Find Academic Supervisors

• Social Media Analysis
• Sentiment Analysis
• Computational Journalism
• +6 more

Fabrício Benevenuto is an Associate Professor in the Computer Science Department at Federal University of Minas Gerais (UFMG), where he conducts interdisciplinary research at the intersection of social media analysis, data science, and computational journalism. His work spans complex networks, machine learning, and natural language processing with strong societal impact. His research focuses on social media dynamics, particularly in Brazilian contexts, with major contributions to hate speech detection, fake news analysis, and political discourse monitoring. He leads large-scale projects against misinformation, including development of systems like WhatsApp Monitor, Media Bias Monitor, and Purple Feed. His work combines technical innovation with real-world applications for election transparency and public discourse integrity. Benevenuto's recent publications demonstrate strong trends in multilingual NLP for social media analysis, with emphasis on Brazilian Portuguese contexts. His team produces both theoretical contributions and practical systems addressing hate speech, misinformation, and media bias. Notable methodological approaches include combining network analysis with linguistic features, developing culturally-aware detection systems, and creating large annotated datasets for understudied languages. CAPES award for best Brazilian computer science thesis (2010) Humboldt Foundation scholarship recipient (2017-2018) Member of TikTok Safety Advisory Council WWW'20 Best Paper Nominee & CNIL-INRIA Privacy Protection Prize winner Multiple best paper awards at CEAS, WBC, and ICWSM conferences Test-of-Time Award at ICWSM'20 Benevenuto actively mentors PhD and MSc students, with numerous advisees securing academic positions at Brazilian universities and research roles at institutions like Max Planck Institute. His projects often receive funding supporting interdisciplinary collaborations across computer science and social sciences. Current work includes large-scale analysis of Telegram political groups, real-time election monitoring systems, and developing culturally-aware NLP tools for Portuguese. He leads research teams working on social media analysis systems with societal impact, particularly focused on Brazilian digital ecosystems. Projects involve cross-institutional collaborations with researchers from MPI-SWS, Max Planck Institute, and various Brazilian universities, emphasizing practical applications for public discourse integrity.

Peter WankeView profile

Professor

• Soft computing applications in business
• Multicriteria modeling for sectoral benchmarking
• Information theory in business analytics and economics
• +3 more

Peter Wanke is a Full Professor of Data Science & Business Analytics at COPPEAD/UFRJ and a Joint Appointment Professor at EBAPE/FGV. He has coordinated the Graduate Program in Business Administration at COPPEAD (2018-2021) and the Doctoral Program in Administration since 2011. He leads the Business Analytics and Economics Research Unit (BAE RU) and has published over 200 high-impact journal articles. B.Sc. in Production Engineering (Poli/UFRJ) M.Sc. and D.Sc. in Production Engineering (COPPE/UFRJ) Visiting Scholar at Ohio State University Research Focus: Soft computing in business (finance, marketing, logistics), multicriteria benchmarking in public management, infrastructure, and finance, with applications of information theory to business analytics and economics. His work spans decision-making modeling, efficiency analysis, and data-driven solutions for global sectors. Scientific Recognition: He has received multiple awards, including the IEOM Outstanding Researcher title (2020), CNPq and FAPERJ scholarships, and global influence rankings in PLOS Biology. He serves as Associate Editor for journals like Socio-Economic Planning Sciences and Tourism Economics.

Marco Antonio CasanovaView profile

Professor

• Database Systems
• Conceptual Modeling
• Semantic Web
• +5 more

Marco Antonio Casanova is a Full Professor at the Department of Informatics and Coordinator of the Central Planning and Evaluation Office of the Pontifical Catholic University of Rio de Janeiro (PUC-Rio). He has held significant leadership positions at PUC-Rio including Graduate Program Coordinator (2005-2007) and Director of the Department of Informatics (2007-2011). His research interests concentrate on database conceptual modeling, construction of database management systems, and applications of Large Language Models. Dr. Casanova's work focuses on technologies that facilitate the dissemination and interpretation of data on the Web, with particular emphasis on techniques for designing databases to facilitate interoperability. His academic journey began with a degree in Electronic Engineering from the Military Institute of Engineering (1974), followed by an M.Sc. in Informatics from PUC-Rio (1976), and culminated with an M.Sc. (1978) and Ph.D. (1979) in Applied Mathematics from Harvard University. His recent publications (2023-2025) demonstrate a strong focus on the intersection of Large Language Models with database technologies, particularly in developing advanced Text-to-SQL and Text-to-SPARQL systems. His research spans both theoretical database concepts and practical applications across various domains including engineering, healthcare, and cultural heritage. Dr. Casanova has been particularly active in exploring how LLMs can enhance traditional database query interfaces while addressing real-world challenges in complex database environments. Scientific Recognition: Recipient of the Scientific Merit Award from the Brazilian Computer Society (2012) CNPq Level 1B Productivity Grant recipient Dr. Casanova maintains an active research program with numerous collaborations across Brazil and internationally. His work bridges theoretical database research with practical applications, particularly in the evolving landscape of AI-enhanced database systems. He has contributed significantly to the field of semantic technologies, knowledge graphs, and natural language interfaces to databases, with a recent emphasis on leveraging Large Language Models to solve longstanding database interoperability challenges. His laboratory and research team at PUC-Rio focus on developing innovative approaches to database management that incorporate cutting-edge AI techniques while maintaining strong theoretical foundations in database systems.

Joaquim Armando Pires JorgeView profile

Professor

• Computer Graphics
• Virtual Reality
• Human-Computer Interaction
• +5 more

Joaquim Armando Pires Jorge is a Full Professor at Instituto Superior Técnico, University of Lisbon, where he holds the UNESCO Chair on AI & Virtual Reality. He is also an Adjunct Full Professor at Victoria University of Wellington and an Honorary Invited Professor at the Catholic University of Rio de Janeiro. His career spans AI, VR, and human-centered computing, with leadership roles in IEEE Computer Society, ACM/SIGGRAPH, and Eurographics. Teaching: Computer Graphics, VR, and Human-Computer Interaction (HCI) Leadership: IEEE Computer Society Board of Governors (2023–present), ACM Europe Council (2015–2019) Research: 400+ peer-reviewed publications, focusing on VR, AI ethics, and interactive systems Jorge’s research bridges theoretical and applied computing, emphasizing ethical AI, reproducible science, and immersive technologies. His work includes foundational contributions to pseudo-haptics, gamification in education, and 3D user interfaces. He advocates for open science and global collaboration in XR (Extended Reality) development. His publications span topics like NFTs, remote assistance in MR (Mixed Reality), and gamified learning analytics, reflecting a career dedicated to innovation in graphics, interaction, and education. Notable awards include IEEE Fellow (2025), IEEE Golden Core (2024), and IFIP Silver Core (2014). He has supervised 18 doctoral theses and 70+ MSc dissertations, including Pedro Duarte Parreira’s Fraunhofer-winning MSc thesis (2016). As Editor-in-Chief of Computers & Graphics (since 2007) and IEEE Computer Society Publications Board member (2025–present), he shapes academic discourse in computing. Selected Awards IEEE Fellow (2025) Golden Core Award (2024) IFIP Silver Core (2014) Best Paper Award, IEEE CG&A (2011) Fellow, Eurographics Association (2010)

Greis Francy Mireya Silva CalpaView profile

Assistant Professor

• Human-Computer Interaction
• Virtual Reality
• Machine Learning
• +7 more

Greis Francy Mireya Silva Calpa is an Assistant Professor at the Department of Computer Science, Pontifical Catholic University of Rio de Janeiro (PUC-Rio). Her work focuses on Human-Computer Interaction, Virtual Reality, and Machine Learning, with applications in healthcare, industry, and education. BSc in Computer Science (University of Nariño, Colombia, 2009) Revalidated BSc in Computer Science (Fluminense Federal University, Brazil) MSc in Computer Science (PUC-Rio, 2012) PhD in Computer Science (PUC-Rio, 2016) Postdoctoral work at National Laboratory for Scientific Computing (LNCC) and PUC-Rio Her research explores the intersection of Human-Computer Interaction and Artificial Intelligence, including: Intelligent interfaces for healthcare, industry, and education Machine Learning techniques in user modeling HCI design for intelligent systems Serious games and assistive technologies Multimodal interaction and affective computing Greis collaborates with Tecgraf Institute/PUC-Rio and has mentored undergraduate, master’s, and doctoral students in research.

Hélio Cortes Vieira LopesView profile

Associate Professor

• Data Science
• Machine Learning
• Process Mining
• +9 more

Hélio Cortes Vieira Lopes is an Associate Professor at the Department of Informatics of the Pontifical Catholic University of Rio de Janeiro (PUC-Rio) . He joined PUC-Rio in 1996, initially working in the Mathematics Department before transitioning to the Computer Science Department (now Informatics) in 2012. He has also held a Visiting Scholar position at Purdue University in 2001. Education: PhD in Mathematics (1996), Master’s in Informatics (1992), and Bachelor in Computer Engineering (1990), all from PUC-Rio. Research Interests: Hélio focuses on developing advanced mathematical and computational techniques for real-world problems, with recent emphasis on Data Science , Machine Learning , and Process Mining in cloud computing and uncertainty quantification. His work spans Computer Graphics and Visualization , Mesh Generation , Computational Geometry and Topology , and Quantitative Methods for Insurance and Finance . He has applied these techniques to industries like oil, banking, and insurance. Scientific Awards: He received the Business Process Intelligence Challenge (BPIC) Best Report Award in 2017. He also holds productivity grants from CNPq, FAPERJ, CAPES, and FINEP. Advising and Industry Collaboration: Hélio has advised over 80 graduate students and collaborated with companies such as PETROBRAS , VALE , BANCO DO BRASIL , and GLOBO.COM . He has served as an associate editor for journals like Computers & Graphics and IEICE Nonlinear Theory and Its Applications . Laboratories and Initiatives: He leads the DASLAB (Data Science Laboratory) and the ExACTa initiative, while also co-coordinating the GALGOS Laboratory (optimization algorithms) and collaborating with the IDEIAS Laboratory (human-computer interaction and visualization).

Valeria de PaivaView profile

Researcher

• category theory
• programming languages
• type theories
• +12 more

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.

Adriano Francisco BrancoView profile

Professor

• Internet of Things (IoT)
• Wireless Sensor Networks
• Embedded Systems
• +8 more

Adriano Francisco Branco is a Tenured Professor in the Computer Science Department at Pontifical Catholic University of Rio de Janeiro (PUC-Rio). He also serves as a researcher and mentor in technological innovation programs, with affiliations to initiatives like ExACTa, Gist LabLua, and Tecgraf at PUC-Rio. PhD in Computer Science (2015) and Master's in Computer Science (2011) from PUC-Rio Bachelor's in Electronic Engineering from CEFET/RJ (1992) His research focuses on bridging distributed systems, IoT, wireless sensor networks, embedded systems, and data science, with emphasis on practical applications in industrial and energy sectors. He leads projects in collaboration with oil & gas and telecommunications companies, integrating energy harvesting and edge AI techniques. Key scientific recognition includes the FAPERJ Excellence Award (Grade 10) .

Eduardo Sany LaberView profile

Associate Professor

• Algorithm Design
• Machine Learning
• Optimization
• +5 more

Eduardo Sany Laber is an Associate Professor in the Computer Science Department at Pontifical Catholic University of Rio de Janeiro (PUC-Rio), with over two decades of academic and industrial experience. He completed his undergraduate (1996), master's (1997), and doctorate (1999) at PUC-Rio, focusing on data compression. Since 2001, he has been a faculty member, with visiting roles at Carnegie Mellon University (2001-2002). Education : PhD in Computer Science (1999), PUC-Rio Master in Computer Science (1997), PUC-Rio BSc in Computer Engineering (1996), PUC-Rio His research centers on designing algorithms with theoretical guarantees for problems in data compression , machine learning , and finance , combining optimization, information theory, and industrial applications. His recent work explores connections between k-means and decision trees , alongside information-theoretic clustering . The 15 most recent publications highlight his focus on algorithm efficiency, spanning machine learning , information retrieval , and combinatorial optimization . Notable venues include ICML , ECAI , and ISAAC , with applications in finance , data analysis , and web engineering . Scientific Awards : Gold Medal, Brazilian Math Olympiad (1990) Bronze Medal, International Math Olympiad (1991) First Prize, Brazilian Computer Society PhD Thesis Contest (2000, 2004 co-advisor) Affiliated Member, Brazilian Academy of Sciences (2008) Best Paper Award, ISAAC (2015) Laber supervises PhD and Master's students intensively, including recipients of national awards like the Brazilian Computer Society's First Prize for Marco Molinaro's Master's Dissertation (2009) and Second Prize for Renato Carmo's PhD Thesis (2006) . He is a CNPq Level 1 Researcher and collaborates with the Greyhounds Laboratory .

Juliana Alves PereiraView profile

Assistant Professor

• Machine Learning
• Software Quality
• Human Aspects in Software Engineering
• +6 more

Juliana Alves Pereira is an Assistant Professor in the Computer Science Department at Pontifical Catholic University of Rio de Janeiro (PUC-Rio), where she supervises master's and doctoral research in Software Engineering. She collaborates with the Software Engineering Laboratory (LES/OPUS) and focuses on Machine Learning applications for software system management, evolution, and maintenance. Education: PhD (summa cum laude) in Software Engineering from Otto-von-Guericke-Universität Magdeburg (2018), Master's in Computer Science from Federal University of Minas Gerais (2015), and Postdoctoral studies at University of Rennes/Inria-IRISA (France) Her research spans Machine Learning, Explainability, Transfer Learning, Generative AI, Deep Learning, Natural Language Processing, and Recommender Systems, particularly in software quality and human-centric contexts. Juliana has received international recognition through awards at conferences like SPLC, ICPE, and CBSoft. Scientific Awards: Dissertationspreis 2018 – Otto-von-Guericke-Universität Magdeburg GI-Dissertationspreis 2018 nomination – German Computer Society ACM Best Paper Awards – SPLC 2021, ICPE 2020 CBSoft Most Influential Paper Award 2022 – Brazilian Computer Society Juliana actively engages in academic leadership through program committee roles at major conferences including ICSE Demo, SANER, MSR, and ICSME. She collaborates with national and international research groups while maintaining memberships in the Brazilian Computer Society (SBC) and LES/OPUS research group.

• Data Visualization
• Scientific Visualization
• Computer Graphics
• +6 more

João Luiz Dihl Comba is a Professor at the Institute of Informatics, Universidade Federal do Rio Grande do Sul (UFRGS), Brazil. With a PhD in Computer Science from Stanford University (1993-2000) under advisor Leonidas J. Guibas, he has established himself as a leading researcher in visualization and computer graphics. His academic journey includes a M.Sc. in Systems Engineering and Computing from UFRJ (1988-1991) and a B.Sc. in Computer Science from UFRGS (1983-1987), along with a sabbatical at the University of Utah (2010-2011). Comba's research focuses on data and scientific visualization, visual analytics, computer graphics, and their applications in medical imaging, sports analytics, and pandemic response. His work bridges theoretical advances in multidimensional projections with practical applications in healthcare, oil and gas, and digital twins. Recent publications demonstrate his continued leadership in applying machine learning techniques to visualization problems, particularly in biomedical contexts. His publication record spans over three decades with consistent output through 2025, showing evolution from foundational computer graphics work to contemporary applications of AI in visualization. The publications reveal strong collaborations with researchers across Brazil and internationally, with particular emphasis on solving real-world problems through visual analytics. As an advisor, Comba has mentored numerous graduate students who have become active researchers in visualization and computer graphics, with several continuing to collaborate with him on publications. His research group at UFRGS appears to be particularly strong in medical visualization applications, as evidenced by multiple recent publications related to COVID-19 analysis and medical image processing.

Marco MolinaroView profile

Professor

• Online Algorithms
• Convex Optimization
• Machine Learning
• +5 more

Marco Molinaro is a Professor in the Computer Science Department at PUC-Rio . He previously held Assistant Professor positions at TU Delft (EWI), Georgia Tech (ISyE, ACO), and was a PhD student in the ACO program at Carnegie Mellon University . Education: PhD in Algorithms, Combinatorics, and Optimization (ACO), Carnegie Mellon University Research Interests: Marco's work lies at the intersection of online algorithms , convex optimization , machine learning , and operations research . His recent publications analyze: Online scheduling and VM allocation in data centers Complexity of branch-and-bound trees Information-theoretic bounds for optimization problems Explainable decision trees and their theoretical limits Stochastic and adversarial bandit algorithms Curvature properties in optimization sets His research often bridges abstract mathematical frameworks (e.g., Lipschitz functions, supermodular norms) with practical applications in cloud computing, machine learning, and game theory. Publication Trends: Marco's recent work focuses on online convex optimization and its extensions to data center resource allocation (Kamino: latency-driven VM scheduling), dynamic bin packing , and information complexity in mixed-integer models. He frequently collaborates with researchers at institutions like Carnegie Mellon University and TU Delft, with recurring co-authors Santanu Dey, Amitabh Basu, and Thomas Kesselheim. Teaching: Marco teaches advanced courses in algorithm design and analysis at PUC-Rio, including: Analise de Algoritmos (2020–2025) Projeto e Analise de Algoritmos (postgraduate, 2020–2025) Algoritmos e Incerteza (postgraduate, 2020) Estruturas Discretas (2021) His curriculum emphasizes online learning , random-order models , and PAC learning , with connections to game theory and practical implementations.

Marcos KalinowskiView profile

Associate Professor

• Software Engineering for Data Science and Artificial Intelligence
• Requirements Engineering
• Software Process and Product Quality
• +3 more

Marcos Kalinowski is an Associate Professor at the Department of Informatics, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), where he has served as a faculty member since 2017. He is the co-founder and coordinator of the ExACTa PUC-Rio laboratory, General Chair of the International Conference on Software Engineering (ICSE) 2026, and Associate Editor of the Journal of Systems and Software. His academic affiliations also include membership on the steering committees of ICSE and the Ibero-American Conference on Software Engineering (CIbSE), as well as being a Senior Advisor and Lead Appraiser for the Brazilian Software Quality Program (MPS.BR). Dr. Kalinowski earned his Ph.D. and M.Sc. in Software Engineering from COPPE/UFRJ under advisor Guilherme Travassos, and his B.Sc. in Computer Science from UFRJ. His academic career includes previous faculty positions at Fluminense Federal University (UFF) from 2014-2017 and the Federal University of Juiz de Fora (UFJF) from 2012-2014. His research focuses on bridging software engineering and data science, with particular emphasis on developing reliable and maintainable AI/ML systems through rigorous engineering practices. His work addresses critical challenges in requirements engineering for ML-enabled systems, investigating how to effectively specify quality attributes and functional requirements for complex AI applications. His research on software process and product quality emphasizes agile methods adapted to modern data-intensive development contexts. Through experimental software engineering approaches, he validates these methods through empirical studies in both academic and industrial settings. His work has significantly contributed to establishing Software Engineering for Data Science as a distinct discipline, including creating Brazil's first extension course on the subject in 2021. His publication record demonstrates a strong focus on the intersection of software engineering and artificial intelligence, with recent work addressing technical debt in ML systems, requirements engineering for AI, and lean methodologies for industry-academia collaboration. His research shows a clear trajectory toward making AI development more systematic, reliable, and aligned with software engineering best practices, evidenced by his over 160 scientific publications and multiple books including the Jabuti Acadêmico award-finalist 'Engenharia de Software para Ciência de Dados' (2023). Dr. Kalinowski has received numerous prestigious awards including: CNPq Research Productivity Grant (Level 1D, 2024-2028) FAPERJ Scientist of Rio de Janeiro State Distinction (2024) Multiple best paper awards at international conferences including SWQD 2025 and CAIN 2024 Recognition as advisor of multiple award-winning Ph.D. and M.Sc. theses SBES 2024 Advisor of the Best Brazilian Software Engineering Ph.D. Thesis As an academic leader, Dr. Kalinowski has supervised numerous doctoral and master's students, many of whom have received national awards for their research. He has secured significant research funding from Brazilian Research Council (CNPq) and Rio State Agency (FAPERJ), as well as industry collaborations with major organizations including CEPEL, Eletrobras, Galp, Petrobras, and Stone. His laboratory, ExACTa PUC-Rio, has established successful industry partnerships through its Lean R&D approach, creating a robust pipeline between research and practical application. Dr. Kalinowski leads the ExACTa PUC-Rio laboratory which focuses on applying lean R&D methodologies to industry-academia collaboration projects. He also created and coordinates PUC-Rio's specialization courses in Software Engineering and Full Stack Development through CCEC PUC-Rio, demonstrating his commitment to bridging academic research with industry needs. His leadership in bringing ICSE 2026 to Brazil represents a significant milestone for the software engineering community in Latin America.

• Neurosymbolic AI
• Artificial Intelligence
• AI Ethics
• +3 more

Prof. Luis C. Lamb is a Professor at the Institute of Informatics (Universidade Federal do Rio Grande do Sul, UFRGS), specializing in Foundations of Computer Science and Artificial Intelligence. His research focuses on Neurosymbolic AI, AI Ethics, and Technology Strategy. He holds a DIC and PhD. Research Interests: Integrated Machine Learning & Reasoning, AI Ethics, Social Computing, and Innovation Strategy. Notable work includes Neurosymbolic AI frameworks and studies on collective intelligence in problem-solving. Recent contributions include talks at AAAI, IBM Neuro-Symbolic AI workshops, and the Montreal AI Debate. His work bridges statistical and symbolic AI approaches, emphasizing ethical considerations and hybrid models.

Simone Diniz Junqueira BarbosaView profile

Associate Professor

• Human-Computer Interaction
• Information Visualization
• Model-based Interactive Systems Design
• +7 more

Simone Diniz Junqueira Barbosa is an Associate Professor at the Informatics Department of Pontifical Catholic University of Rio de Janeiro (PUC-Rio). She leads the IDEIAS-SERG lab, focusing on Human-Computer Interaction (HCI) and Information Visualization research. Her work spans model-based interactive systems design, visual analytics, digital storytelling, and AI techniques for improving system usability and accessibility. Her research explores: Model-based approaches for interactive systems Data science and visual analytics for complex datasets Digital storytelling techniques Accessibility and usability enhancement through AI Semiotic engineering foundations for HCI Publication analysis reveals strong emphasis on HCI methodologies, data visualization literacy, ethical AI development, and applications in legal informatics/industrial automation. Recent works focus increasingly on explainable AI, visualization cognition, and ethical ML practices. Awards & Honors: IFIP TC13 Pioneer Award (2019) Outstanding HCI Career Award (Brazilian Computer Society, 2020) Selo de Inovação SBC Innovation Award (2022) IFIP Fellow Award (2024) She has advised over 45 graduate students (PhD/MSc) and coordinates multiple labs including DasLab, ExACTa, and Americanas Futuro Lab. Funded projects include CNPq, FAPERJ, Microsoft Research, and Petrobras collaborations focusing on HCI innovations and data visualization tools. Leads the IDEIAS-SERG research group merging semiotic engineering with interaction design. Serves on steering committees for ACM CHI and IFIP TC13, previously co-edited ACM Interactions Magazine (2016-2019).