About Me

There are 2 types of people: those who hear music and think about dancing, and those who think about the mathematical patterns behind it. Needless to say, I couldn’t care less about dancing.

I’m fascinated by how music, mathematics, and machine learning intersect. I enjoy exploring how we can teach machines to understand the mathematical patterns in music, and how we can use technology to understand, create, and transform musical experiences. I believe in building solutions that are not merely technically sophisticated, but also transparent and meaningful to the people who use them.

With over 6 years of experience working on machine learning projects across various industries, I’ve picked up knowledge of software engineering practices, end-to-end ML in Python, working with databases, making sense of unstructured text data, and fine-tuning LLMs. I’ve built practical solutions that have saved thousands of manual work hours across Health Tech, Quick Commerce, Social Media, and Reg Tech. I thrive in innovation-driven start up environments, and enjoy working in research and discovery. I also take pride in storytelling, both in writing and in stakeholder communication.

I recently attained a Masters in Sound and Music Computing, which has equipped me with knowledge of music and audio processing methods. Along this journey, I’ve had fun building all sorts of interesting projects - from systems that can generate music (which have been described as “Bach on Drugs”) to creative multimodel systems, and learnt how I could bring my technical expertise into the world of music.

Based in London and always up for a good chat about the music industry, machine learning, or the occasional philosophical debate about whether computers can truly achieve creative autonomy. In my free time, I reminisce about my adventures in the real world, and tackle A&R problems in the music industry with the folks at Octavate. If you’re interested in collaborating on projects where music and tech collide, let’s connect!