Hi(你好), my name is Run Zhang(张润). Welcome to my page!

Short Biography

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I think of myself first and foremost as a software engineer. Over the years, I've gotten quite a bit of experience coding in C/C++ and Python to put algorithms into practice and work with data across a range of fields. That includes some cool projects using deep learning to tackle real-world challenges. These days, I'm broadening my horizons and picking up a wider variety of tech skills, from full-stack development, databases to Cloud computing, so I can grow into a more versatile and accomplished engineer. Besides, I am also learning CUDA to accelerate programs with data parallelism. I'm driven by working on projects that are both impactful and intellectually stimulating.

- Run Zhang

Education

  • MS Information Systems
    New York University
    September 2023 - May 2025(Expected)
  • MSc Artificial Intelligence
    University of Southampton
    September 2019 - October 2020
  • Bachelor Software Engineering
    Guangzhou University
    September 2015 - July 2019

Professional Experience

  • Software Engineer OD
    Huawei Technologies Co., Ltd
    March 2023 - August 2023
  • Software Engineer
    Autowise.ai
    August 2021 - November 2022
  • Intern
    AIIT-PKU
    November 2020 - June 2021
Projects

A ChatGPT-powered AI tool for refining English writing

This full-stack English polishing tool utilizes Spring Boot for RESTful API development and integration. The front-end is crafted with React and styled using Tailwind CSS. Deployed on Amazon Web Services (AWS), the project uses AWS S3 for persistent storage. The ChatGPT API serves as the primary engine powering the polishing algorithm.

Autonomous Surface Vehicle Controller

My Master's dissertation focused on implementing Evolutionary Algorithms to develop a vehicle controller, which selected actions based on the current state of the environment or the vehicle itself. Additionally, the project explored deep reinforcement learning and compared the two approaches.

Text and tabular data processing with regular expression and NLP techniques.

During my first internship, I worked on extracting information from given text and tabular data. It required developing algorithms to identify and allocate relevant information into the database entries, transforming unstructured data into structured data.

Research on Graph Attention Networks and automatical Grap generation on text data, with LSTM.

While working with text data, I realized that structured data is commonly represented using relational graphs, where two nodes are connected if they are related. This observation naturally led to the idea of exploring whether deep learning models can be trained to generate these relational graphs.

3D LiDAR point cloud semantic and noise point recoginition

LiDAR is a crucial component of the perception system in autonomous vehicles. It generates point cloud data by detecting reflected light. The perception module then analyze this data to identify objects and filter out noise points to support safe driving.

Predict the future semantci masks of moving objects.

In the project for the deep learning course at NYU, I work with two classmates to implement SimVP open-source model on a video semantic prediction task .