Our group develops computational methods for understanding the dynamics, interactions and conservation of complex biological systems. As new high-throughput biological data sources become available, they hold the promise of revolutionizing molecular biology by providing a large-scale view of cellular activity. However, each type of data is noisy, contains many missing values and only measures a single aspect of cellular activity. Our computational focus is on methods for large scale data integration. We primarily rely on machine learning and statistical methods. Most of our work is carried out in close collaboration with experimentalists. Many of the computational tools we develop are available and widely used.
Matt awarded NIH Postdoctoral Fellowship Matt Ruffalo received a NIH postdoctoral fellowship grant from the NCI. Matt has been a postdoc in our group since 2016. Matt works on the analysis of cancer genomics data and his proposal focused on methods for integrating genomics data from several different sources to predict survival and drug response. Congratulations Matt!
Bar-Joseph receives FORE Systems Professorship. In a ceremony attended by some of the FORE Systems founders, Ziv Bar-Joseph has been awarded the FORE Systems chair in computational biology and machine learning. While the chair is a great personal honor, it is in large part due to the amazing work over the years by all the students and postdocs in our group!
Sid defends and accepts a new postdoc position. Siddhartha (Sid) Jain successfully defended his PhD thesis titled 'Inferring Temporal Signaling Pathways and Regulatory Mechanisms from High-Throughput Data'. Sid has accepted a postdoc position with Prof. David Gifford at MIT and will start there shortly. Congratulations Sid, and good luck!