جست‌وجوی استاد و پژوهشگر

• Probability Theory
• Random Graphs
• Preferential Attachment Models
• +۳ مورد دیگر

Dr. Jonathan Jordan is a Senior Lecturer in Probability at the University of Sheffield's School of Mathematical and Physical Sciences, affiliated with the Probability Group within the School of Mathematics and Statistics. He holds a DPhil from the University of Oxford, following studies at the University of Cambridge. His research focuses on probability theory, particularly random graphs (including preferential attachment models), reinforced processes, and fractal graphs. He teaches courses such as MAS113 (Introduction to Probability and Statistics) and MAS223 (Statistical Modelling and Inference). His work explores geometric preferential attachment networks, phase transitions, and multi-type network dynamics. He has published extensively in journals like the Electronic Journal of Probability and Random Structures and Algorithms . His research has addressed topics such as condensation phenomena, non-convergence in multi-type networks, and spatial network models. Dr. Jordan's research group is part of the Probability Group at Sheffield, and his work has applications in theoretical computer science and complex systems. He has supervised PhD topics involving random graph models and has collaborated on projects like the Amorph initiative. His academic background includes a shift from Bristol to Oxford during his PhD, followed by his current role at Sheffield since 2003. His research interests also extend to fractal geometry, including spectral properties of fractal graphs and spectral decimation techniques. Teaching responsibilities include undergraduate courses in probability and statistics, reflecting his expertise in foundational mathematical concepts. His publications span over two decades, showcasing contributions to both theoretical and applied aspects of random networks and stochastic processes. He maintains an active research profile, with ongoing interests in network evolution, geometric models, and interdisciplinary applications.

Cong Gaoمشاهده پروفایل

استاد

• Data Science
• Geospatial data management
• Spatio-temporal data mining
• +۷ مورد دیگر

Cong Gao is a Professor and Head of the Division of Data Science at Nanyang Technological University's College of Computing & Data Science. He also holds a courtesy appointment with the School of Physical & Mathematical Sciences. Previously, he served as an Assistant Professor at Aalborg University, Denmark, and worked as a researcher at Microsoft Research Asia. He co-directs the Singtel Cognitive and Artificial Intelligence Lab for Enterprises@NTU (SCALE@NTU). His educational background includes: Ph.D. in Computer Science from National University of Singapore (2004) Master of Engineering from Tianjin University, China (1999) Bachelor of Engineering from Tianjin University, China (1996) Professor Gao's research focuses on Data Science, with particular expertise in geospatial data management, spatio-temporal data mining, recommendation systems, and social media data analysis. His work has significantly impacted areas like spatial-textual indexing, point of interest recommendation, and mining social networks. He has published extensively in top venues including VLDB, SIGMOD, ICDE, KDD, and WSDM, with over 14,000 citations and an H-index of 61. His recent publications demonstrate strong trends in applying machine learning to database systems, with particular focus on spatial and trajectory data management. Key research directions include learned indexing techniques, trajectory data analysis, and integrating large language models with database systems for improved query optimization. Professor Gao has received notable scientific recognition including: Best paper runner-up award at WSDM'22 Best paper award runner-up at WSDM 2020 He has advised numerous students who have become significant contributors in their own right, including Xin Cao, Lisi Chen, Kaiyu Feng, and Kaiqi Zhao. His research has been supported by substantial grants from Ministry of Education, NRF, IAF, Singtel/NCS, Roll-Royce, Alibaba, and Microsoft, including a S$42.4 million funding over 5 years for the SCALE@NTU lab. Professor Gao leads the Data Management Research Group (DANTE) and co-directs the Singtel Cognitive and Artificial Intelligence Lab for Enterprises@NTU (SCALE@NTU), which develops market-leading AI and data science technologies.

• Public Health
• Global Health
• Epidemiology
• +۵ مورد دیگر

Professor James Lewis is a faculty member in the School of Social Sciences at Cardiff University and a co-investigator at the DECIPHer Centre. He leads international research initiatives, including the £2.9m HCRW Sustainability Funding (2025-30) and the £5m ESRC-funded Local Policy and Innovation Partnership for Rural Wales. Previously, he directed the Cardiff University Y Lab (2018-2023), which attracted £12m in funding and won a Cardiff University Excellence Award for Innovation (2022). His research focuses on public health challenges, including tuberculosis and HIV control, health system innovation, and global health interventions. Education: PhD in Epidemiology (Imperial College London, 2005) MSc in Applied Statistics (University of Oxford, 2000) BA in Mathematics (University of Cambridge, 1999) Research Interests: Professor Lewis specializes in pragmatic evaluations of health system innovations, particularly in TB and HIV. His work emphasizes intervention design, implementation science, and the integration of digital tools in healthcare. He has led studies in sub-Saharan Africa, South Asia, and South America, addressing issues like medication adherence, TB preventive therapy, and social determinants of health. Recent projects include evaluating mobile health (mHealth) adherence support for HIV patients in the Philippines and assessing household TB contact tracing in South Africa. Awards & Grants: £29m in total grant funding, including £6.5m as principal investigator 2022 Cardiff University Excellence in Innovation Award Advising & Teams: Supervised PhD students: Nicole Gelfert, Kavi Velen, Cara O'Connor Leads DECIPHer’s Executive Management team Collaborates with global partners (e.g., LSHTM, World Bank) Labs/Teams: DECIPHer Centre: Focuses on complex public health interventions Y Lab: Specializes in public services innovation

Regine Gilbertمشاهده پروفایل

استادیار

• Digital Accessibility
• Inclusive Design
• Immersive Experiences
• +۴ مورد دیگر

Regine Gilbert holds dual roles as an Industry Assistant Professor and James Weldon Johnson Professor at the Department of Technology, Culture, and Society within NYU Tandon School of Engineering. Her work bridges academia and industry through research focused on digital accessibility, inclusive design, and immersive technologies. She actively contributes to advancing accessibility standards in emerging media and technology sectors. Her research emphasizes creating universally accessible digital environments, with a particular focus on applying inclusive design principles to virtual reality (VR), augmented reality (AR), and other immersive experiences. Dr. Gilbert’s expertise spans compliance with accessibility standards like WCAG 2.1, user-centered design methodologies, and the ethical implications of technology adoption. In 2019, she authored Inclusive Design for a Digital World , a foundational text on accessibility practices. She is also collaborating on an upcoming book Human Spatial Computing with Doug North Cook, scheduled for 2025. Her work has been featured in media outlets like Forbes and Medium, reflecting her role as a thought leader in accessibility innovation. Key achievements include her James Weldon Johnson Professorship appointment and leadership in NYU Tandon’s efforts to integrate accessibility into technology curricula. Her speaking engagements at institutions like Stanford University and global conferences highlight her commitment to disseminating inclusive design knowledge.

W. (Weining) Wangمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• High-Dimensional Time Series
• +۴ مورد دیگر

Prof. W. (Weining) Wang is a Professor at the Faculty of Economics and Business, University of Groningen. His research focuses on econometrics, time series analysis, and high-dimensional data modeling. He has collaborated with notable scholars such as Jeffrey Wooldridge and Wei Biao Wu, producing influential work in statistical methodologies for dynamic systems, spatial data, and financial econometrics. His recent contributions include advancements in quantile regression models, change-point detection in time series, and generalized estimating equations for nonlinear models. Publications span prestigious journals like the Journal of Time Series Analysis , Annals of Statistics , and Journal of Business and Economic Statistics . Contact: weining.wang@rug.nl . Research interests emphasize methodological innovations in econometric theory, with applications to economics, finance, and spatial systems. Ongoing work includes dynamic network quantile models and high-dimensional time series inference.

Abdollah Jalilianمشاهده پروفایل

پژوهشگر

• Spatio-temporal Epidemiology
• Epidemiology
• Biostatistics
• +۳ مورد دیگر

Dr. Abdollah Jalilian is a Research Associate in Spatio-temporal Epidemiology at Lancaster Medical School, part of Lancaster University's Faculty of Health and Medicine. His work focuses on developing statistical methodologies for analyzing spatiotemporal patterns in epidemiological data. Research Interests: His research integrates advanced statistical techniques with epidemiological studies, particularly in understanding disease spread dynamics and public health interventions. He applies spatial and temporal modeling to address challenges in healthcare analytics and infectious disease surveillance. Publications: His recent work includes a 2025 article in Biometrics , advancing composite likelihood methods for space-time point processes, which has implications for real-time disease monitoring systems. Consultancy: He contributed to the Evaluation of Virtual Ward deployment in NHS Trusts, demonstrating practical applications of his research in healthcare optimization. Advising & Grants: No specific advising or grant information is publicly listed here. Labs/Teams: Affiliated with Lancaster Medical School's research groups focused on health data science and epidemiological modeling.

Can Liuمشاهده پروفایل

پژوهشگر ارشد

• Social Epidemiology
• Maternal and Child Health
• Health Inequalities
• +۳ مورد دیگر

Can Liu is a Research Fellow at the Department of Public Health Sciences, Stockholm University. Their work focuses on perinatal social epidemiology, leveraging transdisciplinary skills in statistical methods and epidemiological designs to address health inequalities. Liu holds a PhD from Karolinska Institutet (2017) and completed postdoctoral training at Stanford University (2019-2021) and Karolinska Institutet (2017-2019). They lead the FORTE-funded ORCHID project (2024-2026), examining how parental disability impacts children's outcomes. Active projects include studying pandemic mental health consequences, intergenerational health inequities, and health disparities in cancer screening among disabled populations. Research interests emphasize maternal and child health in disadvantaged populations, policy interventions to reduce inequities, and systems-level factors driving health disparities. Collaborations include the Reproductive, Perinatal, and Pediatric Epidemiology group at Karolinska Institutet's Clinical Epidemiology Division. Current projects explore bereavement impacts, child welfare service drivers, and political responses to health inequities during the pandemic. Publications span environmental epidemiology, with recent work on Arctic carbon cycles and permafrost dynamics, reflecting interdisciplinary engagement. Awards and grants include FORTE funding for the ORCHID initiative.

Miles Hansardمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Robotics
• 3D Displays
• +۷ مورد دیگر

Miles Hansard is a Senior Lecturer in the School of Electronic Engineering & Computer Science at Queen Mary, University of London. He is affiliated with the Vision Group and the ARQ Centre for Advanced Robotics. His research focuses on computer vision, robotics, and 3D imaging technologies, including depth cameras, binocular vision, and scene reconstruction. He has contributed to projects like the GRIP Software Framework for robotics integration and Gaussian Process models for UAV localization. His work spans medical imaging applications, such as analyzing eye movements after vision loss, and explores discomfort factors in stereoscopic displays. Hansard has published extensively in journals like IEEE Transactions and the Royal Society Proceedings, and his research often addresses sensor fusion, calibration, and computational photography. He holds a primary email address at miles.hansard@qmul.ac.uk.

Georgy Sofronovمشاهده پروفایل

دانشیار

• Statistical Analysis
• Operations Research
• Change-Point Detection
• +۶ مورد دیگر

Georgy Sofronov is an Associate Professor in Statistics and Director of Research Training at Macquarie University's School of Mathematical and Physical Sciences. He holds a PhD in Probability Theory and Mathematical Statistics from Moscow State University (2002). Previously, he held academic positions at the University of Queensland and the University of Wollongong. He serves on editorial boards for Statistical Papers and Methodology and Computing in Applied Probability , and is Treasurer of the Modelling and Simulation Society of Australia and New Zealand. His research focuses on change-point detection, statistical quality control, optimal decision rules, and computational statistics. Research Interests : Change-point problem, statistical quality control Optimal decision rules, operations research Markov chain Monte Carlo methods, Cross-Entropy method Statistical modelling and data analysis Statistical Software (R-package 'breakpoint') Affiliations : Biomolecular Discovery Research Centre Centre for Risk Analytics Data Horizons Research Centre Smart Green Cities Research Centre Transforming Energy Markets Research Centre Teaching : Courses include Statistical Theory , Probability , Market Research and Forecasting , and Statistical Inference . Grants & Projects : SmartCrete CRC (2020–2027) Multiple change-point detection methods for big data (2017–present) Awards : Learning and Teaching Award (2016) Award for Excellence in HDR Supervision (2021) Collaborations : Active in Australia, New Zealand, and international networks focusing on computational statistics and risk analytics.

• Statistical Ecology
• Wildlife Population Dynamics
• Bayesian Statistics
• +۴ مورد دیگر

Eleni Matechou is an Honorary Academic in Statistics at the University of Kent, affiliated with the School of Mathematics, Statistics and Actuarial Science. Her academic background includes a BSc in Statistics from Athens University of Economics and Business, an MSc in Statistics with Applications in Medicine from the University of Southampton, and a PhD jointly conducted at the University of Kent and the Max Planck Institute for Demographic Research. She has held positions such as Research and Teaching Fellow at Victoria University of Wellington, Departmental Lecturer at the University of Oxford, and is currently a member of the Statistical Ecology @ Kent (SE@K) group and the National Centre for Statistical Ecology (NCSE). Her research focuses on statistical ecology, particularly developing models for wildlife populations, migration patterns, and environmental DNA (eDNA) monitoring. Key areas include Bayesian and frequentist approaches to capture-recapture models, mixture models, and integrating citizen science data for ecological inference. She is actively involved in methodological advancements for biodiversity assessment and conservation, emphasizing the use of innovative technologies like robotics and eDNA. Professionally, Eleni chairs the Environmental Statistics Section of the Royal Statistical Society and serves as Book Reviews Editor for the Journal of Agricultural, Biological and Environmental Statistics . Her work bridges statistical rigor with ecological applications, addressing challenges in monitoring biodiversity and informing conservation strategies. Collaborations span institutions globally, reflecting her interdisciplinary approach to solving ecological problems.

Jim Jiمشاهده پروفایل

استاد

• Magnetic Resonance Imaging (MRI)
• Biomedical Image Processing
• Constrained Imaging
• +۷ مورد دیگر

Jim Ji is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. His research focuses on advanced MRI and biomedical image processing, including constrained imaging, reconstruction algorithms, cardiac and cancer imaging, and image analysis techniques. He leads the Magnetic Resonance Systems Lab, developing 3D real-time imaging capabilities and automated analysis methods. Education: PhD from University of Illinois at Urbana-Champaign (2003), M.S. and B.S. from Tsinghua University (1997/1993). Active in teaching courses like Signals and Systems, Medical Imaging, and Digital Signal Processing. Supervised over 12 graduate students, including notable alumni like Jong Bum Son and Yuttapong Jiraraksopakun. Developed open-source tools: PULSAR (parallel imaging toolbox) and SID-MRI (dynamic MRI simulation) Received NSF CAREER Award (2008) and Texas A&M travel grants Organized sessions at IEEE EMBS conferences (2003-2005) Recent publications emphasize numerical methods for radiation transport, including diffusion asymptotics, model reduction, and parallel computing solutions. His work bridges computational physics with medical imaging, addressing challenges in dynamic biological imaging and radiation-hydrodynamics coupling.

• Recommender Systems
• Artificial Intelligence
• Data Mining
• +۶ مورد دیگر

Dr. Yongli Ren is an Associate Professor in the School of Computing Technologies at RMIT University, located at the City Campus in Australia. His research focuses on advancing Recommender Systems, with particular emphasis on fairness, quantum computing optimization, and spatio-temporal data analysis. He holds expertise in data mining, collaborative filtering, and context-aware systems. Dr. Ren is open to supervising Masters and PhD students in areas such as quantum annealer-based optimization, fairness-aware recommendation, and heterogeneous time-series analysis. His teaching interests include web search algorithms, collaborative filtering, data mining methodologies, and log analysis. Dr. Ren has contributed to over 70 research outputs, with recent works addressing recommendation system evaluation metrics, fairness in AI, and energy-efficient routing protocols in wireless sensor networks. His research has been published in prestigious venues like ACM Transactions on Information Systems, IEEE Access, and the ACM Web Conference (WWW). Key research themes include addressing the precision-diversity trade-off in recommendations, enhancing fairness through dual-temporal networks, and leveraging quantum computing for QUBO optimization. He has also explored applications in mobility pattern analysis, workplace productivity systems, and fog-based distributed recommendations.

Stuart Sandinمشاهده پروفایل

استاد

• Coral Reef Ecology
• Fish Ecology
• Conservation Biology
• +۷ مورد دیگر

Stuart Sandin is a Professor in Marine Biology at Scripps Institution of Oceanography, University of California, San Diego. His research focuses on the structure and functioning of marine communities, particularly coral reef ecosystems across the Pacific Ocean. He leads the Sandin Lab, which employs innovative large-area imaging techniques to study reef ecology and collaborates extensively with conservation organizations globally. Education: B.S. from University of California, San Diego Ph.D. from Princeton University Professor Sandin's research spans multiple interconnected domains including coral reef ecology, fish ecology, conservation biology, and mathematical ecology. His work examines fundamental questions about marine community organization, with particular emphasis on how human activities and biophysical forces shape reef ecosystems. The Sandin Lab has made significant contributions to understanding coral demography, spatial ecology, fish assemblage dynamics, and the application of large-area imaging technologies for ecological monitoring. A notable aspect of his research investigates how coral reefs respond to environmental disturbances, including thermal stress events, while exploring mechanisms of resilience and recovery. Analysis of Professor Sandin's recent publications reveals a strong focus on integrating technological innovation with ecological research. His work increasingly combines large-area imaging techniques with traditional ecological methods to address conservation challenges. Key trends include the development of standardized protocols for reef monitoring, exploration of island-ocean connectivity for holistic conservation, and detailed demographic studies of both coral and fish populations. His research bridges fundamental ecological questions with practical applications for marine spatial planning and community-based conservation efforts across the Pacific region. Collaborations and Field Work: Longstanding partnership with Waitt Institute and Blue Prosperity Coalition Collaboration with OneReef for community-driven coral reef management Island-Ocean Connection Challenge with Island Conservation and re:Wild Field research across multiple Pacific nations including Tonga, Maldives, Samoa, Fiji, and Vanuatu Professor Sandin's laboratory actively engages in capacity building through hands-on workshops that train local stakeholders in standardized data collection methods. His team develops data collection plans, establishes protocols, processes data, conducts analyses, and visualizes findings to support conservation efforts. The Sandin Lab has established field sites at Palmyra Atoll and other remote Pacific locations, creating long-term ecological datasets that track reef community changes over time.

Kei Tsutsuiمشاهده پروفایل

مدرس ارشد

• Experimental Economics
• Game Theory
• Corporate Governance
• +۲ مورد دیگر

Kei Tsutsui is a Senior Lecturer in Economics at the University of Bath specializing in experimental economics. His research examines bargaining behaviors, corporate governance models, and collective decision-making processes through laboratory experiments. Methodological interests include Bayesian approaches and spatial data applications in social science contexts. Tsutsui develops experimental designs testing bargaining protocols and inclusive governance structures. Key research themes: Procedural fairness in organizational decision-making Tacit bargaining coordination Co-determination stakeholder models

Chris Buddمشاهده پروفایل

استاد

• Applied Mathematics
• Climate Modeling
• Numerical Weather Prediction
• +۵ مورد دیگر

Chris Budd is a Professor at the University of Bath's Department of Mathematical Sciences, affiliated with research centers such as the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa) and the Probability Laboratory at Bath. His research focuses on interdisciplinary applied mathematics, including numerical weather prediction, climate modeling, and collaborations with industries like energy and aerospace. He holds roles as Professor of Mathematics at the Royal Institution and Vice-President of the Institute of Mathematics and its Applications. Budd has received awards, including the Knowledge Transfer Award and the 2020 Joint Policy Board Communications for Mathematics Award. His work bridges theoretical and practical applications, emphasizing public engagement through initiatives like the Bath Taps Into Science Festival. Research interests span complex nonlinear systems, climate dynamics, and machine learning applications in scientific modeling. Recent projects include climate modeling networks like CliMathNet and collaborations on sustainable development goals. His contributions to numerical methods, such as adaptive mesh refinement and optimal transport techniques, enhance weather and climate forecasting accuracy. Budd’s engagement with policymakers and public science communication underscores his commitment to translating mathematical insights into societal impact. Key Projects: REMODEL (EPSRC), Electro-mechanical modeling (Innovate UK), Climate Science Citizen Science projects. Awards: Knowledge Transfer Award (Met Office), 2020 Joint Policy Board Communications Award. Labs/Teams: SAMBa, Probability Laboratory, Institute for Mathematical Innovation (IMI).