جست‌وجوی استاد و پژوهشگر

Benedikt Jahnelمشاهده پروفایل

استاد

• Statistical Mechanics
• Stochastic Geometry
• Spatial Epidemiology
• +۳ مورد دیگر

Prof. Dr. Benedikt Jahnel is a Professor at the Institute of Mathematical Stochastics, Carl Friedrich Gauss Faculty, Technical University of Braunschweig, and Head of the Leibniz Junior Research Group on Probabilistic Methods for Dynamic Communication Networks. His research focuses on the modeling and analysis of spatially embedded systems with interacting random components, with applications in physics, epidemiology, and telecommunications. Education: PhD in Organismic and Evolutionary Biology from Ruhr University Bochum (2011), Diploma from Technical University of Berlin. Research interests: Uses tools from statistical mechanics and stochastic geometry to study phase transitions, percolation theory, and spatial random processes. Current projects investigate continuum percolation, interacting particle systems, and probabilistic methods for communication networks. Recent publications demonstrate strong emphasis on spatial stochastic processes and percolation theory, with applications to network connectivity and epidemic modeling. Trends include mathematical analysis of phase transitions in random environments and dynamics of communication networks. Scientific awards include leadership of Leibniz Junior Research Group, EURANDOM Ambassador appointment, and election to the board of the Probability and Statistics Group of the German Mathematical Society. Leads a research team including 3 postdocs and 3 predoctoral researchers. Current projects funded by DFG, ERC, and Math+ Cluster of Excellence. Heads the DYCOMNET research group at Weierstrass Institute Berlin, focusing on dynamic spatial random systems.

Jing Wangمشاهده پروفایل

استاد

• Biostatistics
• Epidemiology
• Diabetes Research
• +۶ مورد دیگر

Jing Wang, PhD, is a Professor of Biostatistics and Neurological Surgery at Washington University in St. Louis. She serves as Director of the Biostatistics Consulting Service and holds affiliations with the Institute of Clinical and Translational Sciences (ICTS) and the Center for Biostatistics and Data Science (CBDS). Her research spans biostatistical methodologies, epidemiological studies, and translational medicine with a focus on diabetes, immunology, and endocrine disorders. Dr. Wang’s work integrates statistical innovation with biomedical applications, including machine learning approaches for clustered data analysis and ciliary mechanisms in pancreatic islet function. Her research interests include advanced statistical modeling, public health disparities, and molecular mechanisms underlying chronic diseases. Notable contributions include studies on survival disparities in cancer patients, post-COVID-19 conditions, and the role of primary cilia in glucose regulation and hormone secretion. She has published over 97 peer-reviewed articles, with recent work emphasizing diabetes pathophysiology, heart failure phenotypes, and ciliary signaling pathways in endocrine cells. Dr. Wang leads interdisciplinary teams in designing robust statistical frameworks for clinical research and has pioneered methodologies for analyzing complex biomedical datasets. Her work on pancreatic islet cilia has advanced understanding of cellular signaling in metabolic disorders, while her epidemiological studies address health inequities in vulnerable populations. Collaborations span global institutions, focusing on translational research with clinical impact.

• Biostatistics
• Infectious Disease Modeling
• Epidemiology
• +۴ مورد دیگر

Associate Professor Tim Schlub is the Associate Dean (Education) and Associate Professor of Biostatistics at the University of Sydney's Faculty of Medicine and Health. His research focuses on applying statistical methods to understand infectious disease dynamics, vaccine efficacy, and genetic disorders such as neurofibromatosis. Key areas include HIV persistence, SARS-CoV-2 variants, and cancer genomics. He leads studies on viral dissemination mechanisms, 3D imaging for neurofibromas, and public health interventions for multidrug-resistant tuberculosis. His work bridges clinical trials and epidemiological modeling, with recent contributions to understanding antibody responses and booster strategies for emerging pathogens. Grants: 2018: Peer feedback mechanisms in education innovation 2016: Cancer genomics and psychosocial outcomes His publications analyze viral latency, vaccine effectiveness, and imaging technologies. He collaborates on global health initiatives and contributes to clinical pathway optimization for cancer patients' mental health management. Labs/Teams: Collaborates with multidisciplinary groups in virology, biostatistics, and global health, though specific lab names are not explicitly mentioned in the text.

Elizabeth Halloranمشاهده پروفایل

استاد

• Epidemiology
• Vaccine Development
• Infectious Disease Modeling
• +۴ مورد دیگر

Dr. Elizabeth 'Betz' Halloran is a Professor in the Department of Biostatistics and Epidemiology at the University of Washington and an Adjunct Professor in Applied Mathematics. She leads the Biostatistics, Bioinformatics, and Epidemiology (BBE) Program at Fred Hutchinson Cancer Research Center's Vaccine and Infectious Disease Division. Her roles include directing the Summer Institute in Statistics and Modeling in Infectious Diseases (SISMID) and the Center for Inference and Dynamics of Infectious Diseases (CIDID), which advises federal responses to outbreaks. Affiliations: School of Public Health, University of Washington; Fred Hutchinson Cancer Research Center Education: MD (Freie Universität Berlin), MPH and DSc (Harvard University), BS (University of Oregon) Her research focuses on vaccine trial design, causal inference in infectious diseases, and modeling strategies to mitigate outbreaks of threats like Ebola, Zika, and influenza. She has pioneered methods for evaluating direct and indirect vaccine effects, contributing to global public health strategies. Key Achievements: Elected to the National Academy of Medicine (2019) Recipient of the Nathan Mantel Lifetime Achievement Award (2019) Developed statistical frameworks for analyzing vaccine efficacy in populations Her work spans collaborations with institutions like NIH, WHO, and universities worldwide. She advises on pandemic preparedness, including modeling SARS-CoV-2 transmission and vaccine strategies during the COVID-19 pandemic.

Spyridoula Vazouمشاهده پروفایل

دانشیار

• Exercise Psychology
• Physical Activity and Cognition
• Youth Physical Activity Motivation
• +۳ مورد دیگر

Spyridoula Vazou is an Associate Professor in the Department of Kinesiology at Michigan State University's College of Education. Her work focuses on exercise psychology and pedagogy, emphasizing physically active environments to enhance learning and cognitive function in youth. She holds a Ph.D. from the University of Birmingham, UK. Her research explores strategies to motivate physical activity through qualitative interventions, integrating movement with academics. Key areas include school wellness programs like SWITCH, classroom-based physical activity, and the cognitive benefits of rhythmic activities like traditional dance. Publications reflect interdisciplinary approaches, combining neuroscience, education, and public health. Vazou's work addresses both acute and chronic effects of physical activity on executive functions, with a focus on lifespan development and intervention efficacy. She has contributed to policy frameworks for school-based physical activity programs and evaluated implementation strategies for community health initiatives.

Irene Kyomuhangiمشاهده پروفایل

پژوهشگر ارشد

• Geospatial Statistics
• Malaria Epidemiology
• Hepatitis D Research
• +۳ مورد دیگر

Dr. Irene Kyomuhangi is a Senior Research Associate in Geospatial Statistical Methods at Lancaster Medical School, part of Lancaster University's Faculty of Health and Medicine. Her research focuses on applying advanced statistical methodologies to public health challenges, particularly malaria and hepatitis D epidemiology. She leads projects analyzing intervention efficacy, such as the Attractive Targeted Sugar Bait (ATSB) trials in Zambia, and geospatial modeling of disease prevalence. Key research interests include vector control strategies, threshold-free statistical frameworks for seroprevalence estimation, and evaluating national healthcare policies. She collaborates with global health organizations to design evidence-based interventions and has contributed to systematic reviews on hepatitis D and malaria diagnostics. Member of CHICAS (Health Innovation and Modelling) and DSI-Health research groups Expertise in spatial epidemiology, survival analysis, and healthcare market surveys Recent work includes assessing Zambia’s vector control micro-planning and studying insecticide resistance patterns in Uganda. Her publications bridge statistical innovation with real-world public health applications.

Raul Mondragonمشاهده پروفایل

مدرس ارشد

• Complex Networks
• Network Science
• Telecommunications
• +۳ مورد دیگر

Dr. Raul Mondragon is a Senior Lecturer in the School of Electronic Engineering and Computer Science at Queen Mary University of London. He holds an MSc and PhD, and is a Senior Member of the IEEE (SMIEEE). His research focuses on complex network analysis, including topics like network cores, rich-club phenomena, and applications in telecommunications and social media platforms. He has contributed to understanding internet topology, network robustness, and Web3 technologies. He teaches modules on analogue electronics and network security, and has supervised interdisciplinary PhD students in areas such as energy-efficient networks and dynamical modeling. His work includes grants like the EPSRC-funded RAVEN project (2010-2013) on complex energy networks. Mondragon is affiliated with the Centre for Networks, Communications and Systems, where he explores network science applications in diverse domains. Affiliations: School of Electronic Engineering and Computer Science, Centre for Networks, Communications and Systems Education: PhD and MSc (specific fields and institutions not explicitly stated) Research Interests: Dr. Mondragon’s work spans theoretical and applied network science. He investigates structural properties of networks (e.g., rich-club effects, core-periphery organization) and their implications for system resilience. His research applies to telecommunications, social media, and ecological systems. Notable contributions include analyzing cryptocurrency transaction networks, Web3 platform moderation, and drought impacts on food web structures. Teaching: Dr. Mondragon instructs undergraduate courses on analogue electronics and postgraduate/undergraduate modules on network security and authentication principles. His teaching emphasizes foundational circuit theory and cryptographic techniques for secure communications. Grants: Principal Investigator (PI) for the EPSRC RAVEN project (£355,446), focusing on complex energy networks’ resilience and adaptability. Students: Supervised PhD candidates include Mr. Iftekharul Mobin (energy-efficient ad-hoc networks), Mr. Sabri Zaman (functional topology), and interdisciplinary students like Mr. Gibson Ikoro (chat room dynamics) and Mr. Massimo Cavallaro (stochastic processes). Labs/Teams: Active in the Centre for Networks, Communications and Systems, collaborating on projects involving network modeling, social media analysis, and interdisciplinary applications.

Esubalew Assefaمشاهده پروفایل

پژوهشگر

• Health Economics
• Health Policy Evaluation
• Economic Evaluation
• +۷ مورد دیگر

Dr. Esubalew Assefa is a Health Economist at the Health Economics and Policy Research Unit (HEPRU) and Pragmatic Clinical Trials Unit (PCTU) within Queen Mary University of London's Centre for Evaluation and Methods (CEM). He also serves as a methods advisor (health economics) for the NIHR Research Support Service (RSS) Hub, collaborating with Imperial College London and other institutions. Previously, he was a postdoctoral fellow at the Centre for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), focusing on multimorbidities in low- and middle-income countries and enhancing research-to-policy translation. His research interests include outcome assessment, economic evaluation, health technology assessment, policy evaluation, evidence synthesis, and healthcare financing. Dr. Assefa has experience in teaching economics and program management in higher education. Research Trends: His work spans global health challenges, particularly in Ethiopia and Southeast Asia, addressing mental health interventions, neglected tropical diseases, healthcare access disparities, and pandemic socioeconomic impacts. He emphasizes evidence-based approaches to policy and sustainable academic partnerships. Recent studies include randomized trials on depression detection, cost-effectiveness analyses of mental healthcare models, and scoping reviews on research uptake in low-resource settings. Advising & Grants: While no specific grants or trainees are listed, his advisory role in the NIHR RSS Hub indicates involvement in methodological guidance for health economics research. Collaborations with institutions like CDT-Africa highlight his commitment to translational research in global health contexts. Labs/Teams: Affiliated with HEPRU, PCTU, and the Wolfson Institute of Population Health, he contributes to multidisciplinary teams focused on population health challenges and clinical trial methodologies.

• Fuzzy systems
• Computational mathematics
• Numerical optimization
• +۵ مورد دیگر

Professor Gleb Beliakov is a faculty member at Deakin University's School of Information Technology within the Faculty of Science Engineering and Built Environment. He has held roles including Associate Head of School (Research) from 2009–2012 and currently leads the Computational Intelligence and Data Analytics Research cluster and the Data to Intelligence research centre. His expertise spans fuzzy systems, computational mathematics, numerical optimization, and high-performance computation. Education: PhD and MSc from the Russian Peoples Friendship University. Research Interests: Focuses on fuzzy systems, aggregation functions, parallel programming, and their applications in decision-making and data analysis. Grants: Includes projects on Trusted Autonomy (CRC), Satellite Communication Systems (Defence), and Explainable AI (ARC). His research emphasizes fuzzy measures, Choquet integrals, and their use in addressing complex problems like resource optimization and multimedia piracy. He has supervised numerous PhD and master's students, contributing to advancements in preference mining, image processing, and watermarking techniques.

Fabien HELIOTمشاهده پروفایل

مدرس

• Wireless Communications
• Signal Processing
• MIMO Systems
• +۵ مورد دیگر

Fabien HELIOT is a Lecturer in Wireless Communications at the University of Surrey's School of Computer Science and Electronic Engineering, affiliated with the Institute for Communication Systems. His research focuses on wireless communication theory, signal processing for MIMO systems, electromagnetic exposure reduction, and visible/terahertz communications. He leads modules like Applied Mathematics for Communication Systems and chairs the EEE Student/Staff Liaison Committee. His awards include an IEEE Senior Membership and Advance HE Fellowship. Research highlights include projects on MIMO-RIS capacity analysis, EMF exposure measurement in 5G, and waveform design for terahertz systems. He has supervised 8 PhD students and contributed to over 100 peer-reviewed publications. Notable collaborations include EU-funded projects (LEXNET, EARTH) and industry partnerships. His work bridges theoretical analysis and practical implementations, with contributions to standards and patents like the pDCO-OFDM waveform. Teaching responsibilities include courses on 5G, mobile communications, and electronic laboratories. He has examined 18 PhD vivas and supervised 40+ student projects. His research also addresses energy-efficient communication and machine learning applications in network automation, reflecting a commitment to both academic rigor and real-world impact.

Rob Oxobyمشاهده پروفایل

استاد

• Behavioral Economics
• Experimental Economics
• Applied Microeconomic Theory
• +۳ مورد دیگر

Rob Oxoby is a Professor in the Department of Economics at the University of Calgary's Faculty of Arts. He holds a PhD in Economics from the University of California (2000), an M.A. in Economics from San Jose State University (1992), and a B.S. in Business Administration from the University of Santa Clara (1989). His research focuses on Behavioral Economics, Experimental Economics, and Applied Microeconomic Theory, with recent emphasis on pandemic-related behavioral responses, public health policy effectiveness, and decision-making under uncertainty. Education: Ph.D. Economics, University of California, 2000 M.A. Economics, San Jose State University, 1992 B.S. Business Administration, University of Santa Clara, 1989 His research explores how cognitive biases, social preferences, and contextual factors influence economic decisions. Recent work includes analyzing public compliance with pandemic measures, vaccine hesitancy, and experimental methods to study risk-taking and intertemporal choice. He has conducted cross-sectional surveys on non-pharmaceutical intervention adoption and virtual focus groups on health messaging strategies. His experimental research extends to topics like asset integration, sentencing decisions, and performance systems' cognitive impacts. Publications Trends: Over 60% of his recent work (2020–2023) addresses pandemic-related behavioral economics challenges. His experimental methodologies investigate policy compliance, vaccine adoption barriers, and health communication efficacy. Scientific Awards: No awards explicitly listed. Advising & Grants: No specific advising/grant details provided in the profile. His research is supported through institutional and experimental platforms. Research Infrastructure: Leverages laboratory experiments, cross-sectional surveys, and digital trace data analysis for health and behavioral studies.

• Econometrics
• Panel Data Analysis
• Stochastic Modeling
• +۲ مورد دیگر

Louise Laage is an Assistant Professor in the Department of Economics at Georgetown University, specializing in econometrics. Her research focuses on advanced econometric methodologies, including panel data analysis, stochastic modeling, and nonparametric techniques. She develops statistical tools for applied economics, such as the MFELOGIT Stata module for estimating marginal effects in fixed-effect logit models. Her recent work addresses topics like time-varying endogeneity in panel models, stochastic block models with covariates, and identification strategies in fixed-effect frameworks. These contributions bridge theoretical econometrics and practical applications in policy analysis and social sciences. Laage's publications span journals and modules since 2018, reflecting her expertise in both methodological innovation and software implementation. She holds no listed awards or advisees, but her work demonstrates a strong focus on advancing quantitative research tools in economics. Contact: louise.laage@georgetown.edu

• Epidemiology
• Public Health
• Cardiovascular Risk Factors
• +۴ مورد دیگر

Linda O'Keeffe is a Senior Lecturer in Epidemiology at University College Cork (UCC), Ireland. She holds a BSc in Public Health (1st class honours) and a PhD in Epidemiology from UCC. Her research focuses on cardiovascular risk trajectories, maternal and child health, and life course epidemiology. She leads the Epidemiology Methods Cluster and the LEAP Group at UCC, and serves as European Regional Councillor for the International Epidemiological Association. O'Keeffe has secured over €1.8M in grants as Principal Investigator, including an HRB Emerging Investigator Award and MRC Fellowship. Education: BSc Public Health (UCC, 2010) PhD Epidemiology (UCC, 2014) PgCert Teaching & Learning in Higher Education (UCC, 2021) Research Interests: Her work examines sex-specific cardiometabolic trajectories, maternal alcohol use, and socioeconomic determinants of health. Key areas include puberty timing effects, obesity genetics, and systematic reviews on health disparities. She leads large-scale studies like the ROLO birth cohort and the SocialPaths project. Grants & Awards: HRB Emerging Investigator Award (€699,252, 2020) MRC UK Strategic Skills Fellowship (£442,976, 2015-2019) HRB Investigator-Led Project (€357,507, 2022) Teaching: Coordinates the MPH module EH6031 Applied Epidemiology and teaches EH2010 Epidemiology II. Supervises PhD/MPH students in public health and epidemiology. Key Contributions: Published over 100 peer-reviewed papers, including landmark studies on breastfeeding's cardiovascular benefits and sex differences in metabolic risk. Leads international collaborations on health policy and systematic reviews.

Zhirong Yangمشاهده پروفایل

استاد

• Machine Learning
• Information Visualization
• Data Representation Learning
• +۶ مورد دیگر

Zhirong Yang is a Professor at the Department of Computer Science at the Norwegian University of Science and Technology (NTNU). He is affiliated with the Norwegian Open AI Lab and focuses on machine learning, information visualization, and data representation learning. His work integrates techniques like neural attention models, nonnegative matrix factorization, and dimensionality reduction algorithms such as Neighbor Embedding (NE) for visualization. Yang's research has applications in bioinformatics, medical imaging, and engineering systems. His educational background includes advanced studies in pattern recognition and computational methods, though specific details are not provided. Research collaborations span institutions globally, including work on bacterial population genomics and chromosome classification. Key contributions include scalable optimization frameworks for visualization (NE) and novel clustering algorithms using graph random walks and low-rank matrix decompositions. Yang has developed software tools like ChordMixer for sequence processing and HSSNE for robust data visualization. His recent publications emphasize advancements in attention mechanisms, sparse factorization, and medical diagnostics. He actively contributes to open-source projects and collaborates with industry on applications like subsea pipeline inspection and energy scheduling systems.

• Enumerative combinatorics
• permutations
• bijections
• +۷ مورد دیگر

Sergi Elizalde is a Professor of Mathematics at Dartmouth College, affiliated with the Department of Mathematics within the Arts & Sciences school. He holds the position of Faculty Advisor for the Byrne Scholars Program. Education: B.A., Universitat Politècnica de Catalunya Ph.D., Massachusetts Institute of Technology Research Interests: Elizalde specializes in enumerative and algebraic combinatorics, with a focus on permutations, pattern avoidance, lattice paths, bijections, generating functions, and applications to computational biology and dynamical systems. His work bridges combinatorial theory with interdisciplinary fields like cancer modeling through Markov chain models of chromosomal instability. Teaching & Mentoring: Elizalde has taught courses in graph theory, algebraic combinatorics, and calculus. He currently mentors several Ph.D. students and has advised numerous former students who have completed their doctoral degrees under his supervision. Professional Contributions: He maintains a personal research website and collaborates on projects such as the implementation of Markov chain models for studying tumor evolution. His research has been featured in high-impact journals like Nature and Advances in Applied Mathematics .