Hi!

I am a Research and Development Engineer at the Qualcomm Institute, specializing in Deep Learning for Spatial Audio and Generative AI for Music. With a Master’s degree in Computer Science from UC San Diego, my journey spans diverse AI domains, including Deep Generative Networks, Statistical NLP, Distributed Systems, and Recommender Systems.

With 2+ years of professional ML experience (beyond internships), I have worked across the ML lifecycle—data wrangling, feature engineering, model development, hyperparameter tuning, and deploying models to production. My work has brought me to Health-tech, where I built multimodal ML pipelines, to retail operations at Ernst & Young leveraging AWS and databases, and to a Data Science startup shaping Ed-tech and hiring solutions.

As a Founding Team Member at DPhi, I’ve been part of a mission to democratize Data Science education—leading AI bootcamps, creating intuitive courses, and fostering a global data culture through open innovation.

Research excites me as much as hands-on impact. At DRDO, I explored Neuroscience and AI, dynamically allocating aircraft controls using EEG data to monitor pilot workload. At Qualcomm, I delve into cutting-edge technologies that transform how we experience music and audio.

Beyond work, I thrive in community building, blogging, and hackathons—always seeking to connect, learn, and create.