Nonparametric Inference Methods Professors and Supervisors in Switzerland

Rajita Ramesh Chandakمشاهده پروفایل

مدرس

Swiss Federal Institute of Technology in LausanneSwitzerland

Rajita Ramesh Chandak serves as a Lecturer and Scientist at the Institute of Mathematics within the School of Basic Sciences at École Polytechnique Fédérale de Lausanne (EPFL). Currently holding a Bernoulli Instructorship, she will transition to an Assistant Professor position in the Department of Statistics at the University of Wisconsin-Madison starting Fall 2026. Her academic foundation includes a PhD in Operations Research & Financial Engineering from Princeton University (2024), an MA in the same field (2021), and a BSc with Honors in Applied Mathematics and Economics from Brown University (2019). Her dissertation, 'Adaptive Nonparametric Statistical Theory and Implementation,' was supervised by Matias Cattaneo. Chandak's research centers on mathematical statistics and theoretical machine learning, with emphasis on nonparametric estimation frameworks, statistical inference methodologies, and econometric applications. Her work bridges theoretical rigor with practical algorithm development, particularly in flexible function approximation and regression analysis. Her 2025 publication on oblique regression trees demonstrates convergence rate analysis for complex function libraries, reflecting her focus on unifying nonparametric theory with machine learning implementation challenges across statistics and computer science domains. Notable recognitions include the MVPM 2024 Scholar Award, an Annals of Statistics-invited paper at JSM 2025, the EPFL Bernoulli Instructorship, and multiple Princeton engineering awards. Professional grants include the Princeton SEAS Travel Grant and Bernoulli funding supporting her theoretical research. She actively contributes to EPFL's academic ecosystem through graduate instruction in nonparametric methods and statistical inference, while collaborating with the Bernoulli Center on mathematical statistics initiatives and preparing for expanded research leadership at Wisconsin-Madison.

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Stefan Sperlichمشاهده پروفایل

استاد

University of GenevaSwitzerland

Stefan Sperlich is a Full Professor and Director of the Research Institute for Statistics and Information Science at the University of Geneva's Geneva School of Economics and Management. He holds dual appointments in the Department of Econometrics and Statistics, with affiliations in both the Research Institute for Statistics and Information Science and the Institute of Economics and Econometrics. Dr. Sperlich earned his diploma in mathematics from the University of Göttingen and completed his PhD in economics at Humboldt University of Berlin. His academic career includes professorships at University Carlos III de Madrid (1998-2006) and the University of Göttingen (2006-2010), before joining the University of Geneva in 2010. Professor Sperlich's research spans nonparametric and semiparametric statistics , small area estimation , and impact evaluation methods . His work bridges theoretical econometrics with practical applications in development economics, policy evaluation, and poverty measurement. He has made significant contributions to specification testing, causal inference methodologies, and the development of robust statistical techniques for small area estimation. His research often addresses real-world problems through collaborations with international institutions and development programs. His recent publications reveal a strong focus on advancing methodological frameworks for small area statistics, causal inference, and nonparametric estimation. Key themes include developing robust inference techniques for linear mixed models, improving bandwidth selection methods, and creating model-free approaches to difference-in-differences estimation. His work increasingly integrates computational statistics with traditional econometric methods to address challenges in big data analysis and distributed data environments. Professor Sperlich has received numerous accolades including: Tjalling C. Koopmans Econometric Theory Prize (2000-2002) Augusto Gonzalez Linares award (2014) for attracting international talent Elected member of the International Statistical Institute (since 2025) Special rewards from the Economics Department at University Carlos III de Madrid (2004-2005) Grants from the Institute Flores de Lemus (2001-2003) As an advisor and researcher, Professor Sperlich has supervised numerous graduate students and led significant research initiatives. He co-founded the research center 'Poverty, Equity and Growth in Developing Countries' at the University of Göttingen and serves as a research fellow at the Center for Evaluation and Development in Mannheim, Germany. His consultancy work spans regional, national, and international institutions, with participation in development programs like EUROSOCIAL and UN assessment reports. He has secured multiple research grants supporting his work in statistical methodology and economic applications. Professor Sperlich leads research teams focused on nonparametric methods, small area statistics, and impact evaluation. His research group develops innovative statistical approaches for poverty mapping, causal inference, and composite indicator construction. The team maintains strong connections with statistical offices and international organizations, ensuring their methodological advances have practical applications in policy development and evaluation.

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Badr-Eddine Chérief-Abdellatifمشاهده پروفایل

پژوهشگر

Swiss Federal Institute of Technology in LausanneSwitzerland

Badr-Eddine Chérief-Abdellatif is a CNRS Researcher at Sorbonne Université , affiliated with the Laboratoire de Probabilités, Statistique et Modélisation (LPSM) . His research spans mathematical statistics and machine learning theory , focusing on generalized Bayesian inference , robustness , and variational inference . He previously held a postdoctoral position at the University of Oxford and earned his PhD in 2020 from Institut Polytechnique de Paris under Pierre Alquier. His work addresses robust statistical methods using Maximum Mean Discrepancy (MMD) , PAC-Bayesian theory , and kernel techniques . Recent publications explore meta-learning , VAE reconstruction guarantees , and missing data analysis . He received the Best Student Paper Award at AISTATS 2022 and a Best Paper Award at ACML 2019. Chérief-Abdellatif contributes to online learning , copula estimation , and sparse deep learning . His theoretical results on variational inference have been published in top venues like NeurIPS , JASA , and JMLR . He also co-invented a patent for proportions determination in biological ensembles (2024). Scientific Awards : Best Student Paper Award, AISTATS 2022 Best Paper Award, ACML 2019 Travel Award for Academic Conferences

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Matteo Giordanoمشاهده پروفایل

استادیار

Swiss Federal Institute of Technology in LausanneSwitzerland

Matteo Giordano is an Assistant Professor (Ricercatore a Tempo Determinato di Tipo A) of Statistics at the Department of Economics, Social Studies, Applied Mathematics and Statistics (ESOMAS) of the University of Turin, Italy. He is also a Research Affiliate at Collegio Carlo Alberto, associated with the "de Castro" Statistics Initiative. Education: Ph.D. in Mathematical Statistics, University of Cambridge (2021), supervised by Prof. Richard Nickl M.Sc. in Mathematics, University of Turin (2017) Master in Statistics and Applied Mathematics, Collegio Carlo Alberto (2017) B.Sc. in Mathematics for Finance and Insurance, University of Turin (2015) His research focuses on Bayesian nonparametric inference, uncertainty quantification in large statistical models, and applications of partial differential equations (PDEs) to statistical modeling. He has contributed to asymptotic theory for Bayesian nonparametric methods and is actively involved in the Junior Section of ISBA (j-ISBA) as Chair. Scientific Recognition: Member of ELLIS (European Laboratory for Learning and Intelligent Systems) He previously held a postdoctoral position at the Department of Statistics, University of Oxford (2021–2023), mentored by Prof. Judith Rousseau, and has participated in numerous international conferences and workshops, including ENUMATH 2025 and BNP 14.

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David Ginsbourgerمشاهده پروفایل

استاد

University of BernSwitzerland

David Ginsbourger is a Professor and Head of Research Group at the Institute of Mathematical Statistics and Actuarial Science (IMSV) within the University of Bern, Switzerland. He maintains dual affiliations through his role at IMSV and as a member of the Multidisciplinary Center for Infectious Diseases (MCID), reflecting interdisciplinary engagement across statistical methodology and applied domains. His research program centers on advanced statistical methodologies with emphases on Gaussian process modeling, uncertainty quantification, and experimental design for computer experiments. Key contributions include novel kernel constructions for equivariant systems, sequential design strategies for excursion set estimation, and efficient computational frameworks for spatial distributional modeling. His work bridges theoretical statistics with practical applications in agriculture, chemoinformatics, environmental science, and risk assessment, demonstrating consistent innovation in handling complex prediction problems under uncertainty. Analysis of his 15 most recent publications (2024-2025) reveals persistent methodological development in Gaussian process theory alongside expanding application domains. Recurring themes include integration-free kernel design for structured data, rare event probability estimation, and multivariate forecast calibration. His research exhibits strong continuity in addressing computational challenges for large-scale inverse problems while increasingly incorporating domain-specific constraints from fields like molecular chemistry and agricultural science. Ginsbourger leads a dedicated research group at IMSV focused on advancing statistical frameworks for computer experiments and uncertainty quantification. The group maintains active collaborations across disciplines, particularly evident in recent work connecting statistical methodology to infectious disease modeling through MCID affiliations and agricultural optimization projects.

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Olivier Scailletمشاهده پروفایل

استاد

University of GenevaSwitzerland

Finance
Statistics
Econometrics
+۳ مورد دیگر

Olivier Scaillet is a Full Professor at the Geneva Finance Research Institute (GFRI) of the University of Geneva and holds a senior chair at the Swiss Finance Institute . His academic focus spans Finance , Statistics , and Econometrics , with expertise in Derivatives Pricing , Asset Pricing , Econometric Theory , and Quantitative Risk Management . Education : PhD in Applied Mathematics from University Paris IX Dauphine Leadership : Director of GFRI and Deputy Director (Education) His research includes nonparametric methods , instrumental variable regression , stochastic volatility models , and systemic risk measures . Recent work explores dynamic portfolio optimization , high-frequency jump analysis , and time-varying risk premia in equity and cryptocurrency markets. Professor Scaillet has been recognized with awards for best papers in Journal of Empirical Finance and Banque Privée Espirito Santo , and is a Fellow of the Society of Financial Econometrics . He serves as an associate editor for journals in Econometrics , Statistics , and Finance , and advises BNPParibas research teams in Paris and London.

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Fabio Trojaniمشاهده پروفایل

استاد

University of GenevaSwitzerland

Fabio Trojani is a Full Professor of Finance at the University of Geneva since 2015, holding the AXA Chair in Socioeconomic Risk of Financial Markets at the University of Turin and serving as a Senior Chair of the Swiss Finance Institute (SFI). He previously held Full Professorships at the University of Lugano (Statistics), University of St Gallen (Finance), and Bocconi University (Adjunct Professor of Finance). Currently, he directs the SFI PhD program and serves as Editor of the Journal of Financial Econometrics since 2019. His research focuses on Asset Pricing , Quantitative Finance , Financial Econometrics , and Statistical Methods in finance. His work bridges theoretical finance with empirical validation , addressing model robustness , volatility timing , and international SDF frameworks . Articles like ‘ Tradable Factor Risk Premia ’ (2024) and ‘ Smart Stochastic Discount Factors ’ (2019) highlight his contributions to factor risk analysis and model-free pricing . Scientific Awards and honors are not explicitly mentioned in the provided text. However, his frequent invitations to speak at conferences like the Financial Econometrics Conference (2024) and ESEM Annual Meeting underscore his influence in the field. As a director of the SFI PhD program and a former faculty member at multiple institutions, he has shaped academic and professional finance education globally.

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Florian Brueckمشاهده پروفایل

پژوهشگر

University of GenevaSwitzerland

Florian Brueck is a Postdoctoral Researcher at the Research Center for Statistics at the University of Geneva, under Prof. Sebastian Engelke. He earned his PhD in Mathematics (Dr. rer. nat.) from the Technical University of Munich (TUM) in 2023, with distinction, under Prof. Matthias Scherer. His research focuses on statistical model comparison, Bayesian non-parametric survival analysis, Lévy processes, extreme value theory, and the integration of machine learning into classical statistics. Education: PhD in Mathematics, TUM (2023) MSc in Financial Mathematics and Actuarial Sciences, TUM (2019) BSc in Business Mathematics, Ludwig Maximilians University Munich (2017) Research Interests: Statistical model selection via Maximum Mean Discrepancy (MMD) Exchangeability and non-parametric Bayesian methods in survival analysis Inference for Lévy and stable processes Extreme value theory applications in risk modeling Machine learning techniques for statistical inference Key Contributions: Developed distribution-free MMD tests for model selection with estimated parameters Advanced infinitely divisible priors for multivariate survival functions Applied generative neural networks to characteristic function estimation Awards & Recognition: None explicitly listed. Advising & Grants: Supervised 5 Master’s theses on topics including MMD-based model selection and clustering-based portfolio optimization Co-led industry project with WWK Versicherungen on risk assessment tools Labs/Teams: Research Center for Statistics at University of Geneva.

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Leonardo Santoroمشاهده پروفایل

پژوهشگر

Swiss Federal Institute of Technology in LausanneSwitzerland

Leonardo Santoro is a Postdoctoral Researcher at EPFL's Chair of Mathematical Statistics within the School of Basic Sciences. His academic appointments include current research roles at EPFL (2024–present) and a research stay at UC Berkeley (2023). His research focuses on theoretical intersections of statistics and machine learning. Key interests include kernel methods for machine learning, nonparametric inference for high-dimensional and functional data, and optimal transport theory. His work addresses inverse problems through mathematical frameworks like covariance operators, transport flows, and diffusions. His publication trends reveal specialization in statistical methodology development, particularly for complex data structures. Research outputs emphasize mathematical rigor in machine learning applications, with emphasis on geometric statistics and probabilistic modeling. Professional trajectory includes doctoral training under Victor Panaretos at EPFL, preceded by masters studies at ETH Zurich and undergraduate education at Università di Trento. His technical profile demonstrates applied experience through an industry internship at Teralytics AG developing mobility models from telco data.

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Sylvain Sardyمشاهده پروفایل

دانشیار

University of GenevaSwitzerland

Sylvain Sardy is an Associate Professor in the Department of Mathematics at the University of Geneva, where he conducts research at the intersection of statistics, optimization, and machine learning. He is affiliated with the Analysis, Mathematical Physics and Probability research group and has held significant editorial positions including Associate Editor for Computational Statistics and Data Analysis since 2020. Professor Sardy's research focuses on statistical machine learning, sparsity, and optimization with applications spanning astronomy, chemometrics, finance, and tomography. His work develops innovative methods for high-dimensional data analysis, particularly using wavelet-based approaches and LASSO regularization techniques for feature selection, denoising, and model selection. His publications reveal a consistent focus on finding sparse signals in complex datasets across diverse scientific domains. Professor Sardy has mentored numerous graduate students, currently supervising PhD candidate Maxime van Cutsem and having previously guided Dr. Xiaoyu Ma, Dr. Pascaline Descloux, Prof. Jairo Diaz Rodriguez, and Dr. Caroline Giacobino. His Master's students include Jairo Diaz (now Professor at Universidad del Norte, Colombia), Jean-Luc Baeriswyl, and others who have pursued careers in academia, industry, and education. His academic service includes leadership roles as Swiss representative at the European Regional Committee of the Bernoulli Society (2014-2018), President of the Doctoral School of Applied Statistics and Probability (2010-2013), and Student Advisor for the Mathematics Section (2008-2015). His teaching portfolio includes Optimization with Applications I, Statistical Machine Learning, and Pharmaceutical Statistics and Methodology, reflecting his expertise in statistical methodology and its practical implementation.

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