Everett Rush

PhD Student, Computer Science
Department of Electrical Engineering and Computer Science
Tickle College of Engineering
University of Tennessee

Github: rusheniii
Orcid: 0000-0002-5632-5723

Email: erush3 "at" vols.utk.edu


I have a passion for developing high quality software that utilize machine learning. I help build efficient, reliable, and scalable systems for moving, managing, analyzing, and modelling big data. I have experience with DevOps and DataOps taking software and statistical models through their entire life cycle. I work well in an environment with agile development practices and continuous integration. I am used to working in a secure environment with strict security guidelines.


I am able to handle the five V's of big data. I have training and experience in technologies that support my engineering role at the intersection of big data and HPC in a modern data center.


Amazon Seattle,WA

Software Development Engineer June 2022 to present

Oak Ridge National Laboratory Oak Ridge

Data Engineer Aug. 2017 to June 2022





  1. Jun Wen et al. "Multimodal representation learning for predicting molecule-disease relations". In: Bioinformatics 10.1093/bioinformatics/btad085

  2. Doudou Zhou et al. "Multiview Incomplete Knowledge Graph Integration with application to cross-institutional EHR data harmonization”. In: Journal of Biomedical Informatics 10.1038/s41746-021-00519-z

  3. Chuan Hong et al. "Clinical knowledge extraction via sparse embedding regression (Keser) with multi-center large scale electronic health record data”. en. In: npj Digital Medicine 4.1 (Oct. 2021), pp. 1–11. ISSN: 2398-6352. DOI: 10.1038/s41746-021-00519-z

  4. Everett Rush et al. "JSONize: A Scalable Machine Learning Pipeline to Model Medical Notes as Semi-structured Documents".

    In: 2020 AMIA Technology Summit, Houston, TX, USA

  5. Kathryn E Knight et al. "Standardized Architecture for a Mega-Biobank Phenomic Library: The Million Veteran Program (MVP)" in: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science 2020 (2020), 326—334. ISSN: 2153-4063. URL: https://europepmc.org/articles/PMC7233040

  6. Everett Rush et al. "Characterizing Sub-Cohorts via Data Normalization and Representation Learning." In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). 2020, pp. 173–176. DOI: 10.1109/CBMS49503.2020.00040

  7. Benjamin Mayer et al. "Evaluating Text Analytic Frameworks for Mental Health Surveillance". In: 34th IEEE International Conference on Data Engineering Workshops, ICDE Workshops 2018, Paris, France, April 16-20, 2018. 2018, pp. 39–47. DOI: 10 . 1109/ICDEW.2018.00014. URL: https://doi.org/10.1109/ICDEW.2018.00014

  8. E. Tomes, E. N. Rush, and N. Altiparmak. "Towards Adaptive Parallel Storage Systems". In: IEEE Transactions on Computers

    67.12 (2018), pp. 1840–1848

  9. Everett Neil Rush et al. "Dynamic Data Layout Optimization for High Performance Parallel I/O". in: 23rd IEEE International Conference on High Performance Computing, HiPC 2016, Hyderabad, India, December 19-22, 2016. 2016, pp. 132–141. DOI: 10.1109/HiPC.2016.024

  10. Everett Neil Rush and Nihat Altiparmak. "Exploiting Replication for Energy Efficiency of Heterogeneous Storage Systems". In:

    24th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MAS-

    COTS 2016, London, United Kingdom, September 19-21, 2016. 2016, pp. 79–84

  11. Ranjeet Devarakonda et al. "Next-gen tools for big scientific data: ARM data center example". In: 2016 IEEE International Conference on Big Data, BigData 2016, Washington DC, USA, December 5-8, 2016. 2016, pp. 3968–3970. DOI: 10 . 1109 / BigData.2016.7841078

  12. Giri Prakash et al. "HPC infrastructure to support the next-generation ARM facility data operations". In: 2016 IEEE International Conference on Big Data, BigData 2016, Washington DC, USA, December 5-8, 2016. 2016, pp. 4026–4028. DOI: 10 . 1109 / BigData.2016.7841098