Curriculum
Our multidisciplinary curriculum includes courses in bioinformatics, statistics, machine learning, computation and simulation, quantitative biology, and genomics. The training emphasizes hands-on computer labs and practical skills to prepare students for careers beyond the classroom.
During the first two semesters, students focus on foundation and competency courses. In the second half of the program, students will join one of our top-notch research labs at either WCGS or SKI to work on an independent project in order to develop more specialized expertise and hone their skills in problem solving, critical thinking, and science communication.
Students are also required to take at least two electives among program-approved WCGS and Cornell Tech offerings. At least one elective must cover statistical or machine learning. Possibilities include courses on applied machine learning, natural language processing, computer vision, AI, and statistical learning. Other electives include courses on biostatistics, health informatics, biomedical entrepreneurship, etc.
Fall 1 Term
Description
This is a unique graduate course, which addresses fundamental data structures and algorithms that are being applied in modern computational biology. The students will focus on algorithmic problem solving and learn several algorithmic techniques. Students will also learn how to design and apply data structures and algorithms to state-of-the-art biology problems such as large-scale genome sequence analysis.
Schedule
The course is scheduled for the Fall semester and meets on Tuesdays and Thursdays from 10:00 am to 11:30 am.
Objective
Upon completion of this course, students will be able to formulate, implement, and analyze different types of mathematical models used to simulate a variety of biological systems.
Description
This course covers fundamental concepts and techniques used for mathematical modeling of biological systems. Course topics include theoretical analysis of nonlinear ordinary differential equations as well as numerical simulation of different classes of models, including stochastic and spatial models. Students will also learn approaches for model development, model validation, parameter identification, and determining parameter sensitivity. Techniques and methods covered in lectures will be implemented and demonstrated in computer labs. Models demonstrated in class will originate from various biological systems, including electrophysiology, gene networks, and immunology.
Schedule
The course consists of twice-weekly lectures and weekly computer labs, held during the Fall semester on Wednesdays from 11:00 am to 12:15 pm and Fridays from 11:00 am to 1:15 pm.