• Details

• Details

• Engineered a prototype using Python Numpy, Pandas, and Scipy libraries. Owned the end-to-end development of the data analysis pipeline, ensuring accuracy and scalability in processing biological and environmental datasets, identifying over 72 types of data events, and powering the lab’s core project.
• Optimized data processing algorithm using sliding windows, accelerating computing overhead by ~1.6x.
• Generated chart reports using Matplotlib, enabling the engineering team to visually identify event periods on a timeline, accelerating the iteration cycle of algorithms from 1 week to 3 days.
• Directed the code version control and documentation within the engineering team, resulting in a well-organized changelog over 78 iterations to ensure transparency and easy tracking of updates.

• Designed and implemented the critical algorithm that enables multiple swarm devices to collaborate.
• Implemented a simulation-based experimental infrastructure that conducts performance and energy evaluation of the proposed technique/system.
• Set up and profiled several neuron network models on low-end edge devices such as NVIDIA Jetson devices.
• Participated in weekly and daily sync-up meetings, worked closely with the research team.

Languages

Python

C++

C#

GoLang

LaTex

HTML5

CSS3

Shell Scripting

Libraries

NumPy

Pandas

scikit-learn

matplotlib

Frameworks and Database

Flask

PyTorch

MySQL

SQLite

React.js

Django

Other

Git

Unity

MatLab

AWS