Ioan Gabriel Bucur

Research

My research focuses on developing statistical methods for causal inference and time series analysis with a focus on biomedical applications. My goal is to help clinicians and policymakers with extracting meaningful information from data so that they can make better data-driven decisions.

My current position is part of Radboud Healthy Data, a cross-campus collaboration with the Radboud University Medical Center that aims to solidify the campus-wide data and AI infrastructure. I am involved in several projects on biomedical research questions: investigating the role of tremor in the development of Parkinson's disease; uncovering causal mechanisms in respiratory tract infections; finding causal links between vascular surgery and adverse brain outcomes. I also oversee research data management in the Institute for Computing and Information Sciences in my role as data steward.

Research interests: causal inference, Bayesian inference, state space modeling, explainable AI

Teaching

Visiting Address

Mercator I Building - Office M1.05.02
Toernooiveld 212, 6525EC Nijmegen

Research

Teaching

Selected Publications

Greedy Equivalence Search in the Presence of Latent Confounders

Towards Individualized Monitoring of Cognition in Multiple Sclerosis in the Digital Era: A One-year Cohort Study

Understanding the Assumptions Underlying Mendelian Randomization

Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models

MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models

Large-Scale Local Causal Inference of Gene Regulatory Relationships

Inferring the Direction of a Causal Link and Estimating Its Effect via a Bayesian Mendelian Randomization Approach

A Novel Bayesian Approach for Latent Variable Modeling from Mixed Data with Missing Values

Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness

Visiting Address