Hello there! I'm Arav, a dedicated Computer Science enthusiast with a robust grounding in mathematics, programming, and logical problem-solving. My academic journey is fueled by a fervent curiosity for technology and its potential to transform the world.
I thrive on the thrill of bringing ideas to life, whether it's through solo ventures or as part of a synergistic team effort. My portfolio is a testament to my love for innovation, featuring a variety of projects that are as enjoyable as they are instructive.
As I navigate through the intricate landscape of technology, my sights are set on the horizon of Artificial Intelligence and Web Development. I'm committed to not just riding the wave of technological advancement but also contributing to it, one line of code at a time.
Join me on this exhilarating voyage as I explore the endless possibilities of the digital realm and strive to make a meaningful impact through my work. Welcome to my world!
During my tenure as a Software Engineering Intern, I significantly improved financial efficiency by 60% on a key Summary Allocation page, thanks to advanced cron jobs, diligent code management via GitHub, and efficient deployment with Jenkins Pipeline. I pioneered the adoption of AWS DevOps Guru, leveraging machine learning to transform anomaly detection in corporate action events. My integration of Datadog and Plan Funds APIs into Power BI, utilizing advanced DAX queries, resulted in three enhanced reports and five dynamic visualizations that clarified fund assets using SQL and DAX.
I also addressed and resolved eight legacy issues, improving data accuracy and readability for the Scrum Master. My innovative approach to report design involved extracting and interpreting data from JSON structures via Python, leading to the creation of three new reports that dynamically displayed user-input plan numbers and associated funds.
Living in Durham, North Carolina, and commuting from New York to Jersey City in my second year, I not only enjoyed the vibrant city life but also made invaluable connections and memories. The experience of working and living in these diverse locations has been truly enriching, both professionally and personally."
In my role as a Student Consultant, I conducted thorough interviews and leveraged secondary data to uncover transformative tech solutions that boosted operational efficiencies for Student Entertainment Events (SEE). I championed the development of a student-focused smartphone app, centralizing event information to enhance the user experience and improve communication.
I took the lead in providing strategic consultation for Airfare Snacks, utilizing advanced machine learning tools in Python to analyze complex trends across Shopify, Meta Business Suite, and Google Analytics. My data-driven recommendations were designed to propel Airfare's business forward, and I adeptly used Python to reveal key patterns linking social media ads to customer purchase behaviors. Additionally, I improved the dining hall experience by surveying staff and students, refining food disposal processes based on their feedback.
My time working alongside both engineering and business students has been invaluable, granting me a comprehensive view of business operations beyond the technical aspects. This interdisciplinary exposure has allowed me to appreciate the broader business ecosystem, for which I am deeply grateful.
Embarking on the journey to create a Facial Detection System was akin to setting sail on a vast ocean of possibilities, where the waves of innovation and the winds of technology met. This project was not just a test of my programming skills, but also a testament to my perseverance and passion for problem-solving.
The core objective was to develop a system capable of identifying and tracking facial features with precision. The task demanded a deep dive into the realms of machine learning and computer vision, utilizing sophisticated algorithms and neural networks. The project was built upon a foundation of Python, leveraging libraries such as OpenCV to process and analyze real-time video data.
The development phase was a rollercoaster of emotions. There were moments of sheer joy when the code executed flawlessly, faces were detected with pinpoint accuracy, and the system learned to differentiate between various facial expressions. These peaks were contrasted by valleys of frustration, where bugs seemed to crawl out of the woodwork, and the logic I had so carefully crafted hit unforeseen roadblocks.
Debugging was both a challenge and a puzzle. Each error was a riddle waiting to be solved, and with every solution, my understanding of the intricate dance between software and hardware deepened. The project demanded patience and a meticulous attention to detail, as even the most minor oversight could lead to a cascade of unexpected outcomes.
Despite the hurdles, the thrill of overcoming each obstacle was exhilarating. The late nights, the relentless testing, the constant learning, and relearning—it all culminated in a system that not only recognized faces but did so with a level of accuracy that was deeply satisfying.
This project was more than a technical endeavor; it was a personal growth experience. It taught me that the journey of creation is filled with highs and lows, and it's the ability to navigate through them that leads to success. The Facial Detection System stands as a beacon of my dedication, a symbol of the fun, albeit sometimes frustrating, adventure in the world of software development.
As I reflect on the project, I am filled with a sense of accomplishment. The knowledge gained, the skills honed, and the confidence earned from this experience are invaluable. It was a reminder that the process of learning and creating is, in itself, a reward.
The Landmark Explorer is an iOS application crafted with Xcode and SwiftUI, inspired by Apple's own tutorials. This app presents a curated list of world landmarks, offering users a visually engaging experience to explore and favorite the wonders of the world. Utilizing SwiftUI's robust framework, the app features a sleek user interface with interactive views and buttons, elegantly nested views for a structured layout, and subtle shadowing for depth and focus.
At the heart of the app's functionality is a global variable that tracks user-favorited landmarks, ensuring a personalized experience. The integration of MapView allows users to geographically pinpoint each landmark, enhancing the exploratory aspect of the app. With its combination of informative content and intuitive design, the Landmark Explorer app is a testament to the power of SwiftUI in creating immersive and user-friendly applications.
Embarking on the Landmark Explorer project was not only a deep dive into the capabilities of SwiftUI but also an immensely enjoyable journey. The challenges encountered along the way, from debugging to perfecting the user interface, were as rewarding as they were educational. This project has further ignited my passion for iOS development, and I am enthusiastically continuing to expand my skills in this area. With each new feature I implement and every line of code I refine, I find myself more engrossed in the world of app development, eager to explore the next set of features that will enhance the user's interaction with the landmarks they love.
If you'd like to get in touch with me, feel free to send me an email :aravp2319@gmail.com.