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Martin TaylorView profile

Senior Lecturer

• Pure Mathematics
• Applied Mathematics
• Partial Differential Equations
• +3 more

Martin Taylor is a Royal Society Tata University Research Fellow and Senior Lecturer in the Department of Mathematics at Imperial College London. He is affiliated with the Pure Analysis and PDE group and the Geometry group within the Faculty of Natural Sciences. His research focuses on partial differential equations, general relativity, and kinetic theory. He has held postdoctoral positions at Princeton University (2018–2019) and Imperial College London (2016–2018), and completed his PhD in 2015 under Mihalis Dafermos at the University of Cambridge. Education: Martin Taylor earned his PhD in November 2015 from the University of Cambridge's Department of Pure Mathematics and Mathematical Statistics, supervised by Professor Mihalis Dafermos. His postdoctoral training includes roles at: Princeton University (Department of Mathematics and Gravity Initiative, 2018–2019) Imperial College London (Department of Mathematics, 2016–2018) Research Interests: Taylor’s work centers on partial differential equations (PDEs) , general relativity , and kinetic theory . He investigates stability properties of spacetime solutions, particularly focusing on black holes and cosmological models. His research integrates techniques from pure analysis, geometric PDEs, and mathematical physics to address fundamental questions in relativity. Publications: Taylor’s recent work emphasizes the stability of spacetime structures in relativity, including studies of Kerr black holes and Minkowski space. Earlier contributions include geometric analysis on sphere packing and collaborative efforts with leading researchers in the field. Advising & Grants: No formal advisees or grants are listed in the provided information. He has organized significant academic events such as the Münster--Imperial Day in PDE (2024) and workshops on Nonlinear Waves and Fluids (2024). His teaching includes Riemannian geometry courses at Imperial College London, alongside co-organizing seminars like the London PDE Seminar and London Analysis & Probability Seminar. Labs/Teams: Taylor is actively involved in Imperial’s Pure Analysis and PDE group and Geometry group, contributing to both research and academic leadership through event organization and collaborative initiatives.

Kiwon LeeView profile

Lecturer

• Stochastic optimization
• Machine Learning Education
• Probability Theory
• +2 more

Kiwon Lee is a Faculty Lecturer in the Department of Mathematics and Statistics at McGill University. His research focuses on stochastic optimization and machine learning education, contributing to the development of high-dimensional stochastic algorithms and their theoretical foundations. He holds a position within McGill's Probability Group and actively engages in advancing methodologies for optimization in complex systems. His work emphasizes the analysis of algorithm dynamics, parameter selection, and convergence properties in high-dimensional spaces. Lee's contributions bridge theoretical probability with practical machine learning applications, particularly in understanding batch size effects and step-size criticality in stochastic gradient methods. While no specific grants or lab affiliations are explicitly mentioned, his research interests align with computational and applied mathematics within the university's academic framework. He maintains an active presence on LinkedIn, reflecting professional engagements in his academic community.

• Dependence Modeling
• Copula Models
• Multivariate Analysis
• +7 more

Harry Joe is a Professor in the Department of Statistics at the University of British Columbia (Vancouver Campus). His primary research focuses on dependence modeling, copula theory, multivariate analysis, and applications in biostatistics, finance, and psychometrics. He has advised students including Xiaoting Li, Xinyao Fan, and Pavel Krupskiy. Research Interests: - Advanced copula constructions (e.g., vine copulas) - Extreme value theory and tail dependence - Applications in financial risk, biomedical research, and educational measurement - Multivariate time series analysis and non-Gaussian models Publications highlight contributions to copula-based classification methods (2024), factor copula models (2015), and dynamic dependence modeling (2020). His work bridges theoretical developments with practical applications across disciplines. Teaching and advising emphasize methodological innovation. Current research explores high-dimensional dependence structures and computational methods for complex data. No lab/team affiliations explicitly noted in provided materials.

Nic FreemanView profile

Lecturer

• Probability
• Stochastic Processes
• Random Geometry
• +3 more

Dr Nic Freeman is a Lecturer in Applied Probability and Programme Leader for Financial Mathematics at the School of Mathematical and Physical Sciences, University of Sheffield. He holds an academic position within the Department of Mathematics and is a member of the Probability research group. His research focuses on probability theory, stochastic processes, random geometry, fractals, and applications to population genetics and financial mathematics. Dr Freeman teaches courses such as MAS350 Measure and Probability, MAS352 Stochastic Processes and Finance, MAS451 Measure and Probability, and MAS452 Stochastic Processes and Finance. His work integrates theoretical probability with practical applications, including the study of preferential attachment models, coalescent processes, and spatial stochastic systems. He is affiliated with the Hicks Building, located in Room I20. Research interests span topics such as tightness criteria for random processes, Brownian web/net structures, and cluster dynamics in network models. His recent publications explore foundational aspects of stochastic processes and their convergence properties, contributing to both pure and applied probability theory. Dr Freeman collaborates within interdisciplinary teams and maintains an active research profile in mathematical probability.

Francis X. DieboldView profile

Professor

• Financial Markets
• Macroeconomics
• Climate Change
• +5 more

Francis X. Diebold is the Paul F. and Warren S. Miller Professor of Economics, Professor of Finance, and Professor of Statistics and Data Science at the University of Pennsylvania. He serves as Faculty Director of the Masters of Applied Economics and Data Science (MEDS) program. His research focuses on dynamic predictive modeling of financial markets, macroeconomic fundamentals, climate change, and their intersections. Key contributions include work on asset-return volatility, business cycles, yield curves, Arctic sea ice modeling, and network connectedness. Diebold has authored over 150 scientific papers and eight books, including Financial and Macroeconomic Connectedness and Yield Curve Modeling and Forecasting . He is a Fellow of numerous prestigious institutions such as the Econometric Society, American Statistical Association, and Guggenheim Foundation. His academic lineage includes over 75 PhD students. His research spans climate models, financial econometrics, and economic forecasting. Notable projects include analyzing Arctic sea ice dynamics and developing methodologies for measuring financial market connectedness. Diebold has held visiting positions at Princeton, Chicago, Johns Hopkins, and NYU. He is actively involved in editorial and advisory roles, including past presidency of the Society for Financial Econometrics and editorial board memberships for journals like Econometrica . His work bridges theoretical econometrics with practical applications in policy and climate adaptation.

Liudas GiraitisView profile

Professor

• Econometrics
• Time Series Analysis
• Quantitative Methods
• +3 more

Liudas Giraitis is a Professor of Econometrics at Queen Mary University of London, affiliated with the School of Economics and Finance. He holds a PhD from Vilnius University and has extensive research experience at Heidelberg University, Boston University, and the London School of Economics. His research focuses on econometrics and quantitative methods, emphasizing long memory processes, ARCH models, semiparametric inference, and time-varying random coefficient models. He has published extensively in leading statistical and econometric journals. His work bridges econometrics, statistics, and probability theory, with a strong emphasis on time series analysis and asymptotic theory. Recent research explores forecasting under ongoing structural change, heteroscedasticity testing, and dynamic stochastic general equilibrium (DSGE) models with financial frictions. Education: PhD in Econometrics from Vilnius University Key Research Themes: Long memory processes, ARCH/GARCH models, time-varying models, nonstationary time series, and asymptotic theory Affiliations: Queen Mary University of London, School of Economics and Finance Research Interests: Dr. Giraitis’s work addresses theoretical and applied challenges in econometric modeling, including robust inference for time series with general heterogeneity, cyclical time series analysis, and the development of asymptotic frameworks for dependent data. His contributions span both foundational theory and practical applications in finance and macroeconomics. Publications: Over 50 peer-reviewed articles in journals such as Journal of Econometrics , Annals of Statistics , and Journal of Time Series Analysis . Key works include the monograph Large Sample Inference for Long Memory Processes (2012) and recent studies on time-varying volatility and structural change. Lab/Team: Active in the School of Economics and Finance’s research group on econometric theory and applied time series analysis.

Donald E. K. MartinView profile

Associate Professor

• Statistical Theory
• Applied Statistics
• Time Series Analysis
• +4 more

Donald E. K. Martin is an Associate Professor of Statistics at North Carolina State University (NCSU), affiliated with the College of Sciences. He holds a Ph.D. in Mathematical Statistics from the University of Maryland (1990). His research focuses on statistical theory, time series analysis, and pattern distribution in sequences. He has received awards including the 2018 College of Sciences Faculty Diversity Professional Development Award and the 2021-2022 Dennis Boos Citizenship Award. Dr. Martin has extensive professional experience, including roles as Co-Director of Graduate Programs (2015-2017) and active participation in departmental committees. He has supervised numerous PhD students and contributed to initiatives like ADJOINT, promoting diversity in mathematics. His teaching spans courses in statistical theory and applications, such as ST370, ST380, and advanced topics in time series (ST782/783). His research emphasizes computational statistics and probabilistic methods, with recent work on sparse Markov models and scan statistics. Key contributions include methodologies for pattern recognition in sequences and efficient algorithms for statistical inference. Grants include NSF funding for categorical time series analysis (2018-2023) and prior projects on pattern distributions (2011-2014). He is an active reviewer for journals and conferences, and a member of the American Statistical Association's JEDI outreach group.

• Non-parametric statistics
• High-dimensional statistics
• Bioinformatics
• +4 more

Professor Jian Zhang is a Professor of Statistics at the University of Kent's School of Mathematics, Statistics and Actuarial Science. His research focuses on non-parametric and high-dimensional statistics, bioinformatics, computational biology, statistical genetics, neuroimaging methods, and Bayesian modeling. He has advised students including Jie Li and Tong Wang. His work spans theoretical advancements and applied methodologies across diverse fields such as genomics, neuroimaging, and biomedical data analysis. Publications highlight contributions to Bayesian inference, neuroimaging techniques, and statistical genetics. Notable collaborations include studies on mixture models for genetic association analysis and beamforming methods for functional connectivity. He holds an ORCID iD and is based at Canterbury Campus, University of Kent.

Ioannis KyriakouView profile

Professor

• Actuarial Science
• Quantitative Finance
• Financial Mathematics
• +13 more

Professor Ioannis Kyriakou is a leading academic in actuarial finance and quantitative methods at Bayes Business School, City St George's, University of London, where he serves as Professor of Actuarial Finance and Director of the MSc in Actuarial Science and MSc in Actuarial Management. He holds a visiting professorship at the University of Eastern Piedmont and has previously served as affiliate faculty at the Cyprus International Institute of Management. His research spans actuarial science, derivatives pricing, risk management, computational finance, and machine learning applications in finance and insurance. His research interests focus on quantitative finance , stochastic modeling , numerical methods , and machine learning , particularly in the context of derivative pricing , pension product design , energy and commodity markets , and investor sentiment . He has developed advanced computational techniques such as moment-based approximations and transform methods for financial modeling. His work integrates simulation, optimization, and data-driven approaches to solve complex financial and actuarial problems. The recent publications reflect a strong trend toward interdisciplinary research, combining machine learning with financial economics , energy efficiency forecasting , mutual fund performance , and climate risk . His work frequently appears in top journals like Operations Research , Journal of Financial and Quantitative Analysis , and European Journal of Operational Research , showcasing expertise in both theoretical and applied finance. European Journal of Operational Research (2020) Editors' Award for Excellence in Reviewing Cass Business School (2014) Prize for Excellence in Teaching and Learning Dimitris N. Chorafas Foundation (2009) Prize for outstanding PhD research work EPSRC Doctoral Training Award (2008) He supervises several PhD students in areas such as derivatives pricing, machine learning in actuarial science, and pension optimization. He has received research support through editorial leadership, consultancy (e.g., with Lloyd’s Treasury), and academic collaboration. He is actively involved in organizing conferences and workshops, including the Finance and Business Analytics Conference. He is affiliated with research groups focusing on financial modeling , actuarial computation , and machine learning in insurance , contributing to both academic and industry-facing initiatives. His co-authored book Machine Learning in Insurance (MDPI, 2020) highlights his leadership in bridging data science with actuarial applications.

• Quantum Cognition
• Cognitive Psychology
• Decision Making
• +7 more

Emmanuel M. Pothos is a Professor in the Department of Psychology at City, University of London, within the School of Arts and Social Sciences. He has held prior academic positions as Senior Lecturer at City, Reader at Swansea University, and Lecturer at both the University of Edinburgh and Bangor University, reflecting a sustained and progressive academic career in cognitive psychology. PhD in Psychology, University of Oxford (1995–1998) BSc in Physics, Imperial College London (1992–1995) His research primarily focuses on quantum cognition, where he applies the mathematical formalism of quantum theory to model cognitive phenomena such as decision-making, judgment, and similarity perception. He investigates violations of classical probability in human cognition, context effects, and the role of ambiguity and conflict in choices. His interdisciplinary work draws from psychology, physics, and computational modeling. The recent trend in his publications reveals a strong emphasis on formal modeling of cognitive processes using quantum probability, with applications extending into behavioral health, financial systems, and foundational questions in decision theory. His work frequently appears in high-impact journals across psychology, physics, and interdisciplinary sciences, indicating a broad scholarly impact. While no formal awards are listed in the provided data, his extensive publication record in top-tier journals and frequent peer-review activities for leading outlets such as Nature , Psychological Review , and PNAS reflect significant academic recognition and service. Dr. Pothos actively collaborates with researchers internationally and has contributed to theoretical advances in understanding how quantum-like structures can explain human cognitive biases and complexities. Although no direct mention of student supervision is made, his role as a professor and prolific researcher implies involvement in mentoring graduate students and leading research initiatives. He has not indicated any lab or research group name, but his work clearly involves collaborative projects in cognitive modeling and decision science.

Xuebin ZhangView profile

Assistant Professor

• Climate Change Detection and Attribution
• Climate Extremes
• Weather and Climate Extremes
• +7 more

Xuebin Zhang is an Assistant Professor in the Department of Geography at the University of Victoria and serves as the Director, President, and CEO of the Pacific Climate Impacts Consortium (PCIC). His role combines academic research with leadership in applied climate science, focusing on regional climate impacts in Canada and globally. Institution: University of Victoria School: Faculty of Social Sciences Department: Department of Geography Leadership: Director, President & CEO, PCIC Dr. Zhang holds a PhD from Lisbon, an M.Eng, and a B.Eng from Hohai University. His research centers on the detection and attribution of climate change, particularly focusing on weather and climate extremes at regional and global scales. He has made significant contributions to understanding how human activities influence extreme precipitation and temperature events. His recent publications demonstrate a strong trend in analyzing extreme climate events using advanced statistical methods such as optimal fingerprinting and Bayesian analysis. These studies often involve large international collaborations and utilize climate model simulations (e.g., CMIP6) to attribute observed changes to anthropogenic forcing. Key areas include extreme precipitation, temperature variability, drought, and flood events across North America and China. Dr. Zhang has been deeply involved in major scientific assessments: Coordinating Lead Author, IPCC 6th Assessment Report (Working Group I, Chapter on Extremes) Co-chair, Expert Team on Climate Change Detection and Indices (WCRP) Co-chair, Grand Challenge on Weather and Climate Extremes (WCRP) Fellow of the Royal Society of Canada He leads and mentors a broad team of scientists at PCIC, overseeing projects related to climate analysis, hydrologic impacts, and computational support. While specific student names are not listed, his leadership role implies extensive supervision of postdoctoral researchers and graduate students. He has secured significant research funding through PCIC and federal programs to support climate modeling, monitoring, and impact assessments. His work directly informs policy and adaptation planning in British Columbia and across Canada.

• Algorithms
• Computational Geometry
• Discrete Mathematics
• +8 more

Mordecai J Golin is a Professor in the Department of Computer Science and Engineering at The Hong Kong University of Science and Technology (HKUST), School of Engineering. His research lies at the intersection of theoretical computer science, algorithms, and discrete mathematics, with strong applications in information theory and computational geometry. His research interests include algorithms , computational geometry , data structures , dynamic programming , coding theory , and combinatorics . He has made significant contributions to the design and analysis of optimal search trees, prefix-free coding, and minmax regret optimization in dynamic flow networks. His work often combines probabilistic analysis with algorithmic efficiency. The recent publications highlight a sustained focus on optimization problems in graphs and trees, particularly in dynamic flow networks for applications like evacuation modeling, and in data compression via advanced Huffman and AIFV coding techniques. The research spans from theoretical foundations to algorithmic innovation, with recurring themes of efficiency, robustness, and structural analysis. Scientific Awards No specific awards mentioned in the provided text. Advising and Grants : While specific students and grants are not listed, Dr. Golin has an extensive record of collaborative research with colleagues at HKUST and internationally, suggesting active supervision and project leadership. His frequent publications in top-tier venues indicate sustained funding and research activity. Labs and Teams : Though not explicitly named, his work is likely conducted within theoretical computer science or algorithms research groups at HKUST, possibly associated with centers focusing on discrete mathematics or information sciences.

• Network Analysis
• Random Graph Theory
• High-Dimensional Statistics
• +5 more

Can M. Le is an Associate Professor in the Department of Statistics at the University of California, Davis, within the College of Letters and Science. His research lies at the intersection of statistics, network science, and high-dimensional data analysis, with a focus on theoretical and applied aspects of network modeling and inference. Ph.D. in Statistics, University of Michigan, Ann Arbor His research interests include network analysis, random graph theory, community detection, high-dimensional statistical inference, and regularization of network data. He develops methods for analyzing noisy, complex network structures and has contributed significantly to spectral methods and low-rank approximations in network science. His work bridges theoretical statistics with practical applications in social and biological networks. The recent publications demonstrate a strong trend in modeling and inference for network-linked data, with emphasis on robustness, adaptivity, and concentration properties of random graphs. His work combines deep probabilistic analysis with statistical methodology, particularly in community detection and network estimation under noise and heterogeneity. His research is supported by the National Science Foundation (NSF) grant DMS-2015134, indicating active funding and ongoing contributions to the field. While no formal list of advisees is provided, his collaborative work with leading statisticians such as Elizaveta Levina and Roman Vershynin suggests an active research group and mentoring role. He has no listed scientific awards in the provided text. However, his consistent publication record in top journals (JASA, JRSSB, Annals of Statistics, JMLR) underscores his scholarly impact. Dr. Le's work is closely tied to theoretical and applied statistical research on networks, likely involving a research lab or team focused on network data science, though specific lab names or team structures are not mentioned in the text.

Davide La VecchiaView profile

Professor

• Time series analysis
• Robust and semiparametric inference
• Financial econometrics
• +3 more

Davide La Vecchia is a Full Professor at the Research Institute for Statistics and Information Science (RISIS) at the University of Geneva, Switzerland. He holds dual PhDs in Statistics from Bocconi University (2007) and Economics from Università della Svizzera italiana (2011). His academic journey includes roles as an Assistant Professor at the University of St. Gallen and Monash University, and he has held visiting positions at Princeton University, the University of Copenhagen, and CREST (Paris). His expertise spans time series analysis, robust and semiparametric inference, financial econometrics, and spatial statistics. Key research contributions include work on saddlepoint approximations, optimal transportation methods, and latent variable models. Davide has led multiple grants, including from the Swiss National Science Foundation and the Australian Research Council. He serves as a referee for top-tier journals in statistics and econometrics. Teaching focuses on advanced statistical methods, including probability theory, time series analysis, and multivariate inference. His recent work emphasizes high-dimensional data modeling, spatio-temporal factor models, and applications to commodities trading networks. He has been honored with editorial roles and speaker invitations at global conferences, reflecting his leadership in statistical methodology.

• Meteorology
• Climatology
• Land-surface-atmosphere interactions
• +5 more

Mike Brown is a Professor in the Department of Geosciences at Mississippi State University and serves as the State Climatologist for Mississippi . He holds a Ph.D. in Geography from the University of North Carolina at Chapel Hill. Educational Background: Ph.D., Geography, University of North Carolina at Chapel Hill (2000) M.S., Geosciences, Mississippi State University (1995) B.S., Meteorology, Western Illinois University (1991) Dr. Brown's research focuses on land-surface-atmosphere interactions , severe local storms , and applied climatology . His work examines tornado dynamics, lightning risk assessment, and climate impacts on ecosystems. Recent publications analyze vegetation recovery post-tornado, mallard migration patterns linked to climate, and lightning probabilities at athletic venues. He teaches graduate courses in Mesoscale Meteorology , Radar Meteorology , and Advanced Mesoscale Meteorology . Dr. Brown has contributed to applied climatology through collaborations with agricultural and wildlife agencies.