جست‌وجوی استاد و پژوهشگر

Sam Astillمشاهده پروفایل

مدرس ارشد

• Time Series Econometrics
• Financial Econometrics
• Theoretical Econometrics

Dr. Sam Astill is a Senior Lecturer in Finance at the Essex Business School, University of Essex. He holds a PhD from the University of Nottingham and specializes in econometric methodologies applied to financial time series analysis. PhD (University of Nottingham) His research focuses on: Time Series Econometrics Financial Econometrics Theoretical Econometrics Recent research includes developing Bonferroni-type tests for return predictability, CUSUM-based monitoring systems for explosive financial episodes, and robust methodologies for bubble detection. His work addresses challenges in time-varying volatility, trending predictors, and nonlinear deterministic components. Current teaching responsibilities include: Quantitative Methods and Finance (BE303) Financial Time Series: Methods and Applications (BE372) Research Methods in Financial Econometrics (BE990) Current PhD students: Boubacar Drame Yanfeng Xu Previous PhD supervisees: Seth Richard James Sidney Harrison (awarded 2025) Nhat Minh Vuong Chu (awarded 2023) Ioannis Korkos (awarded 2020) Contact: sastill@essex.ac.uk | Office: EBS.3.8, Colchester Campus

Shaobo Liمشاهده پروفایل

دانشیار

• High-dimensional robust statistics
• Nonparametric regression
• Ordinal data analysis
• +۴ مورد دیگر

Shaobo Li is an Associate Professor in the Department of Analytics, Information, and Operations at the University of Kansas School of Business. His research spans high-dimensional robust statistics, ordinal data analysis, and data privacy in business contexts. Ph.D. in Business Administration, University of Cincinnati M.S. in Statistics, University of Cincinnati B.A. in Mathematics, Shandong University Li’s work focuses on nonparametric regression techniques, corporate bankruptcy prediction models, and methodological innovations in ordinal variable analysis. He has contributed to data privacy frameworks for marketing and financial applications, emphasizing flexible statistical approaches. Recent publications highlight his expertise in partial association quantification, point-of-sale data anonymization, and second-party data utilization. Collaborative efforts include applications in marketing science and business analytics.

Akramul Azimمشاهده پروفایل

دانشیار

• Real-Time Systems
• Embedded Software
• Safety-Critical Systems
• +۵ مورد دیگر

Dr. Akramul Azim is an Associate Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University, Faculty of Engineering and Applied Science. His research focuses on real-time systems, embedded software, safety-critical systems, IoT, software verification/validation, and applied machine learning. He leads the Real-Time Embedded Software Research (RTEMSOFT) Lab and has contributed to over 50 peer-reviewed publications. His work spans cybersecurity for automotive systems, edge computing optimization, and AI-driven software testing. Education: PhD (Electrical and Computer Engineering), University of Waterloo (2014) MSc (Computer Science and Engineering) (2009) BSc (Computer Science and Information Technology) (2006) Research Interests: Dr. Azim explores the intersection of real-time systems and machine learning, particularly in embedded environments. Key areas include: Autonomous vehicle safety Edge/IoT security Optimized task scheduling ML-enhanced software testing FPGA vulnerability mitigation Recent Research Trends: His publications (2023-2025) highlight advancements in LLM-based code optimization, lock contention mitigation in Java, energy-efficient vehicular networks, and safety-critical AI applications. Notable contributions include frameworks for situation-aware adaptation in ITS and reinforcement learning for edge task scheduling. Labs/Teams: Directs the RTEMSOFT Lab, collaborating on projects like AD-CGAN for anomaly detection and ESOps agile pipeline development.

• Cardiology
• Atrial Fibrillation Management
• Cardiovascular Devices
• +۵ مورد دیگر

Jeffrey Sean Healey is a Professor of Medicine at McMaster University , affiliated with the Faculty of Health Sciences . His research focuses on cardiovascular diseases, particularly atrial fibrillation, defibrillator technology, anticoagulant therapies, and clinical trial design. He leads studies on stroke prevention, device safety, and arrhythmia management. He has contributed to major trials like RE-LY and ASSERT, investigating novel oral anticoagulants and subclinical atrial fibrillation detection. His work spans device innovation, pharmacological interventions, and improving patient outcomes through evidence-based strategies. Recent studies include analyses of alcohol's role in cardiovascular outcomes, mineralocorticoid receptor antagonists' effects, and prediction models for ventricular tachycardia. He has authored over 375 scholarly works in the last decade, with a focus on atrial fibrillation epidemiology and device-related complications.

Adam Sykulskiمشاهده پروفایل

مدرس ارشد

• Statistics
• Oceanography
• Applied Statistics
• +۵ مورد دیگر

Adam Sykulski is a Senior Lecturer in Statistics at the Department of Mathematics, Imperial College London, within the Faculty of Natural Sciences. His research focuses on time series and spatiotemporal statistics, particularly spectral analysis and Fourier methods, applied to environmental and oceanographic sciences. He holds roles including Trustee of the Royal Statistical Society Council (elected 2024), External Examiner at King's College London, and leads the EPSRC CDT in Mathematics for Future Climate. He has supervised 7 PhD students to completion, with 4 current advisees, and contributed to 32 peer-reviewed publications in top-tier journals like Biometrika and IEEE Transactions on Signal Processing. Education: PhD and MSci in Mathematics from Imperial College London (2008–2011 and 2003–2007). Professional development includes a Postgraduate Certificate in Academic Practice (2020). Research interests span statistical methodologies for environmental data, oceanographic modeling, and interdisciplinary applications. Key roles include Partner Investigator in the Australian Research Council’s TIDE project and leadership in equality, diversity, and inclusion initiatives at Imperial College. His work integrates statistical theory with practical challenges in climate science and operational research.

Rodney Strachanمشاهده پروفایل

استاد

• Econometric and Statistical Methods
• Econometrics
• Macroeconometric Analysis
• +۵ مورد دیگر

Professor Rodney Strachan is a Professor of Econometrics at the School of Economics, University of Queensland (UQ), within the Faculty of Business, Economics and Law. He holds a PhD from Monash University (2000) and has held prior positions, including at the Australian National University (ANU), where he served as Deputy Head of the Research School of Economics. His research focuses on Bayesian econometrics, time-varying parameter models, macroeconometric analysis, and dynamic factor models. He also serves as a Senior Fellow at the Rimini Centre for Economic Analysis and a Research Associate at the Centre for Applied Macroeconomic Analysis. Strachan's research interests include Bayesian methods, time series analysis, cointegration, and the development of large-scale macroeconomic models. His work addresses challenges in model uncertainty, parameter instability, and the application of stochastic volatility models. Notable projects include 'Large dynamic time-varying models for structural macroeconomic inference' (2018–2022) and 'Estimation of the continuous piecewise linear model and macroeconomic applications' (2012–2015). His publications span journals like the Journal of Econometrics, Journal of Business and Economic Statistics, and Studies in Nonlinear Dynamics and Econometrics. Recent work includes advancements in multivariate stochastic volatility models and dynamic factor models with drifting trends. He is actively involved in advising and mentoring graduate students, though specific advisee names are not listed here. Strachan’s contributions to econometric theory and macroeconomic modeling have been recognized through collaborations with institutions like the Rimini Centre for Economic Analysis and the Centre for Applied Macroeconomic Analysis. His research emphasizes methodological innovation, with a focus on practical applications to policy analysis and forecasting.

Oscar L. Olvera Astiviaمشاهده پروفایل

استادیار

• Psychometrics
• Educational Measurement
• Quantitative Methodology
• +۴ مورد دیگر

Oscar L. Olvera Astivia is an Assistant Professor in the Department of Measurement and Statistics at the University of Washington's College of Education . He also holds an affiliation with the Center for Statistics and the Social Sciences . His research focuses on advancing quantitative methodologies in psychometrics and educational measurement through theoretical and mathematical approaches. Research Interests: Monte Carlo simulations and their methodological implications in social sciences. Statistical heuristics and their underlying assumptions. Non-Gaussian data analysis and distribution theory. Meta-science and research methodology critique. Education: Ph.D. in Measurement, Evaluation & Research Methodology, University of British Columbia (2017) M.A. in Measurement, Evaluation & Research Methodology, University of British Columbia (2013) B.A. in Mathematics, University of the Fraser Valley Publications: Over 20 peer-reviewed articles, including work on multivariate outliers, simulation algorithms, and psychometric evaluation. Recent trends emphasize methodological rigor and reproducibility in quantitative research. Awards: None explicitly listed. Grants/Advising: Focus on statistical software development (e.g., a shiny app for power analysis) and mentorship in quantitative methods. Active in promoting open science practices. Labs/Teams: Collaborates with interdisciplinary groups on statistical methodology and applications in education and healthcare.

Kevin Linمشاهده پروفایل

استادیار

• Single-cell Biology
• Statistical Genetics
• Biostatistics
• +۳ مورد دیگر

Kevin Lin is an Assistant Professor in the Department of Biostatistics at the University of Washington, joining in Fall 2023. His research focuses on developing statistical methods for analyzing single-cell data to uncover cellular mechanisms in diseases like Alzheimer’s and immune resistance. He holds a PhD from Carnegie Mellon University and completed postdoctoral training at the University of Pennsylvania. His work bridges computational methods with biological questions, emphasizing matrix factorization, network modeling, and changepoint detection. Education: PhD in Statistics & Data Science (Carnegie Mellon University, 2020) Previous Role: Postdoctoral Researcher at University of Pennsylvania (Wharton Statistics) Research interests include high-dimensional data analysis, statistical genetics, and single-cell RNA-Seq. Notable contributions include methods like Tilted-CCA for multimodal data integration and eSVD-DE for cohort-wide differential expression analysis. His work has been recognized with awards such as the Wikimedia Foundation Research Award (2023). Lin’s lab collaborates on projects involving endolysosomal dysfunction in Alzheimer’s, yeast cell division dynamics, and lineage-aware machine learning. Key achievements include over 20 publications in journals like Nature Biotechnology , PNAS , and Biometrics . He advises on grants related to single-cell technologies and serves on the EDI subcommittee for mental health initiatives at UW. Outside academia, Lin enjoys zumba and cooking.

Steve Kingمشاهده پروفایل

استاد

• Formal Methods
• System Specification
• Safety-Critical Systems
• +۳ مورد دیگر

Prof Steve King is a Professor (Teaching and Scholarship) at the University of York's Department of Computer Science, part of the Faculty of Sciences. He has held roles including Associate PVC for Teaching, Learning and Students, and has been involved in academic leadership positions such as Deputy Head of Department and Associate Dean. His research focuses on formal methods for system specification and development, with applications to safety-critical systems. King holds degrees from Oxford University: MA in Maths, MSc in Computation, and DPhil in Computation. He has contributed to over 30 publications in areas like formal verification, model transformation, and software safety assurance. His work emphasizes practical applications of formal methods in industry, particularly in ensuring safety-critical systems' reliability. Prof King has also served as an external examiner for multiple PhD theses, demonstrating his engagement in academic governance and quality assurance. Key professional roles include membership in the Standing Committee on Assessment and QAA HE Review activities. His research interests span formal specification techniques, model-driven engineering, and the application of rigorous methodologies to complex software systems. Recent publications highlight contributions to test data generation, UML model simulation, and compliance notations for concurrent systems.

Jenny Brynjarsdottirمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Environmental Statistics
• Uncertainty Quantification
• +۴ مورد دیگر

Jenny Brynjarsdottir is an Associate Professor at Case Western Reserve University's Department of Mathematics, Applied Mathematics, and Statistics, within the College of Arts and Sciences. She specializes in Bayesian statistics, environmental modeling, and uncertainty quantification. Her work integrates spatio-temporal processes and model discrepancy analysis to address complex scientific challenges, such as climate data interpretation and energy system optimization. Her research also extends to applications in public health, crowd dynamics, and renewable energy, reflecting a multidisciplinary approach. She maintains an active research website at http://sites.google.com/case.edu/jennybrynjarsdottir/ and can be reached at jenny.brynjarsdottir@case.edu. She is based at two locations on campus: 2145 Adelbert Rd. and Sears 5th Floor, 2083 Martin Luther King Jr Dr, Cleveland, OH 44106-7058. Her articles highlight advancements in Bayesian hierarchical modeling, satellite-based environmental measurements, and data-driven solutions for photovoltaic systems. While no formal awards or grants are listed, her contributions emphasize methodological innovations in statistics with practical environmental and engineering applications.

Dimitris N. Politisمشاهده پروفایل

استاد

• Time Series Analysis
• Nonparametric Statistics
• Bootstrap Methods
• +۶ مورد دیگر

Dimitris N. Politis is a Distinguished Professor in the Department of Mathematics and the Halicioglu Data Science Institute at the University of California, San Diego. He holds the prestigious Halicioglu Data Science Institute Chancellor's Endowed Chair II position and has been affiliated with UCSD since 1997, progressing from Associate Professor to his current distinguished position. His educational background includes a Ph.D. in Statistics from Stanford University (1990), along with multiple master's degrees in Statistics, Mathematics, and Computer and Systems Engineering from Stanford and Rensselaer Polytechnic Institute. Professor Politis's research focuses on advanced statistical methodologies, with particular expertise in: Time Series and Random Fields analysis Computer-Intensive Methods in Statistics Resampling and Subsampling for Dependent Observations Spatial Statistics and Point Processes Nonparametric Spectral and Probability Density Estimation Model-free Prediction and Regression Information Theory and Signal Processing Econometric Analysis of Financial Time Series His scholarly output includes over 100 journal papers and several influential books, most notably "SUBSAMPLING" (1999), "MODEL-FREE PREDICTION AND REGRESSION" (2015), and "TIME SERIES: A FIRST COURSE WITH BOOTSTRAP STARTER" (2020), which has become a key educational resource in the field. Professor Politis has received numerous prestigious awards and honors: Guggenheim Fellowship (2011) Fellow of the American Statistical Association (2011) Fellow of the Institute of Mathematical Statistics (2004) Distinguished Author Award from the Journal of Time Series Analysis (2020) Econometric Theory Multa Scripsit Award (2013) Tjalling C. Koopmans Econometric Theory Prize (2012) He has been principal investigator on numerous NSF and NIH grants, including current funding for "Computer-intensive methods for dependent and complex data" (NSF DMS 24-13718, 2024). Professor Politis has held significant leadership roles, including serving as Chair of the Faculty Council of the Halicioglu Data Science Institute (2019-2023) and Associate Director (Founding) of the Institute (2018-2024). As a co-founder of the International Society for NonParametric Statistics, Professor Politis has made substantial contributions to the organization of major conferences and workshops in his field, including the First Conference of the International Society for NonParametric Statistics in 2012. His editorial work includes serving as Co-Editor of the Journal of Time Series Analysis since 2013 and Senior Editor for the ACM/IMS Journal of Data Science since 2022.

H.A.L. Kiersمشاهده پروفایل

استاد

• Statistics
• Data Analysis
• Multivariate Statistics
• +۳ مورد دیگر

Prof. Henk A.L. Kiers is a Professor in Statistics and Data Analysis at the University of Groningen's Faculty of Behavioural and Social Sciences, within the Department of Psychometrics & Statistics. His research focuses on advanced statistical methodologies, particularly in multivariate analysis, three-way component analysis, and psychometrics. He has contributed extensively to data analysis techniques for large datasets, including methods for uncovering underlying structures in complex data matrices. Key research interests include Bayesian analysis, statistical education, and the application of statistical methods in psychology and psychiatry. He has held editorial roles and received the Psychometrika Reviewer Award in 2017. His work bridges theoretical advancements with practical applications, such as improving classification methods and addressing challenges in missing data analysis. Prof. Kiers is actively involved in academic collaborations, including projects on data fusion and the development of statistical software tools like the R package ThreeWay. His contributions to the field have been recognized through numerous high-impact publications and methodological innovations.

Quefeng Liمشاهده پروفایل

دانشیار

• High-dimensional statistics
• Biomedical research
• Omics data integration
• +۵ مورد دیگر

Dr. Quefeng Li is an Associate Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill (UNC-Chapel Hill), part of the Gillings School of Global Public Health. He earned his Ph.D. in Statistics from the University of Wisconsin-Madison (2013) and completed a postdoctoral fellowship at Princeton University (2013–2015). His research focuses on high-dimensional statistics and its applications in biomedical and neuroimaging data analysis, emphasizing robust methodologies and integrative data analysis techniques. Dr. Li's work has contributed to advancements in variable selection, data integration, and statistical methods for complex biomedical datasets. He has held editorial roles at journals like Biometrics and Biostatistics , and serves on multiple professional committees. His research has been supported by grants from the NIH, including R01AG073259 and R01CA282648, and he collaborates extensively with clinical researchers across disciplines. His research interests span high-dimensional statistics, neuroimaging analysis, and robust statistical methods. Notable contributions include developing the ARTdeConv algorithm for cell-type deconvolution and the glmmPen package for penalized generalized linear mixed models. He has advised numerous PhD students, many of whom now work in industry and academia. Awardees include the Gillings Research Excellence Award (2023), Junior Faculty Development Award (2018), and University Research Council Award (2016). His work bridges theoretical statistics with practical biomedical applications, addressing challenges in data heterogeneity and reproducibility.

• Software Testing
• Formal Methods
• Specification-Based Testing
• +۳ مورد دیگر

Dr. Kirill Bogdanov is a Lecturer in the Department of Computer Science at the University of Sheffield, UK. He completed his PhD in 2000 on specification-based software testing using X-machines and Statecharts. Before his current role, he worked as a Research Associate on the MOTIVE project, focusing on testing object-oriented systems. Education: PhD in Computer Science, University of Sheffield (2000) Research focused on applying X-machine testing methods to Statecharts specifications Research Interests: Specification-based testing methodologies X-machine formalisms and their applications Automated model inference from software code Passive inference of software models from logs Formal methods for safety-critical systems Grants & Projects: PI on EPSRC grants: REGI (2009-2012), StaMInA (2009-2012), and Automated Abstraction (2005-2008) Developed tools like the Statechum project for model inference Labs/Teams: Member of the Verification and Testing Research Group and Foundations of Computation research group. Awards: No specific awards listed, but contributions include pioneering work in formal testing methods.

Eleni Vasilakiمشاهده پروفایل

استاد

• Bioinspired Machine Learning
• Neuromorphic Computing
• Computational Neuroscience
• +۳ مورد دیگر

Professor Eleni Vasilaki holds the Chair in Bioinspired Machine Learning at the University of Sheffield's School of Computer Science, where she serves as Head of the Machine Learning research group and member of the Complex Systems Modelling research group. She joined as Lecturer in 2009 and became Professor in 2016. Education includes: Bachelor's in Informatics and Telecommunications from University of Athens Master's in Microelectronics from University of Athens DPhil in Computer Science and Artificial Intelligence from University of Sussex Research focuses on developing novel machine learning techniques inspired by biological principles, particularly in reinforcement learning and reservoir computing methods. Her team collaborates with material scientists and engineers to design neuromorphic computing hardware. Publications demonstrate strong emphasis on neuromorphic computing, machine learning applications in neuroscience, reservoir computing systems, and computational modeling of biological processes. Recent work addresses device-agnostic modeling, physical neural networks, and stochastic computing platforms. Significant grants include: MARCH: Magnetic Architectures for Reservoir Computing Hardware (£936,815, Co-PI) ActiveAI - active learning and selective attention for robust AI (£953,584, Co-PI) Modeling probabilistic reinforcement learning in Drosophila (Google, £50,769, PI) CausalXRL: Causal explanations in Reinforcement Learning (£309,915, PI) Brains on Board: Neuromorphic Control of Flying Robots (£2,128,934, Co-PI) Leads the Machine Learning research group and collaborates with the Complex Systems Modelling group, focusing on brain-inspired computing architectures and neuromorphic hardware design.