Juho Lee is an Associate Professor at the Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST). Previously, he worked as a research scientist at AITRICS and completed his PhD in Computer Science & Engineering at Pohang University of Science and Technology (POSTECH) under Professor Seungjin Choi, followed by postdoctoral research at University of Oxford with Professor François Caron. His research focuses on: Bayesian deep learning Bayesian inference Meta learning Generative models Uncertainty quantification Graph representation learning Professor Lee's work bridges theoretical Bayesian methods with practical deep learning applications. His recent publications demonstrate significant contributions to neural processes, Bayesian optimization, and scalable inference methods. He has developed novel architectures like Set Transformer for permutation-invariant modeling and advanced techniques for uncertainty quantification in deep networks. His research shows a clear trajectory toward making Bayesian principles applicable to large-scale, real-world machine learning problems. Notable contributions include: Set Transformer: A framework for attention-based permutation-invariant neural networks (ICML 2019) Bootstrapping neural processes (NeurIPS 2020) Deep amortized clustering (NeurIPS 2019 workshop) Learning to pool in graph neural networks for extrapolation Professor Lee actively mentors graduate students and has advised numerous PhD candidates who co-author papers with him across NeurIPS, ICML, and ICLR. His research group SIML@KAIST develops scalable and interpretable machine learning methods with strong theoretical foundations.
João Magalhães is a Full Professor in the Department of Computer Science at the Faculty of Science and Technology, Universidade NOVA de Lisboa, Portugal. He serves as Group Coordinator of the Multimodal Systems Group at the NOVA Laboratory for Informatics and Computer Science and leads the NOVASearch research group at FCT/UNL. His research focuses on vision and language information understanding, with particular emphasis on multimodal information understanding, multimodal conversational AI, multimedia search and summarization, temporal and memory models, and social media information quality. His work spans both theoretical foundations and practical applications across web, social media, and clinical domains. Analysis of his recent publications reveals a strong trajectory in multimodal conversational AI systems, with increasing sophistication in handling both voice and visual inputs. His research has evolved from foundational work in cross-modal embeddings to advanced large language models for dual-goal conversational settings, demonstrating consistent innovation in the field of multimodal understanding. 1st prize winner of the second Alexa TaskBot Challenge (2023) Award-winning solution in the Alexa TaskBot Challenge (2022) Best paper award at the Portuguese NLP conference (PROPOR) (2020) Best paper nominations at ACM conferences (2018) Professor Magalhães has advised numerous graduate students through the NOVASearch group and has secured substantial research funding through projects including Amazon Alexa TaskBot Challenge (2021-2023), iFetch (2020-2023), SmartyFlow (2017-2020), COGNITUS (2016-2019), GoLocal (2016-2020), QSearch (2012-2015), ImTV (2010-2013), and CS4SE (2010-2013). He actively serves the research community as ACM Multimedia 2026 Program Committee Chair and has held leadership roles in numerous conferences including ACM Multimedia 2022 General Chair and ECIR2020 General Chair. He leads the Multimodal Systems Group within the NOVA Laboratory for Informatics and Computer Science, where his team develops cutting-edge solutions for multimodal understanding with applications in conversational AI, multimedia search, and social media analysis.
Minjoon Seo is an Associate Professor at KAIST AI, Korea Advanced Institute of Science and Technology. He holds a BS in Electrical Engineering & Computer Science from UC Berkeley and previously worked as a software engineer at Oracle. His research focuses on natural language understanding, large-scale end-to-end question answering, and multimodal AI systems combining language and vision. Research Interests: His work spans Natural Language Processing, Machine Learning, Deep Learning, and Language-Vision integration. He develops neural network architectures for machine comprehension and multimodal understanding, with applications in question answering systems and diagram interpretation. Publications: His research demonstrates a consistent focus on multimodal AI systems, with recent works advancing neural approaches to machine comprehension and diagram understanding. Publications show strong emphasis on NLP-CV integration and practical applications in healthcare and education. Awards: Best Paper Nomination at UbiComp 2014 for BiliCam research Professional Activities: Maintains active open-source contributions through GitHub repositories related to question answering systems and NLP research. Co-founded Config Intelligence while maintaining academic position.
Paulo Jorge Freitas de Oliveira Novais is a Full Professor of Computer Science at the Department of Informatics, School of Engineering, Universidade do Minho, where he also holds a Habilitation in Computer Science. He leads the Synthetic Intelligence Lab at ALGORITMI Centre and coordinates the research line on Ambient Intelligence for Well-Being and Health Applications. His research spans Intelligent Systems, Machine Learning, Multi-Agent Systems, and their applications in Smart Cities, Health Informatics, and AI Ethics. PhD in Computer Science, Universidade do Minho, 2003 Habilitation in Computer Science, Universidade do Minho, 2011 Research interests include Ambient Intelligence, Ambient Assisted Living, Intelligent Environments, AI and Law, Conflict Resolution, and Explainable AI. His work focuses on enhancing system intelligence and reliability through novel architectures and ethical frameworks. Recent publications highlight applications in wastewater energy prediction, violence detection, student risk modeling, and urban logistics. Awards include multiple Best Paper and IBM Excellence recognitions across 2015–2023, plus a 2022 Career Recognition Award from the Ibero-American Society of Artificial Intelligence. Senior IEEE Member Chair of IEEE Computational Intelligence Chapter, Portugal IFIP TC 12 Artificial Intelligence Working Group Leadership He has supervised 132 PhD and Master’s students and contributed to editorial boards of journals like JAISE and ComSIS . His leadership roles include coordinating LASI – Intelligent Systems Associate Laboratory and serving as former president of APPIA.
Teresa Cristina de Freitas Gonçalves is an Associate Professor at the Department of Informatics, School of Sciences and Technology, University of Évora, where she has been employed since 1999. She serves as an integrated researcher at the ALGORITMI research centre and is the Director of the VISTA Lab (Video, Image, Speech and text Analysis Lab), the unit of the ALGORITMI research centre at University of Évora. Her leadership roles include Director of the Master programme in Informatics Engineering and deputy Director of both the Master programme in Artificial Intelligence and Data Science and the Doctoral program in Computer Science. She earned her PhD in Computer Science from University of Évora and a MSc degree in Informatics Engineering from New University of Lisbon. Her academic journey at University of Évora has included significant leadership positions including Head of the Computer Science Department (2011-2015), Director of the Bachelor programme in Informatics Engineering (2016-2021), and Deputy Director roles for various undergraduate and graduate programs. Dr. Gonçalves' research focuses on intelligent systems, particularly Machine Learning approaches, with substantial contributions in evolutionary algorithms, information extraction and retrieval, and supervised learning across multiple data modalities including tabular data, text (in both Portuguese and English), and images (medical and satellite). Her work bridges theoretical advances with practical applications in healthcare, remote sensing, and natural language processing. She has successfully supervised 6 doctoral theses, 19 master theses, and 3 postdocs, and currently mentors 5 doctoral and 6 master students from diverse international backgrounds including Bangladesh, Cabo Verde, Nepal, Philippines, India, Sri Lanka, China, Mongolia, and Portugal. Her publication record includes over 100 scientific articles indexed by Scopus with 640 citations and an h-index of 12, demonstrating significant international impact with 56% of her work involving international collaboration. Her recent research shows a strong trend toward applying advanced machine learning techniques to healthcare applications, information retrieval systems, and remote sensing analysis, with particular emphasis on transformer networks, learning-to-rank methodologies, and multimodal data analysis. Dr. Gonçalves has made substantial contributions to the academic community through her service as a reviewer for over 50 articles in prestigious international journals and conferences, and as chair for major international conferences including IDEAL 2023, PROPOR 2020, SKIMA 2017 and 2018, and CLEF 2016. She serves on the board of APRP (Associação Portuguesa de reconhecimento de Padrões) and as a jury member for APRP prizes for best MSc and PhD theses. Her current research portfolio includes coordination of the Horizon Europe MSCA Staff Exchange HarmonicAI project and local coordination of WP6 in the NewSpace Portugal mobilising agenda. She is also actively involved in numerous other international research initiatives including Interreg VI-B Sudoe SenforFire, PRR CANTE, La Caixa INCOME, Erasmus+ KA220-HED REDINEST, Interreg POCTEP TID4AGRO, and ATTRACT DIH projects. Previously, she led the FCT AI in the Public Administration SNS24.Scout.IA project and coordinated the FEDER R&D NIIAA project. As Director of the VISTA Lab, Dr. Gonçalves leads a dynamic research team focused on video, image, speech, and text analysis. The lab serves as the Évora hub of the ALGORITMI research centre and has established strong international collaborations. Under her leadership, the VISTA Lab has developed innovative approaches in medical image analysis, natural language processing for Portuguese, and satellite image classification, with applications spanning healthcare, environmental monitoring, and public administration.
Sungsoo Ahn is an Assistant Professor at the Graduate School of AI, KAIST, where he leads the Structured and Probabilistic Machine Learning (SPML) Lab. His research focuses on developing machine learning algorithms for molecular science, particularly in drug discovery, material design, and generative modeling. He directs a team of 13 researchers (including 2 post-docs and 11 students) and maintains collaborations with institutions like Mila and industry partners. His core research integrates probabilistic machine learning , generative models , and AI for science , with applications spanning molecular dynamics simulation, language model reasoning, combinatorial optimization, and graph neural networks. Key methodologies include flow matching, diffusion models, GFlowNets, and equivariant neural networks applied to chemical and biological domains. Recent publications (2023–2025) demonstrate strong emphases on: (1) Molecular generation/optimization for drug design, (2) Enhancing reliability and reasoning in large language models, (3) Graph-based machine learning for scientific discovery, and (4) Efficient training paradigms for generative samplers. These appear predominantly in NeurIPS, ICML, ICLR, and ACL. He advises multiple PhD/master's students and post-doctoral researchers in the SPML Lab. Current research directions include torsion-aware molecular generation, causal AI safety, neural operators for quantum chemistry, and multi-agent systems for molecular optimization.
Kimin Lee is an assistant professor at the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST), where he focuses on developing safe and capable decision-making agents. His research spans multiple aspects of artificial intelligence with a strong emphasis on safety and reliability. Dr. Lee completed his educational journey at KAIST, earning a Ph.D. in Electrical Engineering with a focus on Machine/Deep Learning (2015-2020), advised by Professor Jinwoo Shin. He also holds a Master's degree in Electrical Engineering (Wireless Communication Networks, 2013-2015) and a Bachelor's degree in Electrical Engineering (2009-2013), both from KAIST. His primary research interests include: Physical AI - developing AI systems that can interact safely and effectively with the physical world Alignment - particularly reinforcement learning from human feedback (RLHF) and scalable oversight techniques Monitoring - safety evaluation frameworks and benchmarking for AI systems LLM Agents - enhancing the capabilities and safety of large language model-based agents Dr. Lee's recent publications reveal a strong trajectory toward addressing critical challenges in AI safety. His work consistently bridges theoretical advances with practical applications, particularly in the areas of reinforcement learning, computer vision, and natural language processing. A notable trend in his research is the development of methods to evaluate and enhance the safety of AI systems, especially large language models and diffusion models, while maintaining or improving their capabilities. As an active member of the academic community, Dr. Lee serves as an area chair for major conferences including NeurIPS, ICLR, and ICML, and regularly reviews for top-tier AI venues. He has also organized workshops focused on safe and trustworthy AI agents. Dr. Lee's research group at KAIST appears to focus on AI safety and decision-making, with research projects spanning from theoretical foundations to practical implementations of safe AI systems. His collaborative work with institutions like UC Berkeley and Google Research demonstrates the interdisciplinary nature of his research approach.
Ricardo F. Ramos is an Adjunct Professor at the Oliveira do Hospital School of Technology and Management, Polytechnic Institute of Coimbra (ESTGOH-IPC), and at the Information Management School, NOVA University Lisbon (NOVA-IMS). He serves as an Associate Researcher at ISTAR-Iscte - Research Center in Information Sciences, Technologies and Architecture, focusing on Information Systems. His educational background includes: PhD in Management with specialization in Marketing from ISCTE (2015-2018) Master's degree in Sports Management from University of Lisbon Faculty of Human Kinetics (2012-2015) Bachelor's degree in Sport from Polytechnic Institute of Setúbal Higher School of Education (2009-2012) Ricardo's research spans diverse areas within marketing, with particular focus on consumer behavior in tourism and hospitality, sports marketing, and text mining applications. His methodology often involves analyzing textual data to uncover consumer insights, as demonstrated in publications examining social media, mobile applications, and online reviews. His work frequently explores the intersection of technology and consumer behavior, investigating how digital platforms influence purchasing decisions and service experiences. His recent research trends show a growing interest in sustainability within business contexts, virtual and augmented reality applications in tourism, artificial intelligence in marketing, and the evolving dynamics of digital consumer behavior across various industries including hospitality, aviation, and retail. Ricardo has received significant recognition for his scholarly work, with publications in high-impact journals such as the International Journal of Hospitality Management, Journal of Air Transport Management, and Journal of Hospitality and Tourism Technology. His research on topics like social media's impact on retail websites, airline sustainability, and customer satisfaction in tourism contexts has accumulated hundreds of citations across multiple databases. His academic contributions extend to collaborating with researchers across various institutions, with a methodological approach that frequently employs text mining and data analytics to extract meaningful consumer insights from large datasets.
Nuno Santos is an Associate Professor in the Department of Computer Science and Engineering at Instituto Superior Técnico (IST), University of Lisbon, and a senior researcher at INESC-ID Lisbon, where he leads the SysSec team within the Distributed, Parallel and Secure Systems (DPSS) group. He is an active member of the international security community, serving as Program Vice Co-Chair for USENIX Security 2025 and on program committees for top venues such as IEEE S&P, CCS, and USENIX Security. Education: Ph.D. in Computer Science, 2013 – Max Planck Institute for Software Systems (MPI-SWS) & Saarland University Research internships at Microsoft Research Redmond (2010), Vrije Universiteit Amsterdam (2018), and Technical University of Munich (2024) Research Interests Nuno Santos’s research centers on the security and privacy of computer and networked systems, with particular emphasis on trusted execution environments, secure systems design, and the intersection of machine learning with security. His group investigates vulnerabilities and defenses in widely deployed platforms, including TrustZone, JavaScript runtimes, and cloud infrastructures. Additional themes include censorship-resistant communication, privacy-preserving analytics, and automated exploit generation. Recent Publication Trends Over the past five years, his work has increasingly targeted emerging threat models in confidential computing (AMD SEV-SNP, Intel TDX), large-language-model integration into web applications, and automated security analysis of JavaScript ecosystems. These publications consistently appear in the most selective venues (PLDI, SIGMETRICS, ICSE, NDSS, S&P, USENIX Security), evidencing strong empirical evaluation and real-world impact. Awards & Honors IST Outstanding Teaching Award (2019/2020) ISOC.PT Best Portuguese Internet Research Award (2024) Prémio Científico Universidade de Lisboa / Caixa Geral de Depósitos (2024) Multiple IST Teaching Excellence Awards Advising & Service Nuno Santos has supervised numerous MSc and PhD students whose theses span secure systems, network privacy, and trustworthy computing. Recent defenses include Bernardo Ribeiro, Cristi Savin, Hugo Mantinhas, João Aragonez, João Sá, and Tomás Tavares. He actively participates in doctoral committees and mentors junior researchers within the DPSS group. Labs, Teams & Collaborations He leads the SysSec team inside the Distributed, Parallel and Secure Systems (DPSS) group at INESC-ID Lisbon. The team maintains strong collaborative ties with MPI-SWS, VUSec at VU Amsterdam, the Systems Research Group at TU Munich, and multiple industry partners including Microsoft Research.
Pedro Alexandre Simões dos Santos is an Associate Professor at Instituto Superior Técnico, with dual appointments in the Mathematics Department and Computer Science and Engineering Department. His work spans Machine Learning, Social Artificial Intelligence, and Game Design, focusing on convergence analysis of ML algorithms, Social AI, and AI for Games. He actively contributes to game design, both digital and analogic, with a special interest in historical themes. Affiliations: Mathematics Department, Instituto Superior Técnico Computer Science and Engineering Department, Instituto Superior Técnico INESC-ID Research Unit GAIPS - Group of AI for People and Society His research integrates theoretical and applied aspects, including reinforcement learning in non-stationary environments, neural network optimization, and socially intelligent agent authoring tools. He explores AI applications in games, mental health support systems, and legal domains. His recent publications highlight trends in NFT-based game ecosystems, dialogue systems for mental health, and multi-agent reinforcement learning frameworks. Pedro teaches subjects such as Mathematics for Machine Learning and the 1st Cycle Integrative Project in Applied Mathematics and Computing, bridging mathematical foundations with computational applications.
Paulo Carreira is an Associate Professor at Instituto Superior Técnico , Universidade de Lisboa. His research focuses on Building Automation , Energy Management , and Data Integration , with a particular emphasis on Smart Urban Environments and Cyber-Physical Systems . Fields: Building Automation, Energy Management, Data Privacy, Query Optimization, ETL, Streaming Query Processing Research Trends : His recent work spans from SQL Injection Attacks in LLM-integrated systems to Multi-Paradigm Modeling for Cyber-Physical Systems. Publications highlight his expertise in Energy Efficiency in buildings and BIM Integration with real-time data. Scientific Awards : Best Student Paper Award Nomination for Best Paper Award
Tomás A. Palma is an Assistant Professor at the School of Psychology, University of Lisbon. His research focuses on social cognition, specifically how social categories like race, gender, and age influence face recognition, metamemory, and stereotype formation. He has contributed to understanding the cross-race recognition deficit (CRD) and strategies to mitigate its effects. Research Themes: Cross-Race Recognition Deficit, Metacognitive Awareness, Social Categorization, Stereotyping Dynamics Key Collaborations: James Correll, Luís Garcia-Marques Recent publications explore CRD mechanisms, eye gaze patterns, and the application of large language models to behavioral science predictions. His work also addresses hiring discrimination, stereotype plasticity, and cultural variations in gender perception. While no specific awards or student mentorship details are mentioned, his teaching includes advanced psychology research methods and statistics.
Maribel Yasmina Campos Alves Santos serves as a Full Professor in Information Systems in Organisations and Society at the Department of Information Systems, School of Engineering, University of Minho, Portugal. She is a Senior Researcher at the ALGORITMI Research Centre and the CCG/ZGDV ICT Innovation Institute, where she leads the data engineering and analytics group. Previously, she led the Software-based Information Systems Engineering and Management Group (2017-2022) and currently coordinates the 'Organisational and Analytical Data-intensive Systems' research track since 2009. Her research focuses on Business Intelligence and Analytics, with particular emphasis on Big Data Analytics including data architectures, processing, analysis, and visualization. She contributes to international classifications through the Association for Information Systems (AIS) Topics in Decision Support and Analytics, Geographic Information Systems, and Big Data Application Processes, as well as IFIP Technical Committee on Information Systems. Dr. Santos has held significant administrative roles including Vice-Dean of the School of Engineering (2019-2022), Dean of the Pedagogical Council (2019-2022), and Associate Director of the Department of Information Systems (2010-2014). Since February 2024, she has served as Director of the Doctoral Program in Information Systems and Technologies. She was actively involved with AGILE as Secretary-General (2013-2015) and remains a member of the Association of Information Systems (AIS). Her recent publication portfolio demonstrates strong focus on emerging technologies, with 15 most recent works spanning large language models for conceptual modeling, storytelling dashboards for Industry 4.0, data mesh adoption, and model-to-model transformations. These publications reveal consistent themes in data architecture innovation, visualization techniques, and the integration of AI in information systems. Associate Editor, Business & Information Systems Engineering Journal (Q1, since January 2022) Scientific/Program/Organizing Committee member for over 140 international conferences Co-inventor of two patents (one national, one international) Dr. Santos has supervised 7 PhDs, 60 MSc, and 2 post-doc students, and currently mentors 4 PhD students, 4 MSc students, and 1 post-doc. She has supervised over 45 research grants and participated in more than 30 funded research projects. Her leadership extends to coordinating the Doctoral Program in Information Systems and Technologies and contributing to European research initiatives including a Marie Skłodowska-Curie Actions Joint Doctorate Program in Geoinformatics.
João Pedro Matos-Carvalho is an Assistant Professor at Lusófona University in Lisbon, affiliated with the School of Engineering and the Department of Electrical and Computer Engineering. He is also an Integrated Member of the Center of Technology and Systems (CTS) at UNINOVA and COPELABS, Lusófona University, contributing to interdisciplinary research in robotics and intelligent systems. Ph.D. in Electrical and Computer Engineering, FCT NOVA (2021) M.Sc. (Hons.) in Electrical and Computer Engineering, FCT NOVA (2017) His research focuses on aerial robotics, machine learning, remote sensing, and sensor networks, with applications in UAV navigation, precision agriculture, environmental monitoring, and embedded AI. He has made significant contributions to GPS-denied navigation, multispectral imaging, and AI-driven signal processing. The recent publications reflect a strong trend in integrating deep learning with real-world engineering systems, particularly in UAV autonomy, IoT, and human-centric applications like fall detection and online learning analysis. His work spans algorithm design, software development, and practical deployment in complex environments. Best Paper Award at IEEE Conference (2018) Distinguished Paper Award by LASIGE at FCUL (2021) Best Poster Presentation Award at International Complex Systems and Their Applications Conference (2023) He has secured the competitive Scientific Employment Stimulus (CEEC) grant from FCT and has advised or collaborated on multiple research projects. He has guest-edited special issues in journals such as Drones and Frotiers in Computer Science , and serves as a reviewer for leading scientific journals. He leads the development of open-source tools like AutoNAV and Raster Forge, supporting simulation and geospatial analysis. He is actively involved in research teams at CTS-UNINOVA and COPELABS, focusing on intelligent systems, aerial robotics, and data fusion. His lab work emphasizes practical UAV platforms, sensor integration, and AI deployment in real-time systems.
Luis Cruz is a Professor at the Faculty of Economics of the University of Coimbra (FEUC), actively affiliated with the Centre for Business and Economics Research (CeBER). In April 2025, he conducted research under a Fulbright Scholarship at the University of Virginia (UVA), co-organizing the Regional Economics and Input-Output Modeling Workshop with the Weldon Cooper Center for Public Service. This event fostered collaboration among PhD students and prominent economists like Michael Lahr (Rutgers University) and Geoffrey Hewings (University of Illinois Urbana-Champaign), emphasizing cross-generational dialogue in regional economics. His core research examines Environmental and Socio-Economic Interactions, with specialization in Regional Economics and Input-Output Analysis. The UVA workshop highlighted his focus on community-building in economic modeling, where he described the atmosphere as having "great energy, great conversations, and a strong sense of community across generations of regional economists" . His Fulbright experience facilitated cultural exchange and connections in socio-economic policy research. Analysis of his 15 most recent publications reveals an unexpectedly broad interdisciplinary scope spanning computer vision (LiDAR compression, 3D modeling), electrical engineering (grid inspection), and occupational medicine (dermatitis, radiation exposure). This suggests collaborative work beyond traditional economics, possibly through CeBER's cross-departmental initiatives at the University of Coimbra. Professor Cruz has secured competitive international funding through the Fulbright Program, enabling workshops that mentor emerging economists. His leadership in organizing the UVA workshop demonstrates active engagement in academic community development, though specific grant details beyond the Fulbright are not documented in the provided sources. He operates within CeBER (Centre for Business and Economics Research), a key research unit under FEUC that drives economic policy studies. The center's focus on empirical modeling aligns with his workshop on input-output analysis, though no dedicated lab or specialized team is mentioned in the current materials.
