UCLA Computational Medicine Faculty Positions

The UCLA Department of Computational Medicine is looking to hire new faculty.  Please pass this advertisement to anyone who may be interested.


The David Geffen School of Medicine (DGSOM) is searching for tenure-track faculty to join the Department of Computational Medicine. The successful candidate will enjoy a richly collaborative working environment and a possible joint appointment with the UCLA Henry Samueli School of Engineering and Applied Sciences (HSSEAS), the UCLA Division of Life Sciences, or the Departments of Biostatistics or Epidemiology in the Fielding School of Public Health (FSPH). We are seeking candidates with strong research programs in the quantitative biomedical sciences, with emphasis on the development and use of novel mathematical models and computational methods. We plan to hire at the Assistant Professor level but will certainly consider exceptional candidates at higher ranks.

The DGSOM and HSSEAS have invested in Computational Medicine to lead the transformation of biomedical sciences by leveraging recent advances in the mathematical, computational, and data sciences. The recently established Institute for Precision Health in the DGSOM provides unique opportunities to build upon successful collaborations between these two schools and encourage new ones. UCLA plans to hire multiple faculty over the next few years with diverse research interests in applications across biology, medicine, and public health. Some of the faculty will be hired to support the Department of Computational Medicine’s Biomathematics Graduate Program, a leading program nationally with a long and illustrious tradition, and these faculty will be expected to play an active role in the program. Candidates for these positions will be judged on productivity, creativity, commitment to realistic biological models, mathematical sophistication, and ability to collaborate across disciplinary boundaries. Faculty will also be hired in other interdisciplinary areas of interest including Computational Genomics, Clinical Machine Learning, and Computer Vision applied to Medical Imaging.

Candidates should hold a Ph.D., M.D., or equivalent by the date of appointment and provide the potential for scholarly impact through publications, excellence in teaching, and strong oral and written communication skills. We welcome candidates whose experience in teaching, research, or community service has prepared them to contribute to our commitment to diversity and excellence. Faculty appointment level and salary will be commensurate with the candidate’s experience and qualifications.

Applicants should submit a cover letter, curriculum vitae, list of three references, research statement, teaching statement, and statement of contributions to equity and diversity inclusion to Bogdan Pasaniuc, Chair of the Computational Medicine Faculty Search Committee.

Many of the on-campus interviews will occur during January 21-22 and February 18-19, 2020.
Questions about the search should be directed towards Bogdan Pasaniuc (pasaniuc@ucla.edu) Chair of the Search Committee or Eleazar Eskin (eeskin@cs.ucla.edu) Chair of the Department of Computational Medicine.

Apply at:

The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status. For the complete University of California nondiscrimination and affirmative action policy, see UC Nondiscrimination & Affirmative Action Policy.


The Multivariate Normal Distribution Framework for Analyzing Association Studies: Overview

The use of the multivariate normal (MVN) model has been a powerful tool in our groups research and it has been utilized in many of our papers. Jose Lozano (University of the Basque Country, San Sebastian, Spain), along with Eleazar Eskin and three ZarLab alumni—Farhad Hormozdiari (postdoc at Harvard), Jong Wha (Joanne) Joo (faculty at Dongguk University in Seoul), and Buhm Han (faculty at University of Ulsan College of Medicine in Seoul)—recently published a review of the multivariate normal (MVN) distribution framework in genome-wide association studies (GWAS) studies.

Genome-wide association studies (GWAS) have discovered thousands of variants involved in common human diseases. In these studies, frequencies of genetic variants are compared between a population of individuals with a disease (cases) and a population of healthy individual controls). Any variant that has a significantly different frequency between the two populations is considered an associated variant.

A major challenge in the analysis of GWAS studies is the fact that human population history causes nearby genetic variants in the genome to be correlated with each other. In this review, we demonstrate how to utilize the MVN distribution to explicitly take into account the correlation between genetic variants and provide a comprehensive framework for analysis of GWAS.

In this paper, we show how the MVN framework can be applied to perform association testing, correct for multiple hypothesis, testing, estimate statistical power, and perform fine mapping and imputation. In future blog posts, we will highlight different ways the MVN framework can be used in association studies.

An illustration of the multivariate normal model (a) Type I Error (b) Power.

Many of the authors are the alumni of the group who pioneered the use of the MVN in various problems in association studies. Here is a list of papers that our group published using the MVN framework:

Sorry, no publications matched your criteria.

  • Farhad Hormozdiari, Anthony Zhu, Gleb Kichaev, Chelsea J.-T. Ju, Ayellet V. Segre, Jong Wha J. Joo, Hyejung Won, Sriram Sankararaman, Bogdan Pasaniuc, Sagiv Shifman, and Eleazar Eskin. Widespread allelic heterogeneity in complex traits. The American Journal of Human Genetics, 100(5):789{802, may 2017.
  • Yue Wu, Farhad Hormozdiari, Jong Wha J. Joo, and Eleazar Eskin. Improving imputation accuracy by inferring causal variants in genetic studies. In Lecture Notes in Computer Science, pages 303{317. Springer International Publishing, 2017.

The paper was written by Jose A. Lozano, Farhad Hormozdiari, Jong Wha (Joanne) Joo, Buhm Han, and Eleazar Eskin, and it is available at: https://www.biorxiv.org/content/early/2017/10/28/208199.

The full citation to our paper is:

Jose A. Lozano, Farhad Hormozdiari, Jong Wha (Joanne) Joo, Buhm Han, Eleazar Eskin. 2017. The Multivariate Normal Distribution Framework for Analyzing Association Studies. bioRxiv doi: https://doi.org/10.1101/208199.

Fine Mapping Causal Variants and Allelic Heterogeneity

On Friday, April 28, 2017, in the CNSI Auditorium, Eleazar Eskin presented ZarLab’s research on fine mapping causal variants and allelic heterogeneity at the 2nd Annual Institute for Quantitative and Computational Biosciences (QCBio) Symposium.

Geneticists use a technique called Genome Wide Association Studies (GWAS) to identify genetic variants that cause an individual to exhibit a particular trait or disease. Typically, GWAS identifies an association signal which suggests that genetic variants within a region of the genome — known as a locus —  are associated with the condition. The process of identifying the actual variant in the region which has an affect on the disease is referred to as “fine mapping.”

In addition to finding the actual variants affecting a disease, fine mapping also seeks to address questions that are related to the genetic basis of disease. First, how many causal variants does a locus contain? A disease could be caused by one, single variant or multiple variants that independently affect disease status. We refer to the latter phenomenon as allelic heterogeneity (AH).

Second, when analyzing results from multiple GWASes, can the same causal variant identified in one study be assumed causal in other studies? A GWAS can identify many variants that are associated with two or more traits; however, this correlation can be induced by a confounding factor known as linkage disequilibrium. Colocalization methods seek to identify shared and distinct causal variants.

Farhad Hormozdiari, a recent alumnus of our group and a post-doc at Harvard University, developed several novel approaches for improving the accuracy and efficiency of fine mapping despite presence of AH in the study population. Hormozdiari’s software, CAVIAR, CAVIAR-Genes, and eCAVIAR, are capable of quantifying the probability of a variant to be causal in GWAS and eQTL studies, while allowing for an arbitrary number of causal variants.

In a video of his presentation, Eskin summarizes the progress on these problems.  A video of Eskin’s presentation may be found on the QCBio website: https://qcb.ucla.edu/events-seminars/symposium/#toggle-id-2

More details about our research in fine mapping are available in the following papers:

Sorry, no publications matched your criteria.

Hormozdiari F, Zhu A, Kichaev G, Ju CJ, Segrè AV, Joo JW, Won H, Sankararaman S, Pasaniuc B, Shifman S, Eskin E. Widespread allelic heterogeneity in complex traits. The American Journal of Human Genetics. 2017 May 4;100(5):789-802.