جستجوی استاد راهنمای دانشگاهی

Jackie Liمشاهده پروفایل

استاد

• Stochastic Mortality Modelling
• Longevity Risk Pricing and Hedging
• General Insurance Claims Reserving
• +۳ مورد دیگر

Professor Jackie Li holds a dual PhD in Actuarial Studies (University of Melbourne) and Demography (Macquarie University). As a professor at Monash University's Department of Econometrics & Business Statistics, he specializes in stochastic mortality modeling, longevity risk, and machine learning applications. He leads the Monash Actuarial Program, elevating its global ranking to 7th in UNL's 2020-2024 assessment. Professor Li has secured AUD$800,000 in research funding and serves on editorial boards of journals like Forecasting and Risks . His work addresses UN SDGs through actuarial solutions to longevity challenges. Research Interests: Stochastic mortality modeling Longevity risk pricing and hedging Machine learning in actuarial science General insurance claims reserving Recent Articles: Focus on Bayesian methods, ensemble forecasting, and neural networks applied to mortality and health expectancy analysis. Highlights include systematic vector autoregressive frameworks and manifold learning approaches. Awards: FIAA, FSA, and multiple teaching awards at university, school, and department levels. He is also an Honorary Professor at Macquarie University and serves as an independent examiner for UK actuarial bodies.

Stuart McIntyreمشاهده پروفایل

استاد

• Applied Economics
• Regional Economics
• Labour Markets
• +۳ مورد دیگر

Stuart McIntyre is a Professor of Economics at the University of Strathclyde and Associate Dean (Research Impact) at the Strathclyde Business School. His research focuses on applied economics, regional economies, labor markets, and public policy, with an emphasis on policy-relevant analysis. He is a Fellow of the Office for National Statistics and a Research Associate at the ONS-funded Economic Statistics Centre of Excellence. Stuart's work contributes to UN Sustainable Development Goals related to economic growth and sustainable cities. He holds extensive teaching experience, currently instructing Macroeconomics and the International Business Environment on the Strathclyde MBA. His research has been funded by the ESRC, Nuffield Foundation, and other organizations. Notable projects include the Blue Triangle Recovery Service Evaluation (2025–2026) and Transforming Forecasting Capacity in Government (2023–2028). His research interests span regional nowcasting, mixed-frequency econometric models, and the socio-economic impacts of policy interventions. Recent work includes analyzing homeworking trends via job advertisements and reconciling GDP estimates for the US and UK. Stuart actively participates in workshops and advisory roles, such as the Regional Research Institute and advising the Department for Work and Pensions. Key impacts include improving policy analysis through novel macroeconometric methods, with applications in forecasting and regional development. His publications emphasize timely economic indicators and structural analysis, contributing to both academic and governmental decision-making.

Silvia Liveraniمشاهده پروفایل

استاد

• Bayesian Modelling
• Clustering Methods
• Computational Statistics
• +۷ مورد دیگر

Dr Silvia Liverani is a Reader in Statistics and Head of the Centre for Probability, Statistics and Data Science at Queen Mary University of London's School of Mathematical Sciences. Her research focuses on Bayesian methods, clustering, and spatio-temporal modeling with applications in epidemiology, environmental health, and genetics. She collaborates with institutions like the Royal Botanical Gardens, Kew, and has advised three PhD students. She leads interdisciplinary projects including a £1,000 LMS-funded LGBTQ+ Mathematics Open Day initiative and hosts international seminars. Research interests include Dirichlet process mixtures, causal methods, and health data modeling. Notable publications (2020–2025) address causal adversarial analysis, urban health inequalities, and Bayesian graph structures. Current projects involve biodiversity modeling using spatial statistics and misaligned data techniques. She has been cited in media outlets like phys.org and The Conversation for studies on cerebral palsy health risks and biodiversity loss. Her academic leadership includes roles in Equality, Diversity & Inclusion and collaborations with charities/industry. Recent grants include a Turing Institute Data Study Group project on biodiversity prioritization. Supervised PhD topics include Bayesian epidemiological modeling and biodiversity data analysis.

• Econometrics
• Macroeconomics
• Monetary Policy
• +۳ مورد دیگر

Lance Fisher is a PartTime Lecturer in the Department of Economics at Macquarie University, specializing in econometric modeling and macroeconomic policy analysis. His research spans over four decades with significant contributions to Australian economic studies. His academic qualifications include: B.A.(Hons) in Economics from Macquarie University M.Com. (Hons) in Economics from The University of New South Wales PhD in Economics from the University of Minnesota Fisher's research focuses on Wealth Economics, Error Correction Models, and Monetary Policy, with particular expertise in sign-restricted Structural Vector Autoregression (SVAR) methodologies. His work bridges theoretical econometrics with empirical analysis of Australian economic data, addressing critical issues in fiscal policy, exchange rate dynamics, and retirement income systems. Recent publications demonstrate innovative approaches to rotation matrices and impulse response analysis within macroeconomic frameworks. Analysis of his 2021-2025 publications reveals consistent methodological advancement in SVAR applications, with increasing emphasis on Australian-specific policy questions including government spending multipliers and wage determination. His research shows strong integration of mathematical econometrics with real-world macroeconomic challenges. No scientific awards are documented in the available information. Fisher has served as Chief Investigator on multiple significant research projects: "The use of sign restrictions in macroeconomic modelling" (active since 2017) "Roles for life annuities and related products in Australia's retirement income system" (2015-2016) "Endgame: managing superannuation in later life" (2012-2015) "Security in retirement: forecasting and managing macro economic investment risks" (2008-2011) These projects demonstrate sustained engagement with Australian retirement policy and macroeconomic risk management. His collaborative network includes prominent economists such as G. Kingston and H. Bateman, with recent methodological work developed alongside H.-S. Huh.

• Macroeconometrics
• Time Series Econometrics
• Forecasting
• +۳ مورد دیگر

Domenico Giannone is a researcher affiliated with the International Monetary Fund (IMF), specializing in macroeconometrics, time series analysis, and forecasting. His work focuses on developing statistical methods for large datasets, including Bayesian vector auto-regressions, nowcasting frameworks, and dynamic factor models. He has collaborated with prominent institutions like the European Central Bank and Federal Reserve, contributing to policy-relevant research on monetary policy, financial stability, and global interest rates. His research interests span structural macroeconomic modeling, high-dimensional data analysis, and the integration of real-time data into forecasting systems. Key contributions include methodologies for prior selection in vector autoregressions and analyses of the effects of central bank policies such as Outright Monetary Transactions (OMT). Giannone's publications address topics like the natural rate of interest, vulnerabilities in economic growth, and the informational content of macroeconomic data. His work emphasizes practical applications of econometrics to inform policy decisions, particularly in the context of global financial systems and institutional frameworks.

Emily Barkerمشاهده پروفایل

پژوهشگر ارشد

• Migration
• International macroeconomics
• Fiscal policy
• +۵ مورد دیگر

Emily Barker is a Research Fellow at the University of Southampton specializing in migration uncertainty across time horizons, affiliated with the Demographic and Migration Modelling research group and contributing to the Horizon Europe-funded QuantMig project. Her work integrates international macroeconomics, fiscal policy, and migration dynamics within emerging economies using advanced econometric techniques. Education PhD in Economics, University of Sheffield (2020). Thesis: "Essays on the Macroeconomics of Migration" evaluating migration's impact on open economies through DSGE models and SVAR, including analysis of brain waste in Canada and resource curse-migration relationships. Research Interests Dr. Barker's research centers on Migration and International Macroeconomics , with specific focus on Fiscal Policy implications in Emerging Economies . She examines business cycles, commodity prices, and open economy dynamics through Applied Macroeconometrics , employing DSGE models to quantify migration uncertainty and its economic ripple effects. Her work bridges theoretical frameworks with policy-relevant migration forecasting. Publication Trends Her 2020-2025 publications reveal a consistent focus on European migration forecasting through advanced VAR methodologies, early warning systems for asylum flows, and automation-migration interactions in aging societies. Collaborative work with Jakub Bijak dominates this output, characterized by empirical rigor in mixed-frequency and panel VAR applications within Horizon Europe's QuantMig framework. Scientific Awards Associate Fellow of the Higher Education Academy Advising and Grants Teaching: Undergraduate/postgraduate instruction in macroeconomics, microeconomics, mathematics, and macroeconometrics with project supervision experience. Funding: Horizon Europe support for QuantMig project developing migration forecasting tools and policy resilience models. Research Environment As core member of the Demographic and Migration Modelling research group, Dr. Barker collaborates on migration scenario analysis and early warning system development, contributing econometric expertise to interdisciplinary teams addressing European demographic challenges.

Jerry Gibsonمشاهده پروفایل

استاد

• Data Compression
• Speech Coding
• Image and Video Compression
• +۵ مورد دیگر

Dr. Jerry D. Gibson is a Distinguished Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB). He holds a PhD and MS from Southern Methodist University and a BS from the University of Texas. His research focuses on data/speech/image/video compression, wireless communications, information theory, and digital signal processing. He leads the ViVoNets Lab in Harold Frank Hall. Education: PhD in Electrical Engineering, Southern Methodist University MS in Electrical Engineering, Southern Methodist University BS in Electrical Engineering, University of Texas Research Interests: Dr. Gibson’s work spans foundational and applied topics in communications and signal processing, including predictive coding, rate distortion theory, and multimedia transmission over networks. His research emphasizes practical implementations for emergency communication systems and high-efficiency codecs. Awards and Recognition: IEEE Signal Processing Society Paper Award IEEE Medal of Honor Selection Committee Member of the National Academy of Engineering Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Advising & Grants: While specific student names are not listed, Dr. Gibson has advised numerous graduate students in communications and signal processing. His work has been supported by grants from the NSF Science and Technology Center for Science of Information. Labs & Teams: The ViVoNets Lab focuses on video and voice networking, developing low-complexity coding schemes for UAVs and first responders. Collaborations include cross-disciplinary projects in 3D video communication and emergency communication systems.

Hamza Alkhatibمشاهده پروفایل

پژوهشگر

• Bayesian Modelling
• Monte Carlo Methods
• Uncertainty Modelling
• +۵ مورد دیگر

Hamza Alkhatib is a Senior Researcher at the Geodetic Institute within the Faculty of Civil Engineering and Geodetic Science at Leibniz University Hannover. His research focuses on advanced computational methods in geodetic data analysis, including Bayesian modeling, Monte Carlo simulations, uncertainty quantification, and filtering techniques for multi-sensor systems. He leads projects funded by DFG, BfG, and BMVI, addressing topics like hydrographic monitoring, cooperative sensor networks, and machine learning applications in geodesy. Alkhatib holds a PhD from the University of Bonn (2007) and a Habilitation from Leibniz University Hannover (2020). Education & Career : PhD (2007): University of Bonn, Dissertation on Monte Carlo methods for satellite gravity missions Habilitation (2020): Leibniz University Hannover, Advanced methods in geodetic data analysis Senior Research Staff since 2019 Research Interests : Bayesian inference and robust statistical methods Filtering algorithms (Kalman, Particle Filters) for dynamic systems Machine learning for geodetic point cloud analysis and damage detection Sensor fusion and multi-sensor positioning systems Hydrographic monitoring and marine infrastructure assessment Key Projects : DFG-funded adaptive robust adjustment of geodetic measurement processes Research Training Group GRK 2159 on dynamic sensor networks WaMUT project for hydrographic water change zone monitoring 3D HydroMapper for port infrastructure inspection Awards : DAAD Award for Outstanding Achievements (2000) Syrian Ministry of Academic Education Award (1998) Teaching : Teaches courses on geodetic data analysis, recursive state estimation, and machine learning applications at both bachelor and master levels.

• Macroeconomics
• Monetary and Fiscal Policy
• Economic Epidemiology
• +۳ مورد دیگر

Dr. Lin Liu is a Lecturer in the Department of Economics at the University of Liverpool, where she has been since 2014. She holds a Ph.D. in Economics from the University of Rochester (2014). Her research focuses on macroeconomic policy analysis using empirical and quantitative methods, particularly examining the interplay between infectious diseases and the economy through dynamic general equilibrium models. She teaches courses such as Intermediate Macroeconomics, Advanced Macroeconomics, and Macroeconomics for postgraduates. Dr. Liu's research interests include dynamic stochastic general equilibrium (DSGE) models, economic fluctuations, and health-macro economy interactions. Her work explores policy responses to epidemics, fiscal/monetary policy effects, and the structural impacts of disease dynamics on economies. Recent publications in journals like Journal of Mathematical Economics and Canadian Journal of Economics highlight her contributions to economic epidemiology and fiscal policy analysis. Her advisory role includes supervising Ruoxi Cao. Research outputs emphasize methodological advancements in modeling epidemics within economic frameworks, with applications to real-world policy challenges like optimal lockdown strategies and tax shock impacts in Canada.

Yongli Wangمشاهده پروفایل

استادیار

• Time-series econometrics
• Forecasting under uncertainty
• Structural breaks
• +۴ مورد دیگر

Yongli Wang is an Assistant Professor (Lecturer) in Economics at the Department of Economics, Birmingham Business School, University of Birmingham, where he also serves as the Admissions Tutor for the Year Abroad Programme. He joined the University of Birmingham in March 2022 after previously holding a lectureship at the University of Essex from October 2019. PhD in Economics, University of Leicester, 2020 MSc Financial Economics, University of Leicester BSc Finance, Wuhan University, China His research is centered on time-series econometrics, with a strong focus on forecasting under uncertainty , structural breaks , and inflation modeling . His work applies advanced econometric techniques to analyze macroeconomic dynamics, particularly in the context of central bank forecasting and global inflation spillovers. Yongli Wang’s recent publications, from 2023 to 2024, prominently feature collaborations with leading econometricians and are published in top journals such as the Journal of International Money and Finance and the Journal of Forecasting . These works examine inflation forecasting performance across major central banks, the use of dynamic factor models, and regional spillover effects, reflecting a cohesive and high-impact research agenda in macroeconomic forecasting. He has no listed scientific awards in the provided text. Yongli Wang has held administrative roles including Deputy Director of Undergraduate Final Projects and Deputy Director of PGT Dissertations at the University of Essex. There is no mention of research grants or advising of students in the text. He does not appear to lead or be part of a named research lab or team based on the available information.

Tom McCoyمشاهده پروفایل

استادیار

• Computational Linguistics
• Cognitive Science
• Artificial Intelligence
• +۴ مورد دیگر

Tom McCoy is an Assistant Professor of Linguistics at Yale University, affiliated with the Department of Computer Science and the Wu Tsai Institute. He holds a PhD in Cognitive Science from Johns Hopkins University (2022) and was a postdoctoral fellow in Computer Science at Princeton University (2022–2023). His research bridges computational linguistics and artificial intelligence, focusing on how language is learned and represented in humans and neural networks. Key interests include syntax, inductive biases in AI systems, and connections between linguistic theory and neural network architectures. Education: PhD in Cognitive Science (Johns Hopkins University, 2022); Postdoctoral Fellow in Computer Science (Princeton University, 2022–2023). Research explores computational mechanisms underlying human language, including syntactic representations in neural networks and mechanisms for efficient language learning. He investigates how AI systems can be informed by linguistic insights and vice versa. Active in organizing the North American Computational Linguistics Open competition (NACLO) for high school students. Key affiliations include Yale’s Linguistics Department, Computer Science Department, and Wu Tsai Institute. Current activities include advising PhD students/postdocs and hosting research opportunities for Yale undergraduates.

Nils Thuereyمشاهده پروفایل

استاد

• Computer Graphics
• Fluid Dynamics
• Machine Learning
• +۳ مورد دیگر

Prof. Nils Thuerey is a Professor of Physically Based Simulation at the Technical University of Munich (TUM), within the TUM School of Computation, Information and Technology. He specializes in computer graphics, fluid dynamics, and machine learning applications in physics-based simulations. His work focuses on developing algorithms for fluid simulations, turbulence modeling, and differentiable physics solvers. He earned his doctorate in fluid simulations from the University of Erlangen-Nuremberg (2006) and held positions at ETH Zurich, ScanlineVFX, before joining TUM in 2013. Research Interests: Fluid Dynamics and Turbulence Modeling Machine Learning for Physics Simulations Deep Learning in Computer Graphics Differentiable Simulations and Inverse Problems Key Awards: Technical Oscar (2012) for contributions to fluid effects in films/games ERC Starting Grant (2014) for 'realFlow' research University of Erlangen Staedtler Graduation Award (2008) His recent work emphasizes integrating deep learning with physical models, such as differentiable solvers for fluid dynamics and probabilistic methods for weather forecasting. He leads projects like WeatherBench and develops tools like φ-ML and phiflow for scientific computing.

David Ruppertمشاهده پروفایل

استاد

• Functional data analysis
• Nonparametric regression
• Statistical machine learning
• +۷ مورد دیگر

David Ruppert is the Andrew Schultz Jr. Professor of Engineering at Cornell University's School of Operations Research and Information Engineering, and Professor of Statistics and Data Science. He holds dual appointments and has been a faculty member since 1987. His education includes a B.A. in Mathematics from Cornell University (1970), M.A. in Mathematics from the University of Vermont (1973), and Ph.D. in Statistics and Probability from Michigan State University (1977). Research Interests: His work spans functional data analysis, astrostatistics, neuroimaging (fMRI/ICA), environmental statistics, and semiparametric regression. He has pioneered methods in measurement error models, splines, and Bayesian statistics. His research has been continuously funded by NSF, NIH, and EPA since 1978. Publications: Over 130 refereed articles and 5 books, including foundational texts like Measurement Error in Nonlinear Models and Statistics and Data Analysis for Financial Engineering . Recent work includes astrostatistical modeling of galaxy spectral energy distributions and neuroimaging analysis. Awards/Honors: Wilcoxon Prize (1986), ASA/IMS Fellowships, Highly Cited Researcher (ISI), and Distinguished Alumni Award (2014). Teaching: Courses include Financial Engineering, Bayesian Statistics, and Functional Data Analysis. He co-developed four graduate/undergraduate courses at Cornell. Service: Editor of Journal of the American Statistical Association , Director of the MPS Program in Data Science and Statistics (DSS). Impact: 29 PhD students trained, many now leading researchers in academia and industry.

Özlem TÜRKER BAYRAKمشاهده پروفایل

دانشیار

• Statistics
• Time Series Analysis
• Quality Control
• +۵ مورد دیگر

Dr. Özlem TÜRKER BAYRAK is an Associate Professor at Çankaya Üniversitesi, primarily affiliated with the Department of Statistics under the Inter-Curricular Courses unit. She has held academic roles since 1996, including Assistant Professor at the Faculty of Engineering and Visiting Position at University of Waterloo. Her research focuses on statistical methodologies, time series analysis, quality control, environmental acoustics, and energy economics. She has advised 11 graduate students and contributed to projects like the Turkish soundscape evaluation and robust estimation techniques. She teaches courses such as Engineering Statistics and Systems Analysis. Key research areas include non-normal distribution modeling, soundscape perception, and energy market dynamics. Recent work includes studies on urban nightlife impacts and adaptive statistical inference. She has over 40 publications in journals like Applied Acoustics and Quality and Reliability Engineering International.

Anuradha Royمشاهده پروفایل

استاد

• Multivariate Analysis
• Repeated Measures Data
• Kronecker Structured Covariance Matrices
• +۵ مورد دیگر

Dr. Anuradha Roy is a Professor in the Department of Management Science and Statistics at the University of Texas at San Antonio (UTSA), affiliated with the Alvarez College of Business. She holds a Ph.D. in Applied Mathematical Sciences (Oakland University), an M.Stat. from the Indian Statistical Institute, and a B.Sc. (Mathematics) from Calcutta University. Her research focuses on multivariate analysis, particularly in structured covariance matrices and repeated measures data, with applications in biomedical and engineering fields. She has held visiting professorships at KTH Royal Institute of Technology (Sweden) and Universidade Nova de Lisboa (Portugal). Education: B.Sc. (Mathematics, Honors), 1978, Presidency College, Calcutta University M.Stat., 1981, Indian Statistical Institute, Calcutta Ph.D., 2002, Oakland University, Michigan Research interests include multivariate repeated measures analysis, Kronecker structured covariance matrices, mixed effects models, and symbolic data analysis. Her work bridges theoretical statistics with applied problems in medicine, engineering, and education. She has published in journals like Journal of Multivariate Analysis , Statistics in Medicine , and Biometrical Journal , and presented globally at conferences in over 20 countries. Dr. Roy actively seeks Ph.D. students with strong computing skills to collaborate on multivariate and symbolic data analysis projects. She has organized sessions at international conferences and contributed to methodological advancements in statistical testing and classification. Advising & Grants: Active in mentoring graduate students but specific grant details are not listed. Her research emphasizes collaboration across disciplines, with applications in bone mechanics, medical imaging, and educational analytics. Labs & Teams: Engaged in interdisciplinary projects, though no specific lab affiliations are mentioned.