My research interests lie at the intersection of statistical learning theory, healthcare, personalised algorithms, and trustworthy machine learning.

Predicting Gene Essentiality and Drug Response from Perturbation Screens in Preclinical Cancer Models with LEAP: Layered Ensemble of Autoencoders and Predictors.

B. Bodinier, G. Dissez, L. Bleistein, A. Dauvin. Preprint, 2025. Online access.

Large Language Models as Markov Chains.

O. Zekri, A. Odonnat, A. Benechehab, L. Bleistein, N. Boullé, I. Redko. Preprint, 2024. Online access.

Dynamical Survival Analysis with Controlled Latent States.

L. Bleistein, V.T. Nguyen, A. Fermanian, A. Guilloux, ICML 2024. Online access.

On the Generalisation and Approximation Capacities of Neural Controlled Differential Equations.

L. Bleistein, A. Guilloux, ICLR 2024 (previously presented at ICML Workshop on New Frontiers in Learning, Control, and Dynamics, 2023). Online access.

Learning the Dynamics of Sparsely Observed Interacting Systems.

L. Bleistein, A. Fermanian, A.S. Jannot, A. Guilloux, ICML 2023. Online access.

Parcours Hospitaliers des Patients Atteints de la COVID-19 de Mars 2020 à Janvier 2021.

H. El-Rais, M. Aflak-Kattar, L. Bleistein, Les Dossiers de la DREES, 2021. Online access.