Bioinformatics MSc

Students pursuing biomedical informatics via the Master's degree through the UCLA Bioinformatics Interdepartmental Program can choose to complete degree requirements in two different ways: through the comprehensive exam plan, or completion of a Master's thesis. We encourage all Master's students to discuss early on with their advisor their career plans in order to guide individuals towards appropriate coursework and degree completion. Please note that this is a terminal MSc degree, and its completion does not guarantee acceptance into the PhD program: students must reapply for admittance into the doctoral degree program. Students interested in pursuing bioinformatics specifically should go directly to the Bioinformatics IDP web site for more information.

MSc Comprehensive Exam

The comprehensive exam plan consists of three parts:

  1. Successful completion of the core curriculum. Students must complete the Bioinformatics core curriculum with letter grades of B or higher.
  2. A written report. The master's comprehensive examination is in the format of a written report on a research project. The report will describe the results of the student's investigation of a problem in the area of biomedical informatics under the supervision of a faculty member in the program, who approves the subject and plan of the project, as well as reading and approving the completed report. While the problem may be one of only limited scope, the report must exhibit a satisfactory style, organization, and depth of understanding of the subject. A student should normally start to plan the project at least one quarter before the award of the MSc degree is expected. An advisory committee evaluates and grades the written report as not pass or MS pass and forwards the results to the graduate adviser.
  3. Completion of one additional elective. Students must complete additional electives from the courses below with a letter grade of B or better. Elective can be taken at any point during the student's enrollment as a Master's student.

Students must choose this option by the end of the first year. On average, the MSc comprehensive exam plan can be completed in 6 quarters. In general, the comprehensive exam option is suited to individuals who need exposure and an introduction to biomedical informatics with the intent of using such experience towards careers in various industries, including health information technology (IT) and the biomedical data sciences.

MSc Thesis

The thesis plan consists of two parts:

  1. Successful completion of the core curriculum. Students must complete the Bioinformatics core curriculum with letter grades of B or higher.
  2. Completion of a research thesis. Students complete a written research thesis under the supervision of a faculty research advisor, resulting in a document reviewed and vetted by a thesis committee of three members. Every master's degree thesis plan requires the completion of an approved thesis that demonstrates the student's ability to perform original, independent research. Students must choose a permanent faculty adviser and submit a thesis proposal by the end of the third quarter of study. The proposal must be approved by the permanent adviser who serves as the thesis adviser. The thesis is evaluated by a three-person committee that is nominated by the program and appointed by the Graduate Division. Students must present the thesis in a public seminar.

Students who are interested in research-oriented careers should choose the thesis option in order to gain experience in conducting and evaluating biomedical informatics research. On average, the MSc thesis plan can be completed in a total of 6 quarters.

Electives

CS 143: Database Systems

Information systems and database systems in enterprises. File organization and secondary storage structures. Relational model and relational database systems. Network, hierarchical, and other models. Query languages. Database design principles. Transactions, concurrency, and recovery. Integrity and authorization.

Biomathematics 204: Biomedical Data Analysis

Quantity and quality of observations have been greatly affected by present-day extensive use of computers. Problem-oriented study of latest methods in statistical data analysis and use of such arising in laboratory and clinical research.

Biomathematics M280: Statistical Computing

Introduction to theory and design of statistical programs: computing methods for linear and nonlinear regression, dealing with constraints, robust estimation, and general maximum likelihood methods.

PIC 110: Parallel & Distributed Computing

Introduction to programming of parallel computers. Shared and distributed memory parallel architectures; currently available parallel machines; parallel algorithms and program development; estimation of algorithmic performance; distributed computing; selected advanced topics.