The Bioinformatics Program is interdepartmental, drawing faculty from the school of Medicine, Life Sciences, Physical Sciences, and the School of Engineering. The focus is on the development of methods for the analysis and interpretation of datasets from molecular level measurements, produced by next generation sequencing (NGS), mass spectrometry, and other emergent technologies. These data come from experimental model systems, patients, populations and eco systems. Research may address fundamental or basic science questions, or seek to make discoveries that have immediate impact for ecological or biomedical applications.
Admissions Criteria: Demonstrated research interest in Bioinformatics (e.g. via prior research experience). Typically, an undergraduate degree (major or minor) that involves coursework in bioinformatics or statistics and computational algorithms; a demonstrated interest and coursework (but not necessarily a degree) in biology or life sciences. Strong programming skills.
The Medical Informatics Program is interdepartmental, drawing faculty from the school of Medicine, Life Sciences, Physical Sciences, and the School of Engineering. The focus is on developing and applying computational methods to improve healthcare. Coursework covers a range of foundational and contemporary materials related to biomedical informatics (e.g., clinical, imaging), as well as cross-cutting topics in (bio)statistics and machine/reinforcement learning – all emphasizing biomedical/clinical applications and use cases. Research is focused broadly on methodological, evaluation, and translation of modern techniques into real-world health environments and includes areas related to mobile health (mHealth), image analysis/understanding, text analysis/understanding, and decision support.
Admissions Criteria: This program looks for students with either a quantitative/engineering background (undergraduate training in bioengineering, electrical engineering, computer science, etc.) or a biomedical background who have demonstrated skills in computer/data science or other information-related disciplines.
The Genetics & Genomics Program is supported by multi-disciplinary faculty in Human Genetics and affiliates. It focuses on gaining insights about human genetic diseases and animal model systems. Students often develop networks, systems, and other multilayer approaches combining large data sets at the genomic, transcriptomics, methylomics, proteomics, metabolomics, and phenome level, and also experimental perturbation studies to test hypotheses. The overall research emphasis is on identification and characterization of genes, pathways, and molecular mechanisms, by integrating new and state-of-the-art computational, bioinformatics, and molecular genetic and genomics approaches. Investigation across species, in model organisms, and at the cellular level is also utilized to elucidate fundamental biological principles and disease-causing mechanisms.
Admissions Criteria: This program looks for students with a strong interest in experimental or computational genetics. Undergraduate training may be in life sciences or a quantitative science, and prior research experience is highly valued.
The Biomathematics Program’s goal is to train creative, fully independent investigators in mathematical, theoretical, and computational biology who can initiate research in both applied mathematics and their chosen biomedical specialty. This is reflected in a curriculum providing doctoral-level competence in biology or biomedical specialty; substantial training in applied mathematics, statistics, and computing; and appropriate biomathematics courses and research experience. Individualized programs permit students to select graduate courses in applied mathematics, biomathematics, and statistics appropriate to their area of research and to choose among diverse biomedical specialties.
Admissions Criteria: Strong foundation in mathematics, physics, computer science through undergraduate degree, or other documented experience. Strong interest to extend that background to with coursework to develop substantial expertise in biomathematics.
The Neuroscience Program is interdepartmental, drawing faculty from the school of Medicine, Life Sciences, Physical Sciences, and the School of Engineering. strives to enable its students to develop the critical skills to identify a research question, to obtain the technical and intellectual tools to address the question, and to communicate with both experts and the general public. These skills are indispensable for a successful career in Neuroscience but will also prepare students extremely well for a wide variety of other career paths. Research topics involve both experimental and computational components, ranging from experimental model systems to clinical studies of human disease.
Admissions Criteria: A deep interest in neuroscience, documented by coursework and research experience. An appreciation of how experimental approaches and quantitative and computational approaches may address pertinent questions in the field.
The Molecular Biology Program is interdepartmental, committed to training PhD candidates to become the next generation of scientific leaders in molecular biology. The program comprises four disciplinary home areas in Cell & Developmental Biology (CDB), Immunity, Microbes & Molecular Pathogenesis (IMMP), Biochemistry, Biophysics, and Structural Biology (BBSB), and Gene Regulation, Epigenomics and Transcriptomics (GREAT). All students develop expertise in experimentation and data generation, but quantitative and computational skillsets are key to interpreting the resulting datasets and developing new hypotheses.
Admissions Criteria: Strong foundation in molecular biology, documented by undergraduate coursework and research experience, and a strong motivation to develop expertise in cutting edge experimental skills to address important basic science questions that have clinical and human health relevance and application.
The Ecology and Evolutionary Biology Program offers a creative and highly interactive, intellectual environment that prepares students to excel in positions in academia, industry, and governmental / non-governmental organizations. The program comprises field research, experiments lab research, and computational research – the latter addressing questions in population genetics, ecological systems, and epidemiology. Students regularly engage with faculty across academic disciplines at UCLA.
Admissions Criteria: Academic excellence in a variety of majors but documented interest in ecological or evolutionary biology research. Prospective students are encouraged to contact faculty of interest before applying to our program.
The Computer Science Department strives for excellence in creating, applying, and imparting knowledge in computer science and engineering through comprehensive educational programs, research in collaboration with industry and government, dissemination through scholarly publications, and service to professional societies, the community, the state, and the nation. Some students and faculty pursue biological or clinical questions, particularly in big data analytics.
Admissions Criteria: Strong foundation in computer science as documented by an undergraduate degree and research experience. Strong GRE scores. Prospective students are encouraged to contact faculty of interest before applying to our program.
Biostatistics PhD Program within the School of Public Health aims to meet the demand for well-trained biostatisticians, to address scientific problems encountered in public health and biomedicine, and to actively collaborate with investigators at UCLA and worldwide to solve global health problems. The PhD degree program trains statisticians who can apply statistical methods to solve problems in the health field and who can conduct theoretical research in statistical methodology. Training in the program combines mathematical statistics, biostatistical methods and a third-field specialization.
Admissions Criteria: Strong foundation in mathematics and/or statistics, documented through an undergraduate degree and/or research experience. Motivation to contribute to health and patient care improvements through the analysis of clinical data or the design of clinical studies.
Quantitative & Systems Biology is a vibrant research area at UCLA comprising faculty from Life Sciences, Physical Sciences, the School of Medicine and School of Engineering. It comprises research in cell and developmental biology, microbiology and immunology, physiology and pharmacology. Quantitative and computational analysis address the dynamics of networks, biological heterogeneity using microscopy, single-cell assays, statistical and mechanistic modeling. It is not yet a stand-alone graduate program but affiliated laboratories come together in joint group meetings, joint seminars, and they frequently collaborate. Students interested in undertaking Quantitative & Systems Biology Research should contact faculty of interest and be advised about which of the current graduate programs may be the best fit for them.
|Program / Research Area||Bioinformatics||Genetics & Genomics||Medical Informatics||Biomathematics||Quantitative & Systems Biology||Neuroscience||Molecular Biology||Ecology and Evolutionary Biology||Biostatistics||Computer Science|
Director of Bioinformatics IDP
Home Area Director
EEB Department Chair
|Student Affairs Officer||Gene Gray|
|# 1st year students||8-15||4-8||4-8||3-6||4-8||8-15||15-25||6-10||3-6 PhD||2-4 in bio|
|# active faculty||62||74 total|
30 in comp
30 in comp
20 in comp
8 in comp
6 in bio
|# Depts||>12 Depts across campus||Human Genetics||>12 Depts across campus||Computational Medicine||>12 Depts across campus||>12 Depts across campus||>12 Depts across campus||Ecology and Evolutionary Biology||Biostatistics||Computer Science|
|Coursework||3 of 5 core classes and 2 Electives within year 1, or optional year 2||3 core classes and 2 Electives within year 1, or optional year 2||9 courses in years 1 and 2||10 core classes and 6 Electives in years 1-3||depends on program||5 core classes and 3 Electives within years 1 and 2||3 core classes and 2 Electives within year 1, or optional year 2||4 core classes and 3 Electives in years 1 and 2||5-8 core courses.|
6 elective courses in years 1-3
|Rotations?||3 rotation in 1st year||3 rotation in 1st year||3 rotation in 1st year||Flexible in year 1-3||depends on program||3 rotation in 1st year||3 rotation in 1st year||admitted to specific lab||projects by different faculty and grants||admitted to specific lab|
|GRE for Admission?||no||no||no||yes||depends on program||no||no||no||yes||yes|
|Funding||Program guarantees funding, 1 TAship||Program guarantees funding, 1 TAship||Program guarantees funding, 1 TAship||Funding by PI and TAships||depends on program||Program guarantees funding, 1 TAship||Program guarantees funding, 1 TAship||Funding by PI and TAships||TA-ships, and project RA-ships||Funding by PI and TA ships|
|Time to degree||5-6 years||5-6 years||5-6 years||5-7 years||5-6 years||5-6 years||5-6 years||4-6 years||4-5 years||5-6 years|