Angela Juana Torres Iglesias is a Professor at the University of Santiago de Compostela , affiliated with the Faculty of Medicine and Dentistry and the Department of Psychiatry, Radiology, Public Health, Nursing and Medicine . Her research focuses on depression in non-professional caregivers , preventive mental health interventions , and mathematical modeling in medical contexts . Research Trends : 2019-2015: Analyzed psychometric properties of depression assessment tools, prevalence of clinical depression in caregivers, and long-term efficacy of problem-solving interventions. 2000-2006: Developed nonlinear models for aneurysm rupture prediction, epidemic dynamics, and blood flow instabilities. Caregiver Mental Health : Leading trials on preventive cognitive-behavioral and problem-solving therapies for caregivers with depressive symptoms. Systematic reviews of psychosocial interventions and meta-analyses of caregiver depression trends. Collaboration and Impact : Affiliated with the GRISAMP Mental Health and Psychopathology Research Group and Clinical Psychiatry, Social Psychiatry and Psychotherapy . Her work bridges psychiatry, public health, and applied mathematics.
Michael K. Scullin is an Associate Professor in the Department of Psychology and Neuroscience at Baylor University, College of Arts and Sciences. He leads the Sleep Neuroscience and Cognition Laboratory, focusing on the intersection of sleep, memory, and cognitive health across the lifespan. His educational background includes a Ph.D. from Washington University in St. Louis and a postdoctoral fellowship at Emory University School of Medicine in Neurology and Sleep Medicine. He is actively involved in academic service, including as a columnist for Teaching Current Directions in Psychological Science and co-founding editor of Translational Issues in Psychological Science . Dr. Scullin’s research investigates how sleep physiology influences memory consolidation, cognition, and long-term brain health. He explores prospective memory—the ability to remember to perform future actions—and how digital tools like reminder apps can mitigate memory decline in older adults and early Alzheimer’s disease. His work integrates laboratory studies with real-world behavioral trials, emphasizing translational impact. His recent publications span topics such as technology use and cognitive aging, smartphone interventions for memory, sleep during academic stress, and integrative reviews of sleep and cognition. These reflect a strong trend toward applied cognitive neuroscience with a focus on aging, technology, and public health. NIH/NIA R01 grant: Digital solutions for prospective memory impairments in mild Alzheimer’s disease NSF CAREER grant: Sleep and STEM learning in lab and real-world settings NIST funding: Development of ARKTOS, an environment simulator for extreme condition research Dr. Scullin mentors several Ph.D. students and a postdoctoral researcher, actively recruiting new graduate students. He teaches courses including Introduction to Neuroscience, Cognition, Sleep, and a Grant Writing Seminar. His lab, the Sleep Neuroscience & Cognition Laboratory, fosters interdisciplinary research with real-world applications.
Wilhelm Schlag is a Professor in the Department of Mathematics at Yale University, specializing in partial differential equations, mathematical physics, and harmonic analysis. His research focuses on nonlinear wave equations, spectral theory, and Anderson localization, with significant contributions to the understanding of wave maps, Klein-Gordon equations, and Schrödinger operators. Dr. Schlag's research interests span multiple areas of mathematical analysis with emphasis on energy critical wave equations , spectral theory of Schrödinger operators , and Anderson localization . His work combines techniques from harmonic analysis, dynamical systems, and geometric analysis to study nonlinear phenomena in mathematical physics. He has developed innovative approaches to understanding the stability of solitons, the behavior of waves on curved backgrounds, and the spectral properties of quasi-periodic operators. Analysis of his recent publications reveals a strong focus on non-perturbative methods in spectral theory, particularly for quasi-periodic operators and Schrödinger cocycles. His work often bridges the gap between mathematical physics and pure analysis, with applications to quantum mechanics and field theory. Schlag has developed multiscale techniques for Anderson localization and made significant contributions to the understanding of wave map dynamics beyond symmetric settings. Dr. Schlag serves on the editorial boards of prestigious journals including Communications in Partial Differential Equations , Inventiones Mathematicae , and Calculus of Variations and PDE . He is the co-author of influential books such as Concentration compactness for critical wave maps and Invariant Manifolds and dispersive Hamiltonian Evolution Equations . His collaborative research program involves extensive numerical computations, as evidenced by the NLKG3_WEB repository containing Bash scripts and data for nonlinear Klein-Gordon equation simulations. Schlag has mentored numerous researchers through his collaborative projects and has presented his work at major international conferences including the International Congress of Mathematicians.
Margarida Carvalho is an Associate Professor in the Department of Computer Science and Operations Research at Université de Montréal, where she holds the FRQ-IVADO Research Chair in Data Science for Combinatorial Game Theory. She is also an Associate Academic Member at Mila (Quebec AI Institute), contributing to their research in AI for Humanity. Her academic journey spans from Portugal to Canada, where she has established herself as a leading researcher at the intersection of operations research and game theory. Carvalho earned her bachelor's and master's degrees in mathematics from the Faculty of Sciences of the University of Porto (FCUP), followed by a PhD in Computer Science from the same institution in 2016. Her doctoral work, which focused on game theory applications for kidney exchange programs, earned her the prestigious 2018 EURO Doctoral Dissertation Award, making her the first Portuguese woman to receive this honor. After completing her PhD, she worked as an IVADO Postdoctoral Fellow at Polytechnique Montréal before joining Université de Montréal as an Assistant Professor in 2018. Her research focuses on combinatorial optimization and algorithmic game theory, with applications spanning healthcare (kidney exchange programs, hospital operations), sustainable development (electric vehicle infrastructure, urban planning), and education (school choice systems). She develops novel mathematical programming approaches to model and solve problems involving multiple decision-makers with potentially conflicting objectives. Her work bridges theoretical advances in optimization with practical implementations that address real-world challenges in resource allocation and decision-making under uncertainty. Notably, her research on fairness in kidney exchange programs has contributed to more equitable organ allocation policies. Her 15 most recent publications reveal a strong trend toward integrating game-theoretic concepts with practical optimization challenges, particularly in healthcare and sustainable infrastructure. She has pioneered approaches that balance utilitarian objectives with fairness considerations, developed novel formulations for bilevel and multilevel optimization problems, and created learning-based frameworks for complex decision environments. Her work consistently demonstrates how mathematical rigor can inform practical policy decisions in critical domains. 2018 EURO Doctoral Dissertation Award for her PhD thesis on game theory applications for kidney exchange programs Mathematical Programming 2024 Meritorious Service Award Teaching Excellence Award from Université de Montréal Supervised student Maria Bazotte receiving the Dupačová-Prékopa Best Student Paper Prize in Stochastic Programming Carvalho actively advises graduate students, with Marylou Fauchard (Master's) and William St-Arnaud (PhD) among her current advisees. Her research is supported by grants from Hydro-Québec, the Natural Sciences and Engineering Research Council of Canada (Discovery grant 2017-06054 and Collaborative Research and Development Grant CRDPJ 536757–19), and FRQ-IVADO. She serves as an associate editor for INFORMS Journal on Computing, OR Spectrum, and Dynamic Games and Applications, and is a founding board member and treasurer of the Bilevel Optimization Society. She teaches courses in Mathematical Programming, Operational Research Models, and Discrete Mathematics at Université de Montréal. Carvalho is affiliated with Mila (Quebec AI Institute), where she contributes to research initiatives focused on AI for Humanity, particularly in the areas of algorithmic fairness and sustainable development. Her FRQ-IVADO Research Chair supports her work on combinatorial game theory applications, and she collaborates with researchers across disciplines through the IVADO research community. She has been instrumental in establishing the Bilevel Optimization Society, creating a dedicated forum for researchers working on hierarchical decision-making problems.
Dr. Yue Wu is a Lecturer in the Department of Mathematics and Statistics at the Faculty of Science, University of Strathclyde. She is actively engaged in research and teaching, with a strong focus on stochastic and numerical analysis. She is affiliated with the Alan Turing Institute as a Visiting Researcher and collaborates internationally on advanced mathematical and data science projects. Research Interests: Numerical analysis for stochastic (partial) differential equations (SDEs/SPDEs) Random periodic solutions and their numerical approximation Rough path theory and signature methods Applications in machine learning, data science, and engineering systems Her recent work bridges pure stochastic analysis with practical applications in AI, autonomous systems, and industrial diagnostics. She employs advanced mathematical tools such as log-signatures and randomized numerical schemes to solve complex real-world problems. Publication Trends: Dr. Wu's recent publications (2022–2025) show a strong trend toward integrating stochastic numerics with machine learning. Her work spans theoretical convergence analysis of numerical schemes, feature extraction using rough paths, and PDE-informed deep learning. There is a clear interdisciplinary focus, connecting mathematics with engineering and computer science. Scientific Awards: Strathclyde & TU Braunschweig Joint Collaborative Funding Recipient (2023) ICIAM2023 Financial Support Scheme 2 Recipient (2023) Turing Network Development Award: Trailblazers Competition Recipient (2022) Lower Saxony – Scotland Tandem Fellowship Recipient (2022) Advising and Grants: Dr. Wu is accepting PhD students and offers projects in stochastic numerics and rough path applications. She has secured funding as Principal Investigator (e.g., International Exchanges Round 3) and Co-investigator (e.g., AI-based asteroid navigation project with ESA). Her grants reflect strong international collaboration and interdisciplinary innovation. Labs and Teams: While no specific lab name is mentioned, Dr. Wu is part of active research networks including the Alan Turing Institute and collaborates with teams in aerospace, data science, and applied mathematics. She organizes seminars and workshops, indicating leadership in her research community.
Professor Bixiang Wang is a faculty member in the Department of Mathematics at New Mexico Tech. His research focuses on stochastic partial differential equations, fractional calculus, and infinite-dimensional dynamical systems with a particular emphasis on invariant measures, random attractors, and asymptotic behavior of solutions. He has taught numerous courses including Math 372 (Ordinary Differential Equations), Math 534 (Partial Differential Equations), and graduate-level topics in stochastic analysis. His work explores complex systems such as reaction-diffusion equations, wave equations, and Navier-Stokes equations under stochastic influences. Key areas of interest include fractional equations driven by superlinear noise, large deviation principles, and convergence of invariant measures in unbounded domains. He actively contributes to the theoretical foundations of stochastic dynamics and their applications in nonlinear science. Recent research trends highlight advancements in understanding long-term behavior of stochastic systems, with a focus on fractional operators and non-autonomous dynamics. His publications address critical challenges in global well-posedness, ergodicity, and bifurcation phenomena under random perturbations.
Daniel Cao Labora is a University Professor in the Department of Statistics, Mathematical Analysis and Optimization at the University of Santiago de Compostela, affiliated with the Faculty of Mathematics. He holds a PhD from the same university, awarded in 2019 for his thesis titled New methods for the study and resolution of equations involving fractional operators and their applications , supervised by Dr. Juan José Nieto Roig and Dr. Rosana Rodriguez Lopez. He is a member of the Galician Mathematical Research and Technology Center (CITMAga) and the research group EDNL: Nonlinear differential equations . His research focuses on advanced mathematical analysis, particularly in nonlinear differential equations , fractional calculus , and their applications. Key areas include boundary value problems, fractional operators, and the interplay between differential equations and functional analysis. His work bridges theoretical developments with practical applications in engineering, physics, and computational sciences. Recent publications highlight contributions to Borwein integrals via residue theory, fractional fractals, and time-fractional beam dynamics. His articles frequently explore existence theorems, uniqueness criteria, and novel analytical methods for complex systems. Beyond technical research, he has organized academic events like the VI Encontro da Mocidade Investigadora . No academic awards or grants are explicitly listed in the provided materials. His advising record is not detailed here.
Dr. Víctor Mañosa Fernández is a full Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Mathematics and the School of Industrial, Aerospace, and Audiovisual Engineering of Terrassa (ESEIAAT). He leads research within the UPCDS Dynamical Systems Group and is part of the BarcelonaTech Mathematics Institute (IMTECNBC). Affiliations: Professor at the Department of Mathematics, UPC Member of the UPCDS Dynamical Systems Research Group Institut de Matemàtiques de la UPC-BarcelonaTech Research Expertise: His work focuses on dynamical systems, nonlinear dynamics, difference equations, and bifurcation theory. Specific interests include integrable maps, periodic solutions, invariant manifolds, and applications to mathematical physics and engineering systems. Recent Contributions: Recent studies explore Kahan-Hirota-Kimura maps, invariant graphs in piecewise linear systems, and periodic traveling wave persistence. He also investigates global periodicity conditions and stability indices in both continuous and discrete systems. Grants & Projects: He has led multiple competitive projects, including the UPCDS group's funding and research on dynamical systems applications. Collaborations span control theory, nonlinear analysis, and interdisciplinary applications. Publications: Over 190 academic contributions, including articles in Journal of Differential Equations , Nonlinear Dynamics , and Communications in Nonlinear Science . Key topics include Hamiltonian systems, discrete integrability, and bifurcation phenomena.
Stefano Marchesiello is a Full Professor of Applied Mechanics at the Polytechnic University of Turin, Department of Mechanical and Aerospace Engineering (DIMEAS), a position he has held since 2019. His academic work spans theoretical studies, numerical applications, and experimental tests within the field of Applied Mechanics. He maintains active roles in doctoral education, serving on mechanical engineering doctoral colleges from 2013/2014 through 2024/2025, and teaches courses including Dynamics and Identification of Nonlinear Systems, Dynamics of Mechanical Systems, Vibration Mechanics, and Machine Mechanics for Aerospace Engineering. Marchesiello's research focuses on modal analysis and identification, damage diagnosis in structures and construction materials, damping systems, mechanical vibrations, and nonlinear dynamics. His primary research lines include vehicle-bridge dynamic interaction, dynamic identification techniques in linear and nonlinear fields, damage identification, vibrations of continuous systems with non-proportional damping, innovative vibration damping devices, diagnostics and monitoring of rotating systems, and pantograph-catenary dynamic interaction. His work bridges theoretical mechanics with practical engineering applications, particularly in transportation infrastructure and mechanical systems. His recent publications demonstrate a strong focus on nonlinear system identification, structural health monitoring, and vibration analysis across various mechanical and aerospace applications. Marchesiello's research shows increasing integration of machine learning techniques with traditional mechanical engineering approaches, particularly in system identification and damage detection. His work spans from fundamental nonlinear dynamics to practical applications in railway systems, rotating machinery, and structural components. Certificate of reviewing awarded by Journal of Sound and Vibration - Elsevier, Netherlands (2013) Certificate of Excellence in Reviewing - Mechanical Systems and Signal Processing 2013 awarded by Elsevier, Netherlands (2013) Marchesiello serves as Scientific Director for multiple commercial research contracts, particularly with Officina Fratelli Bertolotti SpA, focusing on vibration damping systems for railway catenaries and rotor dynamics modeling. He has led research projects from 2008 through 2023, demonstrating sustained research leadership and industry collaboration. His editorial work includes membership on the Editorial Board of SHOCK AND VIBRATION since 2018, and he has served on program committees for the International Conference on Damage Assessment of Structures (DAMAS) across multiple years. He is actively involved with the Dynamics of Mechanical Systems and Identification research group (DIMEAS), which focuses on developing advanced methods for analyzing and identifying mechanical systems with both linear and nonlinear behaviors. His research integrates computational modeling, experimental validation, and practical applications across multiple engineering domains.
Antonio Moro is an Associate Professor of Mathematics at Northumbria University's School of Engineering Physics and Mathematics. He co-founded the Mathematics of Complex and Nonlinear Phenomena (MCNP) group and previously held research roles at the University of Salento, University of Loughborough, SISSA Trieste, and University of Milano-Bicocca. PhD in Physics (University of Salento, 2004) Former Senior Lecturer at Northumbria (2013–present) His research focuses on nonlinear partial differential equations, integrable systems, and critical phenomena in statistical mechanics. He develops unified approaches to complex systems across nonlinear mathematics , integrable systems , dispersive hydrodynamics , and random matrix theory . Recent articles explore biaxial nematic systems, KP equation reductions, and phase transitions in mean-field models. Scientific contributions include Fellow of the Higher Education Academy Member of the London Mathematical Society Fellow of the Institute of Mathematics and its Applications Organizer, Isaac Newton Institute thematic programs (2022) As Head of Mathematics and Statistics (2019–2022), he restructured undergraduate programs and introduced Northumbria's first Mathematics MSc. He teaches with a research-led approach connecting classical problems to contemporary research.
Dr. Christine T. Chambers is a Professor and Canada Research Chair (Tier 1) in Children's Pain at Dalhousie University in Halifax, Nova Scotia, Canada. She holds joint appointments in the Departments of Psychology and Neuroscience & Pediatrics. Since 2018, she has served as Scientific Director of Solutions for Kids in Pain (SKIP), a national knowledge mobilization network focused on improving children's pain management through evidence-based solutions. Dr. Chambers is also the Scientific Director of the CIHR Institute of Human Development, Child and Youth Health. Dr. Chambers' research focuses on pediatric pain management, with particular emphasis on vaccination pain, pain in juvenile idiopathic arthritis, and children's cancer pain. Her work bridges developmental, psychological, and family factors to develop effective pain management strategies for children. She has pioneered knowledge translation approaches through social media campaigns like #ItDoesntHaveToHurt and #KidsCancerPain, bringing evidence-based pain management strategies directly to parents and healthcare providers. Her recent publications demonstrate a strong focus on knowledge mobilization, patient engagement, and implementation science in pediatric pain management. Dr. Chambers has led numerous systematic reviews on chronic pain prevalence in children and developed innovative digital health interventions for pain management and postpartum care. Canada Research Chair (Tier 1) in Children's Pain Dr. Chambers leads a research team composed primarily of trainees who collaborate with parents and other experts to address poorly managed pain in children. She has been instrumental in developing the first national Pediatric Pain Management standard in partnership with the Health Standards Organization. Her work emphasizes patient partnership in research and implementation science to translate evidence into real-world practice, particularly through her leadership of the Solutions for Kids in Pain network. Dr. Chambers' laboratory, the Centre for Pediatric Pain Research at the IWK Health Centre, focuses on understanding stakeholders' needs for successfully engaging in activities to improve children's pain management. Her team conducts original grant-funded research while developing and evaluating social media campaigns to mobilize evidence about children's pain to parents and healthcare professionals.
Professor John Moraros is Dean and Professor of the School of Science at Xi'an Jiaotong-Liverpool University (XJTLU), a position he has held since 2022. Prior to this, he served as Dean and Professor at the School of Health Sciences, College of the North Atlantic Doha, Qatar (2020-2022), and Dean and Professor at the Faculty of Health Studies, Brandon University, Brandon, Manitoba, Canada (2019-2020). Professor Moraros holds an impressive academic background: Ph.D. in Molecular Biology from New Mexico State University (2007) M.P.H. from New Mexico State University's Department of Health Science (2004) M.D. from Universidad Autónoma de Ciudad Juárez, Facultad de Medicina (2002) B.A. in Biology & Interdisciplinary Studies from Queens College (1991) As an internationally renowned leader in science, precision medicine, public health, and epidemiology, Professor Moraros integrates data analytics, evidence-based research, and community-engaged approaches to develop culturally tailored, AI-driven health interventions that address inequities. His work bridges AI, precision medicine, epidemiology, and health equity to foster transformative solutions in global health, with particular focus on minority and marginalized population health. Professor Moraros has published over 60 peer-reviewed articles in high-impact journals, with recent work (2025) spanning genetic connections between depression and dysmenorrhea, epidemiological studies of aging and bone health, and AI applications in cancer diagnosis and treatment. His research demonstrates interdisciplinary collaboration across medical, technological, and social domains. Professor Moraros's work has received significant recognition: Featured in 186 news outlets globally Interviewed by CNN Health regarding genetic connections between period pain and depression Featured on BBC Sounds discussing depression and menstrual pain h-index of 22 with over 1,855 citations according to Scopus Actively involved in mentoring, Professor Moraros supervises PhD students working on hepatocellular carcinoma progression and intergenerational technology transfer. He teaches SCI002 Scientific Principles and Methods and SCI003 Scientific Communication and Integrity, emphasizing interdisciplinary approaches that connect his research expertise with educational objectives. As Chair of the School of Science committee at XJTLU, he continues to shape academic direction while maintaining active research collaborations across multiple institutions.
Hany Osman is an Associate Professor in the Master of Data Analytics program at the University of Niagara Falls Canada, holding a PhD in Industrial Engineering from Concordia University and a Professional Engineer (PEng) license in Ontario. His academic-industrial career bridges theoretical research with practical applications across multiple sectors. Dr. Osman's research spans three interconnected domains: Machine Learning & Data Analytics : Specializing in logical analysis of data, cost-sensitive learning, and ensemble techniques for industrial applications Operations Research : Developing nature-inspired metaheuristics (cuckoo search, ant colony optimization) for NP-hard problems in manufacturing and logistics Supply Chain Management : Focusing on sustainable optimization of lot sizing, production planning, and inventory control under stochastic conditions His recent publications (2023-2024) reveal a strategic pivot toward AI-integrated manufacturing systems, notably the CAPP-GPT framework for generative AI in process planning and emission-aware lot sizing models. This work demonstrates consistent translation of theoretical advances into industrial solutions for rail, oil, and smart manufacturing sectors. Professional credentials include: IBM Mastery Certificate in Predictive Data Analytics Professional Engineer (PEng) license from Ontario Dr. Osman leverages extensive industrial experience in supply chain logistics, oil industry optimization, and education technology to inform both research and teaching. His supervision in the Master of Data Analytics program emphasizes hands-on application of machine learning to real-world operational challenges, with students contributing to publications in Manufacturing Letters and related journals. While no formal lab is specified, his research group operates at the intersection of data science and industrial engineering, maintaining strong industry partnerships that drive applied projects.
Waltraud Huyer is a faculty member at the Department of Mathematics, Faculty of Mathematics, University of Vienna. Her office is located in Room 04.120 at Oskar-Morgenstern-Platz 1, 1090 Wien, Austria. She actively teaches courses including Numerical Mathematics and Introduction to Mathematics for both undergraduate and secondary school teacher accreditation programs. Her research spans Global and Local Optimization , Numerical Analysis , Data Analysis , Protein Folding , and Population Dynamics with structured populations. She develops advanced optimization algorithms like SNOBFIT for noisy environments and MINQ8 for quadratic programming, while applying mathematical techniques to biological problems such as protein structure prediction and age-structured population modeling. Analysis of her 14 most recent publications (1994-2018) reveals three dominant research thrusts: (1) Algorithmic development in global optimization (7 papers), including multilevel coordinate search and exact penalty functions; (2) Mathematical biology applications (5 papers), particularly in protein folding and population dynamics; (3) Computational verification methods (2 papers) for linear systems and feasibility problems. Her work consistently bridges theoretical mathematics with practical computational implementations. Her scientific contributions include widely-used algorithms implemented in MATLAB, such as the MCS global optimizer and SNOBFIT for noisy optimization, available through the University of Vienna's software repository. Huyer supervises teaching activities for foundational mathematics courses, emphasizing practical computational skills using MATLAB for numerical analysis and regression problems. Her teaching materials include detailed exercise sets with structured assessment criteria requiring both theoretical understanding and programming implementation.
