Maanav Choudhary

PNC: Technology Intern

During my internship, I utilized cutting-edge machine learning techniques to automate and enhance the quality control process for SOX (Sarbanes-Oxley) reports. My work primarily focused on creating and refining algorithms to detect anomalies and streamline data processing using scikit-learn and JupyterHub. By implementing a range of supervised and unsupervised models, I significantly improved anomaly detection capabilities.

I conducted thorough data preprocessing, including feature selection and dimensionality reduction, which greatly enhanced model input quality and boosted model efficiency by 20%. Furthermore, I integrated advanced statistical methods and employed ensemble techniques to develop more robust and scalable models. This process involved rigorous cross-validation and the application of relevant performance metrics.

In addition to algorithm development, I increased overall data processing efficiency by 30%, contributing to a 60% reduction in manual quality control time. Through iterative tuning and validation, I improved anomaly detection accuracy by 15%, achieving a final model accuracy of 92%. These advancements not only automated a previously manual process but also significantly improved the accuracy and efficiency of quality control for SOX reports, highlighting the potential of machine learning in enhancing regulatory compliance workflows.

Headstarter AI: SWE Fellow

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About Me!

I am currently pursuing a Bachelor of Science in Computer Science and Cognitive Science from the School of Arts and Sciences at Rutgers University, with an expected graduation date of May 2025. My technical skill set includes proficiency in Java, Python, C, and Swift. I have extensive experience with machine learning, probability, low-level programming, cloud computing, and front-end app development. I frequently use APIs and libraries such as Numpy, scikit-learn, pandas, seaborn, matplotlib, and Streamlit to create efficient and effective solutions.

In my free time at Rutgers, I participate in the App Development Club (RUMAD) and the Modeling Club (F.A.C.E). As a member of RUMAD since January 2022, I have collaborated with a team to build an iOS app from the idea phase to a prototype using Swift. This experience has honed my front-end app development skills and taught me the importance of teamwork and long-term project management. Being a member of F.A.C.E. has pushed me out of my comfort zone, as being on stage was something I had never done before joining. It has given me a chance to meet new people from a variety of backgrounds who supported me in leaving my comfort zone. It has also provided me an opportunity to express my creativity and experiment with fashion. I also enjoy playing basketball and participating in hackathons with my roommates. These activities allow us to work as a team and help me get closer to the people I care about.

I am highly adaptable, a quick learner, and eager to take on new challenges. My hands-on experience with technologies like Python, Java, and C, combined with my strong work ethic and academic background, make me a valuable candidate for dynamic roles in technology and software development. I am particularly drawn to companies committed to innovation, diversity, and making a real difference in the world.

Rutgers Hackathon

Worked on a 3 person team to make a web app that displays crowds in our university gyms using live user input as data for the crowd meter. We created an interface to take in user input for what their perceieved crowd level at the gym is, we also deleted data that was older than 15 minutes old to keep meter fresh and accurate. We used python for the back and front end, utilized Streamlit API for the webpage and user input, and we launched online with AWS. I learned how to use python and AWS for this, and was able to experience working on a team for a real world project. Our efforts won us the health track for the 2023 Rutgers Hackathon.

Huffman Coding

Implemented a Huffman coding algorithm to compress text files, achieving up to 50% reduction in file size. Used a priority queue (min-heap) to build a binary tree, reducing time complexity for tree construction to O(nlogn). Assigned binary codes to 256 unique characters based on their frequencies, optimizing for space efficiency. Encoded text into binary format, reducing the average character representation length from 8 bits to as low as 3 bits for the most frequent characters.

Resume

You can view my resume here.

Git Hub

Link to my Git: Git Hub.

Linked in

Heres my Linked in: Maanav Choudhary.

Contact

• Twitter
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• GitHub

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