Program Overview

Doctor of Philosophy in Computational Biology

The Doctor of Philosophy in Computational Biology is a research-based degree that aims to prepare students to become leading scientists in academia and industry. The program focuses on educating students to be highly competent in their chosen area of research while providing a broad knowledge foundation in bioinformatics and computational biology.

Overview

The goal of the program is to equip students with the right skill set to successfully accomplish their research project (thesis). Upon graduation, students will have independently planned and conducted computational research in their chosen area and will be able to conduct original interdisciplinary research across the life sciences.

Program Structure

The program is primarily a research-based degree, with coursework designed to equip students with the necessary skills to accomplish their research project. The minimum degree requirements for the Doctor of Philosophy in Computational Biology program are 60 credits, distributed as follows:

• Core courses: 16 credits
• Electives: 8 credits
• Internship: 2 credits
• Advanced research methods: 2 credits
• Research thesis: 32 credits

Core Courses

All students must take the following core courses:

1. Introduction to Single Cell Biology and Bioinformatics (4 credits): This course provides a broad overview of bioinformatics for single cell omics technologies.
2. Introduction to Molecular Biology for Machine Learning (4 credits): This interdisciplinary course is designed for students with a background in machine learning who seek to understand the fundamentals of molecular biology.
3. Analyzing Multi-omics Network Data in Biology and Medicine (4 credits): This course covers the analysis and modeling of entire biological systems using networks (or graphs).
4. Computational Genomics and Epigenomics (4 credits): This course introduces students to the core concepts, tools, and emerging methods in computational genomics and epigenomics.

Elective Courses

Students will select a minimum of two elective courses, with a total of 8 credits. Available elective courses include:

1. AI and Deep Learning for Biomedical Data (4 credits)
2. Advanced Topics in Machine Learning for Biology (4 credits)
3. Evolutionary Genomics and Population Genetics (4 credits)
4. Advanced Topics in Continuous Optimization (4 credits)
5. Advanced Topics in Reinforcement Learning (4 credits)
6. Federated Learning (4 credits)
7. Causality and Machine Learning (4 credits)
8. Foundations of Machine Learning (4 credits)
9. Advanced Machine Learning (4 credits)
10. Algorithms for Big Data (2 credits)
11. Predictive Statistical Inference and Uncertainty Quantification (2 credits)
12. Collaborative Learning (4 credits)
13. Current Topics in Natural Language Processing (4 credits)
14. Advanced Speech Processing (4 credits)
15. Generative AI-powered Educational Applications (4 credits)
16. Agent Systems Powered by Large Language Models (4 credits)

Admission Criteria

To be eligible for the program, applicants must meet the following criteria:

• Completed degree in a STEM field with a minimum CGPA of 3.5 (on a 4.0 scale) or equivalent
• English language proficiency (IELTS, TOEFL, PTE, Cambridge C1 Advanced, or Duolingo English Test)
• Submission of Graduate Record Examination (GRE) scores is optional
• Statement of purpose (500-1000 words)
• Research statement (including title, problem definition, literature review, proposed research/methods/solution, study timeline, and list of references)

Admission Process

The admission process includes:

1. Submission of application
2. Screening exam (optional)
3. Admission interview (for selected applicants)
4. Decision notification
5. Offer response deadline

Study Plan

A typical study plan for the program is as follows:

• Semester 1: Introduction to Molecular Biology for Machine Learning, Analyzing Multi-omics Network Data in Biology and Medicine, and one elective course
• Semester 2: Computational Genomics and Epigenomics, Introduction to Single Cell Biology and Bioinformatics, and one elective course
• Summer: Ph.D. Internship
• Semester 3: Computational Biology Ph.D. Research Thesis and Introduction to Research Methods
• Semester 4-8: Computational Biology Ph.D. Research Thesis