07.07.2026 aktualisiert
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Clinical Biostatistician | Computational Biologist | Multi-Omics Expert
München, Deutschland
Nur Remote
PhDÜber mich
Simone Anza, PhD, is a clinical biostatistician and computational biologist, co-founder of Kernel Science. 7+ years in multi-omics, biomarker discovery, FDA-registered trials (PREDICTs) and NIH studies (eLABE). Open-source tool developer. PhD Göttingen, MSc Pisa.
Skills
Clinical TrialsBiomarker
I am a clinical biostatistician and computational biologist with a PhD in Mathematics and Natural Science from the University of Göttingen (Magna cum Laude) and an MSc in Biology from the University of Pisa. Over seven years I have built end-to-end analytical pipelines connecting heterogeneous biological data to clinical outcomes, specializing in longitudinal cohort design and multi-omics integration.
My statistical and ML toolkit spans Bayesian mixed-effects models (brms), GLMMs, GAMs, zero-inflated negative binomial models, mediation analysis, causal inference, Random Forest with nested cross-validation, neural networks, longitudinal trajectory modelling, and dimensionality reduction (PCA, PCoA, NMDS). I work fluently in R, Python, Bash, Git, and HPC environments, and have developed and maintained CRAN packages.
In bioinformatics, I work with shotgun metagenomics (HUMAnN3, MetaPhlAn, InStrain), strain-level microbiome profiling, metagenomic assembly, pangenome construction, KEGG/KO annotation, and multi-omics integration tools (MaAsLin, ANPAN, HALLA, LEfSe).
I have led statistics on FDA-registered clinical trials, most notably PREDICTs, the world's largest nutrition intervention study, with the University of Trento SegataLab, ZOE Health, Harvard T.H. Chan School of Public Health, Stanford Medicine, Mass General Hospital, and King's College London, developing predictive models for cardiometabolic disease risk. Currently, as Senior Postdoctoral Researcher at Washington University in St. Louis School of Medicine, I lead statistics for the 399-participant eLABE/March of Dimes study on early life adversity and pediatric mental health, developing novel AI-based approaches for biomarker discovery.
I developed two open-source tools: LongitudinalRandomForest, extending Random Forest to repeated-measures multi-omics and microbiome data, and the Cortisol Sine Score, a validated metric for diurnal cortisol cycles, published as a CRAN package with an accompanying preprint.
As co-founder of Kernel Science, I lead a scientific consulting practice delivering AI-powered multi-omics analytics, biomarker discovery, and preclinical study design to academic medical centers, biotech companies, and research foundations across Europe and the US, managing engagements including partnerships with Harvard/Brigham and Women's Hospital and the University of Pisa/Organo Therapeutics/University of Freiburg.
My work has produced multiple peer-reviewed publications, including papers under review at Nature Food, Nature Microbiology, and Microbiome. I serve as a grant reviewer for the National Science Centre of Poland, a journal reviewer for Frontiers, Microbiome, eLife, and Scientific Reports, and a mentor to PhD students at Washington University and the University of Trento. I completed Y Combinator Startup School and a Strategic Management course at Copenhagen Business School to strengthen the business dimension of my scientific work.
Erfahrung
Berufserfahrung5 - 10 Jahre
Freelancer seit2017
Sprachen
EnglischverhandlungssicherItalienischMuttersprache
Projekthistorie
Lead statistical analysis for a 399-participant longitudinal clinical study examining early life adversity and pediatric mental health outcomes. Developed novel AI-based biostatistical approaches for longitudinal multi-omics data analysis and biomarker discovery. Collaborate with clinical teams, regulatory partners, and cross-functional study investigators. Developed bioinformatics tools.
Active projects and ongoing computational biology work.