“In God we trust. All others must bring data.”
— W. Edwards Deming
I am currently a Postdoctoral Researcher at the Unité d’appui méthodologique of the Clinical Research Center at the Geneva University Hospitals (HUG) led by Prof. Combescure, where I focus on advanced statistical methodologies for meta-analyses. In parallel, I collaborate with Prof. Panteleimon Giannakopoulos, General director of the Cantonal Health Office (OCS) and Professor of Psychiatry at the University of Geneva, on research using Machine/Statistical learning approaches to identify early predictors of Alzheimer’s disease.
I hold a PhD in Statistics from the University of Geneva (co-advisors: Prof. Maria-Pia Victoria-Feser and Prof. Stéphane Guerrier), where I developed new methods for bioequivalence testing, designed a generative AI model for spatial extremes, and contributed to simulation-based privacy-preserving inference techniques. I also completed a research internship at Roche in Basel, applying latent variable models to improve disability progression endpoints in neuroinflammatory diseases.
Overall, my work bridges statistical innovation and practical impact, with over seven publications, ten talks, and open-source tools that foster reproducible and collaborative research in statistics and machine learning.
Research included:
Thesis: Learning Max-stable Distributions with Generative Adversarial Networks. Advisor: Prof. Sebastian Engelke.
Grade: 5.5/6
A unified framework for multivariate bioequivalence assessment of locally acting drugs, providing size control and improved power under realistic conditions.
A simulation-based fiducial framework for differential privacy that delivers practical uncertainty quantification for categorical data.
We propose the multivariate $\alpha$-TOST, a finite-sample correction that preserves size and improves power in multivariate (bio)equivalence studies.
A cross-sectional survey of 982 adults reveals how COVID-19 disrupted care access and medication adherence for people with chronical diseases.
We introduce the $\alpha$-TOST, a finite-sample correction to the TOST procedure that restores nominal error rates and improves power in (bio)equivalence studies.
A faultline analysis of six global partnership boards showing how internal diversity within sectors shapes collaboration and effectiveness.
We introduce evtGAN, a hybrid EVT–GAN model that better captures spatial climate-extreme dependencies than standard GANs or classical EVT methods.
Polymeric micelles improve topical sirolimus delivery to the viable epidermis and offer a promising approach for treating facial angiofibromas.
evtGAN is a lightweight emulator that combines extreme value theory (EVT) with generative adversarial networks (GANs) to model rare compound events with far greater accuracy than traditional tools. Climate models are computationally costly, EVT alone struggles with complex spatial dependence, and standard machine-learning models typically fail in the extreme tail. evtGAN overcomes these limitations by separating marginal behavior from dependence using a copula-based framework: EVT provides theoretically sound marginal modeling and tail extrapolation, while GANs flexibly learn spatial dependence patterns. With strong performance even from as few as 30 annual maxima, evtGAN offers scientists an efficient, ready-to-use solution for simulating extremes. The Zenodo repository provides 2'000 years of simulated annual temperature and precipitation maxima over Western Europe, along with R and Python (TensorFlow) code to reproduce the method from our Environmental Data Science (2022) publication.
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