Computer Vision Engineer, 11/2017 to Current Verizon Communications – Bridgeville, PA,
Building detection and classification models using Python, Tensorflow, Keras, scikit-learn to help auto-generate tags for visual assets
Constructing data ingestion pipeline, implementing APIs, and building model deployment framework to add deep-learning generated intelligence to our system
Deep Learning Engineer, 08/2017 to 11/2017 360Pi – Indianapolis, IN,
Using a distributed Deep Learning framework for Apache Spark, BigDL, to train an Inception V3 CNN and classify 2.4 million images for the World Bank to detect fraud amongst crowdsourced images
Doing feature engineering, image pre-processing, transfer learning, and fine-tuning of pre-existing ImageNet model from Caffe using inception architecture
Published a customized Amazon Machine Image for deep learning in Spark to launch EC2 instances for educational purposes
Speaker at multiple workshops/meetups including Intel AI DevJam Night in SF (video link: https://www.youtube.com/watch?v=KzPWkY6Ng6M&feature=youtu.be)
Project - Netflix Movie Recommendation System, 06/2017 to 06/2017 Syllable – , ,
Engineered dataset for students' opinions on Netflix movies to learn a Naïve Bayes Model for classifying students to a moviegoer type
Used EM algorithm for binary matrix completion, then fine-tuned parameters used to recommend personalized,
best unseen movies to each student
Software Engineer, 02/2017 to 05/2017 Tijuana Red Cross – City, ,
Developed web-application which connects dispatchers with ambulance locations in real time, part of an end-to-end emergency dispatch system deploying in Red Cross of Tijuana, Mexico.
Implementing MQTT protocol, REST APIs, serializations, designing schema, improving data visualization using Python/Django and designing algorithm to route ambulances more efficiently and reduce response times.
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