Adam Watts

Research And Development Engineer at Los Alamos National Laboratory specializing in data science.

Santa Fe, New Mexico, United States

About

R&D engineer applying data science and machine learning to traditional engineering problems. Experienced in image and time series analysis, functional data analysis, statistical intervals, and directional statistics. Skilled in building end-to-end data pipelines and scalable analytics workflows. Educational background in polymers, fibers, and composites with a strong foundation in numerical methods and uncertainty quantification.

Experience

  • Research And Development Engineer at Los Alamos National Laboratory
    Jun 2020 - Present · 6 yrs 1 mo

  • Graduate Research Assistant at North Carolina State University
    Sep 2018 - Jun 2020 · 1 yr 10 mos

    - Modeling of heat and mass transport through textiles. A coupled system of PDEs are solved taking into account of condensation, evaporation, mass change of fabric due to vapor absorption and heat released due to fiber hydrophilicity. Code originally written in MATLAB but now also in Python. - Measured and analyzed the thermal performance properties of three different chemical protective fabrics. Calculated the maximum, average, and standard deviation of heat flux and energy transported through each replicate using Python. Performed statistical analysis using Tukey multiple comparisons of means to identify which fabric(s) were the most insulative under extreme cold exposure.

  • R&D Graduate Intern at Sandia National Laboratories
    May 2019 - Jul 2019 · 3 mos

    Performed data analysis on the thermal and mechanical properties for the constituent materials used in carbon fiber reinforced polymer composites (CFRPC). Prediction intervals and regression techniques were implemented to generate uncertainty in said properties as a function of temperature. Uncertainty quantification (UQ) and sensitivity analysis were performed using the uncertainties in the thermal and mechanical properties using a mesoscale FEA model to derive the final effective properties for CFRPC. Python scripts were written to help generate CSV files and plot the prediction intervals. Additional python scripts were written to import the CSV files in order to generate and populate the input file for the FEA and UQ software.

  • Researcher at Western Washington University
    Jun 2018 - Aug 2018 · 3 mos

    Applied novel combinatorial isoconversional kinetics towards the repeatability of DSC cure data for neat and catalyzed benzoxazine resins. Cure Kinetics software was written in MATLAB which calculated cure time predictions under isothermal and nonisothermal temperature programs, statistical summaries, and activation energy vs. conversion plots.

  • Sales at REI
    Apr 2014 - Dec 2017 · 3 yrs 9 mos