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BioXin Liu is a postdoctoral Research Scientist in the Department of Genetics at Stanford University. Xin holds a PhD in Chemistry from the University of Michigan, Ann Arbor. Her basic research interests include RNA and protein biochemistry, enzymology, cancer immunology, and autoimmune disease. She has published papers in several prestigious journals in the field of biochemistry, including Nature Communications, Journal of American Chemical Society, and Nucleic Acids Research. The highlight of her multidisciplinary research includes the development of high-throughput enzymatic methods to discover anti-microbial agents and to reveal mechanisms behind human mitochondrial diseases, as well as innovative applications of genome engineering and machine-learning to decode principles of RNA editing in human cells. Her current research focuses on the mechanistic study of innate immune pathways.

Romain Lopez

Affiliate, Genetics

BioSince Fall 2021, I am a joint postdoctoral scholar at Stanford University and Genentech Research and Early Development, hosted by Jonathan Pritchard and Aviv Regev. I recently obtained my PhD degree from the department of Electrical Engineering and Computer Sciences at UC Berkeley, advised by Mike Jordan & Nir Yosef. My research interests lie at the intersection of statistics, computation and modeling with a focus on biological applications.

A significant part of my research is driven by building more statistically accurate and faster machine learning software for analyzing single-cell omics data. I developed single-cell Variational Inference (scVI), a flexible model and a scalable inference method for comprehensive analysis of single-cell transcriptomes. I co-developed scvi-tools, an open-source software suite for fully-probabilistic modeling of single-cell multi-omics data. You may learn more about these topics in my guest lecture of the Deep Learning in the Life Sciences class at MIT.

More generally, I am interested in the broader area of ML + Science. Deep generative models provide an appealing and flexible paradigm for learning distributions, but quite some work is needed to fully exploit them as part of a scientific hypothesis testing pipeline (e.g., causality, interpretability, disentanglement, decision-making).

Previously, I worked on counterfactual inference and offline policy learning methods in collaboration with technology companies. In 2018, I visited Le Song at Ant Financial in Hangzhou. In 2019, I visited Inderjit Dhillon at Amazon in Berkeley. Before graduate school, I obtained a MSc in applied mathematics from Ecole polytechnique, Palaiseau in 2016. Additionally, I worked as an intern at the Harvard Medical School with Allon Klein in 2016. I was born and grew up in Bedarieux, France.

Contact Info
romainl@stanford.edu

Web page:

https://romain-lopez.github.io/

Anastasiia Lovchykova

Visiting Instructor/Lecturer, Genetics

Zhifei Luo

Basic Life Research Scientist, Genetics

Morgan Margery Lutje

Life Science Research Professional 1, Genetics

Contact Info

Web page:

http://web.stanford.edu/people/mlutje

Heankel Lyons

Postdoctoral Scholar, Genetics

Quan Maq Ma

Postdoctoral Scholar, Genetics

BioQuan (Maq) Ma is a postdoctoral researcher in the Department of Genetics at the Stanford School of Medicine. His research focuses on developing high-throughput sequencing technologies to study RNA modifications and identify substrates of RNA-binding proteins using biochemical and chemical biology approaches. Maq is also interested in the role of RNA editing in inflammatory chronic diseases.