Luís Miguel Mendonça Rato is an Associate Professor at the Universidade de Évora and a Senior Researcher with a PhD at Centro ALGORITMI. He is affiliated with the CST R&D Group and VISTA Lab R&D Lab, focusing on interdisciplinary research at the intersection of Electrical Engineering, Computer Science, and Agricultural/Biomedical applications. Academic Degree: PhD Current Position: Associate Professor Labs: VISTA Lab Researcher IDs: ORCID 0000-0003-4492-7548, ResearcherID A-9152-2013, CiênciaID A914-6344-CD2D His research spans machine learning applications in Agricultural Engineering (Sentinel-2 satellite data for nutrient analysis), Biomedical Imaging (MRI-ADC texture analysis for tumor classification), and Control Systems (predictive control algorithms for water delivery canals and solar fields). With an h-index of 11 and 51 publications, his work emphasizes hybrid systems combining traditional engineering with computational innovation. Recent publications highlight trends in SLAM efficiency (2024), cloud service optimization (2022), and deep learning for medical imaging (2022-2023). He has contributed to Smart Cities initiatives through projects like M-Traffic (2006) and NanoSen-AQM (2020). As a senior researcher, he leads projects in the CST R&D Group and VISTA Lab , with notable work in the Universidade de Évora ecosystem.
Dr. Kai Li serves as Professor of Finance at the University of British Columbia's Sauder School of Business, holding the Canada Research Chair in Corporate Governance and W. Maurice Young Endowed Chair in Finance. Elected Fellow of the Royal Society of Canada (2022), she is Managing Editor of the Journal of Financial and Quantitative Analysis and previously served on editorial boards of Review of Financial Studies, Management Science, and Journal of Finance. Her academic foundation includes a BSc from Jiaotong University, MA from Concordia University, and PhD from the University of Toronto. Dr. Li's research examines corporate governance through three interconnected lenses: gender dynamics in finance (board diversity, analyst performance), corporate culture quantification (using generative AI and machine learning), and green innovation (environmental governance). Her methodology pioneers computational approaches to analyze textual data from corporate disclosures, bridging finance, computer science, and social sciences. Analysis of her recent publications reveals a distinct trajectory toward interdisciplinary research, increasingly integrating artificial intelligence with traditional finance to study culture and sustainability. This shift demonstrates growing emphasis on empirical measurement of intangible governance factors through NLP and generative models. Her accolades include: Fellow of the Royal Society of Canada (2022) UBC Killam Research Award Sauder Research Excellence Award (junior/senior) Barclays Global Investors Canada Research Award John L. Weinberg/IRRCi Best Paper Award (2024) Best Paper Award at 2024 China International Conference in Finance While the provided materials confirm her editorial leadership and research impact, specific details regarding graduate student supervision and grant funding portfolios were not disclosed. Dr. Li maintains affiliations with the Asian Bureau of Finance and Economic Research (Senior Fellow), European Corporate Governance Institute (Research Member), and FinTech at Cornell Initiative (Research Fellow).
Luís B. Elvas is an Assistant Professor at ISCTE-University Institute of Lisbon's Department of Social and Business Sciences (SINTRA) and a Research Assistant at ISTAR-Iscte Research Center. He holds qualifications including a Technical Specialization in TensorFlow for AI (Coursera, 2021) and certifications in IoT/Blockchain from ISCTE and cybersecurity from Palo Alto Networks. His research spans artificial intelligence, healthcare informatics, smart cities, and blockchain, with applied work in medical imaging, data sharing, and urban analytics. Research interests include: Healthcare AI : Developing deep learning models for cardiac diagnostics, medical imaging analysis, and blockchain-based health data systems Smart Cities : Implementing IoT solutions for urban mobility optimization, disaster management, and sustainable transportation Data Science : Creating predictive analytics frameworks for clinical decision support and urban planning His publications demonstrate a strong focus on AI-driven healthcare solutions (67% of recent works) and smart city technologies (33%), with emerging interests in blockchain and NLP. Research consistently targets real-world applications in clinical settings and urban environments. Awards: Award for best internship, Order of Engineers (2022) Distinction for best internship, Order of Engineers (2021) He leads/contributes to multiple EU research consortia including AMR-EDUCare (antimicrobial resistance education), NEEM (e-health in Nepal), and Blockchain.PT. Coordinates the IEEE Computational Intelligence Society Student Branch Chapter at ISCTE and developed the ManagiDiTH master's program in digital health transformation.
Alberto Rodrigues da Silva is a Professor at the Institute Superior Técnico , part of the University of Lisbon . He teaches Fundamentals of Information Systems , primarily during the 1st Semester of the 2025/2026 academic year. His scientific interests revolve around Information Systems , Model-Driven Engineering (MDE), Requirements Engineering (RE), Social Computing , and Software Engineering . He has extensively contributed to the development of rigorous requirements specification languages like RSL (Requirements Specification Language) and its extensions (e.g., RSL-IL4Privacy for privacy policies). His collaborative work spans automated acceptance testing, GDPR compliance, and domain-specific languages (DSLs) for applications such as mobile development , digital twins , and legal contexts (e.g., LegalLanguage ). His research trends focus on integrating model-driven engineering with privacy policies , IoT applications , and low-code platforms . He has also explored tools like Maestro for data classification and usability testing, and RiverCure for flood simulation. Email: alberto.silva@tecnico.ulisboa.pt .
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.
Adalberto Campos Fernandes is an Associate Professor at the National School of Public Health (NOVA-NSPH), Universidade NOVA de Lisboa, and a researcher at the Centro de Investigação em Saúde Pública (CISP/PHRC) and Comprehensive Health Research Centre (CHRC). He holds a PhD in Health Administration from Universidade de Lisboa. Previously, he served as Minister of Health of Portugal (2015–2018) and held leadership roles in major hospitals and healthcare institutions, including the Board of Directors of Hospital de Santa Maria and Hospital Pulido Valente. Education: PhD in Health Administration (Universidade de Lisboa), Master in Public Health (ENSP-NOVA), Specialist in Public Health, and Medical Degree (Faculdade de Medicina de Lisboa). Research Focus: Health Administration, Health Management, Sustainability of Health Systems, and Public-Private Partnerships in Healthcare. His work emphasizes the integration of social and health sectors, policy analysis, and healthcare system sustainability. Recent research explores decentralized healthcare models, the impact of AI in clinical coding, and obesity management strategies. He has contributed to over 96 publications and actively participates in policy debates on healthcare accessibility and system resilience. Key roles beyond academia include serving on the Board of Directors of several hospitals and leading initiatives in public-private healthcare collaborations. His professional experience bridges academic research, policy-making, and clinical administration, shaping Portugal’s healthcare landscape.
Fernando Manuel Marques Batista is an Associate Professor at ISCTE – University Institute of Lisbon, Department of Information Science and Technology, and an integrated researcher at INESC-ID Lisbon. He serves as the Executive Coordinator of the Human Language Technologies (HLT) Scientific Area at INESC-ID and is a member of its Scientific Council. He previously held leadership roles including President of the Pedagogical Council of ISCTE-IUL (2017–2019) and member of its Standing Committee (2015–2017). Research Interests: Natural Language Processing Machine Learning Text and Speech Processing Sentiment and Emotion Analysis Hate Speech Detection Social Media Analytics Automatic Speech Recognition and Transcription His recent publications reflect a strong focus on applying NLP and machine learning to social media, with particular emphasis on hate speech detection, sentiment analysis, and user behavior modeling. He has also contributed significantly to speech processing, including punctuation restoration and prosody modeling, and to digital humanities through medieval text analysis. His work spans both technical innovation and real-world applications in tourism, finance, and public discourse. Scientific Recognition: Senior Member of IEEE (since 2016) Member of ISCA (International Speech Communication Association) Fernando Batista actively advises numerous PhD and Master’s students, supervising research in areas such as generative AI, hate speech detection, sentiment analysis, and economic forecasting. He has coordinated research projects like SPEDIAL and AppRecommender and is involved in organizing major conferences including PROPOR, EAMT, IPMU, and the Lisbon Machine Learning Summer School (LxMLS), where he has served in editorial and technical roles. Research Labs and Teams: He is a key member of the HLT@INESC-ID research group, contributing to its leadership and scientific direction. This group focuses on human language technologies, including speech processing, natural language understanding, and multilingual systems.
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.
Simo Hosio is an Academy Research Fellow (2022-2027) and Professor of Computer Science and Engineering at University of Oulu's Center for Ubiquitous Computing, where he leads the Crowd Computing Research Group. He also maintains a visiting position at University of Tokyo, Japan. Having graduated as the first Finnish scholar under Microsoft Research Cambridge's Ph.D. scholarship program, he has published over 150 peer-reviewed scientific articles spanning two decades of research. Hosio's research spans three primary domains: crowdsourcing methodologies, human-computer interaction, and digital health applications. His work pioneers novel approaches to online labor markets, investigates the suitability of crowdsourcing for diverse applications, and explores HCI aspects of digital health solutions for chronic conditions. His research group, founded in 2020, has secured nearly two million USD in funding, demonstrating significant research impact and recognition. Analysis of Hosio's recent publications reveals a strong trend toward interdisciplinary research at the intersection of crowdsourcing, healthcare technology, and emerging AI systems. His work increasingly focuses on practical applications of crowd computing in health contexts, with growing attention to mental health, women's health, and workplace well-being solutions. The integration of AI and machine learning techniques with traditional HCI approaches represents another significant trajectory in his recent scholarship. Distinguished Paper Award (2024) Best Paper Honourable Mention Award (2022) PMCJ Best Research Paper (awarded in 2024) Best Paper Award (2022) Best Full Paper Award (2015) Honorable Mention Award (2014) Best Paper Presentation award (2010) As an educator, Hosio has taught Human-Computer Interaction (2019-2025) to over 260 students in 2024, Social Computing (2018-2021) to approximately 60 students annually, and Applied Computing (2015-2018) to around 50 students each year. His research group's nearly two million USD in secured funding demonstrates significant grant acquisition success, supporting innovative work at the intersection of crowd computing, health technology, and human-centered AI systems. The Crowd Computing Research Group, founded by Hosio in 2020, represents a significant research infrastructure focused on advancing methodologies for crowd-powered systems. The group's work spans from fundamental research on crowd labor markets to applied projects in healthcare, workplace well-being, and social computing, demonstrating a strong commitment to both theoretical advancement and practical impact.
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.
Chrysoula Zerva is an Invited Assistant Professor in the Department of Electrical and Computer Engineering at Instituto Superior Técnico and a post-doc researcher in the SARDINE group at the Instituto de Telecomunicações. She holds a PhD from the University of Manchester (2019) and was awarded the EPSRC Doctoral Prize Fellowship for the “Fake Health News” project. Her research focuses on uncertainty quantification, fairness, explainability in NLP, and machine translation evaluation. Key interests include adversarial attacks, bias mitigation, and disentangled representation learning. Dr. Zerva’s work spans conformal prediction frameworks, dialect bias in reward models, and response clarity classification. Her contributions to MT evaluation and quality estimation have been highlighted in WMT shared tasks. Recent studies address ethical AI challenges, such as the societal impacts of LLMs and biases against African American language. Scientific Awards: EPSRC Doctoral Prize Fellowship Labs/Teams: Member of the SARDINE research group Her publications investigate cutting-edge topics like CLIPScore metrics, non-exchangeable conformal methods, and context-aware NMT for business dialogues. Collaborations include Unbabel and participation in international shared tasks.
Inês Cláudia Rijo De Carvalho is a Lecturer at Universidade Europeia in Lisbon and serves as Research Coordinator at the Faculty of Social Sciences and Technology. She holds dual PhDs in Tourism (University of Aveiro) and Management (ISEG-Lisbon School of Economics), with a BSc in Modern Languages and Literatures (University of Coimbra) and an MSc in Tourism Management (University of Aveiro). She was a visiting scholar at Linköping University’s Gender Studies Unit in Sweden. Affiliated with CETRAD and GOVCOPP research centers, her work focuses on gender dynamics in tourism, cultural tourism, language tourism, and AI applications in the sector. She has conducted research on gender equality in tourism leadership and led projects funded by Portugal’s Foundation for Science and Technology. Her recent publications explore topics like chatbot applications in tourism, pandemic impacts on travel industries, and machine translation in traveler experiences. She has co-authored book chapters on women’s voices in tourism research and contributed to policy-oriented studies on tourism development. Education: BSc English/German Literature, MSc Tourism Management, PhD Tourism, PhD Management (ongoing) Key Research Themes: Gender & Tourism, Language Tourism, AI in Tourism, Cultural Heritage, Tourism Resilience Affiliations: CETRAD, GOVCOPP, Universidade Europeia Her research has been published in journals like Tourism Review, Anatolia, and Humanities and Social Sciences Communications. She actively participates in conferences such as the International Tourism Congress and THInC, addressing topics from emotional labor in hospitality to post-pandemic tourism strategies. Her work bridges academic rigor with practical insights for tourism policy and industry innovation.
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.
