These days,

I recently returned from traveling, and these days, I’ve been expanding my skills through personal projects.

I'm working on the “Shazam” for Kirtan project, which is probably my most nuanced machine learning project thus far. Up until now, I’ve worked with ML in different ways—using gradient boosting for structured data and neural networks for image classification. Now, I want to push into audio and expand my skill set even further. This project is a long-term challenge—not just because of the extensive data collection, but also because no existing model does this yet. I’ve always enjoyed NLP and working with language models, and this feels like a natural next step. There’s something really cool about finding new ways to apply ML—especially in areas I never would’ve thought about when I first started.

My previous case study—the Djibouti project for my economic development class—was entirely research-driven, grounded in data and analysis. But behind every data point are real people, real stories, and complexities that numbers alone can’t fully capture. Having just returned from Punjab, I wanted to take a different approach with this new case study—one that blends academic research with firsthand experience. This time, I’m not just analyzing data; I’m drawing from what I saw, the economic realities I witnessed, and the perspectives of those still living in Punjab, navigating its transformation in real-time. By bringing these elements together, I hope to create something deeper and more meaningful—one that bridges research with lived experience.

But my vision goes beyond just a case study or machine learning applications. One day, I want to take these ideas—and many others—and turn them into action, whether in global warming & climate solutions, or broader economic challenges. And not alone, of course.. The greatest privilege wouldn’t just be making an impact, but doing it alongside a team just as determined to change the world.