Manolis Kellis - Courses Taught

6.047/6.878 - Computational Biology: Genomes, Networks, Evolution

Covers the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics.

6.046: Introduction to Algorithms

with Ron Rivest (S06), Srini Devadas (S07), Piotr Indyk (S08)

Introduction to design and analysis of algorithms. Topics include sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; greedy algorithms; amortized analysis; graph algorithms; shortest paths; network flow. Advanced topics include: number theory, computational biology, string matching, database search, hidden Markov models; number-theoretic algorithms; polynomial and matrix calculations.

Previous Offerings:

6.092: Bioinformatics and Proteomics: An Engineering-Based Problem Solving Approach

Gil Alterovitz, Manolis Kellis, Marco Ramoni

This interdisciplinary course provides a hands-on approach to students in the topics of bioinformatics and proteomics. Lectures and labs cover sequence analysis, microarray expression analysis, Bayesian methods, control theory, scale-free networks, and biotechnology applications. Designed for those with a computational and/or engineering background, it will include current real-world examples, actual implementations, and engineering design issues. Where applicable, engineering issues from signal processing, network theory, machine learning, robotics and other domains will be expounded upon. New research areas will be explored using current literature as well as text from book chapter materials being written by the instructors. Guest lectures include speakers from both industry and academia.

(IAP 2005)

6.096 - Algorithms for Computational Biology (meets with 6.046)

This course covers the algorithmic foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, analyze influential algorithms, and apply these to real datasets. Topics include: biological sequence analysis, gene finding, motif discovery, RNA folding, global and local sequence alignment, genome assembly, comparative genomics, genome duplication, genome rearrangements, evolutionary theory, gene expression, clustering algorithms, scale-free networks, machine learning applications to genomics.

All lectures available online

(Spring 2005)

18.417 - Introduction to Computational Molecular Biology