Keynote Speaker

Srinivas Aluru

Professor, School of Computational Science and Engineering
Co-Executive Director, Institute for Data Engineering and Science
Georgia Institute of Technology


Machine learning approaches for reverse engineering genome-scale networks

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Reverse engineering whole-genome networks from large-scale gene expression datasets and analyzing them to discover biological knowledge are important challenges in systems biology. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute-intensive limiting their scale of applicability. In this talk, I will present my group’s research over the last fifteen years in learning genome-scale networks and using them to extract new biological hypothesis. This includes network learning methods based on information theory, Bayesian networks, and deep learning, parallel algorithms to facilitate learning of large networks, and generating ensemble networks combining multiple approaches. The resulting networks can be used for predicting gene function and extracting context-specific subnetworks.


Srinivas Aluru is Executive Director of the Institute for Data Engineering and Science (IDEaS) and Professor in the School of Computational Science and Engineering at the Georgia Institute of Technology. He co-leads the NSF South Big Data Regional Innovation Hub and the NSF Transdisciplinary Research Institute for Advancing Data Science. Aluru conducts research in data science, high performance computing, and bioinformatics and systems biology. He is a recipient of the NSF Career award, IBM faculty award, Swarnajayanti Fellowship from the Government of India, the John. V. Atanasoff Discovery Award from Iowa State University, and the Outstanding Research Program Development Award at Georgia Tech. He is a Fellow of the AAAS, ACM, IEEE, and SIAM, and is a recipient of the IEEE Computer Society Golden Core and Meritorious Service awards.