جستجوی استاد راهنمای دانشگاهی

• Complex Systems
• Financial Mathematics
• Statistical Physics
• +۶ مورد دیگر

Tomaso Aste is a Professor of Complexity Science at the Department of Computer Science, University College London (UCL). He founded the Financial Computing and Analytics group and co-founded the UCL Centre for Blockchain Technologies. His work bridges complex systems, data science, and finance, with applications in blockchain, fintech, and market modeling. Education: PhD in Physics (Politecnico di Milano, 1994); Laurea in Physics (University of Genoa, 1990) Prior Appointments: Reader at University of Kent's School of Physics; Associate Professor at Australian National University's Applied Mathematics His research focuses on data-driven modeling of complex systems , particularly financial systems, complex networks, and statistical physics. He has pioneered information filtering networks and topological machine learning methods for financial applications, including portfolio optimization, risk assessment, and cryptocurrency analysis. Recent publications highlight his expertise in financial time-series analysis, blockchain technology, and AI-driven modeling. Articles explore topics like limit order books, cryptocurrency market fragility, and topological neural networks. His work has influenced regulatory technology (RegTech) frameworks and digital economy strategies. Scientific Awards : Marie Curie Individual Fellowship University of Genoa graduate study specialization Fellowship Bacheflor Boncompagni-Ludovisi Foundation Fellowship Awarded fellowships from European Commission and academic foundations support his interdisciplinary research. He has held editorial roles at journals like Philosophical Magazine and Granular Matter , and contributed to professional societies including American Physical Society and Australian Research Council panels. Teaching & Academic Leadership : Co-created four UCL Master's programs: Financial Risk Management, Computational Finance, Financial Technologies, Emerging Digital Technologies Coordinates executive training on AI, blockchain, fintech, and regtech for regulators and private firms Teaches graduate-level courses in Data-Driven Modeling, Data Science, and Advanced AI

Nikos Tzavidisمشاهده پروفایل

استاد

• Small Area Estimation
• Official Statistics
• Outlier Robust Inference
• +۷ مورد دیگر

Professor Nikos Tzavidis is a Professor of Statistical Methodology at the University of Southampton . He has held academic positions at institutions including University College London and the University of Manchester, and currently leads research initiatives integrating statistical methodology with geospatial and survey data. His work focuses on Small Area Estimation, official statistics, and poverty mapping, with applications across global development and public policy. Research interests: Small Area Estimation and Official Statistics Outlier Robust Inference Quantile and M-quantile Models Geospatial Data Analysis Poverty Mapping Machine Learning Applications in Statistics Recent research trends emphasize machine learning integration, geospatial data utilization, and advanced quantile modeling for poverty and demographic studies. His publications span journals like the Journal of Official Statistics and the Journal of the Royal Statistical Society . Scientific awards: ISI Fellow (2013) American Statistical Association (2004) Vice Chancellor's Teaching Award (2013) Johann von Spix Professor (2023) He supervises PhD students in social statistics, demography, and geography, and serves on editorial boards for journals including the Journal of Official Statistics . Externally, he has held leadership roles in the International Statistical Institute and contributed to United Nations and World Bank projects.

Heather Batteyمشاهده پروفایل

استاد

• Statistical Theory
• High-Dimensional Inference
• Sparsity in Statistics
• +۳ مورد دیگر

Heather Battey is a Professor in the Department of Mathematics at Imperial College London's Faculty of Natural Sciences. Her work bridges foundational statistical theory with practical scientific applications, focusing on parametrization effects, sparsity, and high-dimensional inference. Education PhD, University of Cambridge (2008-2011) Research Interests Battey's research examines how model structure and parametrization influence inferential procedures, particularly in high-dimensional settings. She investigates the equivalence between sparsity and reparametrization, and challenges traditional Fisherian statistical abstractions through modern practices. Her publications reveal a pattern of innovation in high-dimensional regression, covariance matrix analysis, and statistical methodology for complex data. Collaborations span disciplines including machine learning, economics, and biomedical research. Scientific Awards Fellow of the Institute of Mathematical Statistics (2023) EPSRC Early Career Research Fellowship (2020-2026) EPSRC Postdoctoral Research Fellowship (2017-2020) Advising and Grants Battey supervises PhD students Charlotte Edgar, Jakub Rybak, and Rebecca Lewis, with informal guidance to Henrique Hoeltgebaum. Over 15 pre-doctoral researchers have been mentored in topics ranging from support vector machines to spatial point processes. Current funding includes an EPSRC grant for theoretical foundations of inference with nuisance parameters and prior support for covariance matrix inference.

Roberta Fidaمشاهده پروفایل

استاد

• Workplace Ethics
• Organizational Behavior
• Workplace Aggression
• +۷ مورد دیگر

Professor Roberta Fida is the Head of the Leadership and Management Department at Aston Business School, part of Aston University in the UK. She holds a Professorship in Organisational Behaviour and Organisational Psychology, with prior roles including Professor of Work Psychology at the University of East Anglia and Chair of the Ethics Committee there. A Chartered Occupational Psychologist with the British Psychological Society, her research focuses on unethical workplace behaviors, moral disengagement, and workplace stress, particularly in healthcare contexts. She specializes in advanced data analytics like structural equation modeling and has led major grants including EDI initiatives and studies on workplace aggression policy mapping. Her work bridges academic research with practical applications to foster ethical and inclusive organizational cultures. Education: Laurea (5-year degree in Work and Organizational Psychology) from Sapienza University of Rome (2002) MA in Organizational Training (2004–2005) PhD in Psychology from Sapienza University (2006) Postgraduate Certificate in Higher Education Practice (UEA, 2017) Italian National Qualifications as Full Professor (2017) and Associate Professor (2013) Research Interests: Her core areas include moral disengagement, counterproductive workplace behaviors, stress resilience, healthcare leadership, and EDI. She has published over 60 articles and is listed in Stanford's Top 2% Scientists globally. Awards & Grants: NIHR Work & Health Development Award (2023–2024) for EDI Smart-Dash (£95,855) INAIL grants totaling over €1.1 million for psychosocial risk studies and work-related stress analyses ESRC funding for workplace wellbeing research (£659,540) Research Council of Canada grant for nurse leadership studies ($202,852) Teaching & Supervision: She has taught across undergraduate to postgraduate levels, including modules on organizational psychology, multivariate analysis, and conflict management. She supervises PhD students and has led course design for programs focusing on organizational wellbeing and research methods.

• Visual Analytics
• Information Visualization
• Geographic Information Science
• +۷ مورد دیگر

Jo Wood is Professor of Visual Analytics in the Department of Computer Science at City, University of London, where she has been employed since January 14, 2000. Her work bridges computer science, geographic information science, and human-computer interaction, focusing on innovative methods for visualizing complex spatial and behavioral data. Her research interests center on visual analytics , information visualization , and geovisualization , with applications in transportation, public health, crisis response, and citizen science. She investigates how interactive visual interfaces can support exploratory data analysis, decision-making, and storytelling, particularly through small multiples, faceted views, and sketch-based rendering techniques. The trends in her recent publications reflect a consistent focus on user-centered design , spatial data abstraction , and interactive exploration of multivariate datasets. Her work often integrates real-world behavioral data such as GPS tracks, cycling patterns, and crowd-sourced information to build meaningful visual narratives and support analytical reasoning. Throughout her career, Jo Wood has contributed significantly to the advancement of visual analytics through high-impact publications in top-tier venues such as IEEE Transactions on Visualization and Computer Graphics and Computer Graphics Forum. Her collaborations with researchers like Jason Dykes and Aidan Slingsby highlight her role in a vibrant research community. She has supervised numerous research projects and mentored students in visualization and geospatial analytics, though specific names are not listed in the provided text. Her work has been supported by various research grants, particularly in domains involving urban mobility, energy modeling, and crisis informatics, though grant details are not specified here. Jo Wood has also contributed to the design of visual analytics systems for applications including disease spread modeling, bicycle-hire scheme monitoring, and persuasive technology for health and leisure, demonstrating a strong commitment to impactful, interdisciplinary research.

Donald Robertsonمشاهده پروفایل

استاد

• Econometrics
• Applied Macroeconomics
• Financial Economics
• +۳ مورد دیگر

Professor Donald Robertson is a faculty member at the University of Cambridge , holding the position of Professor of Economics and Director of Graduate Studies and PhD Programme within the Faculty of Economics . He is affiliated with Pembroke College and contributes to econometric research and graduate education. Research Interests : His work focuses on Econometrics , Applied Macroeconomics , and Financial Economics , with methodological expertise in Time Series Analysis , Panel Data Analysis , and Predictive Modeling . His publications address topics like cross-sectional dependence, unit root testing, and instrumental variable estimation. Teaching : He instructs modules such as Introduction to Probability and Statistics , Time Series Methods , and MPhil Prep Course - Statistics . Publications : Recent contributions include work on R² bounds for predictive models, factor residuals in panel data, and fiscal fatigue in debt ratios, reflecting his focus on econometric theory and macroeconomic applications. Contact : Email dr10011@cam.ac.uk or phone +44(0)1223 335270. Office hours by email appointment in Room 70.

• Nonlinear Control
• Robust Control
• Adaptive Control
• +۷ مورد دیگر

Mike Grimble is a Research Professor in the Department of Electronic and Electrical Engineering at the University of Strathclyde, Faculty of Engineering. His work is centered on advanced control systems with applications across automotive, aerospace, marine, and industrial domains. He is actively involved in theoretical and applied research, particularly in nonlinear and robust control methodologies. Research Interests: Theory and application of nonlinear and robust control for multivariable systems Adaptive control and estimation methods Benchmarking and performance assessment of control systems Condition monitoring and industrial applications Real-time control and embedded systems His recent publications highlight a strong trend in applying predictive and adaptive control techniques to electric vehicles, battery systems, underwater robotics, and industrial machinery. These works emphasize real-time implementation, energy optimization, and robustness—critical for modern sustainable and autonomous systems. Scientific Recognition and Activities: Invited speaker on the benefits and challenges of advanced control in industrial applications (2013) Contributor to UN Sustainable Development Goals, particularly in sustainable industry and innovation Active research output with over 158 publications, including journals, conferences, and book chapters Research Leadership and Funding: Principal Investigator on multiple EPSRC and RSE-funded projects Co-investigator in interdisciplinary initiatives such as the Medical Devices Doctoral Training Centre Organizer of international workshops on hybrid and predictive control Labs and Research Teams: He is associated with the Industrial Control Centre at the University of Strathclyde, a leading hub for control engineering research. His collaborations span departments and institutions, involving real-time LabVIEW implementations, hardware demonstrations, and partnerships with industry players like National Instruments and Quanser Inc.

Matthew Willsمشاهده پروفایل

استاد

• Evolutionary Palaeobiology
• Macroevolution
• Phylogenetics
• +۱۲ مورد دیگر

Matthew Wills is Professor of Evolutionary Palaeobiology in the Department of Life Sciences at the University of Bath. He is affiliated with the Milner Centre for Evolution and the Centre for Mathematical Biology, where he leads research into macroevolutionary processes using fossil and molecular data. His work integrates phylogenetics, morphological disparity, and stratigraphic congruence to understand large-scale evolutionary patterns. His research interests focus on the role of fossils in building phylogenies, the evolution of morphological complexity, and the testing of macroevolutionary trends such as early high disparity and increasing complexity. He investigates how developmental shifts underpin major evolutionary transitions and how fossilization biases affect our understanding of the Tree of Life. His lab conducts projects on arthropod supertrees, molluscan ontogeny, and the phylogeny of Eumalacostraca using molecules, morphology, and fossils. His recent publications reveal a strong focus on quantifying morphological complexity, vertebral evolution in mammals, species richness in birds, and the impact of boundaries on trait evolution. These works frequently appear in high-impact journals like Nature Communications and Nature Ecology & Evolution , indicating a trend toward integrative, data-rich evolutionary analyses combining paleontological, morphological, and phylogenomic approaches. Principal Investigator, Biodiversity and The Sixth Mass Extinction (Royal Commission for the Exhibition of 1851) Principal Investigator, Susceptibility to Mass Extinctions: Ammonites as a Case Study (NERC) Principal Investigator, PLUTO Project (BBSRC) Principal Investigator, Arthropod Supertree of Life (BBSRC) He has supervised 17 research students and contributes to public discourse through platforms like The Conversation . His work supports UN Sustainable Development Goals related to life on land and climate action through deep-time biodiversity research.

Sylvia Richardsonمشاهده پروفایل

استاد پژوهش

• Biostatistics
• Bayesian Statistics
• Spatial Modeling
• +۷ مورد دیگر

Sylvia Richardson is an MRC Investigator at the MRC Biostatistics Unit and holds a Research Professorship at the University of Cambridge, where she served as Director of the Biostatistics Unit from 2012 to 2021. She is affiliated with the Cambridge Mathematics of Information in Healthcare Hub (CMIH) at the Centre for Mathematical Sciences. Her work bridges advanced statistical methodology with critical healthcare applications, particularly in the analysis of complex biomedical data. Richardson's research spans multiple domains of biostatistics with a strong emphasis on Bayesian approaches. Her work has significantly advanced spatial modeling and disease mapping techniques, developed sophisticated methods for handling measurement error in epidemiological studies, and pioneered mixture and clustering models for integrative analysis of heterogeneous data sources. Her research addresses fundamental challenges in analyzing longitudinal health data, multimorbidity patterns, and complex disease trajectories. Her publication record demonstrates consistent methodological innovation applied to pressing healthcare challenges. Recent work focuses on traumatic brain injury outcomes, multimorbidity progression, genomic analysis, and statistical approaches to pandemic data. The articles reveal a strong pattern of methodological development driven by real-world healthcare challenges, with particular attention to longitudinal analysis, Bayesian computation, and integrative modeling approaches that can handle diverse and complex data structures. While specific awards are not detailed in the available information, Richardson's leadership as Director of the MRC Biostatistics Unit for nearly a decade and her continued Research Professorship reflect significant recognition of her contributions to the field. Her work with major international consortia like CENTER-TBI demonstrates her role in large-scale collaborative research efforts addressing critical health challenges. Richardson's research has substantial implications for healthcare policy and practice, particularly in understanding disease progression, developing predictive models for patient outcomes, and creating methodological frameworks that can integrate diverse data sources to generate meaningful clinical insights. Her work continues to influence both statistical methodology and healthcare applications through ongoing research and leadership in the field.

• Econometrics
• Applied Econometrics
• Financial Econometrics
• +۶ مورد دیگر

Professor Yongcheol Shin is a faculty member in the Department of Economics at the University of York. His academic background includes a BA (Hanyang University), MA (Hanyang University), and PhD (Michigan State University). He specializes in applied and theoretical econometrics, focusing on financial and macroeconomic modeling. Education: BA (Hanyang University), MA (Hanyang University), PhD (Michigan State University) His research spans econometric theory and applications in finance and macroeconomics. Key areas include nonlinear panel data modeling, cointegrating VAR models, regime-switching models, and statistical hypothesis testing for time series. Recent work addresses interactive effects in panel data and multilevel factor models. Recent publications (2023) focus on panel data analysis, canonical correlation, and unit root testing. These studies reflect his expertise in econometric methodology and its application to financial economics, macroeconomics, and trade dynamics. Scientific awards include: 2018 Maekyung-KAEA Economist Award He leads the ESRC-funded project 'New Cross-Sectionally Dependent Panel Data Methods for the Analysis of Macroeconomic and Financial Networks' (2020-2024), collaborating with researchers like J. Chen and W. Wang.

Lars E. Erikssonمشاهده پروفایل

استاد

• Translational Nursing Research
• Biobehavioral Nursing
• Quality of Life
• +۹ مورد دیگر

Professor Lars E. Eriksson is a Registered Nurse with a PhD in Nursing and an MSc in Chemistry, currently serving as a part-time Professor in Nursing at the School of Health and Medical Sciences, City St George's, University of London, and as a full Professor in Nursing at Karolinska Institutet, Stockholm, Sweden, where he leads the Lars E. Eriksson Research Group. His academic journey began with a UC in Nursing in 1985, followed by over a decade of clinical work in hematology, immunology, and HIV care in Stockholm, before transitioning into research and academia. His research is centered on biobehavioral and translational nursing, particularly focusing on health and quality of life among individuals with chronic conditions such as HIV, cancer, and long-term infections. Key areas include disease-related stigma, symptom research, patient-reported outcomes, and biomarker development. He is Principal Investigator for a lung cancer biomarker project funded by the Swedish Research Council and the Swedish PI for the H2020 Magnet4Europe project on mental health in healthcare workplaces. His recent publications show a strong trend in using mixed methods to explore end-of-life communication, death literacy, sexual and reproductive health after cancer, and stigma in chronic illness. He frequently employs patient-reported outcomes, online focus groups, and web-based interventions, often in collaboration with international partners. His work bridges clinical practice, nursing science, and public health. Scientific Awards and Memberships: British HIV Association (BHIVA) National HIV Nurses Association (NHIVNA) Swedish Society of Nursing Swedish Society of Medicine Swedish Society of Medical Microbiology Lars Eriksson has advised numerous PhD and Master’s students, primarily on topics related to quality of life, stigma, and chronic illness. He teaches postgraduate modules including MSc Dissertation, Advanced Practice Dissertation, and Sexual and Reproductive Health. He is affiliated with the Centre for Health Services Research and the Centre for Public Health Research at City St George's.

Iain Murrayمشاهده پروفایل

استاد

• Machine Learning
• Probabilistic Modeling
• Density Estimation
• +۳ مورد دیگر

Iain Murray is Professor of Machine Learning and Inference at the School of Informatics, University of Edinburgh. His research focuses on developing flexible probabilistic models applicable across diverse domains including cosmology, neuroscience, perception, speech, sports, and text. Program Chair for ICLR (2018) Publications Chair for ICML (2017, 2018) Area Chair for AISTATS, ICLR, ICML, NeurIPS, and UAI Amazon Scholar (2018-2024), first appointed in Europe Murray's research interests center on probabilistic reasoning using machine learning, with specific expertise in density estimation and Markov chain Monte Carlo methods. His work spans theoretical foundations and practical applications, with significant contributions to neural autoregressive distribution estimation (NADE), real-valued NADE (RNADE), and pseudo-marginal slice sampling techniques. His research has enabled advances in flexible probabilistic modeling across multiple domains. His publications show consistent focus on advancing probabilistic modeling techniques, with recent work emphasizing neural autoregressive models, density estimation methods, and efficient sampling algorithms. The research trajectory demonstrates progression from foundational work on NADE to increasingly sophisticated deep learning approaches for density estimation and inference. Notable Paper Award for NADE work Amazon Scholar (2018-2024) Murray has supervised numerous PhD students who have gone on to prominent positions at Google DeepMind, NYU, stability.ai, and other leading institutions. His teaching responsibilities include the Machine Learning and Pattern Recognition course and project supervision. His research group focuses on developing tractable probabilistic models with applications across multiple scientific domains.

• Neuroimaging
• Structural brain segmentation
• Multimodal population modeling
• +۶ مورد دیگر

Mark Jenkinson is a Professor of NeuroImaging at the University of Oxford's Nuffield Department of Clinical Neurosciences and also holds positions at the University of Adelaide's Australian Institute for Machine Learning and the South Australian Health and Medical Research Institute (SAHMRI). He heads the Structural Modelling and Analysis Group at the FMRIB Centre, where his research focuses on multimodal population modeling and structural brain segmentation. Education: DPhil in Robotics Research (University of Oxford, 1999) BSc (Hons I) in Mathematical Physics (University of Adelaide, 1994) BE (Hons I) in Electrical and Electronic Engineering (University of Adelaide, 1993) Professor Jenkinson's research spans two major themes: multimodal modeling of populations to describe disease processes and apply to individual patient diagnoses, and structural segmentation and analysis of brain anatomy and pathology, particularly focusing on sub-cortical structures and lesions. His work integrates advanced computational methods with neuroimaging to develop tools for understanding neurological disorders. As the developer of key components of the FMRIB Software Library (FSL), he has significantly contributed to standard neuroimaging analysis pipelines used worldwide. His recent publications demonstrate a strong focus on deep learning applications in neuroimaging, uncertainty quantification in medical AI, and advanced segmentation techniques. There's a clear trend toward developing more robust, anatomically plausible models that preserve topological structures while improving diagnostic capabilities for conditions like multiple sclerosis, Huntington's, and Parkinson's diseases. Scientific Awards: Highly Cited Researcher (Clarivate Analytics 2018-2021, Thomson Reuters 2014-2016) ISMRM Outstanding Teacher Award (2009, 2014) Teaching Excellence Award, University of Oxford (2012) David Phillips Fellowship from BBSRC (2005-2010) Professor Jenkinson has supervised over 25 doctoral students whose work spans brain segmentation, connectivity analysis, and clinical applications of neuroimaging. His research is supported by significant grants including the Medical Research Future Fund (AU$2m), Wellcome Trust Centre for Integrative Neuroimaging (£11m), and NIH Human Connectome Project (US$30m), reflecting the high impact and translational potential of his work. As head of the Structural Modelling and Analysis Group at FMRIB, Jenkinson leads a team developing the FSL (FMRIB Software Library), one of the most widely used neuroimaging analysis packages globally. His group collaborates extensively with clinical researchers on applications ranging from multiple sclerosis to traumatic brain injury, translating computational advances into clinical practice.

Ioannis Kalogridisمشاهده پروفایل

مدرس

• Functional Data Analysis
• Nonparametric Statistics
• Robust Statistics
• +۳ مورد دیگر

Dr Ioannis Kalogridis is a Lecturer in Statistics and Data Analytics at the University of Glasgow , affiliated with the School of Mathematics and Statistics . He joined the university in February 2025, following a postdoctoral researcher role at KU Leuven. His research focuses on the intersection of Functional Data Analysis , Nonparametric Statistics , and Robust Statistics , emphasizing methodological development and theoretical properties of robust and efficient estimation techniques. Key areas include functional regression, penalized splines, and spatial smoothing. Recent publications highlight his work on robust penalized splines for location estimation, resistant dispersion estimation, and adaptive functional logistic regression models. These contributions span theoretical advancements and practical applications in statistical modeling.

• Information Visualization
• Geovisualization
• Cartography
• +۷ مورد دیگر

Professor Jason Dykes is a leading figure in the field of information and geovisualization at City, University of London, where he holds the position of Professor in the Department of Computer Science and co-directs the giCentre , a renowned research centre in visualization. He is affiliated with the School of Mathematics, Computer Science and Engineering and maintains an active research and teaching profile. His academic journey includes a PhD in Geography from the University of Leicester and extensive leadership in both research and education. Education: PhD in Geography, University of Leicester, 2000 MSc in Geographic Information Systems, University of Leicester, 1991 BA/MA in Geography, University of Oxford, 1989 Jason Dykes' research is centered on designing visual methods and tools for exploring, analyzing, and presenting information, with a strong emphasis on geographic data. His work integrates cartography, information visualization, GIScience, and human-computer interaction , leading to the development of innovative techniques such as geowigs, ODmaps, BallotMaps, and AttributeSignatures. He has published extensively in top-tier journals like IEEE Transactions on Visualization & Computer Graphics, with over 20 papers in the last decade, and co-authored the seminal book Exploring Geovisualization (2005). His research is supported by major funders including EPSRC and the EU, with projects like RAMP VIS (Covid-19 response) and VALCRI (criminal intelligence). The most recent articles highlight a consistent trend in applied and human-centered visualization , focusing on responsive design, education, pandemic modeling, and novel visual metaphors for complex data. His work increasingly emphasizes methodological rigor, design exposition, and the role of visualization in interdisciplinary and emergency contexts. Scientific Awards and Recognition: National Teaching Fellow, Higher Education Academy (2005) Best Paper Awards at GIS Research UK (consecutive years) Honorable Mentions, IEEE InfoVis (2009, 2010, 2016, 2018) Security Innovation Commercialisation Award (EU, 2022) Research Supervisor of the Year, City Student Union (2020) Innovations in Teaching Award and multiple teaching grants at City Jason Dykes has supervised eight PhD students to completion and advised many others, including notable researchers like Roger Beecham, Sarah Goodwin, and Susanne Bleisch. His teaching includes modules such as Visualizing Society and Data Presentation. He has received significant grant funding from UK research councils and the EU for projects like DIVA, VALCRI, and RAMP VIS. His service to the community includes leadership roles in IEEE VIS, ICA Commission on GeoVisualization, and editorial positions at IEEE TVCG and the Journal of Visualization and Interaction. He leads the giCentre , a dynamic research group that fosters innovation in visualization, and has been instrumental in establishing the field’s educational and methodological foundations through participation in Dagstuhl seminars and publications on visualization pedagogy.