U.S. Department of Energy

Pacific Northwest National Laboratory

What can (large scale) machine learning do for you?

Tuesday, October 20, 2015
Dr. Abhinav Vishnu
We are producing and recording more data from simulations, experiments and instruments than ever before. With increasing volume, automatic data analysis is critical for generating high fidelity models. Machine Learning consists of a set of algorithms, which can generate models from data, consisting of large number of variables. Yet, the entire process is non-intuitive, and mistake prone. In this talk, we will present a set of steps which domain scientists may use for large scale data analysis. We will provide a brief introduction to these steps, and demonstrate with one or more use cases. We expect that many data producing domains will benefit significantly from this talk, with a potential for longer term collaboration.
Speaker Bio

Abhinav Vishnu is a research scientist in the Advanced Computing, Mathematics, and Data Division, Fundamental and Computational Sciences Directorate. His primary research interests are in high performance computing, parallel programming models, and designing scalable machine learning and data mining algorithms using parallel programming models on high-end systems. He has served as co-editor for multiple journals including Parallel Computing, Journal of Supercomputing, Computation and Concurrency: Practice and Experience, and program co-chair for the International Workshop on Parallel Programming Models and Systems Software, and International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics. He has co-authored 50 journal, conference, and workshop publications and is frequently invited to serve as a Technical Program Committee member for High Performance Computing conferences and workshops.

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