• Designed and implemented an experience launcher for Windows, reducing server acknowledgement times and improving user experience

• Developed a Media Picker functionality on iOS, Mac, Android, and Windows for uploading media from the gallery or file system

• Optimized memory efficiency for media files, reduced HTTP request creation memory footprint by 25% and latency by 80% across iOS, Mac, Android, and Windows

• Set up a CI/CD pipeline for tracking app sizes across platforms and notifying engineers when their change impacts app size significantly, bringing more awareness to app size changes

• Architected and developed a deferred asset delivery system to automatically offload assets to the CDN for deferred downloading and caching, reducing app size

• Worked through weekly lab assignments and readings and provided thoughts on wording improvement, potential challenges, and relevance to course material

• Held 9 hours of office hours per week to assist 600+ students with debugging their code and understanding course material

• Provided feedback on student's completed code, including stylistic decisions, refactoring of code, docstrings, and appropriate commenting during lab checkoffs

• Led weekly staff meetings to discuss tips for helping students understand course material and become better programmers

• Developed and provided feedback for midterm problems

• Worked through weekly lab assignments and provided thoughts on wording improvement, potential challenges, and relevance to course material

• Held 6 hours of office hours per week to assist 600+ students with debugging their code and understanding course material

• Provided feedback on student's completed code, including stylistic decisions, refactoring of code, docstrings, and appropriate commenting

• Attended weekly staff meetings to discuss tips for helping students understand course material and become better programmers

• Queried and analyzed sales and pricing data of products on Amazon using PostgreSQL, Pandas, and Jupyter Notebook to determine user impressions, sales volume, and buy box percentages at various price points

• Deployed an algorithm to automatically and optimally price over 50,000 goods daily to maximize profits (raised profits by 24.61%) by estimating total costs, maximizing buy box percentage, and increasing revenue

• Developed a Slack bot that identifies mismatched listings with 83% accuracy and sends alerts daily

• Designed and maintained a PostgreSQL database of daily analytic data from Amazon, speeding up queries and operations by a factor of over 700

• Trained a neural network using PyTorch to accurately predict the buy box seller on Amazon, and utilized this model to implement a pricing strategy

• Compiled research process and results into MEng Thesis

• Technologies Used: PostgreSQL, Python, Jupyter, Pandas, PyTorch, Plotly, Selenium, Slack SDK

• Worked through weekly lab assignments and provided thoughts on wording improvement, potential challenges, and relevance to course material

• Held 6 hours of office hours per week to assist 600+ students with debugging their code and understanding course material

• Provided feedback on student's completed code, including stylistic decisions, refactoring of code, docstrings, and appropriate commenting

• Attended weekly staff meetings to discuss tips for helping students understand course material and become better programmers