Trevor L. Wrobleski
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Welcome
I am a PhD candidate in statistics at the London School of Economics, interested in Bayesian methods for uncertainty quantification in drug development, toxicology, and risk assessment. My advisors are Dr. Sara Geneletti and Dr. Francesca Panero. My research develops robust statistical models to improve decision-making under uncertainty in biotech, pharmacology, and regulatory toxicology.
Research Interests
Statistical Methodology & Theory
- Bayesian Model Averaging & Stacking: Predictive distribution combination in M-open settings, scoring rules (log score, CRPS), and coherence vs. predictive accuracy trade-offs.
- Optimal Experimental Design: Locally D-optimal and c-optimal designs for nonlinear models, resource allocation strategies (N vs. G), and design robustness under overdispersion.
- Robust Inference: Handling model misspecification, variance propagation in poly-k survival adjustments, and quantifying bias in finite-sample benchmark dose (BMD) estimation.
Quantitative Risk Assessment
- Dose-Response Modeling: Hierarchical modeling of heterogeneity (chemicals/studies), BMD estimation, and derivation of empirical priors for regulatory science.
- Toxicology & Carcinogenicity: Analyzing large-scale bioassay databases such as the Carcinogenic Potency Database (CPDB), accounting for litter effects and differential mortality, and improving points of departure (PoD) for FDA/EMA submissions.
Drug Development
- Evidence Synthesis & Historical Borrowing: Meta-analytic-predictive and robust mixture priors for historical or external controls, prior-data conflict, and probability-of-success estimation from hierarchical models of past programs.
- Clinical Dose-Finding & Model-Informed Drug Development: Model averaging and MCP-Mod for Phase II dose-ranging, Bayesian adaptive dose-finding designs, and exposure-response modeling to support dose selection.
- Translational Safety & First-in-Human Dosing: Bayesian extrapolation from nonclinical points of departure to human-equivalent doses, uncertainty in safety margins, and toxicology-informed first-in-human dose selection.
Application Areas
Academic Research: Advancing Bayesian model averaging theory, developing robust methods for small-sample inference, and creating open-source tools for reproducible risk assessment.
Regulatory Science & Strategy: Benchmark dose estimation for FDA/EMA submissions, prior sensitivity analysis for regulatory compliance, and optimal study design to maximize approval probability while minimizing cost.
Pharmacology & Drug Development: Dose-finding and dose-ranging design, Bayesian adaptive trials, borrowing from historical controls, and first-in-human dose selection from nonclinical data.
Biotech Strategy & Due Diligence: Quantitative due diligence for biotech assets, probability of technical and regulatory success (PTRS) modeling, go/no-go decision frameworks, risk-adjusted NPV analysis, and portfolio optimization across drug development pipelines.
Healthcare Analytics: Causal inference from real-world evidence, predictive modeling for patient recovery trajectories, and synthetic control methods for postmarket surveillance.
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 (Bloomberg Fellow)
- BA, Public Health – Johns Hopkins University
Teaching
Graduate Teaching Assistant – Department of Statistics, London School of Economics
- ST314 – Multilevel and Longitudinal Models
- ST205 – Sample Surveys and Experiments seminar compendium
- ST211 – Applied Linear and Logistic Regression workshop compendium
Highly Commended, LSE Teaching Award, 2026
Skills
| Category | Proficiencies |
|---|---|
| Statistical Methods | Bayesian inference, optimal experimental design, hierarchical (mixed-effects) modeling, survival analysis, MCMC |
| Programming | R (tidyverse, Shiny), Python (NumPy, pandas, Flask), SQL, Git |
| Bayesian & ML | Stan, PyMC, TensorFlow, PyTorch, scikit-learn |
| Languages | English (Native), French (Intermediate), Mandarin Chinese/普通话 (Intermediate) |
About Me
Outside of research, I enjoy running, tennis, hiking, reading, writing, the ballet and opera, and playing piano.
You can contact me through my university page.