Carlos Pedro Gonçalves is an Associate Professor at the Lusophone University of Humanities and Technologies , where he conducts research in Complexity Sciences , Quantum Technologies , and Artificial Intelligence . He holds a PhD in Management with specialization in Quantitative Methods (Risk Mathematics) and has developed innovative software like the Strategy Analyzer and QNeural , preserved in GitHub's Arctic Code Vault. Research Focus: Quantum machine learning, chaos theory, financial risk modeling, and AI in strategic decision-making. Software Contributions: Open-source tools for quantum computing, cybersecurity, and agent-based simulations in NetLogo. His recent publications examine chaotic patterns in pandemic hospital occupancy and quantum neural network dynamics. He has also contributed to Arctic Code Vault as a GitHub-recognized developer. Scientific Awards: Arctic Code Vault Contributor (GitHub)
Manuel Filipe Vieira Torres Santos is an Associate Professor with Habilitation at the School of Engineering of the University of Minho, Portugal. He serves as a Senior Researcher at Centro ALGORITMI (Associate Laboratory in Intelligent Systems) where he coordinates the Intelligent Data Systems research lab and the Information Systems and Technologies research group. His academic career combines teaching responsibilities with extensive research activities focused on data science applications in healthcare settings. Dr. Santos' research interests center on applying advanced computational techniques to healthcare challenges. His primary areas include Machine Learning, Knowledge Discovery from Databases, Data Mining, Agent-based Systems, and Intelligent Decision Support Systems. He has pioneered work in Pervasive and Adaptive Business Intelligence specifically tailored for healthcare environments. His research spans both theoretical advancements in data science methodologies and practical implementations in real-world medical settings, particularly in intensive care units, hospital management, and precision medicine applications. Analysis of his recent publications reveals a strong trend toward integrating emerging technologies like blockchain, Internet of Things, and openEHR standards into healthcare information systems. His work demonstrates a progression from foundational data mining techniques to increasingly sophisticated architectures that support real-time decision making, predictive analytics, and personalized patient care. The publications show particular emphasis on standardization efforts, interoperability challenges, and the development of maturity models for digital transformation in healthcare institutions. Coordinator of Intelligent Data Systems research lab Coordinator of Information Systems and Technologies research group Principal Investigator for multiple funded projects including: Intelligent hospitalization management (2021-present) Data Science applied to diabetes (2021-present) Intelligent Decision Support for response times optimization (2020-present) Intelligent Hospital Infection Control (2020-present) Dr. Santos has secured significant research funding for healthcare analytics projects, demonstrating the practical value of his work. His approach combines technical expertise in data science with deep understanding of healthcare workflows and challenges, resulting in solutions that address real clinical needs while advancing the state of the art in health informatics.
Paulo Alexandre Crisóstomo Lopes is an Assistant Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (IST), where he teaches Computer Architecture and Digital Systems. He is also a researcher at the Signal Processing Systems Group (SIPS) within INESC-ID, focusing on Active Noise Control (ANC), Power Line Communications (PLC) using adaptive OFDM, and medical applications involving protein folding and gene regulatory pathways. His work bridges electrical engineering with biomedical systems, emphasizing practical implementations in embedded and FPGA-based solutions. Research Interests: Active Noise Control (ANC) and vibration suppression Power Line Communications (PLC) with adaptive modulation techniques Medical applications of signal processing, including protein structure analysis Embedded systems and FPGA implementations Publications Trends: His recent work emphasizes robust algorithms for noise control, optimization in OFDMA systems, and biomedical signal processing. Key techniques include adaptive filtering (LMS/NLMS), Bayesian methods, and parallel computing (CUDA/GPU). Labs/Teams: Member of the Signal Processing Systems Group (SIPS) at INESC-ID, collaborating on interdisciplinary projects combining hardware and software innovations.
John Tazare is an Assistant Professor in the Department of Medical Statistics within the Faculty of Epidemiology and Population Health at the London School of Hygiene & Tropical Medicine (LSHTM). He also holds an Honorary Senior Research Associate position at the University of Bristol. As a statistical pharmacoepidemiologist, his work focuses on developing and applying causal inference methods to investigate medication effectiveness and safety using routinely collected electronic health record data. Education: BSc Mathematics from University of Birmingham MSc Medical Statistics from London School of Hygiene & Tropical Medicine PhD Medical Statistics from London School of Hygiene & Tropical Medicine Dr. Tazare's primary research interests center on statistical methods for pharmacoepidemiology, particularly causal inference techniques applied to electronic health record data. His work addresses critical questions regarding medication safety and effectiveness in real-world settings. He has made significant contributions to methodological development in high-dimensional propensity score approaches and has been actively involved in numerous OpenSAFELY platform projects analyzing the impact of medications and health interventions during the COVID-19 pandemic. His recent publications demonstrate a strong focus on comparative effectiveness research, particularly examining anticoagulants, medication safety during the pandemic, and methodological improvements in observational research. His work spans multiple therapeutic areas including cardiovascular medicine, hepatology, and long-term effects of infectious diseases, with consistent application of advanced statistical techniques to large-scale health data. Dr. Tazare is actively involved in teaching, co-organizing the module 'Analysis of Electronic Health Record Data' and the 'Real World Evidence for Pharmacoepidemiology' short course. He also contributes to other courses including 'Concepts and Methods in Epidemiology' and the 'Professional Certificate in Pharmacoepidemiology & Pharmacovigilance.' As a member of the Electronic Health Records (EHR) Research Group at LSHTM, he collaborates extensively on projects utilizing large-scale health data systems. His work on code sharing and transparent research practices has contributed to improving standards in medical research methodology.
Luís M. Alves is a Professor at the Polytechnic Institute of Bragança, Portugal, where he has taught since 1999. He holds an M.Sc. in Computer Science from the University of Porto and a Ph.D. in Information Systems and Technology from the University of Minho. Currently, he serves as Course Director of the Bachelor in Informatics Engineering program at the Technology and Management School's Informatics and Communications Department. His research focuses on empirical software engineering, software project management, educational methodologies in programming instruction, and software metrics. Notable interests include problem-based learning approaches, student achievement metrics using tools like the C Tutor, and cross-country educational studies comparing programming curricula. Publications emphasize programming pedagogy innovations, software risk analysis in academic projects, and empirical studies on software estimation models. Over 20 years, his work bridges academic and industry needs through project-based learning frameworks and visualization tools like GraSMA. No scientific awards are explicitly mentioned in the provided text. He has advised at least one publication in programming education research and contributed to multiple international collaborations on educational technology. No lab affiliations or specific grant details are documented here.
Nuno Gonçalves is a Tenured Assistant Professor at the Department of Electrical and Computer Engineering and Senior Researcher at the Institute for Systems and Robotics (ISR) - Coimbra, University of Coimbra. He also serves as Innovation Manager at the Portuguese Mint and Official Printing Office (INCM) since 2018, focusing on technology transfer and product development. His roles include coordinating the AI group of APDSI and leading research in biometrics, facial recognition, and steganography. PhD in Computer Vision (University of Coimbra, 2008) His research spans computer vision, biometrics, and visual information security, with specific focus on facial recognition, morphing attack detection, presentation attack detection, graphical security, security coding, printer-proof steganography, and robotics. Recent work emphasizes deepfake detection, neural implicit representations, and robust biometric solutions. Article trends reveal expertise in 3D face reconstruction, multimodal deep learning for medical applications, and security-enhancing steganography. He leads projects integrating geometric modeling with neural networks for signed distance functions and develops benchmarks for morphing attack robustness in facial recognition systems. Member, IEEE He has coordinated grants from Fundação para a Ciência e a Tecnologia and private industry partners, including projects like BLOCKDFAKE (2025-2026) for deepfake detection in public administration and VISUAL-ID (2022-2024) for unique visual identities. His lab at ISR-Coimbra collaborates with INCM on security document validation and printer-proof steganography, with patents granted by EUIPO and USPTO.
António Carlos da Silva Abelha is an Assistant Professor at the School of Engineering of the University of Minho and a Senior Researcher at Centro ALGORITMI. He is a member of both the CST R&D Group and KEG R&D Lab, focusing on healthcare informatics and information systems. He earned his Bachelor's in Systems and Informatics Engineering (1992), Master's in Informatics Management (1997), and PhD in Informatics (2004), all from the University of Minho. His academic journey has been entirely rooted at this institution, where he has developed a strong research profile in healthcare information systems. Dr. Abelha's research centers on database systems and knowledge representation, multi-agent systems, electronic health records, interoperability and integration of information systems, and the application of artificial intelligence to medical and clinical problem solving. His work bridges computer science and healthcare, developing practical solutions for real-world medical challenges. His research has significant implications for improving healthcare delivery through better information systems and data management. His recent publications demonstrate a strong focus on healthcare interoperability standards like openEHR, clinical decision support systems, and AI applications in medicine. He has been particularly active in standardizing medical records, developing monitoring systems for chronic conditions like diabetes, and exploring ethical dimensions of AI in healthcare. His work spans both technical innovation in health informatics and practical implementation in clinical settings. Dr. Abelha serves as a mentor to numerous graduate students, guiding 16 doctoral projects (9 successfully completed) and supervising over 40 Master's theses. His teaching responsibilities include Database disciplines and Clinical Electronic Process courses for the Master in Biomedical Engineering program, as well as contributing to Database courses for Computer Science undergraduates and Decision Support Systems for Computer Science Master's students. At Centro ALGORITMI, he contributes to research in healthcare information systems through his involvement with the CST and KEG research groups, where he applies his expertise in database systems and AI to solve complex healthcare challenges.
Isabel Maria Pinto Ramos is an Associate Professor with Habilitation at the Department of Information Systems, School of Engineering, University of Minho , where she leads the ISTTOS R&D Lab and contributes to the Algoritmi Research Centre . She has held leadership roles including Head of Department of Information Systems and President of the Portuguese Association for Information Systems . Her career spans academic, research, and international collaboration roles, including a Visiting International Professor position at the University of Münster, Germany. Academic Leadership : Doctoral Programme Director (2014-2019), IFIP TC8 Chair (2019-2023), Vice President for Membership Services at Association for Information Systems (2022-2025) Research Themes : Innovation management, knowledge systems, digital transformation, crowdsourcing, organizational memory, gender in STEM, and technology's socio-cultural impacts International Engagement : Collaborations with Carnegie-Mellon, University of Agder, Georgia State, and Federal University of Santa Catarina through research projects and mobilities Scientific Recognition : IFIP Outstanding Service Award (2009) IFIP Silver Core Award (2013) IIAKM Lifetime Academic Achievement (2021) Editorial Contributions : Associate Editor for Communications of the AIS (since 2020), editorial board member for Enterprise Information Systems , International Journal of Knowledge Engineering , and Revista de Administração Mackenzie Project Involvement : ERASMUS+, Horizon 2020, FP7 programs with focus on digital workplaces, knowledge management, and organizational resilience Her scientific output demonstrates consistent exploration at the intersection of Information Systems and Organizational Innovation , with recent works focusing on Digital Transformation (2021-2025), Crowdsourcing Platforms (2009-2016), and Organizational Memory (2005-2014). The 2023-2025 publications particularly emphasize Smart City Resilience , Multigenerational Workforce Challenges , and AI Development Democratization .
Nuno Fachada is an Assistant Professor at Lusófona University's School of Communication, Arts and Information (ECATI) and a researcher at COPELABS (Cognitive and People-centric Computing). He teaches Programming and AI in the Videogames Bachelor's program and Research Software in the Informatics PhD program, with research spanning Artificial Intelligence, Machine Learning, Modeling and Simulation, High Performance Computing, and Computer Science Education. His work integrates computational methods with applications in game development, wildfire management, and networking. Education: Bachelor's degree in Electrical and Computer Engineering from IST (2005) Master's degree in Electrical and Computer Engineering from IST (2008), focusing on immune system simulation PhD in Electrical and Computer Engineering from IST (2016) with thesis 'Agent-Based Modeling on High Performance Computing Architectures', awarded 'Pass with Distinction and Honour' Research interests emphasize Agent-Based Modeling for complex systems like wildfires and immune responses, Particle Swarm Optimization algorithms, and OpenCL for parallel computing. He develops educational tools for game development curricula and applies AI to environmental monitoring and wireless networks, with strong output in simulation frameworks and human-centric computing. Recent publications (2024-2025) reveal three dominant trends: wildfire modeling using satellite data and agent-based approaches, large language models for engineering code generation (e.g., LoRaWAN), and game AI for procedural content and rehabilitation. His work bridges theoretical computer science with practical applications in environmental science and healthcare, while maintaining focus on education through tools like TextCL and cf4ocl. Scientific Awards: No scientific awards, fellowships, or medals were mentioned in the provided text Advising and Grants: Fachada serves as a researcher in ILIND-funded projects, notably the 'Cybersecurity Awareness Training Simulator' (2024-2025) with six collaborators. He supervises PhD students in Informatics but specific advisees aren't listed. His grant activity primarily involves institutional projects through Lusófona University's research center, with emphasis on simulation-based tools for real-world applications. Labs and Teams: He is a core researcher at COPELABS, focusing on cognitive and people-centric computing projects including wildfire simulation and cybersecurity training. Prior to Lusófona, he conducted postdoctoral work at LaSEEB/ISR (Institute for Systems and Robotics), maintaining connections to IST. His team collaborations span international researchers in environmental modeling and AI, with recent projects involving Portuguese and European institutions.
Luís Ducla Soares is an Associate Professor in the Department of Information Science and Technology at ISCTE – University Institute of Lisbon, where he has been a faculty member since 2000. He is also an Integrated Senior Researcher and the Coordinator of the Multimedia Signal Processing Group (MSP-Lx) at the Institute of Telecommunications - IUL. His academic appointments reflect a strong integration between teaching, research, and institutional leadership. Education: Bachelor's Degree in Electrical and Computer Engineering, Instituto Superior Técnico (1996) PhD in Electrical and Computer Engineering, Instituto Superior Técnico (2004) Aggregate Degree in Information Sciences and Technologies, ISCTE-IUL (2020) His primary research interests lie in image and video processing , light field imaging , multimedia coding , and biometric recognition systems . He has made significant contributions to scalable and error-resilient coding techniques, particularly within the MPEG standardization framework. His recent work extends into biomedical applications of video processing, such as gait analysis and remote diagnostics. The analysis of his recent publications shows a clear trend toward light field processing and deep learning applications in biometrics . His work combines theoretical signal processing with practical implementations in immersive media and healthcare. The integration of AI in 4D light field segmentation and gait classification highlights his interdisciplinary approach. Scientific Awards: Alcatel – Prof. Carvalho Fernandes Award for best student in Telecommunications and Electronics IBM Scientific Award (2016) for a master's dissertation He has supervised 2 completed PhD theses and 14 master's dissertations, with 3 PhDs and 2 master's theses currently in progress. He has led multiple funded research projects, including national and European initiatives such as LIMESA, LIFESYS, QUIS-CAMPI, and COST actions. His editorial roles include serving as Associate Editor for Signal Processing (Q1 journal) since 2018. He has also played key roles in scientific event organization, including co-chairing the International Workshop on Forensics and Biometrics (IWBF) in 2013. Research Labs and Teams: Multimedia Signal Processing Group (MSP-Lx), Institute of Telecommunications - IUL Coordinator since 2021 Active in developing tools for light field coding, biometrics, and immersive media
Paulo Jorge Lourenço Nunes is an Associate Professor in the Department of Information Science and Technology at ISCTE – Instituto Universitário de Lisboa, where he also serves as Coordinator of the Multimedia Signal Processing Group at the Institute of Telecommunications - IUL. He is an Integrated Researcher at the same institute, contributing to advanced research in multimedia and signal processing. PhD in Electrical and Computer Engineering, Higher Technical Institute - UTL (2007) Master's in Electrical and Computer Engineering, Higher Technical Institute - UTL (1995) Bachelor's in Electrical and Computer Engineering, Higher Technical Institute - UTL (1992) His research focuses on multimedia signal processing , particularly in light field coding , 3D holoscopic video , and scalable video representation . He applies deep learning and advanced signal processing techniques to problems in immersive systems and image compression. His work bridges theoretical innovation with practical implementation in next-generation visual media. The recent articles highlight a strong trend in light field processing , including segmentation, coding, disparity estimation, and view synthesis using neural networks. Keywords across publications include computer vision, image compression, deep learning, and immersive media, reflecting a cohesive and impactful research trajectory in next-generation visual technologies. Notable scientific contributions include highly cited works in IEEE Transactions and Signal Processing journals, with over 1300 citations on Google Scholar. While no formal awards are listed, his extensive publication record and leadership in funded research projects underscore significant recognition in the field. He actively supervises postdoctoral researchers, PhD, and Master’s students. His advising portfolio includes ongoing and completed theses on light field coding, deep learning for immersive systems, and multimedia communication. He has also secured and led multiple research projects in multimedia and telecommunications. His teaching includes courses such as Multimedia Communication Systems and Fundamentals of Computer Networks. He leads the Multimedia Signal Processing Group at the Institute of Telecommunications - IUL, a research team focused on advanced multimedia analysis, compression, and delivery. The group works on cutting-edge topics including light field technologies, error-resilient video coding, and machine learning for multimedia.
Ana P. Pinheiro is an Associate Professor and Group Leader at the Voice, Emotion, & Speech Lab (VoicES Lab) at the Faculty of Psychology, University of Lisbon. Her research focuses on the neural mechanisms of vocal communication, emotional processing, and auditory hallucinations. She explores how self-monitoring and sensory prediction relate to schizophrenia symptoms like auditory verbal hallucinations (AVH) through interdisciplinary collaborations and longitudinal EEG studies. Academic Affiliation: Faculty of Psychology, University of Lisbon Key Research: Self-voice prioritization, emotional authenticity detection, sensorimotor processing in hallucination proneness Editorial Roles: Associate Editor for Psychiatry Research: Neuroimaging , Consulting Editor for Cognitive, Affective, & Behavioral Neuroscience Her work spans cognitive neuroscience, affective science, and clinical psychology, with funded projects from FCT and the BIAL Foundation. She has contributed extensively to understanding auditory hallucinations and neuroplasticity in voice perception. Recent publications emphasize hallucination proneness, sensory feedback processing, and cross-modal influences on vocal emotion detection. She mentors students and collaborates with global labs, integrating behavioral, electrophysiological, and computational approaches. Pinheiro also serves on scientific advisory boards, including the Schizophrenia International Research Society (SIRS) Awards Committee and the International Journal of Clinical and Health Psychology.
Raquel Monteiro Marques da Silva is an Assistant Professor at the Faculty of Dental Medicine, Universidade Católica Portuguesa (UCP), where she conducts research in the Centre for Interdisciplinary Research in Health (CIIS). She is a member of the SalivaTec platform, focusing on biomarker research and diagnosis in saliva. Previously, she served as an Invited Assistant Professor at the Department of Medical Sciences, University of Aveiro (2013-2019), and as a Research Associate in the Population Genetics group at IPATIMUP (2008-2013). Her academic background includes: PhD in Biology from the University of Aveiro (2005), with research on molecular mechanisms involved in genetic code alteration Dr. Silva's research spans multiple disciplines within biomedical sciences, with a strong focus on molecular mechanisms of disease. Her work integrates biochemistry, molecular biology, and bioinformatics to investigate NAD metabolic networks, antifungal resistance mechanisms in Candida species, and biomarker discovery in saliva. She has developed computational tools for genome rearrangement detection and pathogen signature identification. Her current research at the SalivaTec platform aims to translate biomarker discoveries into clinical diagnostic applications, particularly in dental and oral health contexts. Analysis of her recent publications (2020-2023) reveals a strong focus on integrating computational and experimental approaches to address biomedical challenges. Her work spans NAD metabolism research, antifungal resistance mechanisms, biomarker discovery (particularly in saliva), and the development of bioinformatics tools for genomic analysis. Notably, she has contributed to SARS-CoV-2 detection strategies using saliva samples and research on Alzheimer's disease biomarkers. Her publications demonstrate a consistent interdisciplinary approach bridging molecular biology, bioinformatics, and clinical applications. Dr. Silva has supervised and collaborated on numerous research projects investigating fungal pathogenesis, particularly Candida species resistance mechanisms, and NAD metabolic pathways. Her work has contributed to understanding the molecular basis of antifungal resistance, which has implications for developing new therapeutic strategies. She has secured research funding supporting her work on biomarker discovery in saliva and computational approaches to genomic analysis. As a researcher in the CIIS SalivaTec platform at UCP's Faculty of Dental Medicine, Dr. Silva collaborates with interdisciplinary teams focused on translating biomarker research into clinical dental applications. Her work bridges basic science and clinical dentistry, with particular emphasis on how molecular diagnostics can improve oral health outcomes. She is actively involved in research that examines the interaction between saliva and dental materials, aiming to enhance implant integration and overall dental treatment efficacy.
Rui Duarte Neves serves as a Professor in the Department of Civil Engineering at the School of Technology of Barreiro, Polytechnic Institute of Setúbal, Portugal. Previously, he conducted research at the National Laboratory for Civil Engineering (LNEC-Portugal), establishing his expertise in concrete technology and structural assessment before transitioning to his current academic role. His research specializes in concrete durability and sustainable construction materials, with primary focus on service life prediction of reinforced concrete structures and development of eco-friendly alternatives like recycled aggregate concrete. He pioneers non-destructive testing methodologies for on-site evaluation of carbonation and chloride penetration, directly addressing infrastructure longevity challenges in aggressive environments. Analysis of his publication portfolio reveals consistent innovation in sustainable concrete technologies, particularly CO 2 sequestration in recycled concrete and AI-driven predictive modeling for carbonation coefficients. His work bridges experimental validation with computational approaches, including genetic programming and tree-based algorithms, while advancing circular economy principles through industrial waste reuse in cement production. Dr. Neves actively contributes to the Resilience R&D Unit, where his research integrates field testing, statistical modeling, and materials innovation to enhance concrete performance under environmental stressors like carbonation and chloride exposure.
Diana Elisabeta Aldea Mendes is an Associate Professor at the Department of Quantitative Methods for Management and Economics within ISCTE Business School at ISCTE - University Institute of Lisbon, Portugal. She is also an Affiliate Member of BRU-Iscte (Business Research Unit). Her academic career spans various leadership roles including Director of the Master's in Data Science program and other academic management positions. Her educational background includes: PhD in Mathematics from Higher Technical Institute - UTL (2005) Pedagogical Aptitude and Scientific Capacity Tests from ISCTE-University Institute of Lisbon (1999) Bachelor's degree in Mathematics from Universitatea Babes Bolyai Facultatea de Matematica si Informatica, Romania (1988-1993) Diana E. Aldea Mendes is a mathematician and applied scientist with extensive expertise in nonlinear dynamics (both stochastic and deterministic), time series analysis, data science, machine learning, deep learning, computational economics and finance, healthcare analytics, and control and synchronization of complex systems. Her research bridges mathematical theory with practical applications in economics, finance, and healthcare. She has developed sophisticated models for analyzing financial markets, economic policy impacts, and healthcare-related time series data. Her work often combines traditional econometric approaches with cutting-edge machine learning techniques to address complex real-world problems. Her recent publications demonstrate a strong focus on the intersection of data science and finance, particularly examining how higher data frequency can improve stock market predictions, applying deep reinforcement learning to portfolio management, and investigating the impact of economic policy uncertainty on financial markets. She has also expanded her research into healthcare applications, developing ambient assisted living technologies and memory training interfaces for elderly care. Her scientific achievements have been recognized through several awards: Research Excellence Award (2018) Melhor professor IBS (1º lugar) (2014) Melhor professor IBS (1º lugar) (2013) Professor Aldea Mendes has been actively involved in numerous research projects and has served on various academic committees. She has coordinated executive training programs including the Professional Diploma in Big Data for Business Engineering and has been involved with the AI Business Hub as a consultant since 2020. Her professional activities also include participation in the Iscte-Health mission group and coordination of the Data Science Mission Group. Her research is conducted within the Business Research Unit (BRU-Iscte) where she collaborates with interdisciplinary teams to address complex business and economic challenges using quantitative methods. She has also been involved in international collaborations, including coordination of the Portuguese component of the COST Action IS1104 project focused on economic systems modeling.
