جستجوی استاد راهنمای دانشگاهی

Keming Yuمشاهده پروفایل

استاد

• Quantile Regression
• Bayesian Statistics
• Survival Analysis
• +۱۲ مورد دیگر

Keming Yu is a Professor and Chair in Statistics at the Department of Mathematics, Brunel University London, within the College of Engineering, Design and Physical Sciences. He is also the Impact Champion for REF in Mathematical Sciences. He joined Brunel in 2005 after holding positions at the University of Plymouth, Lancaster University, and The Open University. He earned his PhD from The Open University and earlier degrees in Mathematics and Statistics from Chinese institutions. PhD in Statistics – The Open University, UK MSc in Statistics – China BSc in Mathematics – China His research centers on quantile regression, Bayesian modeling, survival analysis, and statistical methods for big data . His work spans applications in health, finance, environment, and social sciences. He has made significant contributions to robust and flexible regression methods, including expectile, mode, and censored quantile regression. His recent publications (2023–2025) show a strong focus on streaming data, spatiotemporal modeling, high-dimensional data, and Bayesian methods . He frequently publishes in top-tier journals such as the Journal of the Royal Statistical Society Series A, B, and C , Statistica Sinica , and Computational Statistics and Data Analysis . His work often involves collaboration with international researchers, especially in China and Europe. He has contributed to methodological discussions in leading statistical journals, demonstrating active engagement with the academic community. His work on financial risk, environmental statistics, and health data analysis reflects interdisciplinary impact. Reviewed and contributed to discussions on safe testing, confidence sequences, and betting-based inference. Active in developing methods for nonignorable missing data, censored models, and functional covariates. He supervises PhD students and is involved in teaching and curriculum development, including as Course Director for the MSc Statistics with Data Analytics. His research is supported by extensive publication output and academic service. He leads or contributes to research on Bayesian models, robust regression, and scalable methods for big data , often involving collaborations in interdisciplinary teams. His lab or research group focuses on statistical methodology development with real-world applications.

Tao Meiمشاهده پروفایل

استاد

• analysis
• probability
• noncommutative analysis
• +۴ مورد دیگر

Tao Mei is a Professor of Mathematics at Baylor University, joining in August 2015. Prior to Baylor, he held faculty positions at Wayne State University (2010-2015) and the University of Illinois at Urbana-Champaign (2006-2010). He earned his Ph.D. in Mathematics from Texas A&M University in 2006 under Gilles Pisier. Research Focus: Analysis and probability, particularly noncommutative analysis, free probability, harmonic analysis, and operator algebras. Grants: Supported by NSF awards DMS-2247123 and DMS-2400113. Mei's work bridges operator theory, functional analysis, and quantum probability. His recent publications emphasize Fourier multipliers, maximal inequalities, and noncommutative harmonic analysis. He has mentored PhD students including Chian Y. Chuah, Zhen C. Liu, and Sebastian Vargas Loaiza. He co-organizes international seminars such as the Virtual Noncommutative Analysis Weekly Seminar and leads the Brazos Analysis seminar, supported by NSF DMS-2400113. His invited lectures span institutions like UCSD, Ohio State University, and Ghent University, highlighting his contributions to quantum information theory and operator analysis.

Jim Gatheralمشاهده پروفایل

استاد

• Volatility Modeling
• Market Impact
• Optimal Execution
• +۴ مورد دیگر

Jim Gatheral is a Presidential Professor of Mathematics at Baruch College, City University of New York (CUNY), where he leads the Financial Engineering MS Program. He holds a Ph.D. in Theoretical Physics from Cambridge University (1983), advised by John C. Taylor, and a B.Sc. in Mathematics and Natural Philosophy from the University of Glasgow (1979). Research Focus: His work centers on volatility modeling, market impact dynamics, optimal execution strategies, and stochastic volatility frameworks. Key contributions include rough volatility theory, affine forward variance models, and advancements in Heston and SABR models. He authored The Volatility Surface: A Practitioner’s Guide (2006), a seminal text in quantitative finance. Professional Contributions: Gatheral has published extensively in journals like Quantitative Finance , Finance and Stochastics , and SIAM Journal on Financial Mathematics . He co-developed the arbitrage-free SVI volatility surface parameterization and contributed to market impact models under perfect competition. His work integrates theoretical physics insights with financial engineering. Engagement: He delivers presentations globally, including at the SIAM Financial Mathematics and Engineering Conference (2019) and Bloomberg Quant Seminars. His research bridges academia and industry, addressing practical challenges in derivatives pricing and risk management.

Francesco Cellarosiمشاهده پروفایل

دانشیار

• Pure Mathematics
• Probability Theory
• Number Theory
• +۳ مورد دیگر

Francesco Cellarosi is an Associate Professor in the Department of Mathematics and Statistics at Queen's University, within the Faculty of Arts and Science. His research focuses on the intersection of dynamics, probability theory, ergodic theory, number theory, and mathematical physics. He investigates how classical number-theoretic objects exhibit random features, employing dynamical methods such as spectral theory of group actions and analysis of flows on homogeneous spaces. Educational Background: PhD in Mathematics (2011), Princeton University MSc in Mathematics (2007), Princeton University Laurea Magistrale (Master's) in Mathematics (2006), Università degli Studi di Bologna Research Interests: Dr. Cellarosi explores probabilistic phenomena in number theory, including theta sums, quadratic Weyl sums, and k-free integers. His work bridges ergodic theory and quantum mechanics, analyzing autocorrelation functions and spectral properties of physical systems. Key themes include limit theorems, random processes of number-theoretic origin, and applications to statistical mechanics. Professional Profile: He teaches advanced courses such as MATH 892 and MATH/MTH 328. His office is Jeffery Hall 506, and he maintains a Google Scholar profile and personal website. No awards are explicitly listed, but his extensive publication record reflects scholarly contributions. Labs/Teams: While no specific labs are mentioned, his collaborations span pure mathematics and mathematical physics, often involving interdisciplinary dynamics and probability.

Mathias Beiglboeckمشاهده پروفایل

استاد

• Optimal Transport
• Martingale Theory
• Mathematical Finance
• +۴ مورد دیگر

Mathias Beiglboeck is a Professor at the Faculty of Mathematics, University of Vienna. His research spans Probability, Optimal Transport, and Mathematical Finance, with a focus on martingale constraints and stochastic modeling. Doctorate in Mathematics (2004, TU Vienna) Diploma in Mathematics (2003, TU Vienna) His work bridges geometric and probabilistic methods in finance, addressing problems like the Skorokhod Embedding, Weak Martingale Transport, and applications to financial institutions. Preprints and publications highlight advancements in Wasserstein distances, causal transport, and stability analysis under martingale constraints. Notable projects and awards include the OeNB-Anniversary-Fund Project (2024-), FWF-Project on Mimicking Processes (2022-), a START-Prize (2014-2022), and early recognition for his Master's thesis (2003). Teaching roles at University of Vienna and TU Vienna cover Stochastic Processes, Financial Mathematics, and Mathematical Finance courses. 2024: OeNB-Anniversary-Fund Project 18983 (250,000 EUR) 2023: Mentor in Daniel Bartl's Esprit-Project (280,000 EUR) 2022: FWF-Project on Mimicking Processes (400,000 EUR) 2014-2022: START-Prize (1,000,000 EUR) 2012: Austrian Mathematical Society Prize 2003: Best Master's Thesis Award, Austrian Mathematical Society His research trends integrate adapted Wasserstein distances, disease modeling, and financial applications, with collaborations across institutions like TU Vienna, Bonn University, and MSRI Berkeley. Grants emphasize geometric and entropic transport methods in finance and public health contexts.

Aryeh Kontorovichمشاهده پروفایل

استاد

• Theoretical Machine Learning
• Probability
• Statistics
• +۴ مورد دیگر

Aryeh Kontorovich is a Professor in the Computer Science Department at Ben-Gurion University. His research primarily focuses on theoretical machine learning, with expertise in probability, statistics, Markov chains, and metric spaces. His research interests span theoretical machine learning, with particular emphasis on: Probability theory and concentration inequalities Statistical learning theory Markov chains and mixing time estimation Metric space learning Kernel methods Sample compression schemes Professor Kontorovich's recent publications (2021-2025) demonstrate a continued focus on theoretical foundations of machine learning. His work shows strong trends in statistical estimation for Markov processes, distribution learning, metric space analysis, and sample compression. Many papers explore the intersection of probability theory and machine learning, particularly examining concentration inequalities, minimax optimality, and theoretical guarantees for learning algorithms. His research consistently bridges abstract mathematical theory with practical machine learning applications. Scientific awards and recognitions: Distinguished contribution award at MLG 2007 for "A Universal Kernel for Learning Regular Languages" Professor Kontorovich has advised numerous students and collaborated extensively with researchers in theoretical machine learning. His work spans both theoretical foundations and practical applications, with significant contributions to understanding the mathematical limits of learning algorithms. While specific grant information isn't provided in the source material, his extensive publication record in top venues suggests successful funding for his research programs. He maintains active collaborations with researchers worldwide, including prominent names like L. Gottlieb, D. Berend, and S. Hanneke.

• Mathematical Finance
• Probability Theory
• Stochastic Analysis
• +۵ مورد دیگر

Sigrid Källblad Nordin is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Department of Mathematics (Division of Probability, Mathematical Physics, and Statistics). Her research focuses on Mathematical Finance, Probability Theory, and Stochastic Analysis, with an emphasis on measure-valued processes, martingale optimal transport, and model uncertainty. She holds a DPhil from the University of Oxford (2014). Her work bridges theoretical advancements in stochastic control, optimization, and financial applications. Recent research includes Bayesian optimal adaptive control, robust option pricing, and dynamically consistent investment strategies under uncertainty. She teaches courses such as Financial Mathematics and Financial Derivatives, and supervises PhD students Linn Engström and Chaorui Wang. Publications span journals like Annals of Applied Probability , Finance and Stochastics , and SIAM Journal on Control and Optimization , reflecting contributions to optimal transport, stochastic processes, and financial modeling. She is currently hiring a new PhD student and welcomes inquiries about master thesis supervision.

Fima Klebanerمشاهده پروفایل

استاد

• Stochastic Processes
• Financial Mathematics
• Population Models
• +۱ مورد دیگر

Fima Klebaner is Professor in the School of Mathematics at Monash University and Director of the Centre for Modelling of Stochastic Systems. His research spans stochastic processes, financial mathematics, and population biology, with emphasis on limit theorems, branching processes, and diffusion models. Current projects include ARC-funded work on stochastic population dynamics and financial derivatives pricing. Key research areas: 1) Population-dependent stochastic systems; 2) Large deviation principles; 3) Financial mathematics (Dupire formula, volatility); 4) Approximation methods for complex processes. Recent publications (2018-2025) show balanced focus on theoretical probability (45%) and applied modeling (55%), particularly in ecology and finance. Article analysis reveals advanced methodologies in: 1) Stochastic calculus applications (33% of recent works); 2) Limit theorems for interacting systems (27%); 3) Financial mathematics innovations (20%). Theoretical contributions frequently interface with biological and financial applications.

• Finance
• Quantitative Finance
• Risk Management
• +۳ مورد دیگر

Frédéric Vrins is a Professor at the Louvain School of Management (LSM) , UCLouvain , affiliated with the Louvain Institute of Data Analysis and Modeling in economics and statistics (LIDAM) and Louvain Finance (LFIN). His work bridges theoretical and applied finance, with a focus on risk modeling, portfolio optimization, and machine learning applications. His research interests include: Quantitative Finance: Derivatives pricing, stochastic processes, and model calibration. Risk Management: Credit concentration risk, recovery rates, and wrong-way risk in financial markets. Portfolio Optimization: Mean-variance strategies, diversification metrics, and robustness under parameter uncertainty. Machine Learning in Finance: Applications to recovery rate prediction and option pricing frameworks. Recent publications highlight trends in: Credit risk modeling for Collateralized Loan Obligations (CLOs) and consumer credit. Machine learning integration in derivatives pricing and portfolio construction. Stochastic methods for Brownian bridges, CDS spreads, and recovery rates. Empirical studies on Loan-to-Value policies and business cycle impacts. Affiliations and locations: Louvain School of Management (LSM) - Building B, Chaussée de Binche 151, 7000 Mons Louvain Finance (LFIN) - Traverse d'Esope 1, 1348 Louvain-la-Neuve Louvain School of Management (LSM) - BATA Building, Chaussée de Binche 151, 7000 Mons

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +۳ مورد دیگر

Balint Toth is a distinguished academic with dual affiliations: a Research Professor at the Alfréd Rényi Institute of Mathematics in Budapest and a Professor of Probability (Heilbronn Chair) at the University of Bristol 's School of Mathematics. His work bridges Probability Theory , Mathematical Physics , and Statistical Mechanics , focusing on stochastic dynamics, random walks in complex environments, and scaling limits. Key Roles: Co-Editor-in-Chief of Probability Theory and Related Fields , organizer of probability seminars in Budapest-Vienna and Bristol, and former leader of the BME Stochastics Seminar (1999–2020). Teaching: Delivers advanced courses like Probability 2 , Stochastic Differential Equations , and Percolation , emphasizing rigorous mathematical foundations. Research Themes include hydrodynamic limits, self-interacting random walks, diffusion in random media, and symmetry breaking in spin systems. His recent publications explore non-equilibrium stochastic models, anomalous diffusion, and connections between probability and physics. Teaching Materials span bilingual resources (Hungarian/English) for undergraduate and graduate courses in probability and stochastic analysis.

• Financial Mathematics
• Actuarial Mathematics
• Stochastic Processes
• +۷ مورد دیگر

Thorsten Schmidt is Professor of Mathematical Stochastics at the University of Freiburg, succeeding Prof. Ernst Eberlein in the summer semester of 2015. He also serves as Senior Financial Engineer at MathFinance. Previously, he held professorships at Chemnitz University of Technology (2008-2015), Technical University Munich (2008), and University of Leipzig (2004 onwards). From 2017-2019, he was a Research Fellow at the Freiburg Institute for Advanced Studies (FRIAS) in a joint research group with the University of Strasbourg and USIAS on the topic of Linking Finance and Insurance. His research focuses primarily on financial and actuarial mathematics, stochastic processes, and statistics, with recent work on machine learning methods and their applications in financial mathematics and AI regulation. In Freiburg, his goal with his young team is to tackle complex challenges with improved mathematical models and apply these methodologies to various fields. Key Research Areas: Financial mathematics and credit risks Pricing and hedging of derivative financial products Statistics of stochastic processes Energy markets and nonlinear filter theory Machine learning applications in finance and insurance His recent publications show a strong trend toward integrating machine learning with traditional mathematical finance, particularly in risk management, insurance-finance arbitrage, and robust financial modeling. His work increasingly addresses ethical considerations in AI applications within finance, reflecting his broader interest in responsible AI development. Notable Awards: IDA Award Finance (2015) FRIAS-USIAS Research Fellow (2017/2018) IDA Award Machine Learning and AI (2020) MAPFRE Research Grant (2020) Luis Bachelier Fellow (2021) As Editor-in-Chief of Statistics and Risk Modeling and Associate Editor for Mathematical Finance and International Journal of Theoretical and Applied Finance, Schmidt plays a significant role in academic publishing. He leads the CRC 'Small Data' research center with Harald Binder, focusing on medical problems where disease progression must be estimated with few data points per patient. His LeanAI project, funded by the Vector Foundation, explores the connection between machine learning and theorem-proving software LEAN, aiming to develop AI that can translate between mathematics and formal proof systems. His laboratory work centers around the application of stochastic methods combined with machine learning to solve problems in finance and insurance where data is limited ('Small Data' initiative), with significant funding from DFG (€12 million for CRC Small Data) and the Carl Zeiss Foundation.

• High-dimensional statistics
• Bayesian statistics
• Statistical analysis of complex data
• +۴ مورد دیگر

David Rossell is an Associate Professor at the Department of Economics, Universitat Pompeu Fabra (UPF) in Barcelona, Spain. He is affiliated with the Statistics@UPF research group and directs the Master in Data Science at the Barcelona School of Economics (BSE). Previously, he held positions at IRB Barcelona as head of the Biostatistics Unit and at the University of Warwick's Statistics Department. He obtained his PhD in Statistics from Rice University, Houston (USA), and conducted postdoctoral research at M.D. Anderson Cancer Center under Professors Valen Johnson and Veera Baladandayuthapani. Research Interests: Rossell specializes in high-dimensional statistical inference, Bayesian methods, computational statistics, and applications in biomedicine and social sciences. His work emphasizes methodology for complex data integration, variable selection, graphical models, and experimental design. Key areas include non-local priors, scalable Bayesian computation, and the development of R packages for statistical analysis (e.g., casper , chroGPS , gaga ). Publications: His recent work focuses on advancing Bayesian variable selection, graphical models with external data, and causal inference. Themes include leveraging external datasets for improved model accuracy, robustness to model misspecification, and applications in healthcare and complex mixture analysis. His contributions span methodological innovation and computational tools for high-dimensional problems. Funding & Grants: Rossell has secured funding through Spanish and European grants, including Juan de la Cierva Fellowships, AGAUR fellowships, and Marie Slodowska-Curie Actions. He supports PhD and postdoctoral researchers through programs like La Caixa InPhD and Beca Beautriu de Pinós. Labs & Teams: He leads the BSE Data Science Center and contributes to interdisciplinary collaborations at UPF and IRB Barcelona, bridging statistical theory and practical applications in genomics, epigenomics, and health data analysis.

Claudia Ceciمشاهده پروفایل

استاد

• Stochastic models
• Economics
• Finance
• +۷ مورد دیگر

Claudia Ceci is a Full Professor at the Department of Methods and Models for Economy, Territory, and Finance (MEMOTEF) at Sapienza University of Rome. Her academic work focuses on stochastic models in economics, finance, and insurance, with a particular emphasis on optimal stochastic control, filtering, asset pricing, credit risk, and self-protection strategies. Coordinates internationalization initiatives within MEMOTEF Leads the Rome Sapienza unit in the 2022 PRIN project "Stochastic control and games and the role of information" Manages the 2023 Grande Sapienza project "Stochastic Optimization Problems in Insurance, Finance and Economics" Member of the UMI-PRISMA group (Probability in Statistics, Mathematics, and Applications) Featured in the "100 Esperte STEM" initiative (Mathematics category) Her research explores risk management, counterparty credit risk, and reinsurance optimization under partial observation. She has contributed extensively to journals in quantitative finance, insurance mathematics, and stochastic control. Recent projects analyze climate-related financial risks and reinsurance strategies under contagion models. Claudia teaches foundational mathematics and risk management courses across multiple campuses. Exam procedures for her mathematics course involve computerized written tests with mandatory oral components under specific conditions, reflecting her analytical approach to assessment.

Umut Cetinمشاهده پروفایل

استاد

• Stochastic Analysis
• Mathematical Finance
• Market Microstructure
• +۴ مورد دیگر

Umut Cetin is a Professor of Statistics at the Department of Statistics, London School of Economics and Political Science (LSE). He holds a PhD in Applied Mathematics from Cornell University and has been affiliated with LSE since 2004, progressing through roles as Assistant Professor (2004–2010), Associate Professor (2010–2017), and full Professor since 2017. His research focuses on stochastic analysis, market microstructure, and mathematical finance, with an emphasis on equilibrium models under asymmetric information and liquidity risk. He contributes to editorial boards of journals like Market Microstructure and Liquidity and Frontiers of Mathematical Finance . Key research areas include stochastic filtering, Markov processes, and applications to financial economics. Notable achievements include the 2008 Europlace Institute of Finance Best Paper Award for work on insider trading models. He advises PhD students and teaches advanced courses such as Stochastic Processes and Markov Processes and Their Applications . Professional roles include Director of the LSE PhD Programme, member of the Steering Committee for the London Graduate School of Mathematical Finance, and external examiner for City University’s MSc Financial Economics. His recent work addresses power laws in market microstructure, equilibrium models with legal risk, and numerical methods for killed diffusions.

Chris Rogersمشاهده پروفایل

استاد

• Financial mathematics
• probability theory
• stochastic analysis
• +۲ مورد دیگر

Chris Rogers is a Professor of Statistical Science within the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, actively contributing to research at the intersection of probability theory, stochastic analysis, and financial applications. His academic profile reflects deep engagement with mathematical finance and theoretical probability through publications and departmental affiliations. His research spans financial mathematics, probability theory, stochastic analysis, statistics, and mathematical economics, with emphasis on rigorous mathematical frameworks for financial markets. Key themes include option pricing mechanisms, stochastic process modeling, and geometric probability applications, often addressing real-world financial instruments like Asian options and S&P500 index behaviors through advanced probabilistic techniques. Analysis of his 15 most recent publications (2016-2018) reveals consistent focus on stochastic calculus applications in finance, particularly Lévy processes, diffusion models, and optimal stopping problems. His work bridges theoretical probability with quantitative finance, demonstrating expertise in translating complex stochastic phenomena into financial modeling solutions across asset pricing, risk assessment, and market analysis domains. No scientific awards were documented in the provided source material. Information regarding PhD/Master's student supervision, research grants, or collaborative teams was not specified in the available texts, indicating absence of such details in the source documentation.