Zhaoqi Liu is an associate research scientist, studying cancer genomics in the Department of Biomedical Informatics at Columbia University. He received his Ph.D. in Applied Mathematics from the Academy of Mathematics and Systems Science, where he won the President's Scholarship prize. Zhaoqi joined Dr. Rabadan’s group in September 2015. His current work focuses on developing computational methods to analyze biological problems in cancer genomics.
Junfei Zhao is an associate research scientist, studying cancer genomics in the Department of Biomedical Informatics at Columbia University. His current work is focusing on developing computational methods to study biological problems in cancer genomics. He received his Ph.D. in Applied Mathematics from University of Chinese Academy of Sciences. Junfei joined Dr. Rabadan's group in September 2016.
Wesley Tansey is an associate research scientist in the Department of Systems Biology. He received his Ph.D. in Computer Science from the University of Texas at Austin in 2017 with a focus on machine learning and computational statistics. Wesley's work in the Rabadan lab revolves around leveraging latent structure in biological data to develop more powerful analysis tools for cancer. His projects include dose-response prediction for cancer treatments, single cell RNA-Seq denoising, and spatial modeling of tumor samples.
Ioan Filip is a postdoctoral research scientist in the Department of Biomedical Informatics. He received his Ph.D. in Mathematics from Columbia University in 2016. As a member of the Rabadan Lab, Ioan is currently studying the genetic basis of the immune response to viral infections and he is interested in further developing algebraic and topological methods to model evolution.
Luis Aparicio is a postdoctoral research scientist in Columbia University Systems Biology department. He did his graduate degree in Physics combined with studies in Mathematics at Universidad Autonoma in Madrid and Music at the Madrid Conservatory specializing in ancient music. He received his M.Sc. and PhD in Theoretical Physics at the Institute for Theoretical Physics in Madrid. He did his first postdoctoral research at the International Centre for Theoretical Physics working at the interface between String Theory, Particle Physics and Cosmology. His current research interest is focused on developing mathematical methods to address problems in Systems Biology, in particular the role that Random Matrix Theory plays in the context of single-cell and cancer genomics.
Juan Angel Patiño-Galindo
Juan Angel Patiño-Galindo is a postdoctoral research scientist, working on virus evolution in the Department of Systems Biology. His PhD focused on studying different aspects of the mid- and long-term evolution of RNA viruses, with special interest in molecular epidemiology of HIV and HCV. His current research involves the application of topological and phylogenetic methods to the analysis of viral evolution.
Francesco G. Brundu is a postdoctoral research scientist in the Department of Systems Biology. He received his B.S., M.S. and Ph.D. from the Polytechnic University of Turin. During his Ph.D., he collaborated with the Candiolo Cancer Research Institute and the Academical Medical Center of Amsterdam, with specific focus on the stratification of Colorectal Cancer through gene expression and copy number variation analysis. His current research interests focus on i) the analysis of genomic variation through computational tools and ii) the application of single-cell RNA sequencing to Cancer Genomics.
Mathieu Carrière is a postdoctoral research scientist in the Department of Systems Biology. He received his PhD in Informatics from Inria and Université Paris Saclay in 2017. He is interested in the application of Topological Data Analysis in Machine Learning frameworks, with an emphasis on biological data. He is currently working on bootstrap methods for Mapper complexes computed on gene expression data, as well as predictive analysis of single cell Hi-C contact maps through persistence diagrams.
Andrew Chen is an MD/PhD student at Columbia University rotating with the Rabadan lab. He graduated with a B.S. in Physics from MIT in 2015. His undergraduate work was in population dynamics, as well as researching combination therapeutic design at Takeda Pharmaceuticals. He is interested in applying computational methods to cancer evolution
Karen Gomez is an MD/PhD student at Columbia University College of Physicians and Surgeons. She graduated from Temple University with a B.S. in Biochemistry in 2017. Her undergraduate research focused on studying cancer evolution through tumor phylogenetics. Currently, she is investigating associations between genetic data and phenotype as well as clinical outcomes in cancer. She is interested in cancer genomics.
Oliver Elliott received his B.A. from Amherst College and his M.S. from Columbia University. In the Rabadan Lab, Oliver has worked on pathogen discovery in high throughput sequencing data, transcriptome annotation, and identifying somatic variants in cancer. He is an all-purpose programmer and works on a mix of web development, bioinformatics programming, and sysadmin.
Richard Wolff is a graduate of Columbia University with a degree in mathematics and significant coursework in computer science. He goes by Ricky, and in his free time enjoys playing the guitar. He hopes one day to use his background in pure math to approach problems in medicine in new and fundamental ways.
Monika is an undergraduate at Columbia University pursuing a B.A. in computer science and mathematics. She is interested in algebraic topology and big data analytics techniques. At the Rabadan Lab, she is studying bacterial and viral interactions in the human microbiome and their role in infection using topological data analysis.
Rose Orenbuch is a senior at Columbia University, where she is majoring in Information Science with a concentration in biology and neuroscience. She is investigating associations between genetics and immune response during viral infections, using genomic data. Furthermore, she is developing an application for genotyping highly polymorphic regions using standard RNA sequencing reads.