Christoforos Panagiotis is a Lecturer in the Department of Mathematical Sciences at the University of Bath. His research focuses on advanced topics in probability theory, statistical mechanics, and mathematical physics, with particular expertise in random-cluster models, lattice systems, and phase transitions. Panagiotis holds a Ph.D. in Mathematics and has contributed to foundational studies on Gibbs measures, critical phenomena, and geometric representations in stochastic models. His research interests span probability theory, including self-avoiding walks, phase transitions in lattice models, and scaling limits of stochastic processes. Notable work includes investigations into the Blume-Capel model's tricritical behavior and the quantitative analysis of sub-ballisticity in hexagonal lattice systems. Panagiotis leads the project Ising universality in the two-dimensional Blume-Capel model , funded by the Heilbronn Institute for Mathematical Research. Panagiotis' work often integrates analytical and combinatorial methods to explore complex systems, with applications to critical exponents, cluster distributions, and geometric probability. He collaborates internationally on topics such as random current models and stochastic processes on graphs, contributing to both theoretical advancements and applied mathematical frameworks.
Andrew Forbes is a Professor of Biostatistics and Head of the Division of Quantitative Research Methodology at Monash University's School of Public Health and Preventive Medicine. His research focuses on statistical methodology for clinical trials, particularly cluster randomized trials, and their applications in healthcare. He leads the Biostatistics Collaboration of Australia and co-leads the Australian Clinical Trials Alliance Statistics Interest Group (ACTA-STInG). He has been awarded Fellow of the Australian Academy of Health and Medical Sciences (2020) and twice received the Excellence in Trial Statistics award from ACTA (2021, 2022). His work contributes to UN Sustainable Development Goals related to health and well-being. Key projects include the Flexible Stepped Wedge and Cluster Randomised Crossover Designs (2025-2028), Comprehensive Geriatric Assessment for perioperative care (2024-2029), and the CALIPSO trial on antimicrobial prophylaxis in cardiac surgery (2022-2027). He has collaborated on over 420 research outputs, emphasizing rigorous trial design, reporting standards, and innovative statistical methods.
Yuehao Bai is an Assistant Professor of Economics at the University of Southern California (USC). For the 2024–25 academic year, he is on leave at the Becker Friedman Institute at the University of Chicago. His research focuses on econometric theory, particularly in randomized experiments, statistical inference, and causal inference. He holds a Ph.D. in Economics from the University of Chicago (2020). His work emphasizes methodological advancements in experimental design, including matched pairs, cluster randomized trials, and stratified experiments. He has contributed to addressing challenges such as imperfect compliance, attrition, and covariate adjustment in experimental settings. His publications appear in top journals like the Journal of Political Economy, Journal of Econometrics, and Journal of the American Statistical Association. Bai has developed software tools like the 'sreg' package for stratified randomized experiments. His research bridges theoretical econometrics with practical applications, aiming to enhance statistical rigor in empirical studies.
Velda Gonzalez-Mercado is an Assistant Professor at New York University Rory Meyers College of Nursing specializing in symptom science and management for GI/GU cancer populations. Her research employs multi-omics approaches including microbiomics and genomics to investigate bio-behavioral mechanisms of cancer-related symptoms. PhD, MSN, and BSN in Nursing National Institute of Nursing Research (NINR) K23 and F32 grant recipient Research Focus: Explores gut microbiome associations with cancer treatment symptoms (fatigue, sleep disturbance, depression) through patient-centered phenotyping. Notably examines disparities in Puerto Rican populations and develops microbiota-based classifiers for symptom prediction. Notable Awards: Goddard Fellowship (2023) Sousa Award of Excellence (2015) International Sigma Theta Tau Induction (1992) Publications demonstrate expertise in microbiome functional pathways, symptom clustering analysis, and culturally adapted interventions. Current work examines interplay between social determinants and biological factors in urban cancer populations.
Dr. Allan Tyler is a Senior Lecturer at London South Bank University, affiliated with the Future Communities Research Centre and the Nicotine, Tobacco, and Vaping (NTV) Research Group. He specializes in qualitative methods, LGBTQ+ mental health, homelessness, and smoking cessation interventions. As Course Leader for the MSc in Mental Health and Clinical Psychology and its part-time variant, he teaches courses on qualitative research, mental health methodologies, and the psychology of sex, gender, and relationships. Education: PhD (2007–2016) MSc Gender Culture Politics (2004–2006) Bachelor of Human Ecology (1989–1994) Research Focus: Dr. Tyler’s work examines decision-making patterns in marginalized populations, such as LGBTQ+ youth, sex workers, and individuals experiencing homelessness. He collaborates with organizations like Croydon Safeguarding Children Partnership (CSCP) to improve safeguarding practices for trans/non-binary youth and co-authored the RaRE Report on LGBT mental health. His recent SCeTCH trials evaluate e-cigarette interventions for smoking cessation among homeless populations. Articles Trends: His research spans e-cigarette efficacy in marginalized communities, LGBTQ+ resilience, and qualitative analyses of street drinking and sex work. Key themes include harm reduction, cultural competence, and disrupting homogenization of minoritized groups. Grants & Advising: Supervises multiple PhD/MPhil students and leads projects like the SCeTCH trial. Previously advised PACE Health and held visiting roles at the University of East London and Birkbeck. Labs/Teams: Engaged with the Centre for Addictive Behaviours Research (CABR) and the NTV Research Group to design interventions addressing addiction and mental health in underserved populations.
Ashish Goel is a Professor at the Stanford University , with primary affiliations in the Management Science and Engineering department, and by courtesy, the Computer Science department. He is actively involved in the Social Algorithms Lab , the Stanford Crowdsourced Democracy Team , and the RAIN seminar . Goel's research centers on algorithm design, analysis, and applications , integrating optimization, probability, stochastics, and game theory to solve real-world problems. His current focus areas include Social Networks, Social Choice, Crowdsourced Democracy, Internet Commerce, Reputation Systems, and Large-Scale Data Processing . He emphasizes tangible impact in application domains, particularly through algorithmic innovation in networked systems. The research trends in his publications highlight Personalized PageRank, Social Network Analysis, Crowdsourced Decision-Making, and Randomized Algorithms . These works often intersect computational social science, distributed systems, and economic incentives , demonstrating his interdisciplinary approach. Scientific Awards: Best Paper Award, WWW 2009 Goel has mentored students in algorithmic research and collaborated on computational social science through the Stanford Crowdsourced Democracy Team. His industry experience, particularly at Twitter, Teapot, and Stripe , has significantly influenced his academic work, especially in recommendation systems, ad targeting, and search algorithms .
Thomas N. Massey is a Research Professor in the Department of Physics and Astronomy within the College of Arts and Sciences at Ohio University. He is a member of the Institute of Nuclear and Particle Physics (INPP) and conducts his research at the Edwards Accelerator Lab on the Athens campus. His work is centered on experimental nuclear physics, with significant contributions to nuclear structure, neutron interactions, and reaction modeling. Dr. Massey earned his Ph.D. from the University of California in 1988, following an M.S. and B.S. from the same institution. His academic career at Ohio University has spanned from Research Scientist (1989–1994), to Research Assistant Professor (1994–2007), and currently Research Associate Professor (2007–present), reflecting a long-standing and active role in the department. His research interests are focused on Experimental Nuclear Physics , particularly Nuclear Structure Studies using gamma-ray spectroscopy, Measurement of (z,n) Reactions , and Neutron Elastic and Inelastic Cross Sections via time-of-flight techniques. He has pioneered methods for calculating reaction cross sections using a combined shell-model and R-matrix approach and has developed advanced techniques for calculating nuclear level densities. His work has direct applications in nuclear data, medical physics (e.g., BNCT), and reactor shielding. The 15 most recent publications reflect a consistent research trajectory in nuclear data and structure. His work spans from fundamental studies of exotic nuclei like 8 He and 11 Li to practical applications such as neutron source characterization for detector calibration and cancer therapy. A strong theme is the precise measurement and theoretical modeling of neutron interactions, with a focus on light nuclei and practical nuclear data needs. Research Professor, Ohio University (2007–present) Research Assistant Professor, Ohio University (1994–2007) Research Scientist, Ohio University (1989–1994) Dr. Massey has been involved in significant projects, including the NERI Iron Sphere Experiments for neutron transport validation and detector calibration work at Lawrence Livermore National Laboratory. He has also developed a suite of data analysis programs for nuclear experiments, including codes for Rutherford backscattering, kinematics, and time-of-flight data replay. His extensive publication record and ongoing research activities indicate a sustained and impactful career in nuclear physics.
Todd Young is a Professor and Chair in the Department of Mathematics at Ohio University , part of the College of Arts and Sciences . He is also a member of the Quantitative Biology Institute and the Infectious and Tropical Disease Institute . His research integrates dynamical systems theory with biological modeling, particularly in cell cycle regulation, clustering phenomena in yeast, and biomedical informatics. Education: Ph.D. in Mathematics, Georgia Institute of Technology, 1995 M.S. in Mathematics, University of California at Riverside, 1991 M.S. in Engineering Mechanics, University of Kentucky, 1987 B.S. in Mathematics, University of Kentucky, 1985 Research Interests: Todd Young's work centers on the qualitative theory of ordinary and random differential equations , with applications in cell cycle dynamics , biological feedback systems , clustering in populations , and binary classification in biomedical informatics . He develops mathematical models to understand how feedback mechanisms lead to synchronization in yeast and how dynamical systems principles apply to neural and immune responses. His research spans pure theory, such as bifurcation and ergodic theory, to applied problems in public health, like ventilator-associated pneumonia detection. Research Trends: His recent publications reveal a strong focus on mathematical biology , particularly in modeling the cell cycle with feedback and clustering behavior . He increasingly integrates numerical methods and tensor approximation dynamics into his work, collaborating across disciplines in physics, engineering, and medicine. His articles frequently appear in journals like SIAM Journal on Applied Dynamical Systems , Journal of Mathematical Biology , and Nonlinearity , reflecting a blend of rigorous analysis and biological relevance. Scientific Awards and Roles: Joint Editor-in-Chief, Dynamical Systems journal Recipient of the College of Arts and Sciences “Dean's Outstanding Teacher Award” Editorial Board memberships in Discontinuity, Nonlinearity and Complexity and Annual Review Chaos Theory, Bifurcations & Dynamical Systems Founding member of the Quantitative Biology Institute Active member of SIAM, AMS, and MAA Advising and Grants: Todd Young has advised numerous Ph.D., M.S., and undergraduate students, many of whom have pursued academic or data science careers. His research has been funded by the National Institutes of Health (NIH) and the National Science Foundation (NSF) , notably through the NIH-NIGMS R01GM090207 grant on mathematical biology. He emphasizes student training through structured research participation, including exploratory and paid assistantships. Labs and Teams: He leads the Dynamics in Biology Research Group , which includes students and collaborators working on projects ranging from theoretical dynamics to computational biology. The group fosters interdisciplinary collaboration, particularly with biologists and medical researchers at Ohio University and beyond.
Antonio Manuel López Quilez is a Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, Universitat de València, Spain. He is affiliated with several research groups, including POpE (Public Opinion and Elections), VABAR (Valencia Bayesian Research Group), and VIO-STRAT (Advanced research strategies in family and gender violence). His email is antonio.lopez@uv.es. Doctor by the Universitat de València (1997) Thesis: Modelos lineales generalizados espaciales Supervisor: Dr. Juan Ferrándiz Ferragud His research focuses on statistical methodology with applications in spatial statistics, Bayesian inference, public health, environmental science, and social sciences. Key areas include spatial modeling, disease mapping, electoral analysis, and Bayesian hierarchical models. He frequently applies his methods to public health surveillance, such as influenza outbreak detection, and environmental modeling, including acoustic mapping with barriers. His recent publications highlight a strong trend in Bayesian modeling, particularly using integrated nested Laplace approximations (INLA), spatial smoothing, and Dirichlet regression for compositional data. He has contributed to dynamic forecasting of infectious diseases and the statistical analysis of bioclimatic indices. His work bridges theoretical statistical development with practical applications in epidemiology and social policy. No scientific awards are mentioned in the provided text. There is no information available about student advisement or research grants. However, his extensive collaborative work suggests active participation in research projects. He is a member of multiple interdisciplinary research teams, including those focused on public opinion, Bayesian methods, and gender violence research, indicating a strong team-based research approach. He leads or contributes to research in advanced statistical strategies for social and health issues, particularly through the VIO-STRAT group, which focuses on family and gender violence. His methodological expertise supports applied research in public policy and epidemiological decision-making.
Miguel Ángel Martínez Beneito is an Associate Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, University of Valencia. His research focuses on Bayesian statistics, spatial epidemiology, and disease mapping, with applications in public health and risk cluster detection. Education: PhD in Statistics from the University of Valencia (2005), thesis on statistical methods for detecting risk foci in epidemic outbreaks. His research interests include Bayesian modeling, spatial statistics, and computational epidemiology. He is a member of the Valencia Bayesian Research Group (VABAR), contributing to advanced statistical methodologies in health sciences. The recent publications attributed to him span topics in stochastic processes, biomechanics, and mathematical biology. However, there is a possibility of name disambiguation, as some works on biomechanics appear more aligned with a researcher from the University of Zaragoza. The core research at UV remains in statistical and epidemiological modeling. Scientific Contributions: Development of Bayesian methods for disease mapping. Application of statistical models to public health surveillance. Potential contributions to stochastic modeling in biological systems. He advises students in statistics and public health, though no specific advisees are listed. He has collaborated extensively with researchers in applied mathematics and biomechanics, though the nature of these collaborations requires further clarification due to potential name overlap. He is affiliated with the VABAR research group, focusing on Bayesian inference and its applications in real-world health problems.
Laura K. Beres is an Associate Research Professor in the Department of International Health at the Johns Hopkins Bloomberg School of Public Health. She is also a Visiting Scholar at the Centre for Infectious Disease Research in Zambia. Her work spans global health, implementation science, and social and behavioral interventions, with a focus on HIV prevention and care in low-resource settings such as Zambia and Lesotho. Education: PhD, Johns Hopkins Bloomberg School of Public Health, 2019 MPH, Emory Rollins School of Public Health, 2011 BS, Northwestern University, 2004 Laura Beres specializes in mixed-methods research, utilizing implementation science and human-centered design to improve health outcomes. Her research interests include re-engagement in HIV care, person-centered interventions, participatory research, and causal inference through quasi-experimental designs. She has led formative and rapid qualitative studies to inform health interventions in sub-Saharan Africa. Her recent publications focus on patient re-engagement, tracer contact effectiveness, and person-centered care models in Zambia. These works employ diverse methodologies including instrumental variable analysis, stepped-wedge trials, and narrative analysis, contributing significantly to the understanding of HIV care delivery in real-world settings. Scientific Awards: Advising, Mentoring and Teaching Recognition Award (AMTRA), JHSPH, 2021 R. Bradley Sack Family Scholarship, JHSPH, 2019 Procter & Gamble Fellowship, JHSPH, 2016 Delta Omega Honor Society, 2010 Robert W. Woodruff Merit Scholar, Emory, 2009–2011 Laura Beres has served as an HIV & AIDS Advisor to the Lesotho Ministry of Local Government and contributed to national strategic planning. She co-authored the WHO Consolidated Guideline on SRHR for women living with HIV and supports the Baltimore City Health Department in monitoring and evaluation. She teaches courses in formative research and human-centered design at JHSPH. Research Projects: She leads initiatives on long-acting injectable PrEP for adolescents in Zambia, risk stratification for pregnant women with HIV in Kenya, stigma reduction integrating mental health and violence prevention, and health systems strengthening in Zambian prisons and clinics.
Charity G. Moore Patterson is a Professor and the founding Director of the SHRS Data Center at the University of Pittsburgh’s School of Health and Rehabilitation Sciences. She is affiliated with the Department of Physical Therapy and leads major biostatistical and data coordination efforts in rehabilitation research. Her work is supported by the National Institutes of Health, PCORI, and the Department of Defense. Education: PhD in Biostatistics, University of South Carolina MSPH in Biostatistics, University of South Carolina BS in Mathematics and Statistics, Eastern Kentucky University Dr. Patterson’s research focuses on biostatistics, clinical trials, and data coordination for exercise, rehabilitation, and physical therapy studies. Her work emphasizes rigorous trial design, longitudinal data analysis, and translational applications in musculoskeletal and neurological disorders. She has contributed extensively to studies on low back pain, Parkinson’s disease, stroke recovery, and health disparities. Her recent publications reflect a strong trend in multicenter clinical trials, pragmatic study designs, and data harmonization , particularly in pain and neurorehabilitation. She frequently employs cluster-randomized, stepped-wedge, and randomized crossover designs, with a focus on real-world outcomes and healthcare utilization. Dr. Patterson serves as a reviewer for leading scientific journals and national funding agencies, contributing to the advancement of methodological standards in rehabilitation research. Advising and Grants: Mentors research teams and collaborates on federally funded projects. Principal and co-investigator on NIH, PCORI, and DoD grants focused on low back pain, vestibular recovery, and data science in rehabilitation. Labs and Teams: She leads the SHRS Data Center, a hub for biostatistical support, data management, and research coordination across interdisciplinary projects within the School of Health and Rehabilitation Sciences.
Andreas Coppi is an Associate Research Scientist in the Department of Cardiovascular Medicine at Yale School of Medicine, with affiliations in the Center for Outcomes Research & Evaluation (CORE) and Internal Medicine. He is actively engaged in research at the intersection of artificial intelligence and cardiovascular health. Research Interests: His work focuses on leveraging machine learning and deep learning to improve cardiovascular diagnostics and outcomes. Key areas include AI-driven analysis of electrocardiograms and echocardiograms for early detection of structural heart disease, heart failure, and cardiomyopathies. He also contributes to innovative research in long COVID and digital health applications. Publication Trends: His recent publications demonstrate a strong trend in developing and validating AI models using real-world clinical data, particularly ECGs and imaging. These models aim to enhance screening, risk stratification, and early diagnosis across diverse patient populations. Scientific Collaborations: He frequently collaborates with leading researchers including Harlan Krumholz, Rohan Khera, and Akiko Iwasaki, contributing to high-impact studies published in journals such as The Lancet Digital Health , JAMA Cardiology , and European Heart Journal - Digital Health . Advising and Grants: While no formal students are listed, his role in large collaborative trials and AI development suggests involvement in mentoring and team-based research. He is likely supported by institutional and federal grants related to cardiovascular outcomes and AI in medicine, though specific funding is not detailed. Laboratories and Teams: He is associated with research teams at the Center for Outcomes Research & Evaluation (CORE), focusing on data science applications in cardiology and patient-centered outcomes.
Sébastien Ott is a Lecturer in the Department of Mathematics at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with both the SB-SMA-SMA-ENS and SB-MATH-MATH-GE units. He contributes to teaching courses such as Probability and Statistics and Large Deviations , focusing on mathematical foundations of statistical physics. Research interests centered on Statistical Mechanics and Probability Theory , particularly in systems like the Ising and Potts models. His work investigates critical phenomena, correlation decay, interface behavior, and large deviation principles. Articles often address topics such as Ornstein-Zernike asymptotics, phase transitions, and spatial mixing properties. His publications reveal a focus on Ising/Potts model phase transitions Large deviations and fluctuation regimes Surface localization/delocalization Renormalization group applications Correlation length analyticity with connections to mathematical physics and percolation theory.
Yi-Qian Sun is a Senior Researcher in the Department of Clinical and Molecular Medicine at the Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU). Her work focuses on epidemiology, causal inference, and the interplay between lifestyle factors (e.g., vitamin D, body mass index) and chronic diseases such as cancer, dementia, and oral health conditions. PhD in Medicine, Linköping University, Sweden (2004) Medical Degree, Capital Medical University, China (1992) Her research combines genetic epidemiology, epigenetics, and biostatistical modeling to investigate causal relationships between risk factors and disease outcomes. She has led projects on vitamin D as a biomarker for lung cancer, causal links between oral health and systemic health, and Mendelian randomization studies for lifestyle-disease associations. Recent publications highlight her focus on vitamin D metabolism, obesity-related disease risks, and oral-systemic health connections. Her work spans population-based (HUNT Study) and clinical research, utilizing databases like UK Biobank for cross-cohort analyses. Prof. Sun collaborates internationally and applies advanced statistical tools (Stata, R) for study design and data analysis. She leads the HUNT4 Oral Health Study within the GLIDE2 Oral Health Genomics Consortium.
