• Engineer and optimize data pipelines by cleaning, standardizing, and integrating diverse datasets, ensuring high-quality, actionable data for predictive analysis.• Design and deploy machine learning models to uncover critical predictors of chronic absenteeism, driving data-driven insights and delivering a comprehensive report to inform strategic interventions.

• Design lessons using Python, Pandas, Matplotlib, Seaborn for data cleaning, data manipulation, visualization.Tools used: Github, Jupyter Notebook, and Google Colab• Spring 2024: Created two Jupyter notebooks for a community college Chemistry class, focusing on BECO2N air quality data analysis.• Fall 2023: Developed approximately 10 Jupyter notebooks introducing data science that will be deployed for 1 class: Principles of Engineering Economics, and 1 educational project called Berkeley Environmental Air-quality & CO2 Network for high school students.• Spring 2023: Developed 4 Jupyter notebooks introducing data science adopted for UC Berkeley’s Anthropology class with 100+ enrolled students.

• Led the analysis of educational disparities using Python, Pandas, and Google Colab, identifying key factors influencing African American college enrollment and providing actionable insights for educational equity.• Guided a team of four students in sourcing and managing datasets, conducting a comprehensive literature review, and contributing to the development of a curriculum module for future Data Science courses.• Contributed to the creation of data-driven strategies addressing systemic issues in higher education, enhancing support for African American students' college enrollment.

• Led a weekly 2-hour discussion section/lab for Data 8-Foundations of Data Science course that provides academic support for 50+ students.• Course Content: Python Programming, Data Analysis, Data Visualization, Distributions, Probability, Designing Experiments, Hypothesis Testing, A/B Testing, Sampling, Regression, Classification.

• Trained a segmentation model over 500 images that detected smoke using toolbox based on Pytorch, resulting in an approximately 45% increase in mIoU; laid groundwork for further development by subsequent Data Scientist.• Presented a comprehensive final report to a diverse audience of 100+ individuals, including stakeholders, professors, and academic faculties, at a prominent symposium.

• Developed and managed the Mentorship Program for Payroll Facility across 15 medical centers in North California using Human Centered Design method.• Interviewed 11 both tenured and new employees in the Payroll Facility in one month. Analyzed and synthesized data using OneNote to detect the patterns and trends in the answers.• Presented successfully the Mentorship Program to stakeholders and Payroll Facility. • Performed strong communication in business settings using Microsoft Office tools, including Outlook, PowerPoint, Excel, WordDocument, and SharePoint.

• Organized weekly office-hour to assist approximately 5 students per semester in Statistics classwork.• Assisted professor in lecture by answering students questions.• Topics covered: Distributions, Hypothesis Testing, Five-number Statistics Summary, Correlation versus Causation

• Delivered quality service to students in vocational training programs over one year.• Enrolled new students with precise information and useful resources as needed.• Answered over 10 phone calls and a minimum of 5 emails per day• Handled inquiries from students regarding tuition payments, State Board application, and academic reports• Used strong communication skills with instructors, current students, clients, and other faculties to maintain a professional environment.