CS undergrad passionate about building and researching intelligent systems. I write code, ask hard questions, and occasionally publish papers about it.
I'm a Computer Science & Engineering student who's genuinely excited about the intersection of machine learning, data, and real-world impact. Whether it's training deep learning models on environmental datasets or building tools that help people understand their digital conversations — I like working on problems that matter.
My research on air quality prediction using deep learning techniques reflects my broader interest in using AI to tackle environmental and social challenges. I'm always looking for the next meaningful problem to sink my teeth into.
When I'm not writing code or reading papers, I'm probably thinking about how to make models work better with less data — or hunting for good chai.
Focusing on machine learning, deep learning architectures, data structures, and software engineering. Coursework spans neural networks, algorithms, databases, and operating systems — with hands-on research that extends beyond the classroom.
Comparative analysis of ML and deep learning models — RF, LSTM, CNN, and hybrid CNN-LSTM — for AQI forecasting. Incorporates event-aware modeling for festivals, traffic surges, and industrial activity patterns.
A tool to extract meaningful insights from social media conversations — message patterns, sentiment trends, activity heatmaps, and more. Works across popular chat export formats to surface stories hidden in your data.
Whether it's a research collaboration, internship opportunity, or just a good conversation about AI and data — my inbox is open.
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