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Evan Racah

Evan Racah
Data Analytics Engineer
Data & Analytics Group
Phone: (510) 486-6133
Fax: (510) 486-6459
1 Cyclotron Road
Mailstop: 59-4010A
Berkeley, CA 94720 US

Evan joined NERSC as an intern in January 2015 and started as a Data Analytics Engineer in August 2015. He primarily works on implementing machine learning methods for various science problems and on exploring and managing data analytics and machine learning frameworks on Cori and Edison, like Spark and Caffe.

Conference Papers

Alex Gittens et al, "Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies", 2016 IEEE International Conference on Big Data, July 1, 2017,

Evan Racah, Seyoon Ko, Peter Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat, "Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks", ICMLA, 2016,

Michael Ringenburg, Shuxia Zhang, Kristyn
Maschhoff, Bill Sparks, Evan Racah, Prabhat,
"Characterizing the Performance of Analytics Workloads on the Cray XC40", Cray User Group, May 13, 2016,

Jialin Liu, Evan Racah, Quincey Koziol, Richard Shane Canon,
Alex Gittens, Lisa Gerhardt, Suren Byna, Mike F. Ringenburg, Prabhat,
"H5Spark: Bridging the I/O Gap between Spark and Scientific Data Formats on HPC Systems", Cray User Group, May 13, 2016,

Mostofa Patwary, Nadathur Satish, Narayanan Sundaram, Jialin Liu, Peter Sadowski, Evan Racah, Suren Byna, Craig Tull, Wahid Bhimji, Prabhat, Pradeep Dubey, "PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures", IPDPS 2016, April 5, 2016,

Alex Gittens, Jey Kottalam, Jiyan Yang, Michael F Ringenburg, Jatin Chhugani, Evan Racah, Mohitdeep Singh, Yushu Yao, Curt Fischer, Oliver Ruebel, Benjamin Bowen, Norman Lewis, Michael W Mahoney, Venkat Krishnamurthy, Prabhat, "A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark", The 5th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics, IPDPS, February 1, 2016,

Web Articles

"Deep Learning for Science", Prabhat, Kris Bouchard, Wahid Bhimji, Evan Racah, NERSC Science Highlight, December 8, 2015,


Annette Greiner, Evan Racah, Shane Canon, Jialin Liu, Yunjie Liu, Debbie Bard, Lisa Gerhardt, Rollin Thomas, Shreyas Cholia, Jeff Porter, Wahid Bhimji, Quincey Koziol, Prabhat, "Data-Intensive Supercomputing for Science", Berkeley Institute for Data Science (BIDS) Data Science Faire, May 3, 2016,

Review of current DAS activities for a non-NERSC audience.

Prabhat, Yunjie Liu, Evan Racah, Joaquin Correa, Amir Khosrowshahi, David Lavers, Kenneth Kunkel, Michael Wehner, William D. Collins, "Deep Learning for Climate Pattern Detection", American Geophysical Union Meeting 2015, December 8, 2015,

Evan Racah, Silvia Crivelli, Yushu Yao, "Machine Learning with Spark: Exploring MLlib Random Forests Performance on Edison", BIDS Data Science Faire, May 15, 2015,