Resume

CORE COMPETENCIES

Python, MATLAB, C++, SQL, Quantitative Data Analysis, Supervised and Unsupervised Machine Learning, Applied Math, Computer Vision, Image Processing, Data Preparation, Data Processing, Data Modeling

EXPERIENCE

University of Washington Department of Physics

Supervised Research, Large Hadron Collider / ATLAS Group – September 2018 to present

  • Machine Learning – Created and tuned proof of concept decision tree models to separate signal data from background data in search for long lived exotic particles from Higgs Boson decays, using AdaBoost algorithms with cuts based on Fisher discriminants
  • Wrote C++ code to process data from Monte Carlo simulations including complex linear algebra calculations with sets of data ~1 GB in order to provide variables for machine learning
  • Created Monte Carlo simulations of particle collisions and used three types of clustering algorithms to classify particle trajectories into jets

Machine Learning Projects

  • Recognizing metal manufacturing defects in machine vision inspection images using models based on Naïve Bayes Classification coupled with Wavelet Analysis, and models using Neural Networks
  • Recognizing integrated circuit lead leg defects with Naïve Bayes Classification and Wavelet Analysis of microscope image (Kaggle)
  • Neural Networks for modeling and prediction of dynamic systems
  • Recognizing genres and artists of music with Naïve Bayes Classification and Support Vector Machines

Micro Encoder, Seattle / Mitutoyo, Japan

Intellectual Property Manager / Patent Agent – September 2017 to February 2019

Patent Agent / Patent Support Engineer – January 2006 to September 2017

  • Led patent team and managed the in-house portfolio consisting of 300+ patents for a variety of in-house technology including machine vision, image processing, machine learning, and optics
  • Researched and analyzed competitive intelligence in the metrology/precision measurement industry
  • Worked iteratively on new patent applications with 40 engineers and scientists around the globe, collaboratively drafting > 50 patents, increasing throughput of patents filed by almost 30%

EDUCATION

  • University of Washington, Seattle, WA. M.S. Physics student, graduating August 2019

Coursework: Applied Math – Data Analysis (Spectral Analysis, Singular Value Decomposition, Principal Component Analysis, Machine Learning) | Applied Math – High Performance Scientific Computing (Parallel Programming, CUDA, MPI) | Applied Math – Modeling for Complex Systems (Statistical Inference, Model Discovery, Neural Networks)

  • University of Washington, Seattle, WA. B.S. Physics with Departmental Honors
  • Deeplearning.ai, Deep Learning, Hyperparameter Tuning, Convolutional Neural Networks, Residual Networks
  • Udemy, Python for Data Science and Machine Learning Bootcamp: Scikitlearn, Linear Regression, Logistic Regression, K Nearest Neighbors, Support Vector Machines, K-Means Clustering, Principal Components Analysis, Natural Language Processing, PySpark, Neural Nets, Tensorflow
  • Coursera, Digital Image and Video Processing – Spectral Filtering, Segmentation, K-Means Clustering, Motion Estimation