This page highlights current thematic areas and representative publications from the lab.

• Probabilistic Inference and Generative Models
• Modeling Regulatory Circuits from Multimodal Perturbation Screens
• Causal Structure & Representation Learning
• Machine Learning for Single-Cell Omics Data Analysis
• Spatial Transcriptomics Data Analysis

Probabilistic Inference and Generative Models

We develop ML methodology for making generative models more interpretable and usable for downstream tasks such as decision-making and hypothesis testing. These models are particularly useful in handling high-dimensional, noisy, and incomplete data typical in applied scientific research.

• Lopez, R., Regier, J., Jordan, M. I., & Yosef, N. (2018). "Information constraints on auto-encoding variational Bayes" Advances in Neural Information Processing Systems
• Lopez, R., Boyeau, P., Yosef, N., Jordan, M. I., & Regier, J. (2020). "Decision-making with auto-encoding variational Bayes." Advances in Neural Information Processing Systems
• Rohbeck, M., Bunne, C., Huetter, J-C., De Brouwer, E., Biton, A., Chen, K. Y., Regev*, A., & Lopez*, R. (2024). "Modeling Complex System Dynamics with Flow Matching Across Time and Conditions". International Conference on Learning Representations.

Modeling Regulatory Circuits from Multimodal Perturbation Screens

We build interpretable models that predict how cells respond to genetic and chemical perturbations and reveal their mechanisms of action, aiding drug discovery and therapeutic design. Increasingly, we work with multimodal screens that profile perturbations across transcriptomic, optical, proteomic, and epigenomic readouts, developing methods that integrate these modalities, predict and denoise across them, and infer the regulatory circuits linking perturbations to cellular state.

• Kudo*, T., Lopez*, R., Meireles, A. M., et al., & Regev, A. (2026). "Scalable multimodal mapping of macrophage regulatory architecture by integrating optical and transcriptomic pooled screens." bioRxiv (preprint).
• Chen*, K. Y., Lopez*, R., Eraslan*, B., et al., Regev, A., & Sakaguchi, S. (2026). "Massively multiplex multimodal chemical screens at single-cell resolution." bioRxiv (preprint).
• Ryu, J., Bunne, C., Pinello, L., Regev*, A., & Lopez*, R. (2025). "Crossmodality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport". International Conference on Artificial Intelligence and Statistics.

Causal Structure & Representation Learning

We develop causal machine learning approaches that can leverage high-dimensional data. Towards this goal, we are interested in tractable approaches to causal structure learning that have the potential to scale to tens of thousands of variables. Additionally, we are interested in causal representation learning, where interventions are conducted on latent variables of a deep generative model.

• Lopez, R., Huetter, JC., Pritchard, J., & Regev, A. (2022). "Large-scale differentiable causal discovery of factor graphs." Advances in Neural Information Processing Systems.
• Lopez*, R., Tagasovska*, N., Ra, S., Cho, K., Pritchard, J. K., & Regev, A. (2023). "Learning causal representations of single cells via sparse mechanism shift modeling." Conference on Causal Learning and Reasoning.
• Lopez, R., Huetter, JC., Hajiramezanali, E., Pritchard, J., & Regev, A. (2024). "Towards the Identifiability of Comparative Deep Generative Models." Conference on Causal Learning and Reasoning.

Machine Learning for Single-Cell Omics Data Analysis

Our lab develops advanced algorithms to analyze single-cell omics data, enhancing our understanding of cellular states and dynamics. We focus on improving methods for differential expression analysis, integration of multi-omics data, and robust modeling of cellular heterogeneity. These innovations are vital for deciphering the complexities of single-cell data and driving biological discoveries.

• Lopez, R., Boyeau, P., Regier, J., Gayoso, A., Jordan, M. I., & Yosef, N. (2018). "Deep generative modeling for single-cell transcriptomics." Nature Methods.
• Gayoso*, A., Lopez*, R., Xing*, G., Boyeau, P., Wu, K., Jayasuriya, M., Regier, J., & Yosef, N. (2022). "A Python library for probabilistic analysis of single-cell omics data." Nature Biotechnology.
• Boyeau, P., Regier, J., Gayoso, A., Jordan, M. I., Lopez*, R., & Yosef*, N. (2023). "An empirical Bayes method for differential expression analysis of single cells with deep generative models." Proceedings of the National Academy of Sciences.

Spatial Transcriptomics Data Analysis

Leveraging spatial transcriptomics, we aim to map cellular organization within tissues, combining computational biology techniques with experimental data to uncover spatial patterns and interactions at the molecular level. Our research focuses on developing robust methods for analyzing spatially resolved transcriptomics data, leading to new insights into tissue architecture and cellular function.

• Lopez*, R., Nazaret*, A., Langevin*, M., Samaran*, J., Regier*, J., Jordan, M. I., & Yosef, N. (2019). "A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements." ICML Workshop in Computational Biology.
• Lopez*, R., Li*, B., Keren-Shaul*, H., Boyeau, P., Kedmi, M., Pilzer, D., et al. (2022). "DestVI identifies continuums of cell types in spatial transcriptomics data." Nature Biotechnology.