Tom Shafer, Ph.D.
Principal Data Scientist
Elder Research, Inc.
Raleigh, NC
contact-at-tshafer
GitHub, Google Scholar, LinkedIn
Serving as a Principal Data Scientist, I get to mentor other data
scientists, study interesting problems, and contribute
technically to help our clients. Over the last few years, I've
led and contributed to major projects applying Bayesian modeling,
graph analytics, deep learning for computer vision, and much
more.
Skills in Brief
Programming Languages
- Python, R, Bash, Stan, SQL, Make, Fortran, HTML, CSS (Proficient)
- Julia, C, Perl, Ruby (Occasional use)
Frameworks and Tools
- PyTorch, Keras, FAIR’s Detectron2 (Neural networks & deep learning)
- Stan, RStan, CmdStanPy, CmdStanR (Stan probabilistic programming language)
- Gensim, Numba, Numpy, Pandas, Scikit-Learn (Python tools)
- R data.table, Tidyverse, Tidymodels, R Shiny (R tools)
- MLFlow, Tensorboard (Model monitoring)
- Docker, GitLab CI/CD, GitHub Actions (CI/CD and deployment)
- Git, Subversion, and other version control systems (Collaborative code)
Platforms
- EC2, S3, Batch, ParallelCluster (Amazon Web Services)
- Azure Cognitive Services, Azure ML Studio, Databricks (Microsoft Azure)
- macOS, Unix, Windows (Operating systems)
Publications, Talks, and Writing
Publications
- Shahid, M. B. et al. Uncertainty Quantification using Deep Ensembles for Decision Making in Cyber-Physical Systems. AIAA SCITECH 2024 Forum, 0108 (2024).
- Robison, R. et al. Differential Equation Approximation Using Gradient-Boosted Quantile Regression. AIAA SCITECH 2024 Forum, 0107 (2024).
- Bastaki, M. et al. A chemical structure-based approach for estimating the added levels of flavourings to foods for the purpose of assessing consumer intake. Food Additives & Contaminants: Part A (2020).
- Kingi, H. et al. A numerical evaluation of the accuracy of influence maximization algorithms. Social Network Analysis and Mining 10, 1–10 (2020).
- Shafer, T. et al. decay of deformed -process nuclei near and , including odd- and odd-odd nuclei, with the Skyrme finite-amplitude method. Phys. Rev. C 94, 055802 (2016).
- Shafer, T. Calculation of beta-decay rates in heavy deformed nuclei and implications for the astrophysical r process. (2016).
- Mustonen, M. T., Shafer, T., Zenginerler, Z. & Engel, J. Finite-amplitude method for charge-changing transitions in axially deformed nuclei. Phys. Rev. C 90, 024308 (2014).
Talks
- Shafer T. Stay at Home, or at Least Tread Lightly: Using County-Level Data to Study the Effectiveness of COVID-19 Policy. Data Science Conference on COVID-19 (2020).
- Elder Research. Using R and AWS for Random Forests. Research Triangle Analysts (2017).
Writing
- Shafer, T. Policy impact on COVID-19 spread. Elder Research Blog (September 4, 2020).
- Shafer, T. The 42 V’s of big data and data science. Elder Research Blog (April 1, 2017).
Education
Ph.D., Physics The University of North Carolina at Chapel Hill (2009–16) Advisor: Jonathan Engel Thesis: Calculation of Beta-decay Rates in Heavy Deformed Nuclei and Implications for the Astrophysical r Process
B.S., Physics and B.A., Mathematics The University of North Carolina at Wilmington (2005–09) University Honors with Honors in Physics