Graduate Programs - Degrees

The graduate program in Computer Science was inaugurated in 1975. Beginning as a combined Computer Science/Mathematics masters degree, students now complete an M.S. in CS. A new doctoral program in Computer Science and Informatics began in Fall 2007.

Concentration

The departments of Computer Science, Biomedical Informatics and Biostatistics jointly offer graduate programs with a number of concentrations including:

Masters Program

Students are required to take 3 core courses and 4 concentration electives, which could be structured to focus primarily on their chosen concentration (depth), or from multiple areas (breadth). They are also required to take 9 credits for fulfilling either the thesis, project, or coursework track. Students are required to complete a practicum related to their course of study (see practicum details in the MS program description)

The academic course work is expected to be completed within 2 years. Students choosing to complete a thesis are expected to perform original research with their advisors.

Doctoral Program

Students are required to take 3 core courses and 7-8 concentration electives, which could be structured to focus primarily on their chosen concentration (depth), or from multiple areas (breadth). They are also required to take 2 rotation projects to explore a potentially research area.

​The academic course work is expected to be finished within the first 2-3 years followed by a qualifying examination in the student's chosen concentration, and a thesis proposal followed by the thesis defense. By year 3 or often earlier, students are expected to begin working closely with an advisor on original research. ​ On average, a PhD degree takes 5-6 years to complete.

Courses

Click here for brief descriptions of the courses offered in our program.​

Data Science

Core
  • Algorithms.
  • Machine Learning.
  • Systems Programming.

Data Science:
  • Artificial Intelligence.
  • Data Mining.
  • Natural Language Processing.
  • Information Retrieval.
  • Data Privacy and Security.

​Systems:
  • Programming Languages and Compilers.
  • Computer Systems.
  • High Performance Computing.
  • Operating Systems.
  • Systems Security.
​
Foundations and Applications
  • Statistical Methods.
  • Probability Theory.
  • Theory of Computing.
  • Digital Image Processing.
  • Numerical Analysis.
  • Matrix Analysis and Applications.
  • Network Science.

Biomedical Informatics

Core
  • Introduction to Biomedical Informatics.
  • Statistical Methods.
  • Machine Learning.
​
Foundations:
  • Probabilistic Theory.
  • Statistical Inference.
  • Fundamentals of Epidemiology.
  • Theory of Computing.
  • Algorithms.

Applications:
  • Cancer Biology.
  • Introduction to Bioinformatics.
  • Introduction to R Programming.
  • High-throughput Data Analysis.
  • Digital Image Processing.
​
Computing and Informatics
  • System Programming.
  • Database Systems.
  • Programming Languages and Compilers.
  • Artificial Intelligence.
  • Data Mining.
  • Natural Language Processing.
  • Information Retrieval.
  • Data Privacy and Security.
  • Operating Systems.
  • High Performance Computing.