Ashley Batchelor

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


University of Washington, Department of Physics – 2018 to 2019
Research, Large Hadron Collider, Switzerland

Machine Learning – Created and tuned 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 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 – 2018 to present

Recognizing metal manufacturing defects in machine vision inspection images using models based on Naïve Bayes Classification coupled with Wavelet Analysis, and models using Deep Neural Networks and Convolutional Neural Networks
Recognizing integrated circuit lead leg defects with Naïve Bayes Classification and Wavelet Analysis of microscope images (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 –2017 to 2019

Patent Agent / Patent Support Engineer – 2006 to 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%

·         M.S. Physics, University of Washington, 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)

·         Data Science Certification, Deep Learning Specialization: Deep Learning, Hyperparameter Tuning, Convolutional Neural Networks, Residual Networks, Natural Language Processing

Udemy, Python for Data Science and Machine Learning: Scikitlearn, Regression, K Nearest Neighbors, SVM, K-Means Clustering, PCA, NLP, Neural Nets, Tensorflow

Coursera, Digital Image and Video Processing: Spectral Filtering, K-Means Clustering, Motion Estimation

·         B.S. Physics with Departmental Honors, University of Washington; Mary Gates Scholar, awarded merit-based scholarship for research with Low Temperature Physics Lab


·         Search and Rescue responder for Everett Mountain Rescue for nine years

·         Rock and glacier climbing instructor with Everett Mountaineers Climbing Program

·         Solo international and domestic travel, including remote regions in the Canadian and US Arctic

  • Updated 3 years ago

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