Yewon Lee Email & Phone Number

Pittsburgh, Pa, Us

미국

미국

M365 Core intern- Streamlined spec for Named Property Cleanup feature that removes specific named properties from all items in mailbox, and store them in hidden property by serializing it into byte array for efficient implementation which in turn reduced time and effort taken from both support engineer and customer and prevents useful data from being lost- Shipped a thorough end-to-end mailbox repair feature using C# that detects corrupted messages including unnecessary named properties using search folder and execute query and stores it in MDBEF collection- Drove the standardization of new inputs given to MailboxRepairRequest StoreQueryFilter parameter, enabling the inputs to be used in both the XSO-based repair tasks and on the XSO-layer itself- Tested repair task on Tabular Data Stream(TDS) box by putting inputs retrieved from Named Property Table using Powershell to ensure good automated/unit test coverage

Pittsburgh, Pennsylvania, United States

- Analyzed behaviors of 3 types of cyber attackers(financial, reputational, ideological) by running over 1,000 trials on different inputs like level of difficulty, team size, duration, etc.- Authored paper with the team to present to cyber-security organizations such as CERT planning to launch a new project that involves creating new graph patterns using dataset stored in MongoDB

Seoul, South Korea

- Optimized recommendation system by building functions based on Hybrid Recommendation Model using Python counting multiple variables into calculation (distance, weather, duration, transportation, etc)- Built functions that quantify attractions on different features and return top n that best matches user taste- Implemented equations to return most efficient travel schedule by counting distance between attractions, weather of the day, transportation being used, average activity duration, etc into consideration- Tested recommender function by fetching user data and travel data from MongoDB- Increased by 14% in predicting shortest travel route and travel plan that better match user preference

Seoul, South Korea

- Conducted personalized recommendation algorithm through Python by applying Collaborative Filtering and Content-based Filtering to calculate similarity between users and contents- Built functions to rank educational contents and group users with similar features for optimization- Constructed Web Crawler with senior programmers in Python to extract necessary data from online bookstores and stored them in MySQL- Led a team of 8 interns to brainstorm ideas to improve user experience and potential ideas for new features

Bachelor Of Science - Bs, Information System And Human Computer Interaction

High School Diploma