Meenakshi Prabhakar

- Designed and implemented a scalable customer request analysis system using Scala and AWS (S3, DynamoDB, Lambda, SNS), helping reduce Zelkova team’s impact analysis time from 2 weeks to 2 days. - Created an intelligent deduplication system using SHA-256 hashing and timestamp-based write controls, cutting duplicate entries by 90% and lowering storage costs. - Developed a robust testing framework in Python and Scala with >80% coverage, including synthetic data generators and automated validation, ensuring performance across millions of requests.

- Led 9 university tech initiatives effectively, enhancing functionality and scalability with AWS. - Maintained SGA websites and managed technical aspects of undergraduate and graduate elections.

- Developed an intelligent, Swift-based tutoring chatbot integrated with various APIs and incorporated a caching system solution to reduce API call expenses and improve response times. - Enhanced the chatbot’s functionality by incorporating question similarity scoring algorithms, utilizing machine learning principles, and integrating Piazza to improve response accuracy. - Improved a virtual assistant by implementing intelligent features such as predictive material suggestions based on past queries and a dynamic guide to help students learn topics comprehensively.

- Contributed to the Fidelity Health App’s iOS development through Agile sprints, utilizing Swift to enhance the UI/UX of managing expenses, implementing GraphQL logic for effective data retrieval, and resolving backend issues to streamline the one-time contributions feature and the save receipts feature in the app with over 100,000+ users. - Achieved certification in GraphQL and became proficient in Swift, ensuring efficient, user-friendly app performance. - Integrated Health Kit into the Fidelity Health app, enabling client-driven employee health challenges and increasing user engagement resulting in winning an internal company hackathon with 100+ participants

- Developed and implemented neural network models utilizing machine learning techniques, specializing in computer vision with TensorFlow/Keras, while following data science principles. - Proficiently utilized Python with various libraries including numpy, matplotlib, and scikit-learn to design, evaluate, and fine-tune machine learning algorithms

- Collaborated with engineers to develop and understand design criteria to create the final website using Javascript. - Participated in the early stages of product development by analyzing interfaces and reporting results, conducting market research and providing feature implementation ideas.

Concentrations: Intelligence and Media Relevant Coursework: Machine Learning, Deep Learning, Computer Vision, Data Structures and Algorithms, Computer Organization and Programming, Object-Oriented Programming, Design and Analysis of Algorithms, Objects and Design, Artificial Intelligence, Robotics and Perception, Computer Graphics, Statistics, Linear Algebra, Multivariable Calculus, Discrete Mathematics