Trevor Wrobleski

Welcome

I am a Statistics PhD student at the London School of Economics, interested in using Bayesian methods, machine learning, and computational tools to solve complex problems in risk assessment, toxicology, and healthcare analytics. My advisors are Dr. Sara Geneletti and Dr. Francesca Panero. My research focuses on developing robust statistical models to improve decision-making in health, pharmacology/biotech, toxicology, and risk systems under uncertainty.

Research Interests

Bayesian inference, dose-response modeling, model averaging, Gaussian processes, probabilistic risk assessment, survival analysis
Technical Skills: R, Python, and SQL, with experience in Bayesian modeling (PyMC3, Stan) and machine learning (TensorFlow, PyTorch)
Methodological Focus: Uncertainty quantification, robust statistical inference, experimental design optimization, model selection and averaging

Application Areas

Academic Research: Advancing Bayesian model averaging theory, developing robust methods for small-sample inference, creating open-source tools for reproducible risk assessment

Investment Decision Support: Quantitative due diligence for biotech assets, probability of technical success (PoTS) modeling, risk-adjusted NPV analysis for drug development pipelines, portfolio optimization under uncertainty

Regulatory Science & Strategy: Benchmark dose estimation for FDA/EMA submissions, prior sensitivity analysis for regulatory compliance, optimal study design to maximize approval probability while minimizing cost

Drug Development Optimization: Adaptive trial design, go/no-go decision frameworks, cross-regional data harmonization (US/EU/Asia), in-silico patient population generation for trial planning

Healthcare Data Science: Causal inference from real-world evidence, synthetic control methods for post-market surveillance, generative models for rare disease data augmentation

Education

PhD Candidate, Statistics – London School of Economics
MSc, Statistics (Research) – London School of Economics
MMSc, Global Affairs, Economics and Business – Tsinghua University, Schwarzman College
BA, Public Health – Johns Hopkins University

Skills

Statistical Methods: Bayesian inference, MCMC, optimization, survival analysis, random effects models

Programming Languages: R, Python, SQL

Bayesian Modeling: PyMC3, Stan

Machine Learning: TensorFlow, PyTorch, Scikit-learn

Optimization: Gurobi, CPLEX, PuLP

About Me

Outside of research, I enjoy running, tennis, hiking, reading, and playing piano.

You can contact me from my university page.