John Hawkins

About John Hawkins

Currently leading applied research efforts in synthetic data generation to support the development of robust 3D and multi-image deep learning based computer vision algorithms that operate in the non-visible light spectrum (x-ray, computed tomography, millimeter-wave radar). Other research efforts include applications of unsupervised anomaly detection, clustering and classification algorithms in hyperspectral imaging for detection of trace explosives, machine learning approaches to accelerating computationally expensive physics based simulations, and a variety of predictive geospatial intelligence tools using multi-spectral, synthetic aperture radar and lidar satellite data.Experienced in leading engineering teams, developing technology road maps, writing proposals, communicating with partners, customers and relevant stakeholders, and executing on program and technical objectives with general autonomy.Interested in designing novel solutions to difficult problems utilizing Machine Learning, Deep Learning, Computer Vision, Natural Language Processing and Artificial Intelligence techniques.

Experience

1. Machine Learning Engineer

   Synthetik

   Oct 2020 — Present

   Researching and developing solutions to environmental and global security problems at the intersection of deep learning and computer vision.

Education

• University of California, Berkeley

  Bachelor of Science - BS, Mechanical Engineering

• UC San Diego

  Master of Science - MS, Mechanical Engineering

• Georgia Institute of Technology

  Master of Science - MS, Computer Science

Skills

• Nastran
• Stress Analysis
• Composites
• Abaqus
• Hypermesh
• Finite Element Analysis
• Structural Analysis
• Femap
• Aerospace
• Fatigue Analysis
• Random Vibration Analysis
• Buckling Analysis
• Composite Structures
• Matlab
• Visual Basic
• Fe-Safe
• Hypersizer
• Structural Testing
• Ptc Creo
• Solidworks
• C
• C++
• Siemens Nx
• Python
• Numpy
• Scipy
• Pandas
• Matplotlib