Find Academic Supervisors

George TzougasView profile

Associate Professor

• Actuarial Science
• Applied Statistics
• Computational Statistics
• +5 more

George Tzougas is an Associate Professor in the Department of Actuarial Mathematics & Statistics at Heriot-Watt University, Edinburgh. He serves as Academic Director of the Scottish Financial Risk Academy and Academic Chair of the IFoA’s Data Science Research working party on Statistical Learning in Actuarial Applications. Previously, he held a tenured position at the London School of Economics (LSE), where he was Programme Director for the BSc in Actuarial Science and a member of the Royal Statistical Society. His research focuses on applying statistical learning and computational methods to non-life insurance, finance, and climate change impact analysis. Key areas include insurance ratemaking, optimal reinsurance, green bond pricing, and climate-related risk modeling. He has published extensively on topics such as multivariate claim count models, machine learning for carbon price forecasting, and zero-inflated regression techniques. Education: Tenured position at LSE Department of Statistics (previously Programme Director for BSc Actuarial Science) Lecturer at University College Cork, Ireland Notable awards include multiple Excellence in Education Awards from LSE (2017–2020) and the Best Class Teacher Award (2017). His work contributes to UN Sustainable Development Goals related to climate action and sustainable finance. He advises on PhD projects involving climate change hazards in insurance and has taught courses such as Time Series and Machine Learning. Research collaborations span institutions globally, with a focus on interdisciplinary applications of actuarial science and data science.

John HughesView profile

Associate Professor

• Biostatistics
• Spatial Statistics
• Bayesian Methods
• +4 more

John Hughes is an Associate Professor and Chair of the Department of Biostatistics and Health Data Science at Lehigh University's College of Health. He holds a PhD in Statistics from Penn State University and has over 29 years of experience in academia, with previous appointments at institutions like the University of Minnesota and the University of Colorado. His research focuses on methodological advancements in statistical modeling for dependent data, Bayesian methods, and statistical computing. His interdisciplinary work spans environmental health, bioimaging, magnetic resonance safety, and vaccine hesitancy. He has developed numerous software packages for R and Perl, including copCAR and batchmeans. Education: PhD in Statistics, Penn State University MS in Statistics, Penn State University MS in Applied Computer Science, Frostburg State University BS in Mathematics and Computer Science, Frostburg State University Teaching: Courses include Advanced R Programming, Biostatistics, Population Health Data Science, and Computational methods. His research interests emphasize spatial and spatiotemporal data analysis, with applications to public health and medical imaging. He has consulted for organizations such as the Minnesota Center for Chemical and Mental Health and Temple University. His software contributions include packages like krippendorffsalpha for agreement measurement and copCAR for spatial regression modeling. Recent work focuses on improving statistical inference methods, analyzing vaccination refusal patterns, and developing frameworks for copula-based agreement coefficients. His articles highlight innovations in Bayesian computation, spatial epidemiology, and nonparametric statistics. Dr. Hughes leads academic initiatives in biostatistics and health data science, fostering interdisciplinary collaborations and advancing statistical methodologies for real-world applications.

Julien ChiquetView profile

Part-Time Lecturer

• Statistics
• Machine Learning
• Sparse Methods
• +10 more

Julien Chiquet is a researcher and part-time lecturer at École Polytechnique (2020-2024), and the head of the UMR MIA Paris-Saclay research unit. He is affiliated with AgroParisTech on the Paris-Saclay campus and leads the SOLsTIS team. He serves as the chief editor of the journal Computo , focusing on computational statistics and data science. Education: Habilitation (2015) Research Interests: His work bridges statistical methodology and life sciences, with a focus on developing and applying advanced statistical methods such as sparse models, multivariate analysis, and optimization techniques. Key areas include dimension reduction in high-dimensional count data, probabilistic graphical models, and applications in ecology, genomics, and metagenomics. He emphasizes reproducible research practices, recently developing a Quarto template for HCERES reports integrating LaTeX and Pandoc. Recent Contributions: Recent work includes advancements in the Poisson lognormal model, stability selection via the sharp R package, and Python tools like pyPLNmodels for analyzing multivariate count data. His research often addresses challenges in ecological network inference, scRNA-seq analysis, and genomic prediction. Labs & Teams: Based at UMR MIA Paris-Saclay, he contributes to the SOLsTIS team, advancing computational methods for biological data analysis. The lab relocated to the new Palaiseau campus in 2022, fostering interdisciplinary collaborations.

Benjamin LauderdaleView profile

Professor

• Political Science
• Public Opinion
• Political Behavior
• +5 more

Benjamin Lauderdale is a Professor of Political Science and Head of Department at University College London's Department of Political Science, within the Faculty of Social and Historical Sciences. His research focuses on developing innovative survey experimental designs to measure public opinion and political behavior, including studies on voter priorities, political rhetoric, fiscal preferences, and moral foundations. He has held roles as Senior Data Science Advisor at YouGov (2016–2021) and Associate Editor of the American Political Science Review (2016–2020). Previously, he worked at the London School of Economics (2011–2018). His work spans methodological advancements, including social science measurement frameworks and legislative/judicial preference analysis. Key recent contributions examine polarization in political priorities, the persuasiveness of populist rhetoric, and moral foundations across ideological groups. His research integrates experimental, observational, and text-based data to address core questions in political behavior and representation.

• Particle Physics
• Higgs Phenomenology
• Top Quark Physics
• +4 more

Christoph Englert is a Professor of Theoretical Physics at the University of Glasgow, affiliated with the School of Physics and Astronomy. He joined the university in July 2013 and focuses on Particle Physics beyond the Standard Model, particularly Higgs and top quark phenomenology, radiative corrections, and Monte Carlo event generation. He has held roles on committees such as the STFC's CASE studentship panel and the CERN Fellowship committee, demonstrating leadership in academic governance and research promotion. His research explores BSM physics with an emphasis on Higgs boson interactions, electroweak phase transitions, and collider signatures. Englert has contributed to studies at the Large Hadron Collider (LHC) and future colliders like the High-Energy LHC (HE-LHC) and the Future Circular Collider (FCC). His work bridges theoretical predictions with experimental data, advancing understanding of fundamental interactions and new physics scenarios. Englert's publications span over 130 articles, addressing topics from Higgs self-couplings to dark matter probes and gravitational wave detector applications. He actively engages in interdisciplinary collaborations, including work on optical triangulations of curved spaces and machine learning techniques for particle physics analysis.

• Economic Theory
• Asset Pricing
• Monetary Policy
• +7 more

Jianjun Miao is a Professor recognized for his significant contributions to economic theory, evidenced by his 2025 election as Fellow of the Society for Advancement of Economic Theory. His academic work bridges theoretical economics with practical policy applications across multiple domains. His research spans economic theory, asset pricing, monetary and fiscal policy, rational inattention, and asset bubbles. Miao's work demonstrates particular expertise in modeling decision-making under uncertainty, financial market dynamics, and macroeconomic policy interactions. His research integrates advanced mathematical techniques with real-world economic phenomena, focusing on how information frictions and ambiguity affect market outcomes and policy effectiveness. Analysis of his 2022-2025 publications reveals three dominant research trajectories: (1) asset bubbles and their macroeconomic implications, (2) rational inattention frameworks in discrete choice and asset pricing, and (3) fiscal-monetary policy coordination in constrained environments. His work consistently addresses methodological challenges in modeling uncertainty while providing policy-relevant insights for financial stability and economic growth. Scientific Awards: Fellow of the Society for Advancement of Economic Theory (2025) Professor Miao maintains an active research program with substantial scholarly output. His work appears in leading economics journals and influences both academic discourse and policy discussions regarding financial stability, economic growth, and optimal policy design under uncertainty. He contributes significantly to academic knowledge through theoretical model development and empirical applications.

Agostino NobileView profile

Lecturer

• Bayesian Statistics
• Computational Statistics
• Finite Mixture Distributions
• +5 more

Agostino Nobile is a Lecturer in Statistics at the University of York, affiliated with the Mathematics department. He holds a PhD in Statistics from Carnegie Mellon University, following undergraduate studies in Economics at La Sapienza in Rome. His career includes post-doctoral roles at NISS/Duke University and the University of Bristol, and a previous Lectureship at the University of Glasgow before joining York in 2014. His research focuses on Bayesian Statistics and Computational Statistics, with emphasis on finite mixture distributions, time series models, discrete choice models, and hierarchical models applied to forensic compositional data. His work includes methodological advancements in Monte Carlo methods and practical applications in forensic science, such as classification of glass fragments. Teaching responsibilities include Bayesian Statistics, Survival Analysis, and Mathematical Skills courses at both undergraduate and postgraduate levels. Education : Bachelor's in Economics (La Sapienza, Rome) PhD in Statistics (Carnegie Mellon University)

Robert KieschnickView profile

Associate Professor

• Corporate Finance
• Mergers and Acquisitions
• Corporate Restructuring
• +5 more

Robert Kieschnick is an Associate Professor of Finance and Managerial Economics at the Naveen Jindal School of Management, University of Texas at Dallas. He has held academic positions since 1987, including roles at George Mason University, University of Manitoba, and the University of Texas at Austin. His research focuses on corporate financing, mergers & acquisitions, working capital management, IPOs, and corporate governance. He directs the MS in Financial Technology and Analytics program and previously coordinated the Finance and Managerial Economics group. Education includes a PhD in Finance from the University of Texas at Austin (1987), MA in Economics from UT Austin (1976), and B.A. in Economics from Southern Methodist University (1972). Professional experience includes roles as a Senior Economist at the Federal Communications Commission (1994–1999) and Senior Financial Analyst at Southern Union Gas Company (1984–1986). Research interests emphasize corporate restructuring, valuation methods, and risk management strategies. Key publications address IPO underpricing, dual-class stock decisions, and the impact of operating leases on corporate finance. Awards include the Teppo Martikainen Best Paper Award (2001) and membership in Phi Kappa Phi. Grants include a three-year SSHRC Canada grant (2000–2003) for studying Canadian dual-class firms and a National Cable Television Association grant (2001) on cable diffusion. Current research explores algorithmic trading, spillover effects in executive compensation, and nonstationarity in governance-accounting relationships.

Gauti B EggertssonView profile

Professor

• Macroeconomics
• Monetary Policy
• Liquidity Traps
• +3 more

Gauti B. Eggertsson is the Dupee Family Professor of Social Science at Brown University, specializing in macroeconomics and monetary policy. He holds a PhD from Princeton University (2004) and a BS from the University of Iceland (1997). His research focuses on liquidity traps, secular stagnation, fiscal and monetary policy interactions, and historical economic crises like the Great Depression and Great Recession. Teaching includes advanced macroeconomics courses (ECON 1225, 2070, 2080) and specialized topics in macroeconomics and monetary policy. His work bridges theoretical models with empirical analysis, addressing contemporary challenges such as inflation dynamics, debt management, and central bank strategies. Collaborations span global institutions, emphasizing policy relevance and historical context. Key contributions include the 'Unified Theory of Liquidity Traps' and analyses of post-2008 financial crises. Eggertsson critiques conventional policy frameworks, advocating for adaptive approaches to modern economic complexities. His research underscores the interplay between institutional structures, policy design, and long-term economic stability.

Sergio BacalladoView profile

Associate Professor

• Bayesian methods
• Bayesian nonparametrics
• Markov models
• +3 more

Sergio Bacallado serves as an Associate Professor in the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, where he is affiliated with the Statistical Laboratory. His academic home is within one of the world's leading mathematics departments, contributing to both theoretical and applied statistical research. Dr. Bacallado's research program centers on Bayesian methods and nonparametrics, with particular emphasis on Markov models and their applications to biological systems and biophysics. His work bridges theoretical statistics with practical applications in drug discovery, pandemic analysis, and microbiome research, demonstrating how sophisticated statistical methods can solve complex problems in the life sciences. His publication record from 2015-2024 reveals a consistent trajectory of methodological innovation with increasing interdisciplinary impact. Early work focused on theoretical foundations of Bayesian nonparametrics and Markov processes, while more recent publications address pressing challenges in pharmaceutical science (molecular docking, adverse drug reaction prediction) and public health (pandemic analysis). This evolution demonstrates his ability to adapt statistical theory to emerging scientific needs. Though no specific awards are listed in the available information, his publications in top-tier journals including the Journal of the American Statistical Association, Annals of Applied Statistics, and Journal of the Royal Statistical Society Series B indicate recognition within the statistical community. As an Associate Professor at Cambridge, Dr. Bacallado likely supervises PhD students and postdoctoral researchers in statistical methodology, though specific advisees aren't mentioned in the available materials. His collaborations span computational chemistry, epidemiology, and microbiome research, suggesting an active role in interdisciplinary grant-funded research. Based at the Statistical Laboratory within DPMMS, he contributes to one of the world's premier centers for mathematical statistics, working alongside colleagues who advance both theoretical foundations and practical applications of statistical science.

Edouard ChalleView profile

Professor

• Macroeconomic Policy
• Household Heterogeneity
• Monetary Policy
• +5 more

Professor Edouard Challe is a Professor of Economics at the Paris School of Economics and a Senior Researcher at CNRS (Centre National de la Recherche Scientifique). He also serves as an Associate Editor for the European Economic Review. His academic career spans multiple institutions and research collaborations, with a focus on macroeconomic policy with household heterogeneity. His primary research interests include: Macroeconomic Policy with Household Heterogeneity Monetary Policy and Inequality Business Cycle Fluctuations Precautionary Saving Behavior Financial Frictions in Macroeconomic Models Professor Challe has made significant contributions to the field of macroeconomics, particularly in understanding how household heterogeneity affects macroeconomic outcomes and optimal policy design. His work on precautionary saving over the business cycle has been influential in developing tractable models of heterogeneous agent economies. He has extensively researched the implications of incomplete markets, unemployment risk, and monetary policy in various economic contexts. His recent publications focus on the intersection of inequality and optimal monetary policy, examining how distributional concerns should factor into central bank decision-making. His work on HANK (Heterogeneous Agent New Keynesian) models has provided new insights into the transmission mechanisms of monetary policy and the importance of household-level heterogeneity for macroeconomic stabilization. Professor Challe has received recognition for his scholarly contributions through invitations to present his work at numerous international conferences and workshops. His textbook 'Macroeconomic Fluctuations and Policies' published by MIT Press in 2019 serves as an important resource for students and researchers in the field. He maintains active research collaborations with economists across Europe and has been involved in several discussion panels analyzing recent economic developments and policy challenges.

• International Economy
• Climate Policy
• Macroeconomics
• +4 more

Dr. Larry Liu is a researcher at the Centre for Applied Macroeconomic Analysis (CAMA) at the Australian National University (ANU). His work focuses on macroeconomic analysis with an emphasis on climate policy, demographic change, and international economic dynamics. He specialises in large-scale economic modelling frameworks (e.g., G-Cubed model) to assess policy impacts on global and regional economies. Key research areas include climate change mitigation strategies, the macroeconomic implications of ageing populations, and the interplay between environmental taxation and international trade. His research explores topics such as: Long-term economic projections under climate scenarios Demographic shifts and their effects on savings/investment patterns Climate policy design and border carbon adjustments Public goods provision mechanisms at international level Modelling global production networks and trade dynamics Dr. Liu collaborates extensively with international institutions including the International Monetary Fund (IMF) and the Network for Greening the Financial System (NGFS). His current project examines China's population ageing and its intersection with climate policy. He has published widely in top economics journals and contributed to policy-oriented reports for global stakeholders.

Zhengyang ZhouView profile

Associate Professor

• Aging
• Alzheimer's disease
• Biostatistics
• +5 more

Dr. Zhengyang Zhou is an Associate Professor in the College of Public Health at the University of North Texas Health Science Center, Department of Population and Community Health. His research focuses on statistical methodology development and collaborative interdisciplinary studies in aging/Alzheimer’s disease, health behaviors/substance use, and statistical genetics. He holds a PhD in Biostatistics from Southern Methodist University and has been actively funded since 2017 with 16 sponsored projects including NIH grants. His work includes 56 peer-reviewed publications with an h-index of 14 and 523 citations. Education: BS in Mathematics, Beijing Institute of Technology MS in Statistics, Southern Methodist University PhD in Biostatistics, Southern Methodist University Research Interests: Statistical method development for real-world challenges Evaluation of existing statistical methods Large-scale interdisciplinary collaborations in Alzheimer’s disease, substance use, and genetic epidemiology Awards: 2021 Junior Investigator Award, Research Society on Alcoholism Grants & Projects: PI/Co-I on 16 projects including NIH/NIA grants (e.g., Health & Aging Brain Study-HD) Focus areas: computational tools, genomic screens, and population health disparities Labs/Teams: Leads a research group studying Alzheimer’s disparities in Mexican/African American populations using advanced ML/AI and statistical techniques.

Dipak DeyView profile

Professor

• Bayesian analysis
• Bioinformatics
• Biostatistics
• +8 more

Dipak Dey is a Professor in the Department of Statistics at the University of Connecticut . His work bridges theoretical and applied statistics, with a focus on Bayesian methodologies and computational statistics. Affiliations : University of Connecticut, Department of Statistics Contact : dipak.dey@uconn.edu , Office: AUST 327, Phone: (860) 486-4755 His research interests span Bayesian analysis , Biostatistics , Computational statistics , Statistical genetics , and Spatio-temporal modeling . He has pioneered techniques in spatial curvature processes and scalable Bayesian algorithms for large datasets. Recent publications highlight his contributions to Bayesian spatial modeling (blockNNGP, curvature processes), survival analysis (skew-t distributions, cure rate models), and computational statistics (variable selection in Gaussian processes, fast inference algorithms). Applications include insurance data , epidemiology , and environmental statistics . Scientific Awards : Board of Trustees Distinguished Professor (University of Connecticut) He actively collaborates on interdisciplinary projects and mentors researchers in advanced statistical methodologies for complex data structures.

Nicole ReinfeldView profile

Researcher

• Transportation Planning
• Commuting Behavior Analysis
• Cluster Analysis
• +3 more

Nicole Reinfeld is a Researcher at Frankfurt University of Applied Sciences, affiliated with Department 1: Architecture, Civil Engineering, and Geomatics. Her work focuses on transportation planning, cycling mobility, and data-driven analysis of urban commuting behavior. Specializes in entropy balancing and cluster analysis for decomposing mobility trends Key projects: ModelRad (bicycle traffic modeling), ParkRight (parking violation analysis) Active in teaching econometrics and production logistics courses Her research spans methodological innovations like zero-inflated regression models and behavioral studies on weather-clothing interactions. She contributes to policy discussions through conference presentations and publications in journals such as Transportation Research Record. Collaborates with colleagues like Dr. Tobias Hagen and Dr. Shahram Saki, particularly on parking violation studies. Leads focus group methodologies and participates in mobility tech development (e.g., FlowMo gamification app).