Neeraj Patil

Research Intern

Syracuse University•  June 2024 - Present•  Syracuse, New York

Developed a deep learning model for violence detection using CNNs with TensorFlow, MobileNet, and LSTM in Python, achieving faster inference time than InceptionV3 and ResNet50. Targeted 90% accuracy by optimizing performance for low-powered GPUs, reducing computational load by, and enhancing image quality through data augmentation and noise reduction on a large video dataset.

Software Engineer

Fidelity National Information Services Global•  July 2021 - August 2022•  Unknown

Maintained and enhanced code for Ambit Asset Finance product as a software developer for clients including Toyota, Scania, and BMW, achieving 80% code coverage with NUnit tests, collaborating with cross-functional teams, and ensuring high quality through code reviews. Executed SQL queries to handle over 100k data and fixed bugs for clients ranging from Orix, Scania, BMW, Toyota, Angle Finance, resolving JIRA tickets, and gaining familiarity with financial concepts and fintech software. Developed and implemented a CI/CD pipeline using Jenkins, integrating ETL processes, and optimized web apps with 10+ microservices, enhancing scalability.

Machine Learning Intern

Cognifront•  June 2020 - August 2020

● Built a classifier model with 93% accuracy using 30k student dataset to predict whether a student gained employment or not, based on diverse academic records, standardized test scores, and other features. ● Implemented feature engineering and optimized models through techniques like cross-validation and hyperparameter tuning. ● Performed statistical analysis using Pandas, Matplotlib, NumPy, enhancing the data validation process and improving data accuracy. Used models like Logistic Regression and Decision Trees.

Android Developer Intern

TechR Business Solutions•  July 2019 - October 2019•  Unknown

Boosted app downloads through agile development and strategic enhancements in a city guide app using Android SDK, Java, XML, and Firebase. Reduced load times through integration of RESTful APIs, Material Design, and rigorous testing with Espresso and JUnit, optimizing app performance and user experience. Implemented advanced caching strategies and optimized database queries using Firebase Realtime Database, further enhancing app responsiveness and reducing data retrieval times.