Research

Research Fellow, Department of Mathematical Modeling and Machine Learning, University of Zurich

Responsibilities

• Associate editor of the International Journal of Biostatistics

Research interests

• Statistical learning
  • high-dimensional data
  • graphical models
  • penalized linear regressions
  • sparse methods
  • clustering
  • statistical modeling
• Computational biology
  • gene regulatory network inference
  • multi-omics data integration
  • applications to clinical and medical research

Current projects

• 🕸️ Additive Bayesian Network (ABN) Inference: A joint collaborative initiative between the University of Zurich (UZH) and the Zurich University of Applied Sciences (ZHAW). This project focuses on structural inference within Additive Bayesian Networks (ABNs) to predict targeted nodes across complex biological systems. A core strength of this ABN architecture is its capacity to model highly heterogeneous, multi-distribution data (Poisson, Multinomial, Binomial, and Gaussian). We are building scalable simulation pipelines to optimize predictions, with future clinical applications aimed at evaluating structural risks of intracranial aneurysm.

• 🦷 Obesity and Oral Microbiota: In collaboration with INSERM (Vincent Blasco-Baque and Charlotte Thomas, InCOMM) and Camille Champion (Université Paris Cité), we are working on a collaborative project to model and decode the complex structural roles of bacterial interactions within saliva and periodontal environments to better understand the underlying mechanisms driving periodontal disease.

• 🐱 Machine Learning Pipelines for FIP Diagnostics and Prognostics: In collaboration with the clinical laboratory (Vetlabor) at the University of Zurich, this project focuses on developing a machine learning pipeline for Feline Infectious Peritonitis (FIP) diagnosis. We will then expand this work into prognostic modeling by analyzing clinical timelines. This longitudinal analysis aims to evaluate overall drug efficacy, determine a cat’s individual chances of therapeutic success, and map treatment outcomes over time.

Supervision of students

Past supervisions:

• Semester thesis:
  • Julia Netzel (Master of Applied Mathematics, ETH Zürich) on “Handling Gender Bias in NLP Models” (4 months in 2022)
• Bachelor thesis:

  • Clements Kirchner (Bachelor of Mathematics, ETH Zürich) on “Clustering algorithms of gene networks using cancer data” (3 months in 2023)

  • Michael Vollenweider (Bachelor of Computational Science and Engineering, ETH Zürich) on “Benchmark of gene regulatory network inference methods” (5 months in 2022)

  • Riccardo Fumagalli (Bachelor of Mathematics, ETH Zürich) on “Identification of genes involved in the development of ER+ breast cancer” (3 months in 2022)

• Semester projects:

  • Gauthier Pervieux (Undergraduate student in data science, IUT de Paris) on “Breast cancer statistical study” (2 months in 2021)

  • Marina Atangana and Michael Tsimi (Undergraduate students in Data Science, IUT de Paris) on “Statistical analysis of airbnb data” (2 months in 2021)

  • Reyna Zhang (Master of Statistics, Stanford University) on “Data fusion for predicting cancer survival” (2 months in 2016)

  • Teun de Planque and Christopher Elamri (Bachelor of Computer Science and Electrical Engineering, Stanford University) on “Identifying genes with prognostic DNA methylation rates for breast cancer survival” (2 months in 2016)

• High-school student internship
  • Nabeel Mamoon (High school student, winner of the Stanford Institutes of Medicine Summer Research program) on “Analysis of statistical signatures in methylation-guided automated carcinoma diagnosis” (2 months in 2015)