Romain Lopez
 |
Ph.D. student, Electrical Engineering & Computer Sciences, UC Berkeley Affiliations: Berkeley Artificial Intelligence Research & Center for Computational Biology |
Research Interests
My interests include mathematical statistics, applied statistics and applications to machine learning in biology. More recently, I am focusing on probabilistic modeling of single-cell RNA sequencing data. Its ecosystem includes Bayesian modeling, variational inference, and more precisely approximate posterior inference with neural networks. I am the lead contributor to single-cell Variational Inference (scVI).
We offer opportunities for MSc students anywhere in the world to apply for a funded visiting research position at UC Berkeley to work on projects related to scVI
Example of an assignment I proposed in the past to select the students.
Publications
Cost-Effective Incentive Allocation via Structured Counterfactual Inference
Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael I. Jordan, Yuan Qi and Le Song
Submitted, 2019 [preprint]
Harmonization and Annotation of Single-cell Transcriptomics data with Deep Generative Models
Chenling Xu*, Romain Lopez*, Edouard Mehlman*, Jeffrey Regier, Michael I. Jordan and Nir Yosef
In review, 2019 [preprint]
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Samuel L. Wolock, Romain Lopez and Allon M. Klein
Cell Systems, 2019 [journal, preprint])
Deep Generative Modeling for Single-cell Transcriptomics
Romain Lopez, Jeffrey Regier, Michael B. Cole, Michael I. Jordan, and Nir Yosef
Nature Methods, 2018 [journal, preprint, News & Views, BAIR Blog]
Information Constraints on Auto-Encoding Variational Bayes
Romain Lopez, Jeffrey Regier, Michael I. Jordan and Nir Yosef
Advances in Neural Information Processing Systems, 2018 [proc., preprint]
A Deep Generative Model for Semi-Supervised Classification with Noisy Labels
Maxime Langevin, Edouard Mehlman, Jeffrey Regier, Romain Lopez, Michael I. Jordan, Nir Yosef
Bay Area Machine Learning Symposium (BayLearn), Oral presentation, 2018 [abstract, video]
A deep generative model for gene expression profiles from single-cell RNA sequencing with applications to detecting differentially expressed genes
Romain Lopez, Jeffrey Regier, Michael B. Cole, Michael Jordan, and Nir Yosef
NIPS Machine Learning in Computational Biology Workshop (MLCB), Oral presentation, 2017 [extended abstract]
A deep generative model for gene expression profiles from single-cell RNA sequencing
Romain Lopez, Jeffrey Regier, Michael Jordan, and Nir Yosef
Bay Area Machine Learning Symposium (BayLearn), Oral presentation, 2017 [abstract, video]
* These authors contributed equally to this work.
About me
I grew up in Bedarieux, a small town in southern France, where I spent my time riding motorcycles, learning and teaching Judo as well as thinking about quantitative models for evaluating the temperature of the different planets of the solar system. I then followed the traditional French system of classes preparatoires in Toulouse at lycée Pierre-de-Fermat. After that, I worked with Laurent Massoulie (Optimal stochastic control on random graphs) at Ecole polytechnique (France) where I received the Diplome d'Ingenieur (2016). After several real-world experiences at Cardiologs (Time-series classification) and in Systems Biology, HMS with Allon Klein (Sparse Matrix Factorization), I decided to maintain this versatile orientation between theory and applications. Recently, I also worked with Le Song at Ant Financial in Hangzhou on Counterfactual Inference and Offline policy optimization.