Summary

  • Qinghao Li is pursuing an MSc in Data Science and AI at the University of Liverpool, UK(GPA is expected over 70%).
  • Holds a BSc in Electronic and Electrical Engineering from the University of Strathclyde, UK.
  • Completed a BSc in Electric Automation at Lanzhou University of Technology, China, graduating in the top 20% of the class.
  • Involved in writing two academic article on autonomous driving with PhD students from the University of Glasgow(Co-first author).
  • Co-inventor of a utility model patent for a quick turnover tooling for heavy plates(During undergraduate).
  • Developed context-aware machine learning models for energy disaggregation in smart homes/buildings as part of an undergraduate graduation project.
  • Worked as a Technical Consultant at Jiayuguan Keding Labor Service Co., Ltd., participating in the design of tooling for paint roller frames and anti-corrosion tooling for water pipelines.
  • Awarded a scholarship at Lanzhou University of Technology for being in the top 20% of the major.
  • Enjoys playing guitar, piano, and drums, and has experience as a resident singer in China.

Years of Experience

1

Residence

UK

Graduation Year

2023

Age

23

Title

Technical Consultant

Marital status

singer

Birth Date

2001/05/18

Graduated From

University of Strathclyde, UK

Nationality

china

Gender

male

Graduated From

Lanzhou University of Technology, China

Projects

Undergraduate Graduation Project

Project Developer

March 2023 - March 2023

The aim of this project is to develop context-aware machine learning models for energy disaggregation in smart homes/buildings. Energy disaggregation, also referred to as non-intrusive load monitoring (NILM), is a set of techniques used to break down the aggregate power consumption into contributions of individual appliances. The state-of-the-art NILM algorithms achieve good performance in disaggregation of individual appliances, but there are still many challenges. The algorithms are usually developed for certain houses/buildings, and performance decreases when transferring to new houses. In addition, the appliances change over time – that is, they start to malfunction, are often replaced with new ones. For some appliances, for example, washing machines, power consumption depends on the load of laundry inside it; some appliances, like toasters, ovens, and microwaves, have different settings. All of this impacts the performance of already trained NILM algorithms – their performance usually reduced, compared with the performance in the original conditions. By including context information (which is getting more and more attention recently) and updating NILM algorithms when needed, they could retain good performance even if the context in which they perform.

Public journals in the field of autonomous driving

Academic Contributor

September 2024 - September 2024

I am currently involved in the writing of an academic article. Two PhD students from University of Glasgow are responsible for the main content.

Patent

Inventor

May 2022 - May 2022

Patent name: a kind of thick plate quick turnover tooling. The utility model relates to a heavy plate quick turning tooling, including a clamping groove, the groove bottom of the clamping groove is provided with a fixed ear, the two sides of the groove wall close to the upper edge of the left and right sides of the corresponding position is connected with a screw through a screw hole; The end of the jacking screw is provided with a lining plate, the long side length of the lining plate is less than the groove wall width of the slot parts; One side of the groove wall is externally fixed with a horseshoe shackle, and the other side of the groove wall is externally fixed with a tensioning round roller, and the tensioning round roller is triangular distribution; The horseshoe shackle is arranged in the middle position of the groove wall of the clamping groove. The inner shackle is used to fix the sling, and the outer shackle is used to fix the lifting ring. The tensioning round roller is provided with a fixed roller shaft, and both ends of the fixed roller are provided with a fixed block. The utility model makes the plate turn over conveniently, the lifting is stable, the rope is not easily knotted, and the safety accident is avoided.

Skills

  • Technical Skills

    Data Science

    AI

    Machine Learning

    Data Mining

    Data Visualization

    Programming

    Database Management

    Electrical Engineering

    Electronic Design

    Instrumentation

    Microcontrollers

  • Soft Skills

    Teamwork

    Communication

    Problem Solving

    Innovation

    Project Management

  • Languages

    English

    Chinese

Languages

Language

Chinese

Mother Tongue

Language

English

Good