malvin Gattinger is a researcher at the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam, specializing in Theoretical Computer Science. His work focuses on applying formal logic to computational problems, with particular emphasis on dynamic epistemic logic and model checking techniques. He maintains an active research profile with numerous publications in leading venues, including recent work extending into 2025. Dr. Gattinger's research centers on the intersection of logic and computer science, particularly exploring how knowledge and information propagate in multi-agent systems. His work on gossip protocols has significantly advanced our understanding of information dissemination in distributed systems, while his contributions to symbolic model checking have made complex epistemic logic verification more computationally feasible. His research demonstrates both theoretical depth and practical application potential, especially in areas requiring formal verification of knowledge-based systems. Analysis of Dr. Gattinger's publication history reveals a consistent focus on epistemic logic and its computational applications. His work shows a clear trajectory from foundational theoretical work to increasingly sophisticated implementation techniques, with a notable emphasis on symbolic methods using Binary Decision Diagrams and Zero-suppressed Decision Diagrams. The recent inclusion of topics like topological evidence models and perspective shifts indicates expanding research horizons while maintaining core methodological approaches. His publications appear primarily in specialized logic and theoretical computer science venues, reflecting deep expertise in these domains. While no specific scientific awards are listed in the available information, Dr. Gattinger's research has clearly made significant contributions to the field of dynamic epistemic logic. His work on model checking implementation (SMCDEL) and GoMoChe represents practical tools that have likely advanced research capabilities in the community. The inclusion of Logic4Peace demonstrates engagement with broader social applications of logical methods. Dr. Gattinger appears to be actively involved in research supervision and collaboration within the ILLC environment. His work often involves computational implementations, suggesting involvement with research groups focused on logic-based computation. The consistent publication record spanning over a decade indicates sustained research productivity and likely involvement in multiple research projects and collaborations within the Theoretical Computer Science group at ILLC. Based on his research focus and publications, Dr. Gattinger is likely associated with research groups or labs working on formal methods, multi-agent systems, and logic-based computation at the University of Amsterdam. His work on SMCDEL and GoMoChe suggests involvement with teams developing computational tools for epistemic logic verification.
Conrado Martinez Parra is a Professor in the Department of Computer Science at the Faculty of Computer Science, Universitat Politècnica de Catalunya (UPC). He is a core member of the ALBCOM research group, which focuses on Algorithmics, Bioinformatics, Complexity, and Formal Methods. Affiliation : Department of Computer Science, Faculty of Computer Science (FIB), UPC Research Group : ALBCOM - Algorísmia, Bioinformàtica, Complexitat i Mètodes Formals Email : conrado@cs.upc.edu ORCID : 0000-0003-1302-9067 Researcher ID : G-4629-2015 His research spans theoretical computer science with a strong emphasis on the design and analysis of algorithms and data structures. His work includes average-case analysis of algorithms, combinatorial generation, probabilistic methods in algorithmics, and applications in information retrieval and data stream processing. He has extensively studied multidimensional data structures such as quadtrees, K-d trees, and skip lists, analyzing their performance under various query models including partial match and orthogonal range searches. His recent publications reveal a sustained focus on algorithmic efficiency, sampling techniques, and probabilistic modeling in data structures. Trends indicate a deep engagement with randomized algorithms, unbiased estimation, and cache-efficient selection methods, reflecting both theoretical rigor and practical applicability in modern computing environments. Scientific Contributions Extensive publication record spanning over three decades, from 1989 to 2024. Active in major algorithmic conferences such as ANALCO, AofA, and AAAI. Contributions to foundational algorithm analysis including Hoare’s FIND, Quickselect variants, and deletion in binary search trees. Collaborative research with prominent figures in theoretical computer science across Europe. Professor Martinez Parra has advised or collaborated with several doctoral students, including Gustavo Lau, whose thesis on partial match queries he supervised. He has participated in numerous competitive R&D projects funded by national and regional programs, focusing on large-scale information processing and graph-based computing models. His work is supported by long-standing grants from Spanish and Catalan research councils. He is affiliated with the ALBCOM research group, a leading team in algorithmic research at UPC, contributing to both theoretical advances and practical implementations in combinatorics and data structure optimization.
Farhad Arbab is a researcher at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands, affiliated with the Computer Security department. His work focuses on formal methods for modeling and analyzing cyber-physical systems (CPS) and coordination models. Current affiliation: Researcher at CWI's Computer Security department Research areas: Cyber-Physical Systems, Formal Methods, Workflow Modeling, Constraint Automata Arbab's research develops component-based semantic models for CPS using constraint automata and the Reo coordination language. His framework enables: Algebraic composition of cyber-physical components Formal modeling of priority constraints in workflows Runtime composition with lazy expansion techniques Verification via Maude rewriting logic system Recent publications analyze: Parallel composition of constraint automata (2025) Concurrency in rule-based machines (2025) Runtime composition techniques (2023) Formal frameworks for distributed CPS (2022) Awards & Projects: FACS Best Paper Award (2015) Bronzen Achievement Award (2009) EU/NWO-funded initiatives: COMPAS (2008), WoMaLaPaDiA (2007), CREDO (2006)
Antonio Violi is an Associate Professor at the University of Sannio (UNISANNIO) in the Department of Law, Economics, Management and Quantitative Methods (DEMM). His research focuses on stochastic programming applications in industrial optimization, energy systems, and logistics. Key research areas include: Stochastic Optimization Risk Management Energy Procurement Smart Mobility Solutions Industrial Performance Modeling Logistics Network Optimization Recent publications (2024) address advanced models for: Production error prevention Dynamic performance evaluation systems Tourism car-pooling optimization Earlier works cover energy market modeling (2008-2021), logistics e-marketplaces (2018), and agri-food supply chain dynamics (2023).
Stephen Baek is a faculty member at the University of Iowa, Department of Industrial and System Engineering, with a PhD from Seoul National University's School of Mechanical and Aerospace Engineering. His research spans interdisciplinary applications of machine learning in computational modeling, biomedical engineering, and geometric data processing. Current Affiliation: University of Iowa, Department of Industrial and System Engineering PhD Institution: Seoul National University Stephen's work focuses on physics-informed machine learning , multiscale modeling , and geometric deep learning . He develops algorithms that integrate physical principles with neural networks for applications in energetic materials , human pose estimation , and medical imaging . His research also includes federated learning and interpretable AI for constraint-based synthesis and text classification. Recent publications highlight a physics-aware deep learning framework (PARCv2) for spatiotemporal dynamics, graph convolutional networks for airway mesh smoothing, and prototype trajectory methods for explainable AI. This work bridges computer science with applied physics and healthcare domains. Stephen actively collaborates across disciplines, evidenced by co-authors from institutions like Iowa, Seoul National University, and Samsung Electronics, with publications in venues such as CoRR , J. Mach. Learn. Res. , and NeurIPS . His methodological innovations address challenges in model heterogeneity , 3D surface processing , and constraint satisfaction .
Sihem Amer-Yahia is a distinguished Research Professor at the University of Grenoble Alpes (affiliated with Grenoble Informatics Laboratory ), with significant contributions to database systems , data exploration , and fairness in AI . Her work bridges human-computer interaction and machine learning to create systems that enhance data-driven decision-making. Research Pillars : Algorithmic fairness, interactive data mining, recommender systems, and human-AI collaboration Recent Advances : 2023-2025 publications focus on statistically sound hypothesis testing , multi-objective recommendation , and conversational analytics Leadership : Co-organized major conferences (DASFAA 2024) and led DEI initiatives in database communities Her 15 most recent articles (2020-2025) span topics like producer fairness in recommendation , statistical hypothesis frameworks , and AI-powered education systems , with keywords covering database optimization , reinforcement learning , and ethical data mining . She actively contributes to ACM/IEEE journals and VLDB/SIGMOD conferences.
Mihaela Curmei is a researcher active in machine learning, recommender systems, and algorithmic fairness, with significant contributions to privacy-preserving methods, behavioral modeling, and optimization. Her collaborative work spans institutions and co-authors like Benjamin Recht, Georgina Hall, and Sarah Dean. Research Themes: Shape-constrained regression using polynomial optimization Privacy in matrix factorization with public features Dynamic preference modeling grounded in psychology Multi-learner systems under participation dynamics Temporal impacts of recommendation algorithms Technical Domains: Sum-of-squares programming Federated and distributed learning Stochastic reachability analysis Gradient flow-based dataset modeling Key Venues: Published in Operations Research , NeurIPS, ICML, RecSys, FAccT, and preprint archives. Her recent work (2023-2025) focuses on temporal effects in recommendations, private computation methods, and equilibrium analysis in multi-agent games. Earlier projects (2017-2021) include search engine indexing techniques (BitFunnel) and foundational studies on offline/online metric correlations in recommendation systems.
Prof. Dr. Matthias Tichy is a Full Professor and head of the Institute of Software Engineering and Programming Languages at Ulm University, Germany, since 2015. His research focuses on domain-specific languages (DSLs), model-driven engineering (MDE), self-adaptive software , and cyber-physical systems , with an emphasis on safety-critical applications and graph transformation formalisms. He employs empirical research methods to evaluate technical contributions and human factors in software engineering. University: Ulm University Role: Professor & Institute Head Research Interests span domain-specific languages for mechatronic systems, collaborative modeling , performance prediction in model transformations, and software evolution in industrial contexts. His work often bridges graph transformations and safety assurance for self-adaptive systems. Recent Publications highlight trends in model versioning (e.g., operation-based caching), DSL design (e.g., flowR for R code analysis), and automotive software testing (e.g., clustering test case specifications). He frequently collaborates with international institutions on topics like cyber-physical systems and IoT resilience . Key Collaborations include projects with Chalmers University, University of Gothenburg, and industrial partners like dSPACE GmbH. His grants and industry partnerships focus on automotive software , robotics , and self-healing systems .
François Pirot is an Associate Professor (Maître de Conférences) at Université Paris-Saclay since September 1, 2021. He conducts research at the LISN laboratory within the GALaC team and teaches at the Faculty of Science of Orsay. PhD in Mathematics (Radboud University) and Computer Sciences (Université de Lorraine), 2019 Postdoctoral experience: ULB (2019), G-SCOP (2019-2020), Inria Sophia Antipolis (2020-2021) His research focuses on graph coloring problems in diverse contexts such as graph powers, locally sparse graphs, and distributed algorithms, utilizing probabilistic methods and connections to bio-informatics through circular codes. He has advanced bounds for h -conflict-free coloring, acyclic coloring, and dichromatic numbers in oriented graphs, with applications to minor-closed families and geometric group theory. Scientific contributions include: Asymptotically tight bounds for chromatic numbers in sparse graphs Efficient fractional coloring algorithms for K_t-minor-free graphs Structural analysis of comma-free and mixed circular codes in genetic alphabets Charles Delorme Prize for outstanding thesis in Graph Theory (2019) Collaborations span institutions like ULB, G-SCOP, Inria, and cross-disciplinary fields from computer science to mathematical biology.
Enrico Tronci is a Full Professor in the Department of Computer Science at Università degli Studi di Roma La Sapienza , Italy. His research focuses on model checking, formal verification, and synthesis of cyber-physical systems, with applications to mission-critical and safety-critical domains such as space systems, smart grids, and healthcare. He leads the Model Checking Lab (MCLab) and has coordinated numerous national and international research projects funded by organizations including the European Community (EC), European Space Agency (ESA), and Italian Ministry of University and Research (MUR). Research Highlights : Automatic control software synthesis from closed-loop specifications Model checking algorithms for hybrid and stochastic systems Technology transfer in sectors like energy, transportation, and aerospace Teaching : Undergraduate: Software Engineering (Fall 2024) Graduate: Automatic Verification of Intelligent Systems (Fall 2024), Verification and Validation of Intelligent Systems (Spring 2025) Scientific Awards : Recipient of the IBM-Italia 1987 prize for best thesis in Artificial Intelligence Publications Trends : 2024: Scaling up model checking for cyber-physical systems via HPC 2023: Hormonal impact on behavior and fault-tolerant sensor deployments 2021-2022: In silico clinical trials, smart grid management, and scenario enumeration 2020: AI-guided diabetes patient modeling and forensic psychiatry applications Software Tools : QKS (Quantized Kontrol Synthesizer) NashMV (MAD systems verification) CMurphi (Hybrid systems model checker) FHP-Murphi (Probabilistic verification) BSP (Boolean symbolic programming)
Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University, Director of the Stanford AI Lab (SAIL), and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI). He also serves as Chief Scientist at Visual Layer and Virtue AI, and is a Member of the National Academy of Engineering. His research centers on Machine Learning Methods, Explainability, Fairness & Ethics of AI, and Machine Learning Systems. He develops interpretable and reliable models, addresses algorithmic fairness, and builds efficient large-scale ML systems through frameworks like XGBoost. His work bridges theoretical rigor with real-world applications in healthcare and human-centered AI. His recent publications (2023–2025) demonstrate leadership in generative AI evaluation, model reliability, and ethical frameworks. Key trends include developing live benchmarks for research synthesis, on-device calibration techniques, multi-objective optimization with constraints, and societal impact assessment tools—showcasing a trajectory from foundational ML systems to responsible AI deployment. Honors include: Member of the National Academy of Engineering Details about his advising and grant activities were not provided in source materials, though his leadership roles indicate extensive mentorship and funding oversight. As Director of SAIL, he shapes one of the world’s premier AI research centers, while his HAI fellowship drives interdisciplinary initiatives ensuring AI advances human welfare. His industry roles at Visual Layer and Virtue AI translate academic research into practical AI solutions.
Horia Popa is a Lecturer at the Faculty of Computer Science , West University of Timișoara. He has taught courses such as Artificial Intelligence , Network Administration , and Functional and Logic Programming since the 2022-2023 academic year, with additional historical courses dating back to 2011-2012. His teaching emphasizes hands-on lab work, software tools (Jess, CLIPS, WEKA), and project-based learning. Education: Not explicitly mentioned in the text. Research: Focuses on multi-agent systems, distributed constraints, asynchronous search algorithms, and system administration. Research Interests: Horia Popa specializes in Artificial Intelligence and Multi-agent Systems , particularly in asynchronous search techniques and constraint satisfaction problems. His work explores scale-free networks, nogood processors, and distributed execution environments. He also investigates Network Administration (DHCP, firewall configuration, kernel recompilation) and Knowledge Discovery through agent-based modeling. Article Trends: His publications (2001-2015) span Computer Science , Artificial Intelligence , and Multi-agent Systems . Key subfields include Asynchronous Algorithms , Constraint Networks , Protein Folding Simulation , and Kernel-Level System Management . He frequently uses NetLogo for large-scale simulations and integrates Samba/ldap for networked environments. Teaching and Projects: Students in his courses work on projects involving Jess , Prolog , and JADE . Assignments include implementing search algorithms (A*, Hill Climbing, RBFS), configuring NIS and Samba servers, and analyzing system monitoring tools like sar and top . He emphasizes practical implementation and cross-language diversity (e.g., Racket, Prolog).
Yaoxin Wu is an Assistant Professor at the Eindhoven University of Technology, affiliated with the Department of Industrial Engineering and Innovation Sciences. His research bridges deep learning and combinatorial optimization to solve complex problems in transportation, scheduling, and network design. Education : PhD in Computer Science from Nanyang Technological University (2023). Wu specializes in artificial intelligence and operations research , focusing on graph neural networks, stochastic programming, and multi-objective optimization. His work has significant applications in UAV routing and on-demand delivery systems. His 2025 publications highlight trends in neural combinatorial optimization for stochastic job shop scheduling, ride-hailing, and drone logistics. Key subfields include deep reinforcement learning, preference modeling, and topological graph learning. He has supervised 9 students, including PhD candidates Xia Jiang and Igor Smite, and Master’s students like Venkata Roshan Mannepu and Floor Halkes. Wu's research is funded by projects like LEO (Holland High Tech | TKI HSTM) and SURF Cooperative grants. His educational activities include teaching Fundamentals of Algorithmic Programming and AI-Driven Business Operations , emphasizing data-driven methods for manufacturing processes.
