Curriculum Vitae

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

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

- 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.

Major: Computer Engineering, PhD

Graduation Date: December, 2014

GPA: 3.81

Major: Electrical Engineering, BS

Graduation Date: May, 2010

GPA: 3.88

- 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

- 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

- Developed unsupervised machine learning techniques for analysis of cyber security data
- Developed deep learning techniques for large scale datasets

- Developed deep machine learning algorithms
- Collaborated with analog device researchers to implement deep machine learning algorithms in analog electronics

- Taught lab for freshman level computer science course
- Developed new lab assignments to replace assignments previously taught using outdated software

- 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

- 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

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

- 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)

- 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)

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

- IEEE – Member
- Tau Beta Pi - Member