Hi there! My name is Justin and I am currently a masters student at Carnegie Mellon University (CMU) in the Electrical and Computer Engineering Program (ECE). My interests are focused in embedded systems and machine learning. I am working on several proejcts relating to edge-ML: how we can apply computationally intensive algorithms on resource-constrained embedded and mobile platforms. I am really passionate about collaborating with others to build amazing things, so browse my projects and feel free to reach out!

I am currently looking for full-time opportunities!

Outside of software development and engineering, I love spending time outdoors, vibing to 70’s jazz fusion music on my AT turntable, and cooking all kinds of international dishes!



  • 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.


Lawrence Livermore National Lab

Lawrence Livermore National Lab
This is where I currently work. 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.


Evaluation of ASR in Musical Environments

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.


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


Feel free to reach out to me via these platforms: