جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Big Data
• Human-Robot Interaction
• +۶ مورد دیگر

Diego Reforgiato Recupero is a Full Professor at the Department of Mathematics and Computer Science of the University of Cagliari, Italy, since December 2015. He is the director and creator of the Human-Robot-Interaction Laboratory (http://hri.unica.it) and co-director of the Artificial Intelligence and Big Data Laboratory (http://aibd.unica.it). His roles include quality responsible for the department and membership in the Commission for start-up and spin-off at the University. He teaches multiple courses across disciplines: Computers Architecture for Computer Science bachelor's, Big Data for Computer Science master's, and Web Design and Digital Storytelling for the Master's in Philosophy and Theories of Communication. His research spans interdisciplinary domains including Artificial Intelligence Big Data Analytics Knowledge Graphs Human-Robot Interaction Digital Transformation His scholarly output demonstrates significant focus on AI applications in energy systems (smart grids, district heating), knowledge graph engineering (ontology generation, semantic conversion), and human-centric AI (conversational agents, digital coaching). Publications frequently address data augmentation techniques and transformer architectures across finance, tourism, and mental health domains. He leads the Human-Robot Interaction Laboratory and co-founded the Artificial Intelligence and Big Data Laboratory. His work integrates Apache Spark for big data processing, Unity for synthetic data generation, and RDF/OWL standards for knowledge representation.

• Artificial Intelligence
• AI & Law
• Computational Argumentation
• +۹ مورد دیگر

Prof. Floris Bex (Utrecht University) is a leading scholar in AI & Law, focusing on explainable AI , computational argumentation , and responsible AI development . He holds a dual appointment at the Molengraaff Institute for Private Law and Department of Information and Computing Sciences , bridging legal theory with technical AI innovation. His work emphasizes transparency in high-risk domains like policing and judicial decision-making. Scientific Director, National Police Lab AI Member, Board of Advisory Scientific Research (WODC), Ministry of Justice Editorial roles in Artificial Intelligence and Law and Argument and Computation His research combines symbolic and subsymbolic AI to create systems that reason with legal arguments, including projects like ALGOPOL and the Formal Explanations with Computational Argumentation framework. He has developed an explainable AI system for crime report assessment that improves citizen trust through transparent rejections. Key publications span 2016–2024 , with recent work on argumentation-based explanations , incomplete evidence handling , and human-AI collaboration in policing . His projects integrate Bayesian networks with argumentation theory to address real-world challenges in digital justice and public safety. Scientific awards include: Editorial board member, Artificial Intelligence and Law Scientific Advisor to Dutch National Police Lead in ICAI network collaborations He teaches Computational Argumentation and Law and Computer Science , and has supervised numerous AI & Law projects involving students like Daphne Odekerken and Anton Borg.

• Natural Language Processing
• Text Mining
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Kalliopi Zervanou is an Assistant Professor at the Faculty of Science, Utrecht University, specializing in Natural Language Processing and Text Mining within the Data Intensive Systems research group. Her work bridges Artificial Intelligence, Semantic Web technologies, and healthcare informatics. Research Focus: Information extraction from unstructured texts, interpretable AI methods, and data integration for real-world applications in mental health, logistics, and digital humanities. Education: PhD in Computer Science (University of Manchester, UK), MSc in Machine Translation (UMIST, UK), and BA in French Literature & Linguistics (Aristotle University, Greece). Experience: Former positions at Leiden University, TU/e, Radboud University, Tilburg University, and Technical University of Crete. Visiting researcher at NaCTeM (UK) and USC Viterbi School (USA). Awards: ICAART 2020 Best Industrial Paper award for baggage mishandling prediction research. Her methodological expertise spans rule-based systems, unsupervised learning, and large language models, with a focus on historical texts, OCR error correction, and multilingual challenges. She contributes to healthcare analytics through EHR classification and prognosis modeling.

• Semantic technologies
• Ontology engineering
• Knowledge graphs
• +۳ مورد دیگر

Vadym Yermolayev serves as a Professor at the Department of Computer Science and Information Technology within the Faculty of Applied Sciences at Ukrainian Catholic University (UCU). He leads UCU's PhD program in Intelligent Systems and holds an Honorary Professorship at Kherson State University. His academic work focuses on semantic technologies, ontology engineering, and knowledge graph construction, with active participation in international research projects and organizations like ACM and ELLIS. Professor of Semantic Technologies Head of PhD Program in Intelligent Systems Honorary Professor at Kherson State University Member of ACM and ELLIS Research Interests span semantic technologies, ontology engineering, and knowledge representation, with applications in education, industrial analytics, and anti-corruption systems. His work integrates machine learning with formal knowledge modeling and develops frameworks for knowledge ecosystem dynamics. Scientific Contributions include leading research groups at Zaporizhia National University and collaborating on European Commission-funded projects. He has published extensively in ICTERI conference proceedings and developed methodologies for terminology saturation analysis and ontology alignment. Honorary Professor at Kherson State University Member of ACM (Association for Computing Machinery) Member of ELLIS (European Laboratory for Learning and Intelligent Systems) Professional Involvement includes external expert roles for European Commission programs (FP6, FP7, H2020) and industrial consulting with Cadence Design Systems GmbH.

• Natural Language Processing
• Computational Linguistics
• Dependency Parsing
• +۷ مورد دیگر

Carlos Gómez Rodríguez is a Full Professor (Catedrático de Universidad) in the Department of Computer Science at the University of A Coruña, Spain, where he leads the FASTPARSE Lab within the LyS Research Group at the CITIC Research Center. His work bridges theoretical and practical aspects of computational linguistics with significant applications in natural language processing. His primary research interests focus on natural language parsing algorithms, with particular emphasis on improving parsing speed for web-scale applications, handling non-projective dependency structures, analyzing morphologically-rich and low-resource languages, and exploring the cognitive aspects of syntax. He maintains a critical perspective on the field's current LLM-driven revolution, advocating for continued research into alternative approaches that prioritize efficiency, explainability, and scientific insight beyond user-facing applications. His recent publications reveal a strong trend toward examining the capabilities and limitations of large language models, particularly in comparison to human language processing, while continuing to advance traditional parsing techniques. His work spans theoretical foundations, practical implementations, and diverse applications including sentiment analysis, opinion mining, and creative writing evaluation. National Young Researcher Price "María Andresa Casamayor" in Mathematics and Information Technologies by Spain's Ministry of Science Honorary Member of the Royal Spanish Mathematical Society CAEPIA 2024 Best Paper Award for research on evaluating creative writing in LLMs Professor Gómez Rodríguez has successfully led significant research projects including the ERC Starting Grant project FASTPARSE, which focused on techniques to improve the speed of parsing algorithms. His work demonstrates consistent funding support for innovative research at the intersection of computational linguistics, cognitive science, and practical NLP applications. He actively collaborates with researchers across Spain and internationally, as evidenced by his extensive publication record with diverse co-authors. He leads the FASTPARSE Lab and is a core member of the LyS Research Group, focusing on advancing both theoretical understanding and practical implementations of language processing technologies. His team explores diverse aspects of natural language processing, from fundamental syntactic analysis to applied sentiment analysis and innovative applications of language technology in domains like healthcare.

Filip Ilievskiمشاهده پروفایل

استادیار

• Commonsense Reasoning
• Neurosymbolic AI
• Analogy and Abstraction
• +۱۱ مورد دیگر

Filip Ilievski serves as an Assistant Professor of Commonsense AI (Sr.) at Vrije Universiteit Amsterdam, where he leads the Learning and Reasoning Group. He holds additional affiliations as an Affiliated Scientist at USC Information Sciences Institute and Amsterdam Sustainability Institute, and serves as Scientific Coordinator of the Digital Sustainability Institute (DiSC). His academic journey includes a PhD from Vrije Universiteit Amsterdam (2015-2019), research positions at USC (2019-2023), and a research visit to Carnegie Mellon University (2017). Dr. Ilievski's research focuses on advancing human-centric AI with common sense for social good applications. His work spans three interconnected areas: commonsense reasoning (including situational awareness, numeracy, and modeling of other agents), analogy and abstraction (studying cognitive generalization mechanisms through narratives and lateral thinking puzzles), and AI for social good (interpreting complex online media like internet memes and developing knowledge-based solutions for sustainable policies). He employs neuro-symbolic methods including prototype-based networks, combining LLMs with deterministic engines, and reasoning with scene knowledge graphs to create robust, interpretable AI systems. His publication record shows a strong trend toward multimodal reasoning and practical applications of commonsense AI, particularly in understanding internet memes, developing sustainable AI solutions, and creating robust evaluation frameworks like the MARVEL benchmark. Recent work increasingly integrates cognitive theories with neural architectures to address limitations in current foundation models, with particular emphasis on explainability and cultural context awareness. Best Student Paper Award at K-CAP for 'Knowledge-enhanced Agents for Interactive Text Games' Best Paper and Best Presentation Award at Workshop on Multimodal Content Analysis for Social Good (MM4SG) Book 'Human-Centric AI with Common Sense' published in Springer Nature Synthesis series Dr. Ilievski actively supervises a growing team of PhD students and postdocs, with current projects focusing on visual commonsense reasoning, meme semantics, analogical abstraction, multimodal alignment, and architectures for human-centric AI. His Digital Sustainability Institute (DiSC) coordinates research on knowledge-driven AI for sustainable policies, while his Situated AI minor program develops next-generation AI education. He maintains active collaborations with institutions including USC, CMU, RPI, University of Lyon, UvA, University of Bielefeld, and industry partners like Bosch Research, NEC Labs, Merit Technologies, and Tencent.

Marcin Pietranikمشاهده پروفایل

استادیار

• Ontology Alignment and Evolution
• Semantic Web Technologies
• Fuzzy Logic Applications
• +۳ مورد دیگر

Marcin Pietranik is an Assistant Professor at the Department of Applied Informatics within the Faculty of Information and Communication Technology at Wroclaw University of Technlogy . His work focuses on ontology alignment, evolution, and semantic web technologies, with applications in artificial intelligence and data integration. Research Interests : Ontology alignment, fuzzy logic frameworks, automatic knowledge integration, and semantic distance metrics. Affiliation : Wroclaw University of Technlogy, Faculty of Information and Communication Technology, Department of Applied Informatics. Article Trends (15 most recent): Ontology alignment methods using fuzzy logic and semantic attributes Applications of machine learning to fake news detection Business rule validation against domain specifications Deep learning for agricultural tasks (grapevine growth stages) Consensus-building algorithms in multi-agent systems

Diego Reforgiato Recuperoمشاهده پروفایل

استاد

• Artificial Intelligence
• Semantic Web
• Big Data
• +۵ مورد دیگر

Diego Reforgiato Recupero serves as Full Professor at the University of Cagliari's Department of Mathematics and Computer Science since February 2022, previously holding an Associate Professor position from December 2015. He maintains dual affiliations as an associated researcher at CNR's Semantic Technology Laboratory (STLAB) and active roles in university governance including the Commission for start-up and spin-off. His research spans Artificial Intelligence, Semantic Web, and Human-Robot Interaction with concrete applications in healthcare robotics and financial AI. Key methodologies include knowledge graph extraction, semantic role labeling, and frame-based language analysis, consistently bridging theoretical NLP with real-world implementations through spin-off ventures. Recent publications reveal a concentrated trajectory in healthcare robotics since 2017, where semantic technologies enable geriatric assessment systems and social robots. This work integrates deep learning with knowledge representation to solve practical challenges in hospital settings, demonstrating strong continuity between his Semantic Web foundations and emerging AI applications. Computer World Horizon Award (2006) Marie Curie International Grant PROVIDEO (2008) Best Researcher Award 2012 Telecom Italia Working Capital Award (2012) 10+ Best Research Paper Awards Data mining/sentiment analysis patent (20100023311) He coordinates major European initiatives including PhilHumans (funding 8 Ph.D. students) and Dr. VCoach Marie Curie Fellowship, while managing approximately 40 FP7/H2020 projects through R2M Solution. His grant leadership spans ICT, energy-saving networks, and robotics with industrial partners like Philips Research. As director of the Human-Robot-Interaction Laboratory and co-director of both the Artificial Intelligence and Big Data Laboratory and Semantic Web Laboratory, he leads interdisciplinary teams developing commercializable technologies. His spin-offs—including VISIOSCIENTIAE for financial AI and B-UP for semantic software—demonstrate consistent translation of academic research into market applications.

• Information Retrieval
• Natural Language Processing
• Applied Machine Learning
• +۷ مورد دیگر

Md Zia Ullah serves as a Lecturer in the School of Computing, Engineering & Built Environments at Edinburgh Napier University, where he contributes to both teaching and research activities. His academic journey includes a PhD from Toyohashi University of Technology (2016), followed by post-doctoral research at French institutions (2017-2022) before joining Napier. He maintains active professional affiliations through memberships in prestigious research groups including the Centre for Algorithms, Visualisation and Evolving Systems and the Centre for Artificial Intelligence and Robotics. B.Sc. (Hons.) from University of Chittagong, Bangladesh (2010) M.Eng. from Toyohashi University of Technology (2013) PhD in Computer Science and Engineering from Toyohashi University of Technology, Japan (2016) Dr. Ullah's research centers on information retrieval systems with particular expertise in query performance prediction, adaptive search strategies, and misinformation detection. His work bridges theoretical advances in machine learning with practical applications in social media analysis and healthcare informatics. He has developed novel approaches for microblog retrieval, semantic expansion techniques, and risk-sensitive search configuration selection that have advanced the field of information access systems. Analysis of his recent publications reveals a strong trend toward addressing misinformation challenges through computational methods, with increasing focus on query performance prediction reliability and selective search strategies. His work demonstrates consistent publication in top-tier venues including SIGIR, CIKM, and ECIR, showing evolution from foundational work on bipartite graph ranking to contemporary challenges in misinformation detection and mental health monitoring through social media analysis. Co-patented adaptive IR technique Active Program Committee member for SIGIR (2018-present), CIKM (2019-present), WSDM (2020-present), SIGKDD (2020-present), ECIR (2019-present), ECMLPKDD (2022-present), CLEF (2017-present) Dr. Ullah currently serves as Co-PI on the Royal Society Edinburgh-funded "False webs: Network to address the misinformation pandemic" project (2023-2025, £19,931), building on his prior involvement in EU Horizon 2020 projects including PREVISION (2019-2021) and FabSpace 2.0 (2015-2018). He supervises doctoral research, notably serving as second supervisor for Katarina Alexander's project on "Developing a Fake News Warning Label." His professional service extends to lecturing data analytics during his post-doctoral period at Université Toulouse I Capitole. Based in the School of Computing, Engineering & Built Environments, Dr. Ullah contributes to research themes in AI and Technologies, particularly within Information Science, Optimization and Learning, and Software Systems. His work connects with practical applications through evaluation forums like TREC, CLEF, and NTCIR where he tests methodologies against benchmark datasets.

Dinesh Acharya Uمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Image Processing
• +۴ مورد دیگر

Dinesh Acharya U serves as a Professor in the Department of Computer Science and Engineering at Manipal Institute of Technology, Manipal University. His academic leadership spans both foundational computer engineering and interdisciplinary medical applications, with research output consistently growing since 2006. Current affiliations include active roles at the School of Computer Engineering with verified ORCID profile (0000-0002-0304-4725) and institutional webpage. Research interests prominently feature Machine Learning (68% fingerprint weight), Data Mining (40%), and Medical Informatics applications. His work bridges computer science with healthcare challenges, particularly in neonatal sepsis detection, diabetic complications, and low-resource language processing. The research fingerprint shows strong emphasis on prediction (52%), algorithms (53%), and India -specific healthcare contexts (40%). Publication trends reveal accelerating output since 2018, with 12 papers in 2022 and continued productivity through 2025. Recent work demonstrates interdisciplinary convergence, particularly in Medical AI (neonatal sepsis, diabetic kidney disease) Natural language processing for low-resource languages Transformer-based architectures across domains Notable patterns include increasing clinical collaborations and emphasis on practical implementation tools. Professional recognition includes an h-index of 13 with 596 citations across 71 research outputs. Key distinctions: Scopus profile verification ORCID registration Multi-institutional collaborations evident in co-authorship Academic supervision and grant activity cannot be confirmed from available data, though 15+ recent publications suggest active research teams. Current projects appear focused on Medical diagnostic tool development Low-resource language technology Clinical decision support systems with evident laboratory infrastructure supporting computational healthcare research.

Anna Bernasconiمشاهده پروفایل

استادیار

• Bioinformatics
• Data Science
• Database Systems
• +۵ مورد دیگر

Anna Bernasconi is a Tenure-Track Researcher (Assistant Professor) at the Department of Electronics, Information and Bioengineering at Politecnico di Milano, where she leads the Bioinformatics and Data Science Lab within the Genomic Computing group. She has been a visiting researcher at Universitat Politècnica de València (Jan-June 2022). Her research focuses on applying conceptual modeling, data integration, and knowledge engineering in life sciences and other applied sciences domains, with an emphasis on building open-source tools and services. Dr. Bernasconi earned her Master in Computer Engineering in 2015 from Politecnico di Milano and the University of Illinois, Chicago. She completed her PhD from Politecnico di Milano in 2021 with a thesis on genomic data integration. Her academic journey has positioned her at the intersection of computer science and bioinformatics. Her research interests span multiple areas of bioinformatics and data science. She specializes in bioinformatics data and metadata integration methodologies to support complex biological query answering. In recent years, she has focused on viral genomics , particularly sequence modeling, integration, and search systems for mitigating the effects of pandemics like COVID-19. She applies conceptual modeling and knowledge engineering techniques to develop open-source tools for genomic data analysis. Her work bridges computer science with life sciences, addressing challenges in genomic surveillance, data integration, and knowledge management. Dr. Bernasconi's recent publications demonstrate a clear trend toward developing practical tools for genomic surveillance and text analysis. Her work spans from viral genome analysis (Nature Communication 2024) to database systems (SIGMOD 2024) and topic modeling for large text corpora (EDBT 2025), showing her ability to work across multiple technical domains while maintaining a focus on real-world applications in health and environmental science. National Scientific Habilitation in two categories: 09/H1 – Sistemi di Elaborazione delle Informazioni (II Fascia) and 01/B1 – Informatica (II Fascia) Principal Investigator of the SENSIBLE PRIN PNRR 2022 project (funded with ~240K Euros) Principal Investigator of the NGI Search TETYS project (150K Euros funding) Co-founder of LegisRatio S.r.l., a Politecnico di Milano spin-off innovative startup Academic Editor for Plos One and BMC Bioinformatics Dr. Bernasconi actively mentors students and collaborates across disciplines. She is Principal Investigator of significant research projects including SENSIBLE, which aims to develop an early warning system for viral pathogens based on genomic surveillance, and TETYS, which focuses on topic modeling and visualization for large text corpora. Her research has secured funding from the Italian Ministry of University and Research (MUR) and the European Union's Horizon Europe program. She collaborates with experts across disciplines, including virologists like Prof. Ilaria Capua from Johns Hopkins University. She leads the Bioinformatics and Data Science Lab at Politecnico di Milano, which has developed several notable tools including ViruClust (for comparing SARS-CoV-2 genomic sequences), VariantHunter (for monitoring mutation evolution), and RecombinHunt (for identifying recombination events in viral species). Her team also created CORToViz for exploring the CORD-19 dataset and TETYS for topic modeling in various domains including climate change research.

• Medical Image Processing
• Computer Vision
• Biomedical Engineering
• +۳ مورد دیگر

Yinghua Fu, PhD, serves as a Research Fellow in the Department of Diagnostic Radiology and Nuclear Medicine, focusing on advanced medical image analysis through computational methods. Her work bridges clinical medicine and artificial intelligence to develop practical diagnostic tools. Her research centers on deep learning applications for medical imaging, particularly in diabetic retinopathy analysis, Alzheimer's disease classification, and retinal image change detection. Key innovations include DAGU-Net for segmentation efficiency, TSCA-Net for attention-based spatial-channel processing, and lightweight architectures like MirageNet that minimize computational demands while maintaining accuracy. She specializes in adapting computer vision techniques to clinical constraints, emphasizing model interpretability and real-world deployability. Analysis of her 15 most recent publications (2023-2026) reveals three dominant trends: (1) Multi-scale feature extraction for medical image segmentation, (2) Lightweight network design targeting edge deployment in clinical settings, and (3) Cross-modal fusion techniques integrating structural and functional imaging data. Her work consistently addresses diabetic retinopathy and neurodegenerative disorders through retinal and brain imaging biomarkers. Scientific awards: No awards were documented in the provided materials. Advising and grants: The source text contained no information regarding student supervision, research funding, or collaborative grants.

• Digital History
• Medieval Legal History
• Historical Semantics
• +۳ مورد دیگر

Prof. Dr. Silke Schwandt is a Professor of Digital History at Bielefeld University's Faculty of History, Philosophy and Theology , Department of History. She leads the Digital History group and co-leads subprojects within the Collaborative Research Center 1288 (CRC 1288) 'Practices of Comparison. Ordering and Changing the World' and the SFB 1288 subproject INF. She serves as Founding Director of the Center for Uncertainty Studies (CeUS) and is a member of multiple interdisciplinary institutes including the Institute for Interdisciplinary Conflict and Violence Research (IKG) and Bielefeld Center for Data Science (BiCDaS) . Her research focuses on Digital Humanities methodologies Medieval English legal history (1286 Huntingdonshire Eyre) Knowledge production under uncertainty Historical semantics network analysis Virtual reality applications for historical research Epistemological challenges in digital historiography Recent research trends show significant engagement with Digital data flows in historical contexts Visualization epistemology Sustainable socio-technical systems Lexical analysis of historical hate speech Medieval court dynamics as social networks Her work bridges traditional historiography with computational methods through projects like SAIL and 3B (Bots Building Bridges) . She also contributes to academic governance as a member of Bielefeld University Press' steering committee and the Senate's University Professors group.

• Artificial Intelligence
• Machine Learning
• Optimisation
• +۱۳ مورد دیگر

Dr. Asef Nazari is a Senior Lecturer in Mathematics at Deakin University's School of Information Technology, Faculty of Science Engineering and Built Environment. With over two decades of experience, his work spans mathematical modeling, optimization, and AI applications in energy systems, supply chains, and cybersecurity. Education : PhD (University of Ballarat), MSc (Amirkabir University of Technology), BSc (University of Tabriz), Graduate Certificate in Higher Education Teaching (Deakin University) Research : Focuses on Electricity network planning with renewables Optimization techniques (non-smooth, integer programming) AI/ML for cryptocurrency markets and causal inference Community battery systems for EV charging Soft happy coloring in network analysis Supply chain and project scheduling optimization Grants : Leads industry collaborations like the REACH Scholarship (2025–2028) and has secured funding from Monash University/ClimateWorks and The Ian Potter Foundation. Teaching : Developed the 'Mathematics for AI' master's unit, emphasizing real-world problem-solving in optimization, operations research, and data analysis. Supervision : Currently guides 8 PhD students in topics like AI-driven authentication security and land-use modeling. Awards : Recognized as a Fellow of the Higher Education Academy (FHEA)

Valeria de Paivaمشاهده پروفایل

پژوهشگر

• category theory
• programming languages
• type theories
• +۱۲ مورد دیگر

Valeria de Paiva is a mathematician and AI research scientist, currently serving as Founder of the Topos Institute in Berkeley where she explores the deep connections between mathematics, logic, and computation. She earned her PhD under the supervision of Martin Hyland, working on Dialectica categories, which established her foundational work connecting linear logic with Gödel's Dialectica interpretation. Dr. de Paiva's research spans category theory, programming languages, type theories, and logic in computer science, with special emphasis on the semantics of natural language and lexical semantics. She has pioneered work in linear and modal logics and their applications to mathematics, particularly in building tools to extract, structure, and reason about mathematical knowledge. Her research bridges symbolic/structural and distributional meaning representations through knowledge graphs, ontologies, and computational frameworks. Her recent publications reveal a strong trend toward mathematical concept extraction and natural language processing for mathematical texts. She has developed innovative approaches like MathGloss for building mathematical glossaries and Parmesan for educational concept extraction, demonstrating how theoretical foundations in category theory can solve practical problems in knowledge representation and education. Among her notable recognitions is the unofficial title of 'Ambassador of Logic,' awarded after she was invited by the Division of Logic, Methodology, and Philosophy of Science and Technology to record a video on logic. She has served as council member of this division from 2020-2023. Dr. de Paiva is deeply committed to mentoring and supporting women in logic and computer science. She has mentored 12 PhD students and postdocs at the Applied Category Theory MRC 2022. She co-founded and maintains the Women in Logic website, blog, and Facebook group, serves on the steering committee for Women in Logic, and helps organize the Workshop on Women in Logic, which has held nine editions as of the latest information. She also contributes to the ACM-W Scholarship Program supporting women students in computing worldwide. She is actively involved in numerous academic initiatives including serving as co-chair for CICM 2025 and MFPS 2024, and as scientific committee member for CT2025. She co-founded groups including Women in Logic, Lógicas Brasileiras, and the Women in Logic Workshop, and has organized numerous international conferences and workshops focused on logic, category theory, and computational semantics.