Responsibilities：Engaged in the development of the company's key project "Music Therapy Platform," responsible for algorithm development, data analysis, and data visualization development tasks.• Algorithm Development: Based on the product planning and design of the company's "Music Therapy Platform," developed bioelectrical signal processing and adaptive music visualization tools using DSP as the foundation, utilizing technologies such as Python. Completed bioelectrical signal data collection, deep learning applications, and utilized artificial intelligence technology to create music compositions.• Data Analysis: Analyzed and trained bioelectrical signal data, completed algorithm maturity analysis, and machine training effectiveness analysis.• Web Application: Designed and implemented Python code using the Django framework, creating scalable high-performance web applications.• Performance Optimization: Identified and resolved issues with code inefficiency, diagnosed and fixed software errors, effectively improving the overall efficiency of the application by 10%, and achieving 100% error-free application.• System Security: Researched platform security measures, practiced system security standards, and effectively prevented potential security vulnerabilities.• Team Collaboration: Engaged in friendly communication with 5 development team members, effectively explored potential user needs through timely and effective communication, and promoted smooth progress in algorithm development and platform construction through good technical communication and cooperation.

Project Description: Utilized DSP, neural networks, and machine learning technologies to deeply learn from bioelectrical signal data, achieving detection of different cognitive states and using artificial intelligence to generate music.Technology Platform: DSP, neural networks, machine learning, Python.Project Responsibilities: As the project leader, responsible for project planning, technical solutions, data collection, data modeling, code development, and completion of project reports and demonstrations. Main steps included: Obtained bioelectrical signal data from Princeton University for data organization and modeling. Used bioelectrical signals for detecting different cognitive states, including skin conductance, heart rate, and respiration. Applied deep learning to bioelectrical signals, using the collected data to create music generated by artificial intelligence. Utilized generated music as a means to normalize individual bioelectrical signals. Completed all technical documents for the project, including code, reports, and presentation materials.

Responsibilities：During the pandemic, assisted professors in providing post-teaching student guidance at the college, utilizing principles of statistics and data analysis techniques to aid students in thesis formatting consultations, thesis proofreading, and plagiarism checking.• Data Analysis: Established relevant data models in RStudio, utilized Greenhouse-Geisser and Huynh-Feldt corrections to enhance data pattern evaluation, conducted Mauchly's sphericity test. Utilized three-factor analysis of variance method to validate the robustness of results, ensuring the reliability of statistical analysis. Completed visualization and analysis of data patterns, identifying key trends and insights.• Thesis Proofreading: Through data analysis, detected and corrected errors and duplications in thesis reports, enhancing the reliability of data interpretation and decision-making processes.• Work Achievements: During the tenure, assisted dozens of students in successfully completing thesis checks, effectively improving the quality of college theses.