Dr Peninah Murage is an Assistant Professor at the London School of Hygiene & Tropical Medicine in the Department of Public Health, Environments and Society under the Faculty of Public Health and Policy. She specializes in environmental epidemiology with a PhD in Health Geography and focuses on health impacts of environmental changes, particularly climate change mitigation and adaptation strategies. Affiliation: Centre on Climate Change and Planetary Health Her research explores climate change effects on health outcomes like heat-related mortality, cardiovascular diseases, and cognitive development in children. She integrates GIS/spatial analysis with public health and policy frameworks to identify nature-based solutions for sustainable development. Current work examines systemic inequalities in heat risk exposure and interventions such as tree-based strategies in low-income settings. Key grants include: Health Protection Research Unit on Climate Change and Health Security (2025-2030, NIHR) Climate Change and Non-Communicable Diseases in Senegal (2024-2027) Pathfinder 2: Accelerating Climate Action for Health (2023-2026) She actively contributes to the Lancet Pathfinder Commission and serves on the Executive Committee of the International Society of Environmental Epidemiology (African Chapter).
Prof. Raul Fangueiro is a Full Professor and Vice-Dean at the School of Engineering, University of Minho, Portugal. As a Senior Researcher, he leads FIBRENAMICS – Institute of Innovation on Fiber-Based Materials and Composites. His work spans advanced materials (nano, smart, composites) and structures (3D, auxetic, multiscale) with applications in defense, healthcare, construction, and automotive sectors. Supervised over 20 PhD and Post-Doc researchers Scientific coordinator of 20+ national/international research projects Author of 200+ journal papers (H-index: 47), 500+ conference publications, 36 books, 40 patents Research focuses on nanotechnology , electrospinning , and sustainable material systems : Auxetic composites for personal protection Biodegradable nanofibers for medical use Smart textiles for biological/chemical resistance Recycled mineral/wood-based composites Graphene-reinforced green materials Multiscale fiber architectures Scientific recognition includes: Top 2% most influential scientist (Elsevier/Stanford 2020) Founder of AUXDEFENSE and ICNF conferences Editorial board member of leading composite journals Advisor to European Defense Agency/NATO working groups Active in industry-academia partnerships through spin-offs (Sciencentris, B4Logic, Beyond Composites, Givaware, Pixartidea) and collaborative projects with institutions like Instituto Superior Técnico, University of Aveiro, and international universities.
Ruth Keogh is a Professor of Biostatistics and Epidemiology at the London School of Hygiene & Tropical Medicine (LSHTM), affiliated with the Medical Statistics Department within the Faculty of Epidemiology and Population Health. She is Co-Director of the Centre for Data and Statistical Science for Health (DASH) and serves as Departmental Research Degrees Coordinator. Her academic career has spanned roles from Lecturer (2012–2015) to Associate Professor (2015–2019) before attaining her current rank in 2019. Keogh holds advanced degrees including a DPhil in Medical Statistics/Epidemiology (University of Oxford, 2007), MSc in Applied Statistics (Oxford, 2003), and BSc in Mathematics and Statistics (University of Edinburgh, 2002). Her research focuses on causal inference, clinical trial emulation using real-world data, and applications in cystic fibrosis, infectious diseases, and public health. She leads projects on lung function trajectories, vaccine efficacy, and healthcare policy analysis. Her work integrates biostatistical methods with epidemiological studies, emphasizing rigorous analysis of observational data to inform clinical decisions. Notable areas include evaluating antibiotic treatments for cystic fibrosis patients, assessing diagnostic test accuracy for dengue and leptospirosis, and modeling vaccine effectiveness during the COVID-19 pandemic. She teaches courses in survival analysis, electronic health records, and health data science at LSHTM. Keogh has held leadership roles in the International Biometric Society and the STRATOS Initiative, and she has delivered keynote addresses at international conferences on trial emulation and biostatistical methods. Her contributions bridge methodological innovation and practical health challenges, with over 190 publications and active engagement in global health research networks.
Professor Joseph Wu is a faculty member at The University of Hong Kong , specializing in mathematical and statistical modeling of diseases. His research focuses on developing practical analytics for disease control, translating findings into public health policies, and addressing global health challenges through AI technology and tools. He leads the Laboratory of Data Discovery for Health (D²⁴H) and has directed significant educational initiatives, including HKU’s Epidemics MOOC and the Croucher Summer Course in Vaccinology . His research spans epidemiology , infectious disease modeling , and public health policy . He has contributed to understanding COVID-19 , influenza , and yellow fever dynamics, with a focus on vaccine hesitancy and epidemic forecasting. His work has been published in high-impact journals such as Nature Medicine and The Lancet . Scientific Awards: Fellow of the UK Faculty of Public Health Professor Wu serves as co-editor-in-chief of Epidemics and associate editor for PLOS Computational Biology and PLOS Neglected Tropical Diseases . He contributes to global health through membership in the WHO Advisory Committee on Immunization and Vaccines-related Implementation Research (IVIR-AC) , the MIT SOLVE Challenge Leadership Group , and the MIT HK Innovation Node .
Il Memming Park is a Professor and Group Leader at the Centre for Restorative Neurotechnology within the Champalimaud Research division of the Champalimaud Foundation in Lisbon, Portugal. His work bridges computational neuroscience, machine learning, and statistical modeling to understand neural dynamics and computation. Dr. Park's research focuses on developing statistical and machine learning methods for analyzing neural time series data. His lab investigates the appropriate language for neural dynamics that can explain and generate specific predictions on neural data and behavior. He builds on foundations of dynamical systems and stochastic processes to create models of neural computation tightly tied to biology. His publications reveal a strong emphasis on developing methods like variational latent Gaussian processes and exponential family dynamical systems to extract meaningful patterns from complex neural recordings. His work spans both theoretical developments in computational methods and their application to real neural data from areas like visual cortex, parietal cortex, and other brain regions involved in perception and decision making. Dr. Park has previously held positions at Stony Brook University and the University of Texas at Austin, where he was affiliated with departments of Neurobiology and Behavior, Applied Mathematics and Statistics, Psychology, and Neuroscience. His lab at Champalimaud includes multiple PhD students, postdoctoral researchers, and research staff working collaboratively on various aspects of neural data analysis and modeling. The team employs an interdisciplinary approach combining neuroscience, statistics, machine learning, and dynamical systems theory.
Zita Vale is a Full Professor at the Institute of Engineering (ISEP) of the Polytechnic of Porto (IPP), where she holds the first Full Professor position since 2017. She is a co-founder of GECAD (1999) and coordinates GECAD's Power and Energy (PES) activities. GECAD is recognized by FCT since 2004 and classified as Excellent. She has served as GECAD director (2010-2017), vice-director (1999-2009, 2017-present), and is a member of the administration board. She is also co-founder and member of the coordination board of the National Associated Laboratory on Intelligent Systems. Her educational background includes a PhD (1993) and Agregação/Habilitation (2003) in Electrical and Computer Engineering from the University of Porto. She began her academic career at the University of Porto as a Teaching Monitor (1985), Assistant (1985-1993), and Professor (1993-1998) before moving to ISEP in 1998. Zita Vale's research focuses on the design and development of artificial intelligence-based models for Power and Energy Systems. Her work spans knowledge-based systems, multiagent systems, machine learning, metaheuristics, and semantics, with applications in smart grids, energy management, electricity markets, and renewable energy integration. She has an extensive international network and has participated in 80 R&D projects, raising over 22 million Euros for GECAD. Her recent publications demonstrate a strong emphasis on optimization techniques, explainable AI, energy storage systems, and the integration of distributed energy resources in power systems. She serves as Editor-in-Chief of Applied Energy (Elsevier), a leading journal in the field with an Impact Factor of 11.2. Her citation metrics are impressive, with over 17,000 citations on Google Scholar and an H-index of 64. Editor-in-Chief of Applied Energy (Elsevier) Over 17,000 citations on Google Scholar H-index of 64 Zita Vale has supervised 29 PhD students (25 completed) and 72 MSc students (66 completed), demonstrating her strong commitment to academic mentorship. She has also been involved in numerous international and national evaluation processes, including project proposals, faculty positions, and PhD juries across 15+ countries. She has contributed to over 225 evaluation processes from 2018-2023, including 150+ project proposals/execution, 50+ Faculty/Researchers positions, 2 Habilitation juries, and 25+ PhD juries. She leads GECAD's involvement in several major research initiatives, including the National Associated Laboratory on Intelligent Systems and various European projects such as IoTalentum, TRADERES, DOMINOES, and EcoRural-IoT from Horizon 2020, as well as PRODUTECH EU DIH from Horizon Europe. Her leadership extends to international organizations where she serves as President of Intelligent Systems Applications in Power (ISAP) and Technical Committee Program Chair of IEEE PES Analytic Methods for Power Systems Committee.
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 Marcus Keogh-Brown is an Associate Professor in the Department of Global Health and Development within the Faculty of Public Health and Policy at the London School of Hygiene & Tropical Medicine (LSHTM). His position is split between research focusing on macroeconomic modeling of health and serving as Deputy Programme Director for the school's public health distance learning program. Education: BSc in Mathematics and Statistics, Queen Mary University of London (1995-1998) MSc in Computer Studies, University of Essex (1998-1999) PhD in "A Statistical Model of Internet Traffic," Queen Mary University of London (1999-2004) PGCILT Modules 1 and 2, London School of Hygiene and Tropical Medicine (2008-2012) Dr Keogh-Brown's research focuses on analyzing the macro-economic impact of health disorders and developing macro-economic models in health contexts. His areas of interest include infectious diseases (SARS, influenza, COVID-19, tuberculosis, malaria) and non-communicable diseases (Alzheimer's Disease, Dementia). He specializes in health-related applications of Computable General Equilibrium (CGE) Modeling with GAMS, with current work on health and macroeconomic modeling of COVID-19, tuberculosis, child labor, and health-related food policies. His publication record shows a strong trend toward integrated macroeconomic-epidemiological modeling, particularly for infectious disease outbreaks and public health interventions. Recent work focuses on tuberculosis in India, Covid-19 impacts in Pakistan, and food policy interventions like the UK Soft Drinks Industry Levy. His research consistently applies economic modeling frameworks to evaluate the health and economic impacts of disease and health policies across multiple countries including Ghana, India, Thailand, Myanmar, UK, and China. Grants: Current: "Modelling the health social care and macroeconomic impacts of dementia policies in the UK" (NIHR, 2025-2026) Current: "Co-designing food system fiscal policy for healthy people and planet" (University of Oxford, 2022-2025) Completed: "COVID-19 vaccine scenario analysis for health economic and social impacts" (WHO, 2022) Completed: "Evaluation of the impact of the UK industry levy of sugar-sweetened beverages" (University of Cambridge, 2017-2023) Completed: "Macroeconomic Burden of Alzheimers in China" (Jansen Global Services LLC, 2014-2015) Dr Keogh-Brown is affiliated with several research centers at LSHTM including the Malaria Centre, Global Health Economics Centre, and Centre for Mathematical Modelling of Infectious Diseases. His collaborative work spans multiple countries and addresses critical intersections between health, economics, and policy.
Leid Zejnilovic is an Assistant Professor at Nova School of Business and Economics (Nova SBE), where he co-founded the Data Science Knowledge Center and serves as Academic Director, and co-founded the Open and User Innovation Knowledge Center as Scientific Deputy Director. He also co-founded the Patient Innovation platform, enabling patients and caregivers to share self-made healthcare solutions. With a double PhD from Carnegie Mellon University and Católica-Lisbon School of Business and Economics, his career spans over 20 years of international consulting, academic entrepreneurship, and teaching at institutions like Imperial College Business School and Ludwig Boltzmann Institute. PhD in Strategy, Entrepreneurship and Technological Change (Carnegie Mellon University / Catholic University of Portugal, 2014) Master in Engineering and Public Policy (Carnegie Mellon University, 2012) Master in Information Technology (Dzemal Bijedic University, 2007) Bachelor in Telecommunications (University of Sarajevo, 2002) His research focuses on Technology and Innovation Management, Human-Computer Interaction, and data-driven solutions across healthcare, tourism, and education. He has published extensively in journals like California Management Review , PLoS ONE , and Marine Policy , with recent work analyzing big data in tourism, machine learning for oral health, and pandemic impacts on fisheries. As an Associate Editor for Data & Policy Journal , his contributions bridge academic research and real-world applications. Co-founding the Data Science for Social Good Foundation and leading over 100 talks in industry and academia, Zejnilovic's career emphasizes translating innovation into social and economic impact through platforms, policy, and education.
Joe Paton is a Professor and Principal Investigator at the Champalimaud Neuroscience Programme, Champalimaud Foundation in Lisbon, Portugal. He leads the Paton Lab which focuses on understanding how animals determine which environmental cues are predictive of behaviorally relevant events, known as the credit assignment problem. His research combines behavioral experiments with neurophysiological recordings in rodents to investigate neural mechanisms of time perception and decision making. Dr. Paton's research interests center on interval timing, temporal processing in the brain, and the neural basis of learning. His work particularly examines how the striatum and dopamine systems contribute to time perception and how animals solve the credit assignment problem through statistical inference in the time domain. His lab employs advanced techniques including optogenetics, neural recordings, and computational modeling to address these questions. Analysis of Dr. Paton's recent publications reveals a strong focus on striatal function in timing processes, with particular attention to how neural populations encode temporal information. His work bridges behavioral neuroscience with computational approaches, demonstrating how timing mechanisms influence decision making and learning processes. The research spans multiple levels from cellular mechanisms to behavioral outputs. Midbrain dopamine neurons control judgment of time (2016) Striatal dynamics explain duration judgments (2015) A Scalable Population Code for Time in the Striatum (2015) The Neural Basis of Timing: Distributed Mechanisms for Diverse Functions (2018) Dr. Paton has mentored numerous PhD students and postdoctoral researchers through the INDP (International Neuroscience Doctoral Program) and supervises a diverse team including research technicians, postdocs, and students. His lab has contributed significantly to understanding the neural basis of time perception and its role in learning and decision making. The Paton Lab also develops experimental tools and frameworks like Bonsai for behavioral neuroscience research.
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.
José António Ferreira Machado is a Full Professor at the Nova School of Business and Economics, Universidade Nova de Lisboa. He currently serves as Vice-Rector of the university and previously held director roles at the Nova School of Business and Economics (2005-2015) and Angola Business School (2010-2015). His academic career includes consultancy at the Bank of Portugal (1992-2015) and teaching Econometrics, Statistics, and Macroeconomics. Research Interests: Machado's work focuses on Econometrics, Quantile Regression, Wage Distributions, Firm Size Analysis, and Macroeconomic Modeling. His most cited paper (2005) introduced counterfactual decomposition methods for wage distribution analysis. Recent publications examine quantile regression extensions, trade margins, and moment-based statistical inference. His research spans both theoretical and applied economics, with collaborations including J. M.C. Santos Silva and Roger Koenker.
Maria Leonilde Rocha Varela is an Associate Professor with Habilitation at the School of Engineering, University of Minho, Portugal, where she also serves as a Senior Researcher at the Algoritmi Research Centre. She has been an integrated member of the Algoritmi Research Centre since 2012 and works in the Department of Production and Systems. Dr. Varela earned her degree in Production Engineering from the University of Minho in 1994, completed a Master's in Computer Integrated Production at DPS-UMinho in 1999, and received her Ph.D. in Production and Systems from the University of Minho in 2007. Her primary research focuses on Manufacturing Management, particularly Production Planning, Control and Optimization, and Collaborative Paradigms, Networks and Decision Making Models. She maintains extensive international collaborations with institutions worldwide including the National Institute of Industrial Engineering, VSB-Technick Univerzita Ostrava, University of Belgrade, and others. Her research spans Web Applications and Services for supporting Engineering and Production Management, with increasing emphasis on Artificial Intelligence, Robotic Process Automation, and Industry 4.0/5.0 applications. She has made significant contributions to scheduling algorithms, optimization techniques, and decision support systems for manufacturing environments. Analysis of her recent publications reveals a strong trend toward integrating Artificial Intelligence with traditional manufacturing processes, particularly in Robotic Process Automation applications. Her research increasingly focuses on sustainable manufacturing practices, with numerous publications addressing energy efficiency, environmental sustainability, and resource optimization. There is a clear emphasis on multi-objective optimization approaches to solve complex manufacturing problems, particularly in distributed job shop scheduling. Her work demonstrates an evolution from traditional production planning methods to more advanced AI-driven approaches for Industry 4.0 and 5.0 environments. Dr. Varela has held significant academic leadership roles, currently serving as the director of the master's course in Engineering and Quality Management at DPS-UMinho. She previously coordinated the industrial management and systems subgroup from 2012 to 2021 and was part of the steering committee for the master's course in systems engineering between 2016 and 2019. She has successfully supervised more than 70 MSc projects, with over 15 currently ongoing, focusing on Production and Systems Engineering. Her supervision encompasses collaborative management models, traditional decision approaches, and web-based platforms incorporating AI techniques. She coordinates research projects including 2 concluded Ph.D. projects and 6 ongoing ones. She collaborates as a research member in several R&D projects with national and international industrial enterprises and institutions, and in international Erasmus projects. Dr. Varela is an active participant in the academic community, serving on editorial boards of several international journals and as a member of organizing and scientific committees for numerous international conferences. She is a member of several prestigious research networks including the Euro Working Group of Decision Support Systems (EWG-DSS), Institute of Electrical and Electronics Engineers (IEEE), Industrial Engineering Network, and the Institute of Industrial and Systems Engineers (IISE).
Carla Curado is an Associate Professor with Habilitation in Organizational Behavior and Human Resource Management at ISEG – Lisbon School of Economics and Management, University of Lisbon. She serves as a founding researcher at the ADVANCE research center and holds leadership roles including Master's Program Coordinator for Human Resource Management and Business Sciences. She represents the University of Lisbon at the International Organization for Standardization and is an Associate Editor for the Journal of Organizational Effectiveness: People and Performance . Her research explores: Knowledge Management in healthcare, SMEs, and NGOs Digital leadership and workplace happiness HRM's role in innovation and sustainability Mixed-method approaches to organizational analysis Recent publications (2023-2025) emphasize digital transformation, healthcare efficiency, and methodological innovations in operations management. Key trends include configurational analysis of leadership and knowledge behaviors in hybrid work environments. Awards: Stanford's Top 2% World Scientist (2021, 2022, 2023) She supervises 65+ Master's students with theses on: Knowledge sharing mechanisms Leadership/organizational commitment Healthcare workplace dynamics Sustainability in HR practices At ADVANCE Research Center, she contributes to teams studying management innovation and organizational resilience, with active participation in EU research consortia.
Maria de Fatima Ramalho Fernandes Salgueiro is a Full Professor at ISCTE-University Institute of Lisbon, where she leads research in advanced statistical methodologies as Head of the Business Development Research Unit (BRU-Iscte). Her scholarship focuses on longitudinal data modeling, structural equation frameworks, and classification techniques for socioeconomic analysis. She earned her PhD in Social Statistics from the University of Southampton and holds an Aggregation in Statistics and Data Analysis from ISCTE. Salgueiro has coordinated major research initiatives including the CHER Consortium panel studies and received the 2011 Scientific Merit Award for outstanding publications.
