Joseph Ni

About

Hello!

I'm Joe. I like solving problems. I just received my Bachelors in Computer Science from the Georgia Institute of Technology. In addition to my coursework, I have co-oped with Georgia Tech Research Institute's Robotics and Autonomous Systems Division and interned for Amazon's Robotics AI group. I look forward to applying my skills to solve real world problems that improve peoples' quality of life.

Updated 8/9/2022

Resume Summary

Contained here is a compressed version of my full resume, which can be found here.

Intro

Joseph Ni

Adaptable software engineer with experience working on everything ranging from ML infrastructure backends (Python) to novel algorithm design and implementation for unmammed autonomous aircraft (C++) to front end web and GUI design (HTML/CSS/JS/C#).

Education

Bachelor of Science in Computer Science

2018 - 2022

Georgia Institute of Technology, Atlanta, GA

Course concentrations in Intelligence and Devices

GPA: 3.73

Professional Experience

Incoming Software Development Engineer- Amazon Robotics AI

August 2022
  • Exciting things to come!

Software Development Engineer Intern - Amazon Robotics AI

May 2021 - August 2021
  • Worked on a continual learning framework aiming to ease the process of productionizing machine learning models deployed on thousands of workcells across Amazon fulfillment centers.
  • Completed the framework’s missing issue detection step by building a modular, Python AWS-native monitoring and alarming component that parses and computes over model output data and triggers user defined alarms for when model intervention or retraining is needed.
  • Saved framework users annotation costs and developer time on implementing their own monitoring and alarming service. Service deployed as of January 2022.

Software Developer Co-op - GTRI

2019 - 2021
  • Developed tile-based geographical discretization algorithm for GTRI unmanned aircraft flying ISR (search and reconnaissance) missions over complex polygonal geographical areas. Work was field tested in Fall of 2021 to satisfaction of Office of Naval Research project sponsors.
  • Implemented novel zamboni path planning, reducing search path lengths and airtime wasted during ISR missions by GTRI aircraft.
  • Prototyped coarse-grid exploration strategy for edge learning drone swarming project, increasing searched space and accelerating casualty drop off.
  • Conducted ticket-based development for GTRI's in house C# UAV operator interface program, improving user quality of life and effectiveness during field testing.

Contact

Call:

+1 678 907 0550