Yuyu Ren

Research on Detecting the Topics Variation ofOnline Customers by Application of Latent DirichletAllocation and Word Embedding in NLP Supervisor: Yi Feng, Associate Professor, Department ofEconomics and Management, University ofElectronic Science and Technology of China Research on the Assurance ofSystematic Mapping Studies in Software Engineering Objectives: Learn about assurance of systematic mapping studies, and implement application and tool for Goal Structural Notation Research in PSPP-of-Materials Research Group Learning Supervisor: Solomon Boakye-Yiadom, Associate Professor, Department ofMechanical Engineering, York

Researcher

Remote   May. 2022 – Present

Objectives: To find the customers’ attitudes’ variation towards products in Amazon

Responsibilities: 1. data pre-process and classification ofthe product reviews

2. Use LDA(Latent Dirichlet Allocation) model to generate topics and key words ofproducts

3. Use Word Embedding to detect the topic variation

4. Use Statistical model to analysis and generate the customers ’ attitudes ’variation

Achievements: Successfully detected the customers ’ attitudes ’ variation towards apple mac pro and mac air with different attitudes (positive and negative) and did an analysis with comparison. The paper for this research have been done half.

Toronto, Canada

Researcher Jan. 2022 – May. 2022

Supervisor: Alvine Boaye Belle, Assistant Professor, Department ofElectrical Engineering and Computer Science, York University

Responsibilities: Communicated with Professor Alvine Boaye Belle, who comes from York University, selected the research method, conducted the research, reviewed the literature, recorded the research results, and drew conclusions

Review literature to study assurance of systematic mapping studies

Developed software and finished project design and working schedule

Achievements: Constructed plug-in application for the Goal Structural Notation, learned the theory and application ofassurance of systematic mapping, and learned a lot about the areas in computer engineering

R1. Defect Detection During Metal 3D printing   Using Supervised Machine May. 2022 - present

R2. Materials Discovery Using Machine Learning (high entropy alloys) Researcher

University

Objective: Detect the defection in 3D printing with laser and apply it as a factor in the research of advanced material.

Responsibilities: Reviewed the literature, prepared for presentation, and used neural network for the research of advanced material, Build machine learning algorithm and GUI.

Achievements: Gained experience in Computer Vision and improved skills in Neural Network building, data processing, and programming