Alberto Verdejo serves as an Associate Professor in the Department of Computer Systems and Computation at the Faculty of Computer Science, Complutense University of Madrid, Spain. His academic profile demonstrates a strong focus on theoretical aspects of computing and algorithmic foundations. His research interests center around Theoretical Computer Science , with specialization in Algorithms , Data Structures , Formal Methods , and Computational Complexity . Verdejo's work emphasizes the mathematical foundations of computing, particularly algorithm specification, derivation, and analysis. Verdejo has co-authored three significant Spanish-language textbooks that reveal his research trajectory: Introducción a la Computación (2006) focusing on theoretical computer science foundations; Especificación, derivación y análisis de algoritmos (2006) addressing formal program specification; and Estructuras de datos y métodos algorítmicos (2003) covering data structures and algorithmic methods including divide and conquer, greedy algorithms, dynamic programming, and backtracking. His teaching activities demonstrate commitment to foundational computer science education, with particular emphasis on making theoretical concepts accessible to students through practical exercises and structured learning materials.
Dr. José Carlos Cabaleiro Domínguez is a Full Professor in the Department of Electronics and Computing at the University of Santiago de Compostela's Faculty of Computing, Spain. He has been a member of CiTIUS (Centro singular de investigación en tecnoloxías da información e comunicación) since 2010 and was promoted to Full Professor in 2022 after serving as an Associate Professor since 1994. His academic journey began with a BS and PhD in Physics from the University of Santiago de Compostela in 1989 and 1994 respectively, with initial teaching experience at the University of A Coruña from 1990-1994. His research focuses on high performance computing, particularly in parallel systems architecture, development of parallel algorithms for irregular problems with sparse matrices, performance prediction and improvement of parallel applications, memory hierarchy optimization, and applications for grid and cloud computing. He has developed significant expertise in 3D point cloud processing from remote sensors like LiDAR, with applications in urban infrastructure analysis, powerline detection, and route planning. Analysis of his recent publications reveals a strong emphasis on optimizing resource allocation for big data frameworks, developing deep learning applications for point cloud classification, and creating efficient algorithms for powerline detection in LiDAR surveys. His work bridges theoretical computer science with practical applications in geospatial analysis and infrastructure monitoring. His research has been published in top-tier journals including IEEE Transactions, ISPRS Journal of Photogrammetry and Remote Sensing, and Future Generation Computer Systems, reflecting his significant contributions to the field of high performance computing and its applications. Dr. Cabaleiro actively collaborates with researchers across multiple institutions, as evidenced by his extensive publication record with co-authors from various universities and research centers. His work demonstrates a consistent trajectory of advancing parallel computing techniques while applying them to increasingly complex real-world problems involving large-scale geospatial data.
Lakhdar Sais is a Professor of Computer Science at the Centre de Recherche en Informatique de Lens (CRIL), CNRS UMR 8188, at Université d'Artois, Faculty of Jean Perrin Sciences in Lens, France. His research focuses on search and representation problems in Artificial Intelligence, including propositional satisfiability, quantified boolean formulas, constraint programming, knowledge representation and reasoning, data mining, and AI applications in Social and Human Sciences. He has supervised numerous PhD students throughout his career, with recent students including David ING (2021-present) working on migration data knowledge extraction, and previously Ikram NEKKACHE (2021), Sofiane TOUATI (2021), and Kahina BOUCHAMA (2020). His research has been recognized with multiple awards including best paper awards at SAT'11 and ICTAI'2009, and first place in the International SAT 2009 competition. His current research projects include the ANR project HYCI (2023-2026) on Hyper-places, Crises, Migrations and Inequalities, Project ERA (2022-2025) on producing new knowledge in juvenile justice and mental health, and ANR project POSTCRYPTUM (2021-2023) on algebraic cryptanalysis for post-quantum cryptography. He has also edited the Handbook of Parallel Constraint Reasoning (Springer, 2018). Scientific Awards: Best paper award at SAT'11 for 'On freezing and reactivating learnt clauses' Best paper award at ICTAI'2009 for 'Learning for Subsumption' ManySAT - First rank at International SAT 2009 competition (Parallel Track) LySAT - Two bronze medals at International SAT 2009 competition (Sequential Track) ManySAT - First rank at SAT Race 2008 competition Professor Sais has taught numerous courses including Artificial Intelligence, Constraint Programming, Knowledge Representation and Reasoning, Expert Systems, Complexity Theory, Advanced Data Structures, Algorithmics, and Functional Programming. He has served as leader of the inference and decision process research group at CRIL (2002-2013) and as Delegate Director of the CRIL laboratory (2013-2018).
Nicolas Perrin-Gilbert is a CNRS Research Fellow at the Institute of Intelligent Systems and Robotics (ISIR), which is part of Sorbonne University in Paris, France. He has been working at ISIR since 2013 as part of the MLIA (Machine Learning and Artificial Intelligence) team, where he conducts research at the intersection of robotics and machine learning. His office is located at ISIR, Campus Pierre et Marie Curie, 4 place Jussieu, BC173, 75005 Paris. Dr. Perrin-Gilbert's research focuses on developing advanced algorithms for robot locomotion, control, and learning. His work bridges theoretical machine learning with practical robotics applications, particularly in reinforcement learning, motion planning, and humanoid robotics. He has made significant contributions to quality-diversity algorithms, state representation learning, and controller synthesis for dynamic multi-agent systems. His approach often combines insights from control theory with modern machine learning techniques to solve complex robotics challenges. His publication record shows consistent research output since 2010, with a notable acceleration in recent years. His work spans both theoretical contributions (such as unifying frameworks for motion planning and diversity search) and practical implementations (including humanoid robot control systems). A key trend in his research is the integration of motion planning techniques with reinforcement learning, creating more efficient and robust learning systems for robotics applications. Dr. Perrin-Gilbert is the primary maintainer of xpag , a modular reinforcement learning library with JAX agents that supports standard reinforcement learning and goal-conditioned reinforcement learning. This open-source project demonstrates his commitment to developing practical tools for the research community. The library has gained significant traction with 27 GitHub stars and 6 forks, and is used by other researchers in the field. His collaborations span multiple institutions and research groups, with frequent co-authorship with researchers from ISIR including Olivier Sigaud, Alexandre Chenu, and Stéphane Doncieux. He has also collaborated with researchers from other institutions on projects involving humanoid robots like COMAN and Cassie, demonstrating the applied nature of his work.
Yoshifumi Nishimura is an Assistant Professor at Waseda University’s Research Institute for Science and Engineering and concurrently serves in Tokyo University of Science’s Faculty of Science, Division of Applied Chemistry. He completed both his bachelor’s (2008) and doctoral degrees (2013) in chemistry at Nagoya University, followed by post-doctoral research at National Chiao Tung University, Taiwan, and a project-researcher position at the Institute for Molecular Science. Education: Doctor of Science, Nagoya University, 2013 Bachelor of Science, Nagoya University, 2008 Research Interests: Nishimura develops linear-scaling quantum chemical methodologies, especially the divide-and-conquer density-functional tight-binding (DC-DFTB) approach, enabling million-atom simulations on supercomputers. His work spans proton-transfer dynamics in water, ice and proteins, CO₂ absorption chemistry, battery electrolytes, photochemical events in retinal proteins, and growth mechanisms of carbon nanotubes and graphene nanoribbons. Awards: CSJ Presentation Award, Chemical Society of Japan, 2017 President’s Award, Nagoya University, 2008 Software & Leadership: He is the lead developer of the open-source DCDFTBMD package, which has been optimised for the K-computer and Fugaku, and he serves on the editorial committee of the 分子シミュレーション学会 journal “アンサンブル” and on organising committees of domestic theoretical-chemistry conferences.
Andrés Jonathan Abeliuk Kimelman is an Assistant Professor at the University of Chile's Faculty of Physical Sciences and Mathematics , affiliated with the Department of Computer Science . He holds a PhD in Computer Science (University of Melbourne, 2017) and a Civil Engineering degree in Computing (University of Chile, 2012). Research Focus : AI ethics, network analysis, natural language processing, and machine learning applications in social systems. Teaching : Leads undergraduate and postgraduate courses in Discrete Mathematics, Data Mining, and Computational Theory. Students : Advises multiple thesis and research projects, including works on AI-assisted urban planning, misinformation analysis, and algorithmic bias. Collaborations : Participates in the National Center for Artificial Intelligence (CENIA) as a co-investigator (2022-2027). Key Publications explore polarization detection, networked public spheres, and computational models for social systems. Current projects include unsupervised topic quantification and extreme multi-label classification in multilingual contexts.
Shachar Itzhaky is an Associate Professor in the Department of Computer Science at Technion - Israel Institute of Technology, Haifa. His research spans multiple areas of programming languages, formal methods, and software engineering, with a focus on making program development and verification more accessible and efficient. He has served on program committees for numerous prestigious conferences including PLDI, POPL, SPLASH, and ICFP. Dr. Itzhaky's research interests center around program synthesis, automated reasoning, and formal verification. His work in program synthesis explores techniques for automatically generating programs from high-level specifications, with applications in end-user programming and software development. In automated reasoning, he has made significant contributions to e-graph based reasoning, invariant inference, and property-directed verification. His research in formal methods focuses on practical applications for program verification, particularly for data structures and security properties. An analysis of his recent publications reveals a strong focus on leveraging advanced formal techniques for practical program understanding and generation. His work consistently bridges theoretical foundations with practical applications, particularly in program synthesis, verification, and end-user programming tools. The trend shows increasing integration of machine learning techniques with traditional formal methods, as well as expanding applications to security and privacy domains. ACM SIGPLAN John C. Reynolds Doctoral Dissertation Award Dr. Itzhaky has been actively involved in the programming languages research community, serving on numerous program committees and contributing to the advancement of formal methods and program synthesis. His work has practical implications for software development tools, security analysis, and end-user programming environments. While specific grant information isn't detailed in the provided text, his extensive publication record in top-tier venues suggests successful funding for his research endeavors. His work on projects like Object Spreadsheets and Lifty demonstrates a commitment to creating practical tools that address real-world programming challenges. Dr. Itzhaky's research is conducted within the vibrant programming languages and formal methods group at Technion's Computer Science department. His work intersects with multiple research threads including program synthesis, verification, and security, suggesting collaboration across these areas within the department. His tools like EPR-based Verification, PDR∀, and VeriCon represent significant technical contributions that likely form the basis of ongoing research projects with students and collaborators.
Andreas Erbs Hillers-Bendtsen serves as a Researcher (Postdoc) in the Department of Chemistry at the University of Copenhagen, where he conducts advanced computational research in quantum chemical methodologies and materials design. His work bridges theoretical frameworks with practical applications in energy and atmospheric science. His research spans Quantum Chemistry, Computational Chemistry, and Materials Science, with specialized focus on cluster perturbation theory for excited states, isotopic fractionation dynamics, and molecular systems for solar energy storage. Recent work demonstrates expertise in developing scalable quantum chemistry algorithms and applying them to photochemical processes and novel carbon-based materials. Analysis of his 51 research outputs reveals a strong trajectory in methodological innovation, particularly in extending cluster perturbation theory frameworks and implementing parallel computing solutions. His publications show increasing interdisciplinary reach, connecting quantum chemistry with atmospheric science and renewable energy materials, evidenced by publications in high-impact journals including Science Advances and Journal of Chemical Physics . Dr. Hillers-Bendtsen maintains active collaborations with leading researchers including Kurt V. Mikkelsen, Mads B. Johansen, and Trygve Helgaker, with co-authorship spanning theoretical developments, experimental validation, and materials synthesis projects. His work has attracted attention across academic platforms including Mendeley and social media, indicating growing influence in computational chemistry circles.
Antonio Salmerón Cerdán is a Professor in the Mathematics Department at the University of Almería, where he has established himself as a leading researcher in probabilistic artificial intelligence and Bayesian networks. With over 25 years of academic experience, he leads the 'Análisis de datos' research group and serves as Principal Investigator for multiple nationally and internationally funded projects, including the current 'Hacia una Inteligencia Artificial Probabilística Confiable (TOPAI-UAL)' project (2023-2026). His research expertise spans theoretical and applied aspects of probabilistic graphical models, with particular focus on Bayesian networks, causal inference, and their applications across diverse domains. His work demonstrates a consistent trajectory from foundational theoretical contributions to practical implementations in software engineering, genomics, sports analytics, and trustworthy autonomous systems. Professor Salmerón's publication portfolio reveals a strong emphasis on methodological innovations in probabilistic reasoning, with recent work exploring divide-and-conquer approaches for causal computation, noise-robust classification methods, and the integration of observational and randomized data sources. His research shows increasing interdisciplinary reach, connecting computer science methodologies with applications in plant genomics, software maintenance, and healthcare. Journal Publications: 105 articles in high-impact venues including Ecological Informatics (Q1), International Journal of Approximate Reasoning (Q2), and ACM Transactions Research Funding: Principal Investigator for 9 major projects since 2001 totaling over €800,000 in funding Thesis Supervision: Director of 7 doctoral theses on probabilistic graphical models and their applications Metrics: h-index 22 (Web of Science), i10 index 59 His research program demonstrates a unique combination of theoretical rigor in probabilistic reasoning with practical applications across diverse scientific domains, positioning him at the forefront of reliable probabilistic AI development.
Dr. Oyku Eren Ozsoy serves as Assistant Professor in the Department of Electrical, Computer and Software Engineering within Embry-Riddle Aeronautical University's College of Engineering, a position she joined in 2023. Her academic trajectory spans Turkish institutions including Middle East Technical University (Ph.D. in Medical Informatics), Baskent University (M.S./B.S. in Computer Engineering), and Hacettepe University, complemented by industry experience as co-founder of Mantis Software Company. With over seven years of teaching expertise in computer science courses, she bridges theoretical knowledge and practical applications. Her educational credentials include: Ph.D. in Medical Informatics, Middle East Technical University M.S. in Computer Engineering, Baskent University B.S. in Computer Engineering, Baskent University Dr. Ozsoy's research centers on bioinformatics, machine learning, and linear optimization with specific focus on biological network construction and allergen protein classification. She develops computational frameworks integrating protein-protein interaction (PPI) and RNA interference (RNAi) data to model signaling pathways in host-pathogen systems, advancing understanding of temporal dynamics in infections like Salmonella. Her work applies algorithmic approaches to microRNA target recognition and automated protein analysis, creating tools with biomedical applications. Analysis of her publications (2008-2015) reveals consistent contributions to computational biology, with increasing complexity in network modeling and temporal dynamics. The research demonstrates strong interdisciplinary integration of computer science, optimization theory, and molecular biology, particularly in pathogen-host interaction studies. Her methodological emphasis on linear programming for large-scale data analysis remains a unifying thread across publications. Key recognitions include: METU Thesis of the Year Award (2014) Heidelberg University Visiting Scientist Fellowship (2011) Baskent University Graduate Scholarship (2006) Dr. Ozsoy's research program combines EU-funded projects (SYSPATHO FP7), national grants (TUBITAK), and industry experience to support student training in computational biology. Her international collaborations and cross-disciplinary approach provide students with exposure to global research standards and real-world software development. Current teaching responsibilities in core computer science and engineering courses reflect her commitment to foundational education. Although no formal lab name is specified, her research group operates at the intersection of computer science and biology, focusing on algorithmic solutions for biomedical problems. The team leverages interdisciplinary methodologies and international partnerships to advance computational approaches in network biology and protein analysis.
Hiromi Nakai is a Professor at Waseda University, School of Advanced Science and Engineering, Department of Chemistry and Biochemistry. She holds a Doctorate in Engineering from Kyoto University and has been a leading researcher in theoretical and quantum chemistry for over three decades. Her academic career includes positions at Kyoto University as a Research Associate followed by a progression through the ranks at Waseda University from Assistant Professor to full Professor since 2004. She has also held concurrent positions at Kyoto University and as a Visiting Professor at Rice University. Dr. Nakai's research focuses on theoretical chemistry, quantum chemistry, electronic state theory, molecular simulation, and machine learning applications in chemistry. Her work has significantly advanced computational methods for large-scale quantum chemical calculations, relativistic effects in quantum chemistry, and the development of efficient algorithms for electronic structure calculations. She pioneered the divide-and-conquer method for linear-scaling quantum chemical computations, which has enabled calculations on systems with millions of atoms. Her recent publications demonstrate a strong emphasis on extending computational methods to handle larger systems, incorporating machine learning techniques, addressing relativistic effects, and applying computational chemistry to energy-related problems including battery materials and CO2 conversion. The research spans fundamental quantum chemical method development to practical applications in materials science and sustainable chemistry. Fukui Medal (2024) Member of International Academy of Quantum Molecular Science (2023) Asia-Pacific Association of Theoretical & Computational Chemists Award (2023) CSJ Award for Creative Work (2016) Fellow of Royal Society of Chemistry (2014) Pople Medal from Asia-Pacific Association (2011) Dr. Nakai serves on numerous editorial boards including the International Journal of Quantum Chemistry and has held leadership positions in professional societies including the Molecular Science Society and the Japan Society of Theoretical Chemistry, where she served as President. Her research has been supported by major Japanese funding agencies including JST CREST and PRESTO programs. She leads a research group that integrates theoretical development with practical applications in energy materials and sustainable chemistry.
Yintong Huo is an Assistant Professor at the School of Computing & Information Systems, Singapore Management University (SMU), where he leads research in intelligent software engineering. He received his PhD from The Chinese University of Hong Kong (CUHK) in 2024 under Prof. Michael R. Lyu and holds a Bachelor's degree from the University of Electronic Science and Technology of China. His research focuses on empowering AI models (particularly LLMs) for software development, testing, and operations, with two flagship projects: LogPAI (open-source AI platform for automated log analysis) and WebPAI (multimodal intelligence for automatic webpage development). His work spans log analysis, code intelligence, UI generation from prototypes, and configuration diagnostics. Huo's publication record shows strong trends in leveraging multimodal LLMs for practical software engineering challenges, with recent work on interactive webpage generation (Interaction2Code), configuration logging (ConfLogger), and log parsing (LILAC). His research bridges theoretical AI advancements with real-world system reliability needs. ICSE Distinguished Reviewer Award (2025) ISSRE Distinguished Reviewer Award (2024) IEEE Open Software Services Award (2022) ACM SIGSOFT CAPS Travel Grants National Scholarship (2019) Huo actively supervises PhD students (including Shi Ying Chang and Dan Huang) and research engineers. His lab has secured funding for multiple projects including WebPAI and LogPAI. He serves on program committees for major conferences (ASE, ICSE, FSE) and reviews for top journals. Current projects include dynamic webpage generation and configuration diagnostics, with ongoing work on small language models for logging systems. Huo leads the LogPAI and WebPAI research groups, developing open-source tools for automated log analysis and multimodal UI code generation. The LogPAI project has garnered over 3,000 GitHub stars and 70,000 downloads. His team collaborates with industry partners on AIOps challenges and is expanding into configuration diagnostics through the ConfLogger project.
