Philippe Moireau is a Full Professor in the Department of Applied Mathematics at École Polytechnique, where he is also affiliated with the Center for Applied Mathematics (CMAP). He serves as the head of the Inria Project-Team MΞDISIM (Mathematical and Mechanical Modeling with Data Interaction for Simulation in Medicine) and holds the distinguished position of Ingénieur Général of The Corps des Mines. His primary research focuses on inverse problems and data assimilation for partial differential equation models, with particular emphasis on: Observer-based methods from optimal control perspectives Stabilization approaches for evolution equations Numerical analysis of time-dependent control problems Digital twin applications in cardiovascular medicine Professor Moireau's publication portfolio demonstrates consistent focus on mathematical methods for physical systems, with recurring themes in: Data assimilation techniques for PDE-based models Numerical stabilization and discretization methods Cardiovascular biomechanics and hemodynamics Stochastic modeling of biological systems Epidemiological forecasting and control He leads the ANANKΞ project-team at Inria focused on Analysis And Numerics of physical-Knowledge-based Estimation. His educational contributions include lectures on data assimilation theory at CEMRACS and courses on mathematical modeling in cardiac biomechanics at Institut Polytechnique de Paris.
Ulrich Pöschl is Director of the Multiphase Chemistry Department at the Max Planck Institute for Chemistry and Professor in the Department of Chemistry, Pharmacy and Geosciences at Johannes Gutenberg University (JGU) in Mainz, Germany. He has held leadership roles at MIT, the Max Planck Society, and the Technical University of Munich, and is a globally recognized expert in atmospheric and multiphase chemistry. Education: PhD (Doctor technicae) in Chemistry, Technical University of Graz (1995) Habilitation in Geochemistry, JGU Mainz (2007) Habilitation in Chemistry, Technical University of Munich (2006) Research Interests: His research centers on multiphase processes at the interface of atmosphere, biosphere, and hydrosphere. Key areas include aerosol chemistry, climate interactions, oxidative stress, protein modification, and the health impacts of air pollution. His work integrates field observations, laboratory experiments, and modeling. Publication Trends: His recent research spans atmospheric new particle formation in the Amazon, health effects of air pollution, open access science, and the role of bioaerosols in disease transmission. The work is highly interdisciplinary, bridging environmental science, chemistry, public health, and climate science. Scientific Awards: Highly Cited Researcher (Web of Science, 2014–2024) AGU Union Fellow (2023) Copernicus Medal (2015) Pius XI Gold Medal (2012) EGU Union Service Award (2005) Advising and Grants: Pöschl has mentored numerous PhD and postdoctoral researchers, many of whom now hold senior academic positions worldwide. He leads major international collaborations and has secured significant research funding. He is a strong advocate for open science, having founded the journal Atmospheric Chemistry and Physics and co-leading the OA2020 initiative. Labs and Teams: He leads the Multiphase Chemistry Department at MPIC, overseeing a large interdisciplinary team conducting cutting-edge research on aerosols, climate, and health. His group collaborates globally and uses advanced analytical, experimental, and computational methods.
Payam Barnaghi is a Professor and Chair in Machine Intelligence Applied to Medicine at Imperial College London's Department of Brain Sciences, part of the Faculty of Medicine. He holds multiple leadership roles, including Co-Director of the School of Convergence Science in Human and Artificial Intelligence and Deputy Head of Neurology. His research focuses on AI-driven healthcare solutions, particularly in neurosciences and dementia care. He leads the Translational Machine Intelligence group at the UK Dementia Research Institute (UK DRI) and is a Visiting Professor at University College London's Institute of Child Health. His affiliations include the NVIDIA Deep Learning Institute, the British Heart Foundation Centre for Research Excellence, and the UK DRI Care and Research Technology Centre. He has received awards such as the Wellcome Trust Mental Health Ideathon Award (2023) and the IEEE Outstanding Leadership Award (2017). His work emphasizes remote patient monitoring, digital biomarkers, and explainable AI for early health event detection. Key projects include the TIHM (Technology Integrated Health Management) initiative for dementia care, leveraging wearable sensors and machine learning. He contributes to interdisciplinary efforts in smart care ethics and has published extensively on topics like neural network applications, healthcare data analysis, and clinical decision support systems.
Nuno Pinto is a Senior Lecturer in Urban Planning and Urban Design at the University of Manchester's School of Environment, Education and Development. He holds a PhD in Planning from BarcelonaTech (Spain) and a Civil Engineering degree from the University of Coimbra (Portugal). Previously, he held academic positions at the University of Coimbra and served as a Researcher at the Polytechnic Institute of Leiria. His research focuses on quantitative approaches to urban planning, including decision support systems, urban simulation, integrated transport planning, and big data applications. He is particularly known for his work on cellular automata models and agent-based simulations in urban policy analysis. Nuno has secured significant funding, including a £663k EPSRC grant for the 'Resilience Beyond Observed Capabilities Network+' and a £19k Turing-Manchester grant for VR analytics in digital twins. Teaching expertise spans data science applications in planning, GIS, and decision-support methods across multiple master's programs. He advises on PhD topics combining quantitative methods with Iberian/Latin American urban contexts. Nuno is a Fellow of the Higher Education Academy and recipient of the 2011 Breheny Prize for outstanding urban planning research. Notable projects include 'Synthetic Cities' digital twin frameworks, peri-urban climate change analyses (PERI-CENE), and cross-border collaborations like the FAPESP-University of Manchester initiative. Current research explores smart city strategies in Latin America and carbon accounting systems. Supervised over a dozen PhD students, including works on mobility decision systems, metropolitan data analytics, and serious gaming for urban participation. Active in professional networks such as the COST TU1408 Air Transport and Regional Development initiative.
Kamila A. Alexander, PhD, RN, is an Associate Professor and Director of the PhD, DNP/PhD, and Postdoctoral Programs at the Johns Hopkins School of Nursing. She holds the endowed Natalie and Wes Bush Rising Professorship. Her research integrates health equity and social justice frameworks to examine socio-structural determinants of trauma, violence, and sexual/reproductive health outcomes among marginalized youth, with emphasis on intimate partner violence, HIV resilience, and economic opportunity. Education Dr. Alexander earned: B.S. in Exercise Science (Howard University) BSN and MSN/MPH (Johns Hopkins School of Nursing) PhD in Nursing Science (University of Pennsylvania) Research Focus Her work spans: Socio-structural drivers of health disparities Intersections of violence, trauma, and sexual/reproductive health Community-engaged interventions to promote HIV resilience Policy impacts on health equity for Black and Latino communities Publication Trends Recent articles (2019–2024) cluster around reproductive coercion, intimate partner violence, and HIV/STI prevention, employing mixed-methods approaches. Key themes include gender-based power dynamics, structural barriers in healthcare, and culturally responsive intervention strategies. Awards & Honors Notable recognitions: Betty Irene Moore Fellowship for Nurse Leaders (2020) Dean’s Award for Outstanding Nurse Researcher (2020) Johns Hopkins Catalyst Award (2018) New Nurse Faculty Fellowship (2015) Leadership & Affiliations She directs NIH-funded training programs and chairs key initiatives including: Nursing Initiative, Mid-Atlantic CFAR Consortium Violence Working Group, Johns Hopkins Center for Injury Research NIH T32 Training Program in Trauma and Violence She is affiliated with six Hopkins research centers focused on global health equity, adolescent health, and infectious diseases.
Jon Wakefield is a Professor in the Department of Biostatistics at the University of Washington's School of Public Health, with additional appointments in the Department of Statistics. He maintains affiliations with the Fred Hutchinson Cancer Research Center, the Center for Statistics and the Social Sciences, and serves on technical advisory groups for the World Health Organization and United Nations on mortality assessment, child mortality estimation, stillbirths, and pre-term births. Wakefield's research focuses on spatial epidemiology, spatial demography, and small area estimation, with particular emphasis on estimating under-5 mortality in low and medium income countries. His work integrates hierarchical models for survey data, space-time models for infectious disease data, and ecological inference methods for both infectious and non-infectious disease contexts. He has made significant contributions to understanding the links between Bayesian and frequentist statistical procedures, developing innovative methods for spatial modeling and disease burden estimation. His publication record shows a strong focus on methodological development with practical applications in global health, particularly in mortality estimation, infectious disease modeling, and demographic analysis. Recent work has addressed critical issues in pandemic response, including excess mortality estimation during the COVID-19 pandemic and seroprevalence studies. His research increasingly incorporates advanced computational methods, including Template Model Builder and integrated nested Laplace approximations for spatial modeling. Fellow, American Statistical Association (2007) Guy Medal in Bronze, Royal Statistical Society (2000) Member of the National Academies of Sciences, Engineering and Medicine Wakefield leads significant research initiatives funded by NIH/NCI and NIH/NIAID, including projects on spatio-temporal epidemiology and statistical issues in AIDS research. He has developed influential software tools including SUMMER, surveyPrev, and SAE4Health, which enable sophisticated small area estimation and spatial analysis for public health applications. His work with WHO and UN technical advisory groups demonstrates the real-world impact of his methodological contributions to global health measurement.
Abbas Jessani, DDS, MSc, PhD, is an Assistant Professor at the Department of Restorative Dentistry within Schulich School of Medicine and Dentistry at the University of Western Ontario. He also holds cross-appointments in the Department of Epidemiology and Biostatistics and is affiliated with research clusters focused on Behavioral and Environmental Risk Factors, Global Health, and Mental Health and Addiction. Dr. Jessani serves as the coordinator for community dental outreach and course director for preclinical operative dentistry. Education: DDS (Doctor of Dental Surgery) MSc in Public Health Dentistry, University of British Columbia PhD in Public Health Dentistry, University of British Columbia Dr. Jessani’s research focuses on barriers to healthcare access and oral health disparities in marginalized communities, including people living with HIV/HCV, LGBTQ2S+, Indigenous populations, refugees, and pregnant women. His work examines the stigma and discrimination faced by these groups and integrates community-based participatory research methods to address systemic inequities. Globally, he investigates oral health access in low-income African countries like Uganda and Southern Africa. His recent publications highlight trends in LGBTQ+ oral health , pregnancy-related dental care , and barriers in underserved populations . He advocates for service-learning programs to train socially-conscious dentists and has contributed to Canada’s first national oral health research strategy.
Debbi Marais is a Professor (Teaching Focussed) at the University of Warwick within the Warwick Medical School and Health Sciences department. She serves as the Director of Postgraduate Education and is a Principal Fellow of the UK Higher Education Academy , demonstrating strategic leadership in enhancing teaching quality, curriculum development, and student mentorship. With over 25 years of experience in higher education, she has held academic positions at Stellenbosch University , University of Aberdeen , and University of Warwick . Her expertise spans teaching, assessment, program coordination, quality assurance, and reflective practice. Marais’ research interests include pedagogical innovation , with a focus on technology-enhanced teaching and employability & professionalism , as well as public health nutrition covering infant and young child feeding , nutrition transition , and global health . She employs mixed methods in her research and has contributed to understanding exclusive breastfeeding barriers , maternal obesity trends in Africa, and Arabic weight-loss app design . Her work often intersects with technology-enhanced health education and interdisciplinary collaboration . Her recent publications highlight qualitative studies on food-based dietary guidelines in Kenya and South Africa, mobile health interventions for weight loss, and educational innovations in medical and nutrition training. These works reflect her commitment to global health equity and digital pedagogy , with recurring themes in African and Southeast Asian health contexts. She emphasizes participatory research methods and policy translation in maternal and child nutrition. Scientific Awards Principal Fellow of the UK Higher Education Academy Marais has supervised postgraduate research students (PhD and Masters) and contributed to curriculum reform through collaborative international projects. She has secured external funding for research and demonstrated interdisciplinary expertise in nutrition , public health , and health education .
Lisandra Stein Bernardes Ciampi Andrade is an Associate Professor in the Department of Clinical Medicine at Aalborg University's Faculty of Medicine, affiliated with the Center for Clinical Research. She holds an ORCID identifier (0000-0003-2367-2849) and an email lsbca@dcm.aau.dk. Her research focuses on fetal medicine, particularly pain assessment in fetuses, preterm birth prevention, and clinical applications of ultrasound in obstetrics. She was honored as 'Årets Underviser' (Teacher of the Year) by her peers in 2024, recognizing her high-level teaching. Collaborators include Louise Thomsen Schmidt Arenholt, Peter Derek Christian Leutscher, and Anya Sook Goldmann Eidhammer. Her work addresses fetal vascularity, gestational age assessment, and retrospective cohort studies in maternal-fetal medicine. Recent publications span topics like fetal cardiac interventions, chronic pain mechanisms in pregnancy, and international consensus guidelines for twin pregnancy management. She actively contributes to peer-reviewed journals such as Ultrasound in Obstetrics & Gynecology and Fetal Diagnosis and Therapy .
Sascha Bolt is a Researcher at Tilburg University's School of Social and Behavioral Sciences, affiliated with the Tranzo Academic Collaborative Center for Digital Health & Mental Wellbeing. Their work focuses on palliative care, dementia care, and healthcare quality improvement, contributing to UN Sustainable Development Goals related to health and well-being. Recent research includes studies on advance care planning, nursing home residents' autonomy, and person-centered dementia care models. Education details are not explicitly stated in the provided text. Collaborations span international teams, as seen in a Delphi study involving global experts. Research outputs from 2019 to 2025 highlight a growing focus on end-of-life care, nursing home environments, and dementia care innovation. Key topics: Dementia care, palliative therapy, nursing home quality, advance care planning. Presentations include talks on eHealth in dementia care and pandemic preparedness in nursing homes. No specific grants or awards are listed, though activities suggest active engagement in collaborative research and public dissemination.
Sanchayan Banerjee is an Associate Professor of Economics & Public Policy at King’s College London’s Policy Institute, with a dual appointment as Visiting Professor at Grenoble-IAE (Université Grenoble Alpes). He previously held a tenured position as Assistant Professor of Environmental and Behavioral Economics at Vrije Universiteit Amsterdam (2022-2024). His research focuses on citizen-oriented, participatory behavioral public policies, tested through experiments in food, energy, health, and charitable policy domains. He co-developed the NUDGE+ framework emphasizing citizen agency in policy design. Affiliations: Visiting Fellow, London School of Economics Affiliate, Amsterdam Sustainability Institute & Institute for Environmental Studies Academic Lead for Professional Education, Policy Institute Research Interests: Behavioral economics, environmental economics, experimental methods, and policy effectiveness. His work bridges behavioral insights with real-world policy challenges, emphasizing participatory approaches and ethical considerations. Recent Article Trends: Recent work analyzes public support for pandemic policies, vaccine uptake drivers across G7 nations, and the ethical implications of behavioral nudges. He explores how experimental methods (field, lab, online) validate policy impacts while addressing equity concerns. Awards: Three consecutive LSE Teaching Awards Teaching Innovation Grants (VU Amsterdam) FHEA (UK Higher Education Academy Fellow) Grants & Leadership: Co-leads NWO ALIGN4energy (€5.7M), a Netherlands-based energy sustainability project. Serves on editorial boards for Scientific Reports , PLOS One , and Humanities & Social Sciences Communications . Previously co-edited Behavioral Public Policy ’s New Voices section. Labs/Teams: Leads King’s Policy Institute’s behavioral policy research group and collaborates across institutions on interdisciplinary projects like the NUDGE+ toolkit development.
Professor Nikolaos Koutsouleris serves as a Research Group Leader for the Max Planck Fellow Group for Precision Psychiatry at the Max Planck Institute of Psychiatry and holds a position as Senior Physician in the Department of Psychiatry and Psychotherapy at Ludwig Maximilian University (LMU) Munich. His work bridges clinical practice with advanced computational approaches to transform psychiatric diagnostics and treatment. Dr. Koutsouleris specializes in predictive psychiatry, focusing on extracting meaningful patterns from neurobiological, neurocognitive, and clinical data to improve early recognition of functional psychoses. His research employs structural MRI, neuropsychological testing, and clinical evaluations within cross-sectional and longitudinal studies, utilizing advanced machine learning methods to identify and validate biomarkers for single-subject prediction of psychosis. As head of the Early Psychosis Studies and the Workgroup for Neurodiagnostic Applications, he drives initiatives to implement predictive models across healthcare settings for personalized management of high-risk individuals. His publication record reveals a consistent focus on machine learning applications in psychiatry, with recent work addressing critical issues like the generalizability of clinical prediction models, brain aging patterns in large populations, and multimodal approaches to psychosis prediction. His research spans from fundamental methodological challenges to clinical applications, demonstrating how AI and machine learning are transforming psychiatric practice toward precision medicine. Dr. Koutsouleris actively trains pre- and post-doctoral investigators in advanced data analysis techniques, emphasizing comprehensive analysis of complex, high-dimensional datasets using multivariate methods. His leadership in the PRONIA Consortium and other collaborative efforts highlights his commitment to advancing the field through international cooperation and rigorous scientific inquiry.
Hao Yang is an Assistant Professor in the Department of Civil and Systems Engineering at Johns Hopkins University, with dual affiliations at the Johns Hopkins Data Science and AI Institute and the Johns Hopkins Institute for Assured Autonomy. His research develops Trustworthy Machine Learning methods to enhance urban mobility systems, focusing on traffic safety, equity, and sustainability through ethical AI and human-machine cooperative systems. Yang earned dual bachelor's degrees in Electrical and Computer Engineering from Beijing University of Posts and Telecommunications and the University of London, followed by a Ph.D. in Civil Engineering (Transportation) from the University of Washington. His educational background bridges telecommunications, electrical engineering, and transportation systems. His research integrates spatio-temporal modeling, assured autonomous systems, and multimodal representation learning to address transportation equity and safety. Key projects include edge-AI-powered traffic surveillance, real-time crash identification, and cooperative signal assistance for vulnerable road users. His work emphasizes ethical AI deployment in cyber-physical infrastructure to create sustainable urban mobility solutions. Recent publications reveal a strategic shift toward large language models and multimodal AI for transportation challenges, with strong emphasis on explainability, reliability, and equity in traffic crash prediction, flow forecasting, and autonomous driving systems. This evolution demonstrates his commitment to adapting cutting-edge AI for real-world transportation problems. Yang's scientific contributions have earned significant recognition: Michael Kyte Outstanding Student of the Year Award (2022) High-Value Research Award from AASHTO (2022) Best Paper Award from TRB Information Systems Committee (2023) Best and Outstanding Dissertation Awards (2024) IEEE DTPI Outstanding Paper Award (2022) TRANSFOR22 Data Competition 2nd place (2022) ASCE Bridges Photo Contest First Place (2021) He actively mentors graduate researchers and seeks 2-3 PhD students for Fall 2025 to advance trustworthy AI in transportation. His research is supported by NSF, USDOT, and AASHTO grants including the Real-Time Truck Parking Information System project that received the High-Value Research Award. Current work focuses on edge-AI for traffic safety and multimodal data integration. Yang leads research within Johns Hopkins' Data Science and AI Institute and Institute for Assured Autonomy, collaborating with Transportation Research Board committees. His lab develops real-time perception systems using edge computing and representation learning, with active projects on non-motorized user safety and equitable traffic management for people with disabilities.
Peter X. K. Song is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health. With expertise spanning statistical methodology development and interdisciplinary applications, Dr. Song maintains active collaborations across Nutritional Sciences, Environmental Health Sciences, Chronic Disease research, and Nephrology. His work bridges theoretical statistics with practical healthcare solutions, focusing on innovative approaches to complex data challenges in public health and medicine. Based at the M4140 SPH II building in Ann Arbor, he leads the Song Lab and contributes significantly to the academic community through teaching, research mentorship, and scholarly publications. PhD, University of British Columbia, Vancouver, 1996 BS, Jilin University, Changchun, 1985 Dr. Song's research focuses on the statistical foundation of big data analytics, with particular emphasis on data integration, distributed inference, high-dimensional data analysis, longitudinal data analysis, mediation analysis, and spatiotemporal modeling. His methodological innovations address critical challenges in smart health applications, including organ exchange programs, children's health, chronic disease management, environmental health assessment, and nutritional sciences. His approach combines statistical theory, integer optimization, and algorithm development to create practical tools that help researchers understand complex relationships between environmental exposures and health outcomes. Dr. Song's publication record demonstrates a consistent trajectory of methodological innovation applied to pressing health challenges. His recent work shows increasing focus on sleep classification using AI techniques, personalized treatment effect analysis, distributed statistical methods for high-dimensional data, and epigenetic applications in adolescent health. The interdisciplinary nature of his research is evident in publications spanning biostatistics journals, computer science venues, and domain-specific medical publications. His work increasingly addresses the challenges of integrating diverse data sources while maintaining statistical rigor in the era of big data. IMS Fellow ASA Fellow Elected Member of the International Statistical Institute 2017 ENAR John Van Ryzin Award Dr. Song has mentored an impressive 22 PhD students and 6 postdoctoral trainees throughout his career, with many now holding faculty positions at prestigious institutions or working as data scientists in leading technology companies. His lab, the Song Lab, currently supports two postdoctoral research fellows and eight doctoral students working on cutting-edge statistical methodology development. His collaborative research extends across numerous grants that support interdisciplinary projects in kidney paired donation programs, environmental health studies, nutritional sciences, and chronic disease research, demonstrating his commitment to translating statistical innovation into practical health solutions. The Song Lab serves as a hub for interdisciplinary statistical research at the University of Michigan, bringing together experts from statistics, operations research, and machine learning to address complex challenges in medical and public health sciences. Current lab members include eight doctoral students and three postdoctoral fellows working on projects related to optimal organ matching strategies, causal mediation pathways of omics biomarkers, and statistical methods for big data integration. The lab maintains strong connections with clinical researchers across nephrology, pediatrics, environmental health sciences, and nutritional sciences, ensuring that methodological developments remain grounded in real-world applications.
Christian A Parkinson is an Assistant Professor at Michigan State University , affiliated with the Departments of Mathematics and Computational Mathematics, Science and Engineering. His research spans mathematical modeling, computational methods, and interdisciplinary applications in epidemiology, control theory, and differential geometry. Research Interests : Mathematical epidemiology, path planning algorithms, reaction-diffusion systems, stochastic modeling, differential geometry, and network science. Email : chparkin@msu.edu His recent publications focus on: Hamilton-Jacobi equations for optimal path planning in multi-agent systems Reaction-diffusion models for epidemics with human behavior Differential geometry approaches to hyperbolic surfaces Network models for disease-opinion coevolution Environmental crime modeling using level sets He teaches MTH 890: Readings in Mathematics , emphasizing advanced computational and theoretical frameworks.
