Tien Tran

• Business Process Analysis and IT Solutions: Conducted requirement gathering and business process analysis using Confluence and Jira to align IT solutions with business goals. Facilitated the successful delivery of the Zalo Mini App through stakeholder collaboration and user acceptance testing (UAT) coordination.
• Testing and Quality Assurance: Implemented comprehensive testing strategies across internal systems, including regression, automation, and API testing. This effort unified web experiences for various business owners, streamlined the checkout process, and increased insurance policy sales. Additionally, enhanced mobile app quality through manual and automated tests on iOS and Android devices.
• Data Visualization and Process Optimization: Developed Power BI visualizations to track defect life cycles and feature completion within an Agile/Scrum development team. Optimized test efficiency by 50% through a Python script that converted customer data from Excel into JSON for Postman API calls, ensuring compliance with policy criteria and accelerating release cycles.

• Data Refinement and Visualization: Refined data from eddy covariance into informative graphs through algorithmic analysis, enhancing the understanding of ecological processes.
• Scalability Solutions: Developed a scalable Python engine to accommodate large meteorological datasets from global weather stations, improving data processing capabilities.
• Algorithmic Analysis: Utilized algorithmic techniques to analyze complex ecological data, contributing to the research and understanding of environmental patterns.

• Code Debugging and Feedback: Offered detailed code debugging assistance, providing feedback to more than 20 students per session. This effort contributed to the conceptual understanding of over 1600 students, enhancing their problem-solving skills and coding principles.
• Conceptual Review and Support: Played a significant role in conducting conceptual reviews, fostering a deeper understanding of data structures and algorithms among students, thereby improving their academic performance in the course.

• Responsible for digitizing, compiling, and analyzing more than 1000 housing inspection reports for the city of San Francisco.
• Presented weekly to a panel of senior inspectors, providing critical data on the state of local housing conditions.
• Developed a keen eye for detail and a deep understanding of the importance of accurate data analysis in informing policy decisions.

• Gaining valuable experience in networking and technical skills in computers through 32 workshops and meetings.
• Demonstrated strong collaboration and project management abilities by working with fellows to plan and execute successful strategies to meet paper and grant deadlines.

• Performing a variety of tasks including answering phone calls, responding to emails, filing documents, managing schedules, and assisting with event planning.
• Demonstrated excellent communication and interpersonal skills while interacting with students, parents, and staff members.
• Gained valuable experience in office administration, time management, and problem-solving, which will be highly transferable to future roles in any industry.

Data Structures, Data Science Principles & Techniques, Discrete Mathematics & Probability Theory, Linear Algebra, Designing Information Devices & Systems, Structure & Interpretation of Computer Programs, Data Inference & Decisions, Concepts of Probability, Blockchain for Developers, Data Mining, Data Engineering.

Pre-Calculus, Trigonometry, Calculus 1, Calculus 2 & Women and Gender Studies

Ethnic Studies

A Crash Course in Causality: Inferring Causal Effects from Observational Data

Unsupervised Learning, Recommenders, Reinforcement Learning; Advanced Learning Algorithms; Supervised Machine Learning: Regression and Classification

• Conducted exploratory data analysis (EDA) on a housing dataset of 204,792 data points and 61S different features using Python and pandas, identifying key features and trends to inform modeling decisions and improve predictive accuracy for house price.
• Built and fit linear regression models using scikit-learn to predict housing prices based on relevant features, achieving a root mean squared error (RMSE) of 227K, and identified potential areas for model improvement through analysis of residuals and feature importance.

• Conduct an analysis of historical temperature data to identify trends over a prolonged period and utilize this information to make predictions about future weather patterns.
• Categorize and create visual representations of data from 210 U.S cities for the last hundred years.

• Developed and fine-tuned a spam email classifier from a 9,348 emails dataset using natural language processing techniques and machine learning models, achieving an accuracy rate of 96%.
• Leveraged data engineering strategies to extract pertinent features, such as filtering out common words and identifying distinct syntax patterns between spam and non-spam emails.
• Additionally, controlled for confounder factors like reply or forward status in emails to enhance model performance and ensure accurate prediction outcomes.
• Gained proficiency in utilizing sklearn libraries for data processing and model fitting, as well as validating model performance to minimize overfitting and analyzing ROC curves.

• Collected and analyzed 43,537 data points from gapminder.org to create visual representations of 12 statistical features pertaining to mortality, fertility, and poverty rates of 197 countries.
• Utilized graphing and tabling methods to effectively convey trends and changes over time.

• Design and implement a 2D tile-based world exploration engine for educational purposes, simulating product development cycles with minimal starter code.
• Focused on software engineering principles, code management, and problem-solving, while utilizing various data structures and algorithms for simplicity and efficiency.
• Successfully created a grid-based virtual world that allowed user interaction and exploration, demonstrating project management and creative problem-solving skills.

• In this project, I developed AI-based guessers and word choosers for the game "Awakening of Azathoth," gaining hands-on experience with Java's built-in List and Map data structures.
• I successfully created three distinct AI guessers that could play the game, along with two innovative word selection classes that elevated the complexity of the problem.
• This project showcased my ability to utilize data structures effectively, and allowed me to demonstrate my proficiency in Java programming.