جست‌وجوی استاد و پژوهشگر

Xiangfeng Yangمشاهده پروفایل

دانشیار

• Large Deviations Theory
• Probability Theory
• Stochastic Processes
• +۱۱ مورد دیگر

Xiangfeng Yang is an Associate Professor and Docent in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA). His work bridges theoretical probability and applied mathematical modeling, with a strong emphasis on large deviations theory and stochastic processes. His research interests include: Large Deviations Theory Random Matrices and Sample Covariance Matrices Stochastic Bridges (Bernstein and Markov) Longest Runs, Gaps, and Extremal Eigenvalues Hidden Markov and Reciprocal Processes Modern Multivariate Statistical Analysis Xiangfeng Yang's recent publications (2015–2024) reflect a consistent and deep engagement with probabilistic asymptotics and extreme behavior in stochastic systems. His work spans theoretical foundations in probability, applications in random matrix theory, and modeling in financial and environmental contexts. A notable trend is his focus on large deviation principles across diverse settings—ranging from eigenvalues of random matrices to gaps in Poisson processes and runs in Markov chains. He frequently collaborates with PhD students, indicating an active supervisory role. His scientific contributions are published in reputable journals such as Journal of Applied Probability , Stochastic Processes and their Applications , and Theory of Probability and Mathematical Statistics . While no explicit awards are listed, the quality and volume of his output suggest recognition within the mathematical community. Xiangfeng Yang has supervised several PhD students to completion, including Joseph Okello Omwonylee, Denise Uwamariya, and Stefane Saize, with research topics closely aligned with his expertise in large deviations and stochastic modeling. His research likely involves collaboration within the Applied Mathematics division, though no specific lab or team name is mentioned. He continues to contribute actively to modern theoretical and applied probability.

• Econometrics
• Applied Economics
• Causal Inference
• +۳ مورد دیگر

Soonwoo Kwon is the Groos Family Assistant Professor of Economics at Brown University. His research focuses on econometric methods, causal inference, and policy evaluation. He teaches courses such as ECON 1620 (Introduction to Econometrics) and ECON 2950 (Workshop in Econometrics). He holds a PhD in Economics from Yale University (2021), along with MA and MPhil degrees from Yale (2017). His earlier education includes degrees from Seoul National University: MA (2014) and BA (2012). His work emphasizes robust statistical methods and their applications in policy analysis. Recent research includes studies on regression discontinuity designs, Bayesian shrinkage, and panel data models. He has contributed to debates on measurement error correction, parallel trends assumptions, and adaptive inference techniques. His publications span topics like causal mechanisms, model misspecification diagnostics, and international technological trends. Kwon collaborates with peers at Brown and globally, though specific coauthors are not listed here. His research bridges theoretical econometrics and empirical policy evaluation.

Bikramjit Dasمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Operations Research
• +۷ مورد دیگر

Bikramjit Das is an Associate Professor and Associate Head of Pillar (Graduate Programme) at Singapore University of Technology and Design (SUTD). He holds a PhD in Operations Research from Cornell University and prior to SUTD, was a postdoctoral researcher at ETH Zurich’s RiskLab. His research focuses on extreme events analysis using applied probability, optimization, and statistical learning, with applications in finance, telecommunications, federated learning, and climate modeling. He teaches courses in Probability, Stochastic Modeling, and Analytics, and directs the Master of Science in Technology and Design (Data Science) program. Education: PhD in Operations Research (Cornell University), B.Stat & M.Stat (Indian Statistical Institute). Research emphasizes heavy-tailed distributions, risk contagion, and network modeling. Key areas include risk analysis in financial networks, robust optimization under uncertainty, and extreme value theory. His work bridges theoretical probability and real-world applications in data science and public policy. Notable contributions include studies on asymptotic independence in high dimensions, robust newsvendor models, and inference techniques for heavy-tailed data. His articles explore topics ranging from federated learning under noise to climate modeling and congestion phenomena in sparse networks. Collaborations include visiting positions at MIT and the Karlsruhe Institute of Technology. Active in academic leadership, he has contributed to technical reports on healthcare provider choice analysis and probabilistic flood risk assessments for nuclear power plants.

Michael Pittمشاهده پروفایل

استاد

• Statistical Methodology
• Bayesian Inference
• Sequential Monte Carlo Methods
• +۵ مورد دیگر

Professor Michael Pitt is a faculty member in the Department of Mathematics at King's College London, holding the rank of Professor of Statistics within the Faculty of Natural, Mathematical & Engineering Sciences. He completed his doctorate in Statistics at the University of Oxford (Nuffield College) in 1997, followed by postdoctoral research at Imperial College London. His career includes roles at the University of Warwick (1999-2016) as Assistant and Associate Professor before joining King's College. Education: PhD in Statistics, University of Oxford (1997) Postdoctoral Research, Imperial College London (1997-1999) Research Interests: Focuses on advanced statistical methodologies including particle filtering, sequential Monte Carlo (SMC), Markov chain Monte Carlo (MCMC), and their applications in financial econometrics and biostatistics. Key areas include: Development of computationally efficient algorithms for Bayesian inference Analysis of financial time series and stochastic volatility models Multivariate copula models for dependence structures Clinical outcome analysis in cardiology through statistical revascularization studies Publications: Recent work emphasizes methodological advancements in pseudo-marginal methods, correlated particle filtering, and adaptive sampling techniques. Notable contributions include applications in cardiac clinical outcomes and high-dimensional density modeling. Labs/Teams: Active member of the Research Centre for Non-Equilibrium Science (CNES) , focusing on interdisciplinary non-equilibrium systems, and part of the Statistics Group within the Department of Mathematics.

• Machine Learning
• Kernel Methods
• Model Selection
• +۱۱ مورد دیگر

Gavin Cawley is a Professor in the School of Computing Sciences at the University of East Anglia (UEA), with additional affiliations to the Data Science and AI group, the Centre for Ocean and Atmospheric Sciences, and the Statistics group. His research spans machine learning, bioinformatics, climate modeling, and environmental data analysis. His primary research interests include Machine Learning , Kernel Methods , Model Selection , Bayesian Regularization , Bioinformatics , and Climate Modeling . He has made significant contributions to understanding overfitting in model selection, sparse logistic regression for gene and cancer classification, and time series classification using ensemble methods. His work bridges theoretical machine learning with practical applications in biology, archaeology, and environmental science. The recent trend in his publications shows a strong interdisciplinary focus, combining machine learning with climate science (e.g., Arctic sea ice prediction) and molecular biology (e.g., protein domain movements). His work often involves developing and evaluating statistical models for complex real-world problems, emphasizing robustness, interpretability, and predictive accuracy. While no specific awards are listed in the provided text, his extensive publication record in top journals such as Journal of Machine Learning Research , Bioinformatics , and Neural Networks , along with high citation counts (e.g., over 1,800 citations for his 2010 paper on overfitting), indicates significant recognition in the academic community. He has also contributed to organizing major machine learning challenges, such as the ChaLearn AutoML and Active Learning challenges. He has supervised or collaborated with numerous researchers across disciplines, though specific student names are not listed. His work involves methodological development in model selection, kernel learning, and survival analysis, often applied to biological and environmental datasets. He has been involved in projects related to predictive uncertainty, ozone forecasting, and microbial growth modeling. While no specific lab or team name is mentioned, his affiliations with the Data Science and AI group and the Centre for Ocean and Atmospheric Sciences suggest active participation in interdisciplinary research teams focused on data-driven environmental and biological modeling.

Beata Sternalمشاهده پروفایل

استادیار

• Marine Geology
• Sedimentation Processes
• Coastal and Shelf Environments
• +۵ مورد دیگر

Dr. Beata Sternal is an assistant professor at Adam Mickiewicz University, affiliated with the Geohazards Research Unit. Her research focuses on marine geology, particularly sedimentation processes in coastal and shelf environments across diverse regions including the Vietnamese Shelf of the South China Sea, Thailand Coast of the Indian Ocean, Sendai Plain in Japan, SW Spitsbergen in the Arctic Ocean, and Northern Norway's fjords. She employs sedimentological (grain-size, X-ray imaging), geochemical (radioisotopes, TOC, SEM-EDS), and mineralogical (heavy and clay minerals) methods. Education: BSc and MSc in Geology (Vietnamese Shelf studies), PhD in Earth Sciences (Arctic Ocean/SW Spitsbergen research). Her publications highlight expertise in heavy mineral analysis, mine tailing dispersion, and paleoenvironmental reconstruction. She has contributed to understanding human-induced environmental changes in Arctic fjords and sediment dynamics from catastrophic events like the 2004 Indian Ocean and 2011 Tohoku tsunamis.

Chantal van den Bergمشاهده پروفایل

استادیار

• Sexual offending (by juveniles)
• Sexual victimisation
• Labour market discrimination
• +۷ مورد دیگر

Chantal van den Berg is an Assistant Professor in the Department of Criminal Law and Criminology at the Faculty of Law, VU University Amsterdam, and a researcher and program leader at the Netherlands Institute for the Study of Crime and Law Enforcement (NSCR). Her work bridges empirical research and normative studies in criminology, with a strong focus on victimology and the social consequences of criminal behavior. VU University Amsterdam – Assistant Professor, Criminology (2017–present) NSCR – Researcher and Program Leader, Victimology (2022–present) Leiden University – Assistant Professor, Criminology (2014–2015) She earned her BSc and MSc in Criminology from VU University Amsterdam and completed her PhD there in 2015, studying the long-term criminal careers of juvenile sex offenders and their life transitions. Her research centers on sexual offending (particularly by juveniles) , sexual victimization , labor market discrimination against ex-offenders , risk assessment , and the collateral consequences of criminal conviction , especially regarding the Certificate of Conduct (VOG). She employs advanced statistical methods and field experiments to investigate how criminal records and personal characteristics affect employment outcomes and social integration. Her recent publications (2023–2025) reveal a strong trend toward studying digital disclosures of sexual victimization (e.g., during the #MeToo movement), employment barriers for cybercrime offenders , and evidence-based rehabilitation in criminal record screening. These works highlight her interdisciplinary approach, combining criminology, sociology, and data science. She co-supervises two PhD candidates: Marleen Gorissen , researching online disclosure of sexual victimization, and Nora van Buitenen , analyzing comorbidity in mentally ill prisoners using network methods. She also contributes to major research projects such as Evidence-based interventions for victims . Member, Examination Board, Faculty of Law, VU University Amsterdam Former Chair, Programme Committee (OLC) Criminology (2017–2021) Co-editor, What works. Effectieve ondersteuning voor slachtoffers (2023) Chantal teaches Victimology at the BSc level and advanced courses in multivariate analysis and research methods for both BSc and MSc Criminology students. Her academic service includes editorial and peer-review roles, as well as frequent academic presentations on juvenile sex offending and rehabilitation. She is actively involved in research collaboration through NSCR and VU, with a growing network in victimology, labor market reintegration, and digital justice. Her work continues to influence both academic discourse and policy development in criminal justice and victim support.

Angelos Alexopoulosمشاهده پروفایل

استادیار

• Theoretical and Applied Econometrics
• Bayesian Analysis
• Computational Statistics
• +۴ مورد دیگر

Angelos Alexopoulos is an Assistant Professor at the Department of Economics , Athens University of Economics and Business . He has held Research Associate positions at the University of Cambridge, University College London, and University of Exeter in the UK. PhD: Athens University of Economics and Business Research Focus: Computational Statistics, Econometrics, Bayesian Analysis, Network Modelling Publications span Bayesian inference, epidemic forecasting, machine learning for fraud detection, and econometric methodology. Key journals include Journal of the Royal Statistical Society , Journal of Computational and Graphical Statistics , and Statistics and Computing . 2024: Gaussian invariance in MCMC 2024: Epidemic nowcasting models 2023: VAT fraud detection with ML Awards include certifications in Deep Learning (Coursera), Blockchain (edX), and Object-Oriented R Programming (DataCamp).

Kristin Gaineyمشاهده پروفایل

دانشیار

• Depression
• Anxiety Disorders
• Emotion Regulation
• +۷ مورد دیگر

Dr. Kristin Gainey is an Associate Professor at the University of Western Australia (UWA) in the School of Psychological Science, directing the Emotional Wellbeing Lab. She is a registered clinical psychologist in the U.S. Her research focuses on affective processes related to depression, anxiety, and wellbeing, incorporating heart rate variability, mindfulness, and ecological momentary assessment. She has held academic roles at the University at Buffalo (SUNY) and has editorial roles at journals like Psychological Assessment and Self and Identity . Her work is funded by the NIH and recognized through awards from the APA and Society for Research in Psychopathology. Education: PhD in Clinical Psychology, University of Iowa (2011) Clinical Internship, Seattle VA Medical Center (2011) MM in Organ Performance, Yale University (2005) BA in Psychology and Music, Harvard University (2003) Research Interests: Dr. Gainey’s work spans emotion regulation, psychopathology structure, and assessment methodologies. She explores how affective processes interact with clinical outcomes, using innovative techniques like wearable devices and ecological momentary assessments. Her recent projects include developing tools for evaluating experience sampling methods and investigating cultural responsiveness in university curricula. Grants & Awards: National Institutes of Health Grant (2019–2025) Mid-Career Research Award (UWA, 2023) APA Early Career Award (2018) Labs/Teams: She leads the Emotional Wellbeing Lab and collaborates on NIH-funded studies examining emotion regulation in distress disorders. She also engages in curriculum reform efforts to decolonize psychology education through yarning sessions with Indigenous communities.

Vadim Linetskyمشاهده پروفایل

استاد

• Financial Engineering
• Mathematical Finance
• Stochastic Modeling
• +۶ مورد دیگر

Vadim Linetsky is a Professor of Industrial Engineering and Management Sciences at Northwestern University. His research focuses on financial engineering, mathematical finance, and stochastic modeling. He holds a Ph.D. in Theoretical and Mathematical Physics from the P.N. Lebedev Physical Institute of the Russian Academy of Sciences (FIRAN), an M.S. in Electrical Engineering from the Moscow State Institute of Radio Engineering, and a B.S. in Electronics and Automation from the University of Technology. His work emphasizes applications in financial markets, including interest rate modeling, credit risk, derivatives pricing, and algorithmic trading. Notable contributions include advancements in spectral methods for pricing financial instruments and the development of models addressing the zero lower bound in interest rate dynamics. Linetsky has also explored long-term risk frameworks and stochastic spectral theory, bridging econometrics and financial mathematics. His publications span journals such as Finance and Stochastics , Econometrica , and Mathematics and Financial Economics , with a focus on practical computational methods and theoretical rigor. Collaborative research projects include high-performance algorithms for Markov processes and interdisciplinary applications of stochastic modeling. Linetsky’s research has been supported by grants, including work on customer default risk management and computational methods in financial engineering. His contributions have influenced both academic theory and industry practices in quantitative finance.

Gerda Claeskensمشاهده پروفایل

استاد

• Model Selection
• Model Averaging
• Post-Selection Inference
• +۴ مورد دیگر

Gerda Claeskens is a full professor of Statistics at the Faculty of Economics and Business (FEB) at KU Leuven, Belgium. She holds positions in the Operations Research and Statistics Research Group (ORSTAT) and is affiliated with the Leuven Statistics Research Center. Her academic journey includes a Licentiate in Mathematics (Summa Cum Laude) from the University of Antwerp and a Ph.D. in Statistics from Limburgs Universitair Centrum (now Hasselt University). Research Interests: Her work focuses on model selection, post-selection inference, nonparametric methods, and high-dimensional statistics. She has contributed extensively to methodologies like focused information criteria, penalized splines, and quantile regression. Awards and Honors: Notable accolades include Fellow of the American Statistical Association (2019), Goodeve Medal (2017), and Medallion Lecturer (2016). She has also held editorial roles in top journals such as Biometrika and Journal of the American Statistical Association . Teaching and Mentoring: She has advised over 20 PhD students and mentored postdoctoral researchers. Her teaching spans advanced statistical methods, probability theory, and business statistics. She has delivered invited lectures globally, including at the European Meeting of Statisticians and the International Society for NonParametric Statistics. Key Contributions: Her book Model Selection and Model Averaging (2008) is a seminal work in statistical methodology. Her research addresses challenges in high-dimensional data, survival analysis, and model averaging, with applications in finance, insurance, and biostatistics.

• Corporate Finance and Governance
• Financial Regulation
• Contract Theory
• +۳ مورد دیگر

Florian Hoffmann is an Associate Professor of Finance at the Faculty of Economics and Business (FEB) of KU Leuven. He holds a Ph.D. from the University of Frankfurt and previously held academic positions at the University of Bonn and Erasmus University Rotterdam. His research focuses on corporate finance, governance, financial regulation, contract theory, and information economics. He is a co-promotor for projects on ESG information quality and multivariate statistics in finance. Education & Positions: Ph.D. in Finance, University of Frankfurt Associate Professor, KU Leuven FEB (since Fall 2022) Previous roles at University of Bonn and Erasmus University Rotterdam Teaching: Capital Investment Policy (Leuven, Bachelor) The Economics of Insolvency (Leuven, Master) Advanced courses in corporate finance, behavioral finance, and contract theory Research Interests: His work explores dynamic contracting, deferred compensation mechanisms, selective disclosure in marketing, and regulatory frameworks for financial institutions. Recent studies address worker runs in banking, auction vs. negotiation payment structures, and ESG information trustworthiness. Grants & Projects: ESG Information Quality Project (2022–2026) Multivariate Statistics in Finance (2020–2024) Labs/Teams: Active in the Finance Research Group at KU Leuven, collaborating on interdisciplinary projects involving regulation, corporate governance, and financial markets.

• Econometrics
• Nonparametric Statistics
• Treatment Effects Analysis
• +۳ مورد دیگر

Eun Yi Chung is an Associate Professor in the Department of Economics at the University of Illinois at Urbana-Champaign and serves as the DEI and Climate Officer for the department. Her research focuses on econometric methodology, particularly permutation tests and nonparametric statistical methods, with applications to treatment effects analysis and causal inference. She holds affiliations with the College of Liberal Arts & Sciences and contributes to interdisciplinary research in statistical theory and policy evaluation. Her work emphasizes rigorous statistical testing frameworks, including quantile-based approaches and methodologies addressing heterogeneous treatment effects. Recent contributions include advancements in permutation tests at nonparametric rates and robust hypothesis testing techniques. Chung’s articles appear in top-tier journals such as the Journal of the American Statistical Association , Journal of Applied Econometrics , and Journal of Econometrics . No scientific awards are explicitly mentioned in the provided materials. Her research has explored permutation tests in diverse contexts, from causal inference in social experiments to multivariate analysis and U-statistics-based frameworks. While no advising or grant details are listed, her publications highlight a sustained focus on methodological innovation in econometrics and statistics.

• Statistical Learning
• Multivariate Analysis
• Classification
• +۶ مورد دیگر

Yoonkyung Lee is a Professor of Statistics and Computer Science Engineering at The Ohio State University, affiliated with the Department of Statistics in the College of Arts and Sciences. She holds a PhD from the University of Wisconsin-Madison (2002). Her primary research focuses on statistical learning and multivariate analysis, with specializations in classification, kernel methods, and model stability. She has a courtesy appointment in Computer Science and Engineering since 2016 and served as a faculty co-director of the Translational Data Analytics Institute (2020–2022). Her work has been funded by the National Science Foundation (NSF), and she was elected a Fellow of the American Statistical Association in 2015. Her educational background includes a PhD in Statistics from the University of Wisconsin-Madison. Her research interests emphasize developing methodologies for latent structures in multivariate data, computational frameworks for model stability, and predictive modeling. Notable contributions include advancements in kernel discriminant analysis, Bayesian restricted likelihood methods, and sparse logistic tensor decomposition. Prof. Lee serves on editorial boards for journals such as Chemometrics and Intelligent Laboratory Systems , Econometrics and Statistics , and Journal of Machine Learning Research . Her articles span topics like support vector machines, quantile regression, and nonlinear embeddings, reflecting her expertise in bridging statistics and machine learning. Beyond research, she has advised numerous projects and contributed to interdisciplinary initiatives in translational data analytics.

Joseph Antonelliمشاهده پروفایل

استادیار

• Causal Inference
• High-Dimensional Modeling
• Bayesian Methods
• +۳ مورد دیگر

Joseph Antonelli is an Assistant Professor of Statistics at the University of Florida , where he has been since 2018. He holds a PhD in Biostatistics from Harvard University (2015) and completed postdoctoral training there until 2018. His research focuses on causal inference, high-dimensional modeling, Bayesian methods, spatial statistics, and applications in environmental health and criminology. Education: B.S. in Statistics, University of Florida (2011) M.S. in Biostatistics, Harvard University (2013) Ph.D. in Biostatistics, Harvard University (2015) Research Interests: Professor Antonelli develops statistical methods to address complex causal questions in health and social sciences. His work emphasizes robust approaches to confounding adjustment, spatial and environmental data analysis, and policy evaluation. He applies these methods to study air pollution effects, policing policies, and opioid policy impacts, bridging theoretical advancements with real-world applications. Key Contributions: His articles span causal inference techniques, Bayesian modeling, and spatial analysis, with recent work addressing racial bias in policing and air pollution mixtures. His research has been recognized with awards from the Health Effects Institute, JSM Biometrics, and ISBA. Awards: 2020 Health Effects Institute Young Investigator Award 2020 Journal of Speech, Language, and Hearing Research Editors Award 2014 ENAR Distinguished Student Paper Award Advising and Grants: He advises over 15 graduate students and has led grants totaling $3.5M, including NIH funding for auditory deficits in children and CDC studies on firearm policy effects. He also serves as Associate Editor for Bayesian Analysis .