Workshop Proposal: Machine Learning and Discovery Science

University Of Southern California, Los Angeles CA

Investigators

Abstract

The emergence of big data has been transformational in many areas in science and engineering - biology, health sciences, material science, physics, and so on. At the heart of this transformation is statistical machine learning, a subfield of computer science that aims at studying and developing algorithms that can analyze large volumes of data. The goal of this international workshop is to bring together researchers, both from USA and Armenia, who work on machine learning (ML) and other scientific disciplines that are poised to benefit from the recent advances in ML. The workshop will include participation from leading experts in a number of disciplines. In machine learning, the workshop will cover topics such as deep learning, tensor methods, unsupervised learning with high dimensional data, and so on. In computational social sciences, the topics will include social network analysis, behavioral modeling, modeling of socio-technical systems. And in computational biology, the topics will cover gene expression analysis, computational neuroscience, predictive diagnostics. The workshop will serve as a bridge to get these communities talking to one another and explore collaborative research. The workshop will provide a forum for the participating researchers to formulate a research agenda that will help to utilize recent advances in ML in data-intensive disciplines. Second, the workshop will support participation of senior graduate students and early career scientists. Finally, the workshop will promote scientific cooperation between the American and Armenian researchers. This award is cofunded by the Office of International Science and Engineering.