Career Profile

Hi, I’m a software engineer interested in anything AI & ML related. I have recent experience working with Airflow and standing up data and model update pipelines. Have also taken various courses at Stanford and earned a few graduate certificates along the way.

Experiences

Software Engineer, ML

2021 - 2022
DefenseStorm, Seattle
  • Established Airflow orchestration for updating machine learning models and periodic tasks
  • Created daily model updates for similarity recommendations with Airflow and Docker
  • Simplified alert and trigger management to assist in alert triage and incident response

Software Engineer

2010 - 2017
Check Point Software Technologies, San Carlos
  • Adapted network namespaces in Linux cgroups for use in acceleration layer virtualization
  • Virtualized software acceleration layer for the security appliance
  • Authored OS configuration pages for network management protocols (SNMP, VRRP, IGMP, PIM)

Education

Databases Graduate Certificate

2020 - 2021
Stanford

Studied relational systems, query languages, and system implementations.

  • CS145 - Data Management and Data Systems
  • CS245 - Principles of Data-Intensive Systems

Artificial Intelligence Graduate Certificate

2018 - 2019
Stanford

Covered deep learning and worked on projects with multi-modal architectures.

  • CS221 - Artificial Intelligence: Principles and Techniques
  • CS224N - Natural Language Processing with Deep Learning
  • CS231N - Convolutional Neural Networks for Visual Recognition

Mining Massive Datasets Graduate Certificate

2016 - 2017
Stanford

Learned machine learning techniques, social networks, and search algorithms.

  • CS224W - Analysis of Networks
  • CS229 - Machine Learning
  • CS246 - Mining Massive Data Sets
  • CS276 - Information Retrieval and Web Search

Data Mining and Applications Graduate Certificate

2015 - 2016
Stanford

Studied the theory behind most data science models.

  • STATS202 - Data Mining and Analysis
  • STATS216V - Introduction to Statistical Learning
  • STATS315B - Modern Applied Statistics: Data Mining

Projects

Interesting projects from Insight's Data Engineering fellowship and graduate courses at Stanford

Fast Fashion Recognition - Streaming image classification with Inception Neural Network using Kafka, Spark, and Cassandra
Network Anomaly Detection with GCNs and GATs - Graph Convolutional Networks and Graph Attention Networks to detect fake Twitter users
Pix2Code - Automatic Code Generation from UI Screenshots
Attention on Attention - Attention Architectures for Visual Question Answering (VQA)
Bitcoin Trust Networks - Network Analysis of Weighted, Signed Bitcoin Networks
Deep Q-Learning with RNNs - Attention Mechanisms in DQN/DRQNs for Atari Games

Publications

Skills & Proficiency

Python

Java

Javascript

C