213-***-**** email@example.com https://www.linkedin.com/in/vishnupriya-ravibalan-9578bb105/ https://github.com/vpravi EDUCATION
Master of Science, Computer Science University of Southern California, Los Angeles, CA December 2018 Selected Coursework: Analysis of Algorithms, Artificial Intelligence, Web Technologies, Data Mining, Current GPA: 3.25/4 Information Retrieval and Web Search Engines, Databases Bachelor of Technology, Computer Science SRM University, India June 2016 Selected Coursework: Data Structures, Operating Systems, Parallel Computing, Software Engineering CGPA: 8.372/10 Global Student Program, Computer Science University of California, Davis, CA June 2015 Selected Coursework: Artificial Intelligence, Software Engineering, Engineering Economics GPA: 3.4/4 TECHNICAL SKILLS
STUDENT WEB DEVELOPER LONI – USC BIG DATA TRAINING CENTER MAY 2017 – PRESENT
• Liaised with 2 student workers to build a database driven responsive web applications to maintain educational resources (~9540 resources) for rapid, real-time information sharing on Big Data Training Coordinating Center Website.
• Exhibited the ability to learn quickly and resolve issues/refactor code in a large code base. HONORS
• First Place in USC Viterbi Graduate Hackathon for developing a technique for unsupervised face clustering for over 10,000 images at Information Sciences Institute at USC, March 2017. https://viterbischool.usc.edu/news/2017/04/2017-usc-viterbi-graduate-hackathon- isi-facial- clustering-iot/
SEARCH ENGINE INFORMATION RETRIEVAL Spring 2018
• Enhanced the search engine with the spell check, autocorrect and snippet description functionality, which mimics Google. STOCK SEARCH RESPONSIVE WEB DESIGN AND ANDROID APPLICATION Fall 2017
• Designed an Android mobile application that uses REST APIs to fetch details from the website depending on users’ query and displayed results on the app. Built a tabbed view display of stock prices, company profiles and news using the opening, closing, high, low, volume values.
• Both app and webpage included features like autocomplete search, local storage, adding/deleting favorites, filters based on content and order, social media, timed automatic refresh, live news, High Charts and High Stocks graphs to stock values over a time period and error handling. RECOMMENDER SYSTEM DATA MINING Fall 2017
• Designed a Recommender System by implementing User Based Collaborative Filtering using Pearson Correlation in Python using Apache Spark Framework. Optimized this solution by exploiting the scalability of the distributed environment and used nearest neighbor approaches.
• Also designed a similar solution in Scala by implementing a Model based approach and compared their RMSE values. FREQUENT-ITEMSETS IN HUGE DATASETS DATA MINING Fall 2017
• Formulated a solution to find Frequent sets, given large data sets - SON Algorithm in Python using Apache Spark Framework
• Optimized the solution by exploiting the scalability of the distributed environment, Apriori property and two-phase Map-Reduce technique. SAT SOLVER using PL RESOLUTION and WALK-SAT ARTIFICAL INTELLIGENCE Spring 2017
• Worked on a constraint satisfaction problem of seating people at tables using Python 2.7 (only standard libraries).
• Achieved 100% relevancy when tested on ~50 test cases where huge test cases required the application of intuitive optimizations to PL resolution algorithm in order to increase the speed of calculation. BELIEF NETWORKS and INFERANCE using Enumeration/Variable Elimination ARTIFICAL INTELLIGENCE Spring 2017
• Solved a Decision making problem by calculating the specific joint, marginal or conditional probability and the expected utility of a particular decision using Python 2.7 (only standard Libraries).
• Determined the best decision by calculating maximum expected utility and achieved 100% relevance on ~50 test cases. RAMPART SOFTWARE ENGINEERING Spring 2015
• Designed and developed a rendition of the classic Atari game “Rampart” in Objective C for the Mac OS platform.
• Implemented a fully functional Single Player mode, Multi Player mode, and meet all weekly deadlines. I worked and collaborated with many different teams throughout the development of the game. Score: 45.56/50 (based on 10 weeks of score reports worth 50% of grade)