Research and development of machine learning techniques for large datasets.
Fields of Interest
Data Analytics, Deep Learning, Computer Vision, Knowledge Discovery, Text Analysis,
High Performance Computing, GPU Programming.
Selected Publications [Bibtex]
- A. Terwilliger, G. Perdue, D. Isele, R. Patton, S. Young, “Vertex reconstruction of neutrino interactions using deep learning,” Proceedings of the International Joint Conference on Neural Networks (IJCNN), May 2017.
- T. Potok, C. Schuman, S. Young, R. Patton, F. Spedalieri, J. Liu, K. Yao, G. Rose, G. Chakma, “A study of complex deep learning networks on high performance, neuromorphic, and quantum computers,” Proceedings of the Workshop on Machine Learning in High Performance Computing Environments, Supercomputing, 2016.
- S. Lim, S. Young, R. Patton, “An analysis of image storage systems for scalable training of deep neural networks,” The Seventh workshop on Big Data Benchmarks, Performance Optimization, and Emerging Hardware (in conjunction with ASPLOS’16), 2016.
- S. Young, D. Rose, T. Karnowski, S. Lim, R. Patton, “Optimizing deep learning hyper-parameters through an evolutionary algorithm,” Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments, Supercomputing, 2015.
- J. Holleman, I. Arel, S. Young, J. Lu, “Analog inference circuits for deep learning,” in Biomedical Circuits and Systems Conference (BioCAS), 2015 IEEE , vol., no., pp.1-4, 22-24 Oct. 2015.
- J. Lu, S. Young, I. Arel, and J. Holleman, “A 1 tops/w analog deep machine-learning engine with floating-gate storage in 0.13 μm cmos,” IEEE Journal of Solid-State Circuits, vol. 50, no. 1, pp. 270–281, 2015.
- S. Young, “Scalable Hardware Efficient Deep Spatio-Temporal Inference Networks,” PhD thesis, The University of Tennessee, 2014.
- J. Lu, S. Young, I. Arel, and J. Holleman, “A 1 tops/w analog deep machine-learning engine with floating-gate storage in 0.13 μm cmos,” in IEEE Int. Solid-State Circuits Conf.(ISSCC) Dig. Tech. Papers, 2014.
- S. Young, J. Lu, J. Holleman, and I. Arel, “On the impact of approximate computation in an analog destin architecture,” Neural Networks and Learning Systems, IEEE Transactions on, vol. 25, no. 5, pp. 934–946, 2014.
- S. Young, A. Davis, A. Mishtal, and I. Arel, “Hierarchical spatiotemporal feature extraction using recurrent online clustering,” Pattern Recognition Letters, vol. 37, pp. 115–123, 2014.
- J. Lu, S. Young, I. Arel, and J. Holleman, “An analog online clustering circuit in 130nm cmos,” in Solid-State Circuits Conference (A-SSCC), 2013 IEEE Asian, pp. 177–180, IEEE, 2013.
- S. Young and I. Arel, “Recurrent clustering for unsupervised feature extraction with application to sequence detection,” in Machine Learning and Applications (ICMLA), 2012 11th International Conference on, vol. 2, pp. 54–55, IEEE, 2012.
- T. Karnowski, I. Arel, and S. Young, “Modeling temporal dynamics with function approximation in deep spatio-temporal inference network,” in Biologically Inspired Cognitive Architectures, International Conference on, 2011.
- S. Young, I. Arel, T. Karnowski, and D. Rose, “A fast and stable incremental clustering algorithm,” in Information Technology: New Generations (ITNG), 2010 Seventh International Conference on, pp. 204–209, IEEE, 2010.
- S. Young, I. Arel, and O. Arazi, “Pi-pifo: A scalable pipelined pifo memory management architecture,” in Telecommunications, 10th International Conference on, pp. 265–270, IEEE, 2009.
University of Tennessee, Knoxville
Major: Computer Engineering, PhD
Graduation Date: December, 2014
University of Tennessee, Knoxville
Major: Electrical Engineering, BS
Graduation Date: May, 2010
ORNL - Computational Data Analytics, Oak Ridge, TN (Dec. 2016 - Present)
- Developing methods for utlizing HPC for training deep networks for large science datasets
- Developing approaches for applying deep learning to datasets with very few labeled examples.
ORNL - Computational Data Analytics, Oak Ridge, TN (Dec. 2014 - Nov. 2016)
Postdoctoral Research Associate
- Developed unsupervised machine learning techniques for analysis of cyber security data
- Developed deep learning techniques for large scale datasets
Machine Intelligence Lab, Knoxville, TN (Nov. 2008 – Dec. 2014)
- Developed deep machine learning algorithms
- Collaborated with analog device researchers to implement deep machine learning algorithms in analog electronics
University of Tennessee, Knoxville, TN (Aug. 2011 – May 2013)
- Taught lab for freshman level computer science course
- Developed new lab assignments to replace assignments previously taught using outdated software
ORNL - Computational Data Analytics, Oak Ridge, TN (July 2009 – Aug. 2009, May 2010 – Aug. 2010, May 2011 - Aug. 2011)
- Implemented MS SharePoint text mining tool that has been commercially licensed
- Raptor: An Enterprise Knowledge Discovery Engine (Version 2.0)
- Met with sponsors to discuss project requirements
- Became proficient in C#, Java, and MS SQL
GE Consumer and Industrial, Louisville, KY (Jan. 2008 – May 2008)
- Managed cost take out projects on sourced products
- Checked supplier and in house test data for conformance to product specification
- Interfaced with marketing, service, quality, and suppliers
Honeywell Aerospace, Clearwater, FL (Jan. 2007 – May 2007)
- Built simulations in Matlab and Simulink
- Modified process for testing a product
- Oak Ridge National Laboratory
- Technology Commercialization Award (December 2016)
- Technology Commercialization Award (December 2014)
- University of Tennessee
- Outstanding GTA - EECS Department (2012-2013)
- J. Wallace & Katie Dean Graduate Fellowship (2010-2014)
- Billy and Sylvia Moore Scholarship (2008-2010)
- Charles and Martha Sprankle Scholarship (2006-2007)
- Alumni Valedictorian Scholarship (2005-2007)
- James L. Howard Scholarship (2005-2006)
- Machine Learning in HPC Environments (SC Workshop)
- Program Committee Chair (2015-2016)
- International Joint Conference on Neural Networks (IJCNN)
- ACM - Member
- Eta Kappa Nu – Beta Phi Chapter
- Vice President (2009-2010)
- Corresponding Secretary (2010-2012)
- IEEE – Member
- Tau Beta Pi - Member