02-752 Advanced Algorithms for Biological Problems

12 units, Carnegie Mellon University

This course will focus on advanced algorithms and algorithm design techniques for classes of algorithmic problems that frequently arise in biological contexts. The course presents both provably optimal algorithms as well as practical heuristics. Students will learn both proof techniques and procedures for empirical assessment of algorithmic performance. Algorithmic families that will be presented include efficient algorithms for manipulation of strings, graphs, trees, images, and other abstractions of biological data. Modern algorithmic techniques such as succinct data structures, amortized and probabilistic analysis of algorithms, sketching, and convex optimization will be applied to solving large-scale biological problems. Some tentative topics include networks for systems biology, game theory for modeling cancer and epidemiology, string algorithms for genomics, search indexes for biological databases, and phylogenetic trees, but topics will shift in response to developments in the field.

Prerequisites: 02-613 or 15-210 or 15-351 or 15-650

Offered: Spring 2027, Spring 2026

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