Computational Biology

The Computational Life Sciences PhD track in the Biomedical Life Sciences Program unites expertise from basic science and medicine to train students in developing and applying cutting-edge computational methods to complex biological and biomedical challenges. Combining rigorous foundations in computer science, mathematics, and quantitative modeling with hands-on training in genomics, systems biology, structural biology, and clinical informatics, the track prepares students to advance discovery at the interface of biology and computation.

Course Work 

Common Program Course Work 

Each student must complete two required core research courses, three foundations courses, and participate in Research In Progress. At least one course from the Life Sciences Foundations and Computational Foundations series is required as part of the three foundations course requirement.   

Core Research Courses 

Responsible Conduct of Research, Biomedical Research

Life Sciences Foundations

Molecules to Life I: Biochemistry & Molecular Biology (4 credits) or 

Molecules to Life II: Cell Biology & Genetics (4 credits)

Computational Foundations

Bytes to Biology and Health I: Quantitative methods in biology (4 credits) and 

Bytes to Biology and Health II: Quantitative and information theory in biology (4 credits) 

Research in Progress 

Research in Progress (1 credit per semester for 10 semesters) 

Track Specific Coursework 

Course requirements for students who commit to the Computational Biology Track:

  1. Computational Genetics (CMBF4761, three credits) or Machine Learning for Functional Genomics (COMS4762, three credits) 
  2. Electives (6 credits)
    1. Choose 1 VP&S graduate course and
    2. 1 course from: 
  • Advanced Machine Learning (STAT5242, 3 credits) 
  • Biological Sequence Analysis (BINF4013, 3 credits) 
  • Probabilistic Models and Machine Learning (STCS 6701, 3 credits) 
  • Bayesian Statistics (STAT4224, 3 credits) 
  • Statistical Interference (STAT4204, 3 credits) 
  • Natural Language Processing (COMS4705, 3 credits)
  • Microbiome Data Analysis (BINF4018, 3 credits) 
  • Probability Theory (STAT4203, 3 credits)
  • Computer Vision (COMS4731, 3 credits) 

Skills & Competencies 

The VIBRE programs require PhD candidates to demonstrate proficiency in the following key scientific skills: Coding, Statistics, Scientific Writing, Oral Communication, and Literature Review. 

These requirements may be met in multiple ways, including:

  • Prior Experience: Students with strong undergraduate or graduate-level experience can meet requirements by demonstrating proficiency (e.g., a high grade in a relevant course).
  • Coursework: Students may meet requirements through designated Columbia University courses. 
  • Track-based Activities: Communication and literature review skills may be developed through journal clubs, work-in-progress meetings, or specialized track-level courses. 

Each student's progress toward these competencies will be tracked and documented by their program's Director of Graduate Studies (DGS). 

Other Curricular Requirements 

  • Orientation & Boot Camp – These programs offer students a comprehensive introduction to the Columbia University community and the VIBRE PhD Pathway.
  • Advising – Students regularly meet with faculty mentors and program advisors to guide their academic and research decisions, address core research skills, and discuss expectations and needs.
  • Research Rotations – During their first year, students complete at least three research rotations, each lasting about three months, in the labs of training faculty. Students select rotations based on their interests. These experiences help them explore potential dissertation mentors while expanding both their practical skills and theoretical understanding in different areas of research. 
  • First-Year Affiliation and Track Specialization – Students are affiliated with a track of interest during their first year, with formal selection of a program track occurring at the end of the year.