Find Academic Supervisors

Alex RoitershteinView profile

Associate Professor

• Probability Theory
• Stochastic Processes
• Statistical Models
• +5 more

Alex Roitershtein is an Instructional Associate Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on probability theory, stochastic processes, and their applications in statistical models, combinatorics, and complex systems. He has contributed to areas including random walks, Markov processes, evolutionary dynamics, and network science. His work often bridges theoretical developments with practical applications in infrastructure resilience, genetic models, and algorithmic combinatorics. Roitershtein’s recent publications explore topics ranging from optimal resource allocation strategies in financial systems to extinction dynamics in evolutionary models. He has also investigated network robustness through Markovian influence graphs and studied combinatorial patterns in permutations and words. His research frequently employs probabilistic methods, generating functions, and stochastic analysis to address both foundational and applied questions. His articles reflect a multidisciplinary approach, with contributions to statistical physics (e.g., avalanche dynamics in excitable networks), computational biology (e.g., Wright-Fisher population models), and discrete mathematics (e.g., enumeration of staircase patterns). While no specific awards are listed, his extensive publication record demonstrates sustained scholarly activity in probability and its applications.

Jonathan WrightView profile

Professor

• Time Series Econometrics
• Empirical Macroeconomics
• Finance
• +4 more

Jonathan Wright is a Professor of Economics at Johns Hopkins University, affiliated with the Krieger School of Arts & Sciences. He holds a PhD from Harvard University, an M.Sc. from the London School of Economics, and a B.A. from Trinity College Dublin. His research focuses on time series econometrics, empirical macroeconomics, and finance, with particular emphasis on forecasting, high-frequency news effects, term structure analysis, and weak identification issues. He has held editorial roles at prominent journals such as the Journal of Applied Econometrics and the Journal of Econometrics. Notable awards include being a Fellow of the International Association for Applied Econometrics (2017) and the Journal of Econometrics (2024). His work spans central bank policies, asset pricing, and macroeconomic modeling, with extensive contributions to understanding monetary policy impacts and financial market dynamics. Education: PhD, Harvard University (1997); M.Sc., London School of Economics (1991); B.A., Trinity College Dublin (1990). Employment highlights include roles as a Research Associate at the National Bureau of Economic Research and Acting Director of the Center for Financial Economics at Johns Hopkins. He has also served as an economist at the Federal Reserve Board and has taught courses in econometrics, macroeconomics, and financial markets. His research interests span a broad range of topics, including inflation dynamics, yield curve analysis, and the effects of unconventional monetary policy. He has authored over 50 peer-reviewed articles and book chapters, with recent work focusing on structural breaks in the Phillips curve, quantitative easing effects, and forecasting methodologies under model uncertainty.

Piotr FryzlewiczView profile

Professor

• Time Series Analysis
• Change-Point Detection
• High-Dimensional Statistics
• +4 more

Piotr Fryzlewicz is a Professor of Statistics in the Department of Statistics at the London School of Economics and Political Science (LSE). He specializes in time series analysis, change-point detection, high-dimensional statistics, and statistical learning. His research includes methodologies like Wild Binary Segmentation for multiple change-points, Haar-Fisz transformation for non-Gaussian data, and multiscale modeling. Fryzlewicz holds a PhD in Statistics from the University of Bristol (2003) and an MSci in Mathematics from Wrocław University of Science and Technology. Affiliations: London School of Economics (since 2011) Former roles: University of Bristol, Imperial College London, Winton Capital Management Research Interests: Focuses on statistical methods for nonstationary data, multiscale techniques, and applications in finance, neuroscience, and social sciences. His work emphasizes practical implementations through R packages like breakfast and nsp . Key Achievements: Recipient of the Guy Medal in Bronze (2013) from the Royal Statistical Society Author of over 50 peer-reviewed articles and book chapters Developed widely cited methods in change-point detection and volatility modeling Teaching & Supervision: Currently teaches Financial Statistics (ST436) . Past PhD students include notable researchers in statistical theory and applications. Advises on doctoral research in time series and machine learning.

• Macroeconomics
• Macroeconomic Policy
• Macroprudential Policy
• +5 more

Professor Joseph Pearlman holds the position of Professor of Economics at City St George's, University of London. He has a PhD in Control Systems from Imperial College and an MSc in Economics from the London School of Economics (LSE). His research focuses on macroeconomic and macroprudential policy, endogenous growth theories, and the implications of imperfect information on Vector Autoregression (VAR) analysis. His work extends into expectational learning dynamics and the application of DSGE models to real-world economic issues like oil price impacts and financial stability. Key academic roles include research associate positions at the European Central Bank and International Monetary Fund. His publications span over four decades, addressing topics such as monetary policy rules, fiscal-monetary coordination during crises, and global economic integration. Notable contributions include works on robust policy design using prediction pools and validation techniques for DSGE models under uncertainty. His research has explored regional economic disparities in North-South frameworks, the role of innovation cycles in growth, and the mechanics of European monetary integration. He frequently collaborates with institutions like the IMF, Federal Reserve, and academic networks in macroeconomic modeling and policy analysis. His scholarly outputs include edited volumes analyzing the UK economy post-2008, contributions to the Oxford Handbook of the Indian Economy, and methodological guides on DSGE model construction published in the Handbook of Research Methods in Empirical Macroeconomics. Labs/Teams: Active in the DSGE modeling community, collaborating with central banks and international organizations on policy-oriented research. His work bridges theoretical macroeconomics with applied econometric techniques for policy evaluation.

Zeynep Ozde KurterView profile

Lecturer

• Macro-Finance
• Financial Economics
• Macroeconomics
• +3 more

Zeynep Ozde Kurter is a Teaching Fellow in Macro-Finance at the University of Warwick's Department of Economics since October 2021. She coordinates assessments, curricula, and supervises research projects. Her roles include lecturing on EC230 Economics of Money and Banking (BSc), leading EC988 Investment and the Financial System (MSc), and teaching econometrics/macroeconomics seminars. She holds a PhD in Economics and Finance from Birkbeck, University of London (2023 Best Thesis Award), an MSc in Economics from the University of Bologna (2012), and a BA in Economics from Ege University (2010). She was previously a lecturer at LSE, UCL, and Birkbeck, with 6+ years of teaching experience across macroeconomics, econometrics, and financial markets. Her research focuses on macroeconomic determinants of sovereign/systemic risk, employing advanced econometric methodologies like Markov Switching, PARDL models, and panel VAR. Current projects analyze financial stability linkages between macroeconomic factors and market dynamics. Zeynep is a Fellow at the Rimini Centre for Economic Analysis (RCEA) and maintains active collaborations through co-authored research. She advises MSc dissertations, supervises undergraduate research (URSS), and contributes to teaching innovation at Warwick's Institute for Advanced Teaching and Learning (IATL).

• Data Science
• Computational Statistics
• Bayesian Models
• +5 more

Antonietta Mira is a Full Professor of Statistics at the University of Italian Switzerland (USI), where she also serves as the founder and director of the Data Science Lab. She holds a part-time professorship at Università dell'Insubria and has held visiting roles at institutions such as the University of Cambridge and Université Paris-Dauphine. Her academic leadership includes a Vice-Dean role in the Faculty of Economics (2013-2015). She earned a PhD in Computational Statistics from the University of Minnesota (1998), a Master's in Statistics (1996), and a Doctorate in Methodological Statistics from the University of Trento (1995). Her research focuses on Bayesian statistical models, computational statistics, and interdisciplinary applications in healthcare, finance, and social sciences. Mira is a Fellow of the International Society for Bayesian Analysis (ISBA) and the Istituto Lombardo Accademia di Scienze e Lettere. Her work has been recognized with awards for both research and teaching excellence. She leads Swiss National Science Foundation-funded projects and serves on editorial boards of journals like Bayesian Analysis and Statistica Sinica . Her publications span topics such as approximate Bayesian computation (ABC), spatio-temporal modeling of health events, and data science applications in public policy. Mira also engages in public outreach, including scientific exhibitions like Numbed by Numbers! and lectures on data-driven societal issues.

Jackie LiView profile

Professor

• Stochastic Mortality Modelling
• Longevity Risk Pricing and Hedging
• General Insurance Claims Reserving
• +3 more

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 McIntyreView profile

Professor

• Applied Economics
• Regional Economics
• Labour Markets
• +3 more

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 LiveraniView profile

Professor

• Bayesian Modelling
• Clustering Methods
• Computational Statistics
• +7 more

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
• +3 more

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
• +3 more

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 BarkerView profile

Research Fellow

• Migration
• International macroeconomics
• Fiscal policy
• +5 more

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 GibsonView profile

Professor

• Data Compression
• Speech Coding
• Image and Video Compression
• +5 more

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 AlkhatibView profile

Researcher

• Bayesian Modelling
• Monte Carlo Methods
• Uncertainty Modelling
• +5 more

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
• +3 more

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.