Over the past few decades, technological developments have resulted in the accumulation of large genomic data even within a single experiment. The analysis of this data has led to many novel discoveries and medical applications. For this reason, the majority of the biotechnology and pharmaceutical companies today, as well as many labs in academia, include experts in the analysis of biological data. These experts have domain expertise in subfields of genomics, such as cancer genomics, statistical genetics, protein structure prediction, computational medicine, metagenomics, and others.
The analysis of these datasets requires the development of methods that are domain-specific. Over the years, many communities that are dedicated to specific subfields of genomics have emerged. Unfortunately, this resulted in smaller overlap between the different sub-domains; for example, the statistical genomics community has minimal overlap with the metagenomics community. CGSI was formed in order to bridge this gap; it is a methods-oriented program with the goal to create interactions between the different fields of research in computational genomics -- and more broadly, computational medicine -- in an informal setting.
In order to achieve this goal, we structure CGSI in a very non-typical way, resulting in its own unique culture. Specifically, CGSI is a fusion of a summer school and a scientific conference. It has the characteristics of a conference, bringing together over a hundred researchers and trainees who showcase their latest research. It also has the characteristics of a summer school; it is a month-long program that involves a combination of tutorials (broader talks that include an overview of the field) and research talks, with opportunities for in-depth discussions during breaks. It emphasizes the need for interaction between participants. We offer icebreakers and many social activities, including an opening retreat in the mountains, a picnic at the beach, and sports games that involve both faculty and trainees. We hope you will join the community.
We are very proud of the faculty in the CGSI community. It is clear that everyone who joins CGSI views it as an opportunity to be part of a larger community. The vast majority of the CGSI faculty spends at least a week at CGSI, takes part in the social events, and makes an effort to interact with faculty and trainees from other fields within computational medicine in order to foster a sense of a larger methods-based community. We welcome to our community both new and established researchers who are interested in methods development for genomics and medical applications, and who would like to become part of a larger, friendly, and intellectually stimulating community.
The institute has its roots in a program called "Mathematical and Computational Approaches in High-Throughput Genomics", which was held at UCLA's Institute for Pure and Applied Mathematics (IPAM) in 2011. Many of the current CGSI faculty were part of this program. CGSI was later founded in 2015 by Eleazar Eskin (UCLA), Eran Halperin (UCLA), John Novembre (The University of Chicago), and Ben Raphael (Princeton University) with the goals of improving education and facilitating collaboration in genomics and related fields. In 2023, in response to changes to the research environment, the founders made the decision to add other areas of research to its agenda: specifically, machine learning and AI applied to medical imaging and electronic health records.
Head of Computational Medicine Department | UCLA | @ZarEskinCompMed
Computer Science & Anesthesiology | UCLA | @eranhalperin
Computer Science | Princeton University | @Benjraphael
Human Genetics, Ecology & Evolution | University of Chicago | @jnovembre
CGSI Organizing Committee
Mathematics and Statistics | Pomona College
Department of Clinical Pharmacy | University of Southern California School of Pharmacy
Computational Medicine and Human Genetics | UCLA
Computer Science, Computational Medicine, and Human Genetics | UCLA
Computer Science | Courant Institute and the Center for Data Science | NYU