جست‌وجوی استاد و پژوهشگر

Tobias Heilmannمشاهده پروفایل

مدرس

• Statistical Inference
• Change Point Detection
• Differential Privacy
• +۵ مورد دیگر

Dr. Tobias Heilmann is a Lecturer at the Department of Humanities, Social and Political Sciences at ETH Zurich. His research focuses on statistical inference, change point detection, differential privacy, high-dimensional data analysis, network analysis, and machine learning. He holds a PhD in Statistics from Fudan University, advised by Zhiliang Ying, and has contributed to software packages such as changepoints , GMPro , and APPLE . His work bridges theoretical statistics and practical applications, emphasizing privacy-preserving techniques, dynamic systems analysis, and algorithmic robustness. Key areas include nonparametric methods, contextual bandits, federated learning, and functional data analysis. Recent contributions address challenges in distributed systems, adversarial robustness, and high-dimensional model selection. He has published extensively in top journals like Annals of Statistics , SIAM Journal on Mathematics of Data Science , and IEEE Transactions on Information Theory . Dr. Heilmann's research also involves interdisciplinary collaborations, including social psychology of groups and biomedical applications. His software tools are widely used for change point analysis and community detection in networks. Current projects explore optimal privacy-utility trade-offs and adaptive algorithms for non-stationary environments.

Michael Wolfمشاهده پروفایل

استاد

• Nonparametric Inference Methods
• Multiple Testing Procedures
• Financial Econometrics
• +۴ مورد دیگر

Michael Wolf is a Professor of Econometrics and Applied Statistics at the University of Zurich's Department of Economics, where he has been since 2005. He holds a Vordiplom in Mathematics from the University of Augsburg (1991), an MSc in Statistics from Stanford University (1995), and a PhD in Statistics from Stanford (1996). His research focuses on nonparametric inference methods, multiple testing procedures, financial econometrics, and large-dimensional covariance matrices. He has contributed to influential work on covariance matrix estimation, portfolio optimization, and statistical methodology. Wolf has served as an Associate Editor for journals including the Annals of Statistics (2004–2007), Statistics and Probability Letters (2014–2016), and the Journal of Financial Econometrics (2019–2022). His academic career includes roles at institutions like UCLA, Universidad Carlos III, and Universitat Pompeu Fabra, where he advanced from Assistant to Associate Professor before joining UZH. His research explores resampling techniques, financial markets analysis, and statistical methods for handling high-dimensional data. Notable contributions include the development of shrinkage-based covariance matrix estimators and their applications in portfolio management. He collaborates with researchers like Olivier Ledoit on nonlinear shrinkage methods and maintains software packages like covShrinkage for covariance matrix estimation. Wolf advises students on thesis topics in econometrics and maintains active roles in professional associations such as the American Statistical Association and the Institute of Mathematical Statistics. His work bridges theoretical statistics and applied finance, addressing challenges in modern portfolio theory, risk management, and statistical inference.

Michel Besserveمشاهده پروفایل

استاد

• Causal Machine Learning
• Artificial Intelligence
• Complex Systems
• +۳ مورد دیگر

Michel Besserve is a Full Professor in the Department of Empirical Inference at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. His research bridges artificial intelligence, causal inference, and neuroscience to develop trustworthy and interpretable AI systems for understanding complex phenomena in artificial, physical, and socioeconomic systems. Dr. Besserve completed his PhD dissertation titled Analyse de la dynamique neuronale pour les Interfaces Cerveau-Machine : un retour aux sources at Université Paris-Sud 11 in November 2007. His academic journey has led him to become a leading researcher in causal machine learning, collaborating extensively with Bernhard Schölkopf and other prominent scientists in neuroscience and AI. Professor Besserve's research centers on causal machine learning, with a focus on understanding and anticipating changes in complex systems. He investigates principles like the Independence of Causal Mechanisms (ICM) to improve causal model identifiability and develop more robust AI. His work spans theoretical foundations of causal inference to practical applications in neuroscience, brain function analysis, and socioeconomic systems. He has made significant contributions to understanding brain networks through causal inference and machine learning, with publications in major journals including Nature, PLOS Biology, and Neuron. His publication record reveals a clear trajectory from theoretical causal inference toward developing frameworks for real-world applications. Recent work focuses on building Causal Computational Models (CCMs) that integrate data, domain knowledge, and causal structure to improve robustness and interpretability of complex system models. His research shows increasing integration of causal machine learning with applications to neuroscience and socioeconomic systems, particularly in developing causal AI that can address real-world complexity while producing interpretable outcomes for decision makers. Through his leadership in the Department of Empirical Inference, Professor Besserve has established a research program that bridges theoretical machine learning with practical applications in neuroscience and complex systems. His team develops novel causal machine learning tools that uncover internal structure and transformations of complex systems, with potential applications ranging from brain function analysis to sustainable economic modeling.

Francesco Audrinoمشاهده پروفایل

استاد

• Computational Statistics
• Financial Econometrics
• Machine Learning
• +۶ مورد دیگر

Francesco Audrino is a Professor of Statistics at the Department of Mathematics and Statistics, University of St. Gallen, affiliated with the School of Economics and Political Science (SEPS). He holds a Diploma in Mathematics from ETH Zurich (specializing in Financial and Insurance Mathematics) and a Ph.D. in Statistics/Finance from ETH Zurich, with a thesis on statistical methods for high-dimensional financial time series. Research Focus: Computational Statistics applied to Economics and Finance, Financial Econometrics, Sentiment Analysis, Volatility Estimation, and Regime-switching Models. His work emphasizes nonparametric methods (e.g., Functional Gradient Descent) and their applications in asset pricing, interest rate dynamics, and risk management. Recent projects include the SentiVol program for sentiment-driven volatility forecasting and causal machine learning analyses of post-earnings sentiment impacts. Selected Projects: Director of the SentiVol SNF Grant (2017–2020) on sentiment analysis for volatility prediction. Co-Director of the SNF Grant on Behavioral Asset Pricing (2010–2013). Leading research on cross-asset dependency structures and causal machine learning applications. Awards & Recognition: Swiss Society of Economics and Statistics Young Economist Award (2007). University of St. Gallen’s Best Researcher Award (2007). Teaching load reduction for outstanding publications (2009–2018). Teaching & Academic Service: Teaches Statistics, Financial Volatility, and Computational Statistics at bachelor’s, master’s, and Ph.D. levels. Served as Elected Member of the Board of Directors of the European Regional Section of the International Association for Statistical Computing (2016–2020). Active in academic networks like the Computational Financial Econometrics (CFE) Network. Labs & Resources: Developed the FGD Code package for nonparametric high-dimensional time series analysis, widely used in volatility and correlation modeling. Maintains real-time volatility forecasts for S&P 500 constituents via the SentiVol project.

Stefano Favaroمشاهده پروفایل

استاد

• Nonparametric Bayesian Methods
• Empirical Bayes
• Statistical Machine Learning
• +۵ مورد دیگر

Stefano Favaro is a Professor of Statistics at the University of Torino and holds the Carlo Alberto Chair of Statistics and Machine Learning at Collegio Carlo Alberto. His research focuses on Bayesian nonparametric methods, statistical machine learning, and applications to data confidentiality and deep learning theory. PhD in Statistics, Bocconi University, 2009 His research interests include: Nonparametric Bayes and Empirical Bayes methods Statistical Machine Learning with emphasis on learning-augmented algorithms Data confidentiality and fairness in machine learning Mathematical foundations of deep neural networks Recent work explores Bayesian nonparametric models for frequency estimation, deep learning asymptotics, and privacy-preserving statistical methods. He leads DataLab Algorithms at Collegio Carlo Alberto and serves as Associate Editor for Bernoulli and Statistical Science .

Fateme Jamshidiمشاهده پروفایل

پژوهشگر

• Causal Inference
• Bayesian Networks
• Multi-Armed Bandits
• +۳ مورد دیگر

Fateme Jamshidi is a doctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Management of Technology and the Institute for Management, Technology and Economics. Her research focuses on: Causal inference and discovery Bayesian network modeling Multi-armed bandit algorithms Context-specific independence analysis Hidden confounding in causal networks Nonparametric statistical testing Recent work trends demonstrate expertise in causal modeling under uncertainty, with applications to machine learning and decision systems. Her publications explore graph-dependent regret bounds, causal imitability, and structural side information integration.

• Statistical Estimation
• High-Dimensional Data Analysis
• Convex Regression
• +۳ مورد دیگر

Dr. Gil Kur is a Lecturer in the Department of Mathematics at ETH Zürich. His research focuses on statistical estimation, high-dimensional data analysis, convex regression, machine learning theory, optimization, and probability theory. He has contributed to areas such as nonparametric estimation, convex body approximation, and differential privacy mechanisms. His work bridges theoretical foundations with applications in computational statistics and optimization. Key research themes include analyzing convergence rates of estimators, developing optimal algorithms for convex regression, and studying geometric properties of high-dimensional spaces. His recent articles explore topics like debiased LASSO methods, log-concave maximum likelihood estimation, and the performance of empirical risk minimization under various constraints. Kur’s publications demonstrate a strong focus on rigorous mathematical analysis, often combining tools from probability, functional analysis, and convex geometry. While no specific awards or grants are listed, his active publication record reflects sustained contributions to statistical theory and machine learning fundamentals.

• Extreme Value Theory
• Statistical Modeling
• Multivariate Statistics
• +۳ مورد دیگر

Anthony Christopher Davison is a Professor at the Institute of Mathematics within the School of Basic Sciences at École polytechnique fédérale de Lausanne (EPFL). He maintains an active research program in statistical methodology with a particular focus on extreme value theory and its applications across various domains including climate science, environmental statistics, and insurance. His work bridges theoretical developments with practical applications, addressing challenging problems in multivariate and spatial extremes. His research interests span Extreme Value Theory , Statistical Modeling , Multivariate Statistics , Spatial Statistics , and Bayesian Inference . Professor Davison has made significant contributions to the understanding of extremal dependence structures, developing novel methodologies for modeling multivariate extremes using structural equation models, graphical representations, and flexible nonparametric approaches. His work often addresses the challenges of non-stationarity in extreme events, particularly relevant in climate change contexts. Analysis of his recent publications reveals a consistent focus on advancing methodological frameworks for extreme value analysis while maintaining strong connections to real-world applications. His work demonstrates increasing sophistication in handling high-dimensional extremal dependence structures and addressing challenges in causal inference for extreme events. The publications span theoretical developments in statistical methodology alongside applications in climate science, environmental risk assessment, and insurance modeling. Professor Davison has supervised numerous doctoral students including Mario Krali, Timmy Rong Tian Tse, and Sonia Alouini, whose theses address cutting-edge problems in extreme value theory. His research has been supported by various funding bodies including the Swiss National Science Foundation and other Swiss foundations.

Deborah Sulemمشاهده پروفایل

استادیار

• Data Science
• Statistics
• Machine Learning
• +۷ مورد دیگر

Deborah Sulem is an Assistant Professor (tenure-track) of Data Science in the Faculty of Informatics at the Università della Svizzera italiana (USI). She leads the Statistical Science Laboratory and is affiliated with the Institute of Computing (CI). Her research focuses on graphs/networks, Bayesian inference, high-dimensional statistics, explainability, and fairness in machine learning. Education: Ph.D. in Statistics, University of Oxford (2023), supervised by Judith Rousseau, Mihai Cucuringu, Xiaowen Dong, and Vincent Rivoirard Postdoctoral Researcher at Barcelona School of Economics and Universitat Pompeu Fabra (with David Rossell and Gabor Lugosi) Joined USI in 2024 Research Interests: Combines statistical theory with modern machine learning, emphasizing graph deep learning, point processes, and nonparametric methods. Her work addresses challenges in high-dimensional data analysis and ethical AI practices like model explainability and fairness. Grants & Labs: Leads the Statistical Science Laboratory Focuses on interdisciplinary projects involving dynamic networks and Bayesian model selection Contact: Email | Office: East Campus, Sector D, D5.18 (P5)

Loriano Manciniمشاهده پروفایل

استاد

• Financial Econometrics
• Corporate Finance
• Volatility and Liquidity Risks
• +۳ مورد دیگر

Loriano Mancini is a Full Professor of Finance at the Institute of Finance, Università della Svizzera italiana (USI Lugano), where he has been faculty since 2017. He obtained his PhD in Economics from USI in 2004, followed by research positions at Princeton University and professorships at the University of Zurich and Swiss Finance Institute at EPFL. His research focuses on financial econometrics, specializing in volatility and liquidity risks, interbank markets, corporate finance, and statistical analysis of financial data. Recent work examines risk premia modeling, corporate cash flow dynamics, and market microstructure. Publications show sustained focus on quantitative finance methods, with emerging themes in derivatives pricing and systemic risk. His articles consistently appear in top-tier journals including Journal of Finance and Review of Financial Studies . Awards include: Outstanding Paper in International Finance (Eastern Finance Association 2010) Best Paper in Quantitative Finance (Quantitative Methods in Finance Conference 2005) He has advised multiple PhD students at USI, EPFL and University of Zurich. Major grants include Swiss National Science Foundation funding for projects on climate risk (CHF 800k) and corporate short-termism (CHF 590k). He serves as Associate Editor for Journal of Financial Econometrics and directs the Master in Finance program at USI.

Damian Kozburمشاهده پروفایل

دانشیار

• Econometrics
• Machine Learning
• High-Dimensional Data Analysis
• +۳ مورد دیگر

Prof. Damian Kozbur is an Associate Professor of Econometrics at the University of Zurich's Department of Economics, affiliated with the Digital Society Initiative. He holds a PhD from the University of Chicago (2014) and has been at UZH since 2016. His research focuses on integrating economic theory with machine learning tools, particularly in high-dimensional econometric models. Key areas include estimation techniques for weak instruments, panel data analysis, and clustering methods for statistical inference. He has contributed to journals like Econometrica and the Journal of Business and Economic Statistics. His work addresses applications such as market dynamics, causal inference in complex data, and policy analysis. Education: PhD in Econometrics from the University of Chicago Booth School of Business (2014), BA in Mathematics (2008). Previous roles include ETH Zurich Fellow (2014–2016). Teaching includes advanced econometrics and machine learning courses for doctoral students. Research Interests: High-dimensional econometrics, model selection, statistical inference, and applications of machine learning to economic problems. His recent articles tackle spatial dependence robustness, debiased machine learning, and forecast hedging with random forests. Professional Service: Associate Editor of the Journal of Business and Economic Statistics since 2022. Active in reviewing for top econometric outlets like Journal of Econometrics and Econometric Theory.

• Financial Econometrics
• High-Frequency Data Analysis
• Market Microstructure
• +۴ مورد دیگر

Aleksey Kolokolov is an Associate Professor at the Manchester Business School, University of Manchester, specializing in financial econometrics and market microstructure analysis. His research focuses on high-frequency data analysis, jump detection in financial markets, liquidity modeling, and statistical methods applied to financial time series. His research interests span several key areas in modern finance: Developing statistical methods for analyzing high-frequency financial data Studying market microstructure and price formation processes Investigating liquidity dynamics and market stability during extreme events Applying econometric techniques to cryptocurrency markets, particularly Bitcoin Creating robust estimators for financial volatility and jump activity His work bridges theoretical econometrics with practical financial applications, often addressing methodological challenges in analyzing discontinuous trading patterns and irregular sampling schemes. Analysis of his publication record reveals a strong focus on methodological innovations in financial econometrics, with particular emphasis on jump detection, price staleness, and liquidity measurement. His most cited work, 'Nonstandard Errors' (Journal of Finance, 2024), represents a major collaborative effort addressing statistical challenges in finance research. His recent publications show increasing attention to cryptocurrency markets and the application of traditional financial econometric methods to these emerging asset classes. Kolokolov maintains an active research agenda with frequent collaborations, particularly with researchers like Roberto Renò, Federico M. Bandi, and Kim Christensen. His work appears in top finance journals and working paper series, demonstrating his significant contribution to the field of financial econometrics.

• Financial Econometrics
• Quantitative Finance
• Risk Management
• +۷ مورد دیگر

Olivier Scaillet is a Research Fellow at the Swiss Finance Institute, affiliated with the University of Geneva. His work spans financial econometrics, quantitative finance, and risk management, with a focus on stochastic volatility models, copulas, and high-frequency data analysis. Research Interests : Financial econometrics and nonparametric estimation Stochastic volatility and jump-diffusion models Asset pricing and factor models in large panels Systemic risk and recovery rate density estimation Machine learning applications in finance Market microstructure and high-frequency data dynamics Scientific Contributions : Developed methodologies for nonstandard error analysis in multi-analyst studies Advanced techniques for testing stochastic dominance efficiency and latent factor models Explored copula-based goodness-of-fit tests and threshold effects in time series Innovated in American option pricing under complex market conditions

James Hendersonمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

James Henderson is a Senior Researcher at Idiap Research Institute where he heads the Natural Language Understanding group. He currently serves as Action Editor for Transactions of the Association for Computational Linguistics (TACL) and was recently awarded an ERC Advanced Grant for his project 'Interpretable Beliefs and Programmable Knowledge with Bayesian Attention in Large Language Models' (BALM). Previously, Henderson held positions as Chargé de Cours at University of Geneva's Department of Computer Science and Principal Scientist at Xerox Research Centre Europe (now Naver Labs Europe). Henderson's research focuses on machine learning methods for natural language processing, with pioneering work on recurrent neural networks for syntactic and semantic parsing. His current investigations include representation learning for language semantics, graph-to-graph deep learning models, entity induction, and variational-Bayesian attention-based representation learning. His research bridges Bayesian inference, transformer architectures, and structured prediction for NLP tasks. His publication portfolio demonstrates consistent contributions to core NLP methodologies, with recent emphasis on transformer optimization, Bayesian neural methods, efficient model architectures, and graph-based language representations. Research frequently appears in top venues including ACL, EMNLP, ICLR, and NeurIPS. Honors: ERC Advanced Grant (2023) Henderson leads the Natural Language Understanding group at Idiap, currently recruiting PhD students and postdoctoral researchers for his ERC project. He obtained his PhD and MSc from University of Pennsylvania and BSc from Massachusetts Institute of Technology, all in computer science.

Taiji Suzukiمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Learning Theory
• Deep Learning
• +۶ مورد دیگر

Prof. Taiji Suzuki is an Associate Professor at the University of Tokyo in the Department of Mathematical Informatics . He also serves as Team Leader of the "Deep Learning Theory" team at AIP-RIKEN , Japan. With a PhD in Information Science and Technology from the University of Tokyo (2009), he has held academic positions at the University of Tokyo (2009-2013) and Tokyo Institute of Technology (2013-2017) before returning to the University of Tokyo in 2017. University of Tokyo (2004: BEng in Mathematical Engineering) University of Tokyo (2006: MSc in Information Science and Technology) University of Tokyo (2009: PhD in Information Science and Technology) His research interests span statistical learning theory , deep learning , kernel methods , sparse estimation , and stochastic optimization . He investigates how neural networks adapt to function smoothness, avoid the curse of dimensionality, and achieve global optimization through mean-field dynamics. His work bridges theoretical guarantees (minimax optimality, convergence analysis) with practical implementations (transformers, graph neural networks, diffusion models). The 15 most recent articles focus on transformers' representation power, graph neural networks' limitations, diffusion models' convergence, and optimization theories for deep learning. Key themes include information-theoretic bounds , feature learning dynamics , and mean-field analysis . He explores applications in AI for medicine, federated learning, and biomedical modeling. Awards & Recognition: Outstanding Paper Award, ICLR 2021 MEXT Young Scientists’ Prize Outstanding Achievement Award, Japan Statistical Society 2017 Outstanding Achievement Award, Japan Society for Industrial and Applied Mathematics 2016 Best Paper Award, IBISML 2012 Best Paper Candidate, ICDM 2019 He has served as Area Chair for NeurIPS, ICML, ICLR, AISTATS, and as Program Chair for ACML. His scientific contributions include convergence theories for stochastic gradient methods, minimax analysis of deep learning vs kernel methods, and novel frameworks for distributional optimization in diffusion models.