The Biomathematics PhD is the flagship doctoral program of the Department of Computational Medicine at UCLA. This program has been awarding Biomath PhDs continuously since our first doctoral graduate in 1979. The program is designed for students who want a broad education in developing, testing, and implementing cutting-edge mathematical models, computational algorithms, and statistical methods at the interface of the mathematical and biomedical sciences. Student research in the Biomathematics program is usually focused in one of three Areas of Emphasis:

Imaging and Biomedical Data (Chairs: Jeffrey Chiang and Daniel Tward)

This area aims to develop computational tools for analyzing signals and data collected in a healthcare setting. Our goals are to increase understanding of health and disease, and inform clinical diagnosis and decision making. Topics include analysis of medical images in neurodegenerative disease, waveforms collected during anesthesia, and data from electronic health records from diverse populations. In this area, mathematical modeling and machine learning are complemented by a rigorous understanding of normal and pathological anatomy and physiology.

Mathematical and Systems Modeling (Chairs: Tom Chou and Van Savage)

This area aims to develop mechanistic mathematical and computational models that describe biomedical experiments and observations, provide predictions, and inform new directions of investigation. Topics include, but are not limited to, population and ecological modeling, physiological systems modeling, molecular and cellular biophysical modeling, cell development and cancer modeling, theoretical neuroscience, and immune system modeling.

Computational Genomics (Chairs: Brunilda Balliu and Harold Pimentel)

This area aims to develop computational methods that enable insights for populations and individuals from the range of available genomic data from raw sequence data to summary statistics. Topics include high-throughput sequence alignment and analysis, genetic modeling of complex traits, small-sample experimental design and inference, and genetic perturbation inference. Common mathematical techniques include high-dimensional statistics, hierarchical probabilistic models, graphical models, and randomized algorithms.

Our trainees obtain a Ph.D. degree in under five years on average. Over half of our graduates hold academic or medical center faculty positions; the other half work in government or industry in quantitative biosciences, e.g., founding a healthcare AI company or working in Google’s Brain Genomics Team. Our students receive a stipend of more than $40K/year and also have their UCLA tuition & fees and health insurance paid. In addition, students have access to multiple NIH-funded training programs in specific areas of interest including the Genomic Analysis Training Program (GATP) and the Biomedical Data Science Training Program for Precision Health Equity (BDTP)

The goal of the doctoral program is to train creative, fully independent investigators in mathematical, theoretical, and computational biology. These investigators will be able to initiate research in applied mathematics, statistics, and computer science, as well as their chosen biomedical specialty. This biological breadth contends with their quantitative breadth. Further, this breadth is reflected in a curriculum providing doctoral-level competence in a biomedical specialty; substantial training in applied mathematics, statistics, and computing; and appropriate biomathematics courses and research experience.

Trainees typically focus on one of the three Areas of Emphasis during their dissertation research, but the Ph.D. program is structured so that each trainee obtains foundations in all areas.  Trainees in the program will obtain experience in developing new mathematical and computational methods for modeling biomedical systems and work collaboratively to apply these models in their biomedical domain, including within and outside the UCLA Health System. 

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