CV

Curriculum Vitae

Objective

Research and development of novel machine learning techniques for novel applications.

Fields of Interest

Data Analytics, Deep Learning, Computer Vision, Knowledge Discovery, Text Analysis, High Performance Computing, GPU Programming.

Selected Publications [Bibtex]

  • R. Archibald, M. Doucet, T. Johnston, S. Young, E. Yang, and W. Heller, “Classifying and analyzing small-angle scattering data using weighted k nearest neighbors machine learning techniques,” Journal of Applied Crystallography, 2020.
  • K. Hamilton, C. Schuman, S. Young, R. Bennink, N. Imam, and T. Humble, “Accelerating scientific computing in the post-moore’s era,” ACM Transactions on Parallel Computing (TOPC), 2020.
  • M. Parsa, C. Schuman, D. Rose, B. Kay, J. Mitchell, S. Young, R. Dellana, W. Severa, T. Potok, K. Roy, et al., “Hyperparameter optimization in binary communication networks for neuromorphic deployment,” arXiv preprint arXiv:2005.04171, 2020.
  • A. Anderson and S. Young, “Self-taught waveform synthesis and analysis in theamplify-and-forward relay channel,” in 2019 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW), IEEE, 2019.
  • A. Anderson, S. Young, F. Reed, and J. Vann, “Deep modulation (deepmod): A self-taught phy layer for resilient digital communications,” arXiv preprint arXiv:1908.11218, 2019.
  • J. Chae, C. Schuman, S. Young, T. Johnston, D. Rose, R. Patton, and T. Potok, “Visualization system for evolutionary neural networks for deep learning,” in 2019 IEEE International Conference on Big Data (Big Data), IEEE, 2019.
  • D. Hoang, J. Hamer, G. Perdue, S. Young, J. Miller, and A. Ghosh, “Inferring convolutional neuralnetworks’ accuracies from their architectural characterizations,” in 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), IEEE, 2019.
  • J. Johnston, S. Young, C. Schuman, J. Chae, D. March, R. Patton, and T. Potok, “Fine-grained exploitation of mixed precision for faster cnn training,” in 2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC), IEEE, 2019.
  • R. Patton, T. Johnston, S. Young, C. Schuman, T. Potok, D. Rose, S. Lim, J. Chae, L. Hou, S. Abousamra, et al., “Exascale deep learning to accelerate cancer research,” in 2019 IEEE International Conference on Big Data (Big Data), IEEE, 2019.
  • L. Song, F. Chen, S. Young, C. Schuman, G. Perdue, and T. Potok, “Deep learning for vertex reconstruction of neutrino-nucleus interaction events with combined energy and time data,” in ICASSP 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2019.
  • S. Young, P. Devineni, M. Parsa, T. Johnston, B. Kay, R. Patton, C. Schuman, D. Rose, and T. Potok, “Evolving energy efficient convolutional neural networks,” in 2019 IEEE International Conference on Big Data (Big Data), IEEE, 2019.
  • A. Anderson, S. Young, T. Karnowski, and J. Vann, “Deepmod: An over-the-air trainablemachine modem for resilient phy layer communications,” in MILCOM 2018 IEEE Military Communications Conference (MILCOM), IEEE, 2018.
  • K. Hamilton, C. Schuman, S. Young, N.Imam, and T. Humble, “Neural networks and graph algorithms with next-generation processors,” in 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), IEEE, 2018.
  • D. Herrmannova, S. Young, R. Patton, C. Stahl, N. Kleinstreuer, and M. Wolfe, “Unsupervised identification of study descriptors in toxicology research: An experimental study,” arXiv preprint arXiv:1811.01183, 2018.
  • R. Patton, T. Johnston, S. Young, C. Schuman, D. March, T. Potok, D. Rose, S. Lim, T. Karnowski, M. Ziatdinov, et al., “167-pflops deep learning for electron microscopy:From learning physics to atomic manipulation,” in Proceedings of the International Conference forHigh Performance Computing, Networking, Storage, and Analysis, IEEE Press, 2018.
  • G.Perdue, A.Ghosh, M.Wospakrik, F.Akbar, D.Andrade, M.Ascencio, L.Bellantoni, A.Bercellie, M. Betancourt, G. C. Vera, et al., “Reducing model bias in a deep learning classifier using domainadversarial neural networks in the minerva experiment,” Journal of Instrumentation, 2018.
  • C. Stahl, S. Young, D. Herrmannova, R. Patton, J. Wells, “DeepPDF: A deep learning approach to extracting text from PDFs,” Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC), 2018.
  • J. Liu, F. Spedalieri, K. Yao, T. Potok, C. Schuman, S. Young, R. Patton, G. Rose, G. Chakma, “Adiabatic quantum computation applied to deep learning networks,” Entropy, 2018.
  • S. Young, A. Maksov, M. Ziatdinov, Y. Cao, M. Burch, J. Balachandran, L. Li, S. Somnath, R. Patton, S. Kanlin, R. Vasudevan, “Data mining for better material synthesis: The case of pulsed laser deposition of complex oxides,” Journal of Applied Physics, 2018.
  • S. Young, D. Rose, T. Johnston, W. Heller, T. Karnowski, T. Potok, R. Patton, G. Perdue, J. Miller, “Evolving deep networks using HPC,” Proceedings of the 3rd Workshop on Machine Learning in HPC Environments, 2017.
  • T. Johnston, S. Young, D. Hughes, R. Patton, D. White, “Optimizing convolutional neural networks for cloud detection,” Proceedings of the 3rd Workshop on Machine Learning in HPC Environments, 2017.
  • C. Schuman, T. Potok, S. Young, R. Patton, G. Perdue, G. Chakma, A. Wyer, G. Rose, “Neuromorphic computing for temporal scientific data classification,” Proceedings of the Neuromorphic Computing Symposium, 2017.
  • 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.

Education

University of Tennessee, Knoxville

Major: Computer Engineering, PhD
Graduation Date: December, 2014
GPA: 3.81

University of Tennessee, Knoxville

Major: Electrical Engineering, BS
Graduation Date: May, 2010
GPA: 3.88

Academic Service

University of Tennessee - Bredesen Center, Knoxville, TN (Jan. 2018 - Present)

Joint Faculty

  • Teaching various courses on data science, machine learning, and computing
  • Providing input for the admissions process for the Data Science and Engineering program
  • Advising a PhD Student

Experience

ORNL - Computational Data Analytics, Oak Ridge, TN (Dec. 2016 - Present)

Research Scientist

  • 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)

Research Assistant

  • 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)

Teaching Assistant

  • 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)

Intern

  • 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)

Co-op

  • 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)

Co-op

  • Built simulations in Matlab and Simulink
  • Modified process for testing a product

Honors

  • ACM Gordon Bell Prize Finalist (June 2018)
  • 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)

Professional Service

  • Frontiers in Big Data: Big Data and AI in High Energy Physics -Associate Editor (2019 – Present)
  • Machine Learning in HPC Environments (Supercomputing Workshop)
    • Program Committee (2019 – Present)
    • Organizing Chair (2017 – 2018)
    • Program Chair (2015 – 2016)
  • OLCF Director’s Discretion Projects
    • Proposal Reviewer - Deep Learning Proposals (2017 – Present)
  • AAAI Conference on Artificial Intelligence
    • Program Committee (2020)
  • High Performance Machine Learning Workshop
    • Workshop Committee (2019)
  • ACM Journal on Emerging Technologies in Computing Systems (JETC)
    • Reviewer (2018)
  • IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
    • Reviewer (2017 - 2020)
  • International Joint Conference on Neural Networks (IJCNN)
    • Reviewer (2015 – 2016)
  • DOE ASCR Cyber Security Workshop
    • Workshop Participant - Knowledge and Analytics Topic (2015)

Professional Organizations

  • ACM - Member
  • Eta Kappa Nu – Beta Phi Chapter
    • Vice President (2009-2010)
    • Corresponding Secretary (2010-2012)
  • IEEE – Member
  • Tau Beta Pi - Member