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About⌗

About

Hi there! My name is Justin and I am currently a Software Engineer at Zoox. My interests are focused in embedded systems, machine learning, and computer architecture.

Outside of software development and engineering, I love spending time outdoors, vibing to 80’s jazz fusion music on my AT turntable, and cooking all kinds of international dishes!

Github • Linkedin • Twitter • Instagram

Skills⌗

Skills
  • Python and packages such as Numpy, Pandas, PyTorch, Keras, and Sklearn
  • C/C++ for desktop and embedded applications with experience with libraries such as OpenCV, Boost, gRPC, ProtoBuf
  • Proficient with machine learning techniques and libraries. Experience working with computer vision and speech recognition
  • Hardware design PCB layout in Allegro and Altium
  • Experience with embedded systems, particularly: ultra-low power systems, interconnect protocols, network protocols, and RF design

View all my projects and experiences by tag to find something more specific.

Experience⌗

Lawrence Livermore National Lab

Sep 2018 - Present :: Software Developer Part-time
#python  #c/c++  #image processing  #OpenCV  #DevOps  #databases  Lawrence Livermore National Lab
I originally interned at LLNL over the summer of 2017, and joined part-time during university to support the software side of the project longer-term. I am continuing to develop vision algorithms for scientific measurements, but I am also working on streamlining our team’s workflow by using the latest workflow automation tools like Dagster.

Projects⌗

Evaluation of ASR in Musical Environments

Sep 2020 - Dec 2020 :: Speech Recognition and Understanding Semester Project (CMU 18-781)
#python  #machine learning  #deep learning  #speech recognition  Evaluation of ASR in Musical Environments
ASR is used heavily in eyes-busy or hands-busy situations and often time the user may be speaking over noise. My peers and I are particularly interested in how music effects ASR decoding. We use several music datasets of varied genre or broken-down instrumentation to allow us to perform in-depth anaysis of how different aspects of music influences speech recognizer’s performance. We then train a new model from what we have learned to see if we could improve the original model’s performance.

Education⌗

Carnegie Mellon University⌗

M.S. Electrical and Computer Engineering • May 2020 - May 2021

  • Low Power code for IOT: Theory behind designing software (especially RTOS) to take advantage of “dark silicon” on embedded hardware to minimize energy use, thereby maximizing battery life.
  • Speech Recognition and Understanding: Study how speech recognition systems model human speech using HMM+GMM, HMM+DNN, and end-to-end models. Implement simple HMM+GMM word recognizer and Listen, Attend, Spell (LAS) speech recognizer.
  • Machine Learning for Signal Processing: Theory behind how machine learning can be applied to signal processing applications. Investigation of data driven signal processing approaches and parallels with machine learning techniques.
  • Image and Video Processing: Theory and implementation of many popular image processing methods with particular emphasis on sparse approximations and linear inverse problems
  • Machine Learning: Theory and implementation of various supervised and unsupervised machine learning algorithms.

California Polytechnic State University⌗

B.S. Computer Engineering • Sep 2016 - Jun 2020

  • Computer Architecture: Introductory computer architecture, ARM RISC instruction set, computing pipelines
  • Implementation of Operating Systems: Implemented x86-64 operating system kernel from scratch
  • Real Time Embedded Systems: Theory of SIMD and parallelized processing in embedded systems with implementation of parallelized processing on Digilent Zybo using ARM Linux and FPGA co-processor
  • Computer Networks: Introductory computer networking covering OSI model, 802.3, TCP/IP, UDP, network switching/routing, and basic network security on Cisco appliances
  • Capstone Project: Intelligent Sensing and Navigation System for NGCP (Northrop Grumman Collaboration Project) Unmanned Ground Vehicle
  • Computer Vision: Introductory course on computer vision algorithms and their theory of operation building on simple operations like edge detection to full feature detection with modern algorithms like SIFT/SURF and neural networks
  • Senior Project - To be published

Contact⌗

Feel free to reach out to me via these platforms: