Ziv Bar-Joseph
Location: (Pittsburgh, PA)
Personal Research Web Page: http://www.sb.cs.cmu.edu/
Keywords: Systems biology, computational biology, machine learning, graphical models, time series analysis, networks, data integration
Posted on: Monday, May 3rd, 2010
Broad Research Area: AI / Machine Learning / Robotics / Vision, Scientific/Medical Informatics
Research Interests:
Our group focuses on modeling regulatory and signaling networks and on cross species analysis of high throughput biological data. We develop and use computational methods utilizing techniques from machine learning, statistics and combinatorics in order to solve important biological questions. We are collaborating with multiple experimental groups and most of our algorithms lead to new hypotheses which are experimentally validated by our collaborators.
The open positions are in two major lines of research:
1. Reconstructing dynamic networks in the cell. We are interested in developing methods that utilize graphical models and graph theoretic algorithms to combine dynamic (temporal) and static information from high throughput biological data in order to reconstruct the flow of information in the cell. Applications include modeling of stress responses, cell cycle, infections, predicting timing of binding of transcription factors, their combinatorial interactions and the pathways activating them.
2. Cross species analysis of interaction networks. While sequence is highly conserved, most interaction networks are much less conserved, even between close species. We are interested in developing methods to identify the conserved and divergent elements in interaction networks (including protein interactions, co-expression networks, genetic interactions etc.) and to use these similarities and differences to identify the mechanisms underlying various responses.
Contact Information:
Applicants should email a CV and the names of two potential references to Ziv Bar-Joseph: zivbj@cs.cmu.edu
